Lab_4_Model_Development.ipynb

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      "source": [
        "# **Lab 4 - Model Development**\n",
        "\n",
        "## **Data Analysis with Python**\n",
        "\n",
        "### **Objectives :- Develop Prediction Models**"
      ],
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        "####In data analytics, we often use Model Development to help us predict future observations from the data we have.\n",
        "\n",
        "####A model will help us understand the exact relationship between different variables and how these variables are used to predict the result."
      ],
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      "source": [
        "#### Setup"
      ],
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    {
      "cell_type": "code",
      "source": [
        "# Import Libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "2wq_pj4GF_b_"
      },
      "execution_count": 125,
      "outputs": []
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      "source": [
        "Load the data and store it in dataframe df\n",
        "\n",
        "This dataset was hosted on IBM Cloud object. Click [HERE](https://cloud.ibm.com/objectstorage/create) for free storage."
      ],
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              "   symboling  normalized-losses         make aspiration num-of-doors  \\\n",
              "0          3                122  alfa-romero        std          two   \n",
              "1          3                122  alfa-romero        std          two   \n",
              "2          1                122  alfa-romero        std          two   \n",
              "3          2                164         audi        std         four   \n",
              "4          2                164         audi        std         four   \n",
              "\n",
              "    body-style drive-wheels engine-location  wheel-base    length  ...  \\\n",
              "0  convertible          rwd           front        88.6  0.811148  ...   \n",
              "1  convertible          rwd           front        88.6  0.811148  ...   \n",
              "2    hatchback          rwd           front        94.5  0.822681  ...   \n",
              "3        sedan          fwd           front        99.8  0.848630  ...   \n",
              "4        sedan          4wd           front        99.4  0.848630  ...   \n",
              "\n",
              "   compression-ratio  horsepower  peak-rpm city-mpg highway-mpg    price  \\\n",
              "0                9.0       111.0    5000.0       21          27  13495.0   \n",
              "1                9.0       111.0    5000.0       21          27  16500.0   \n",
              "2                9.0       154.0    5000.0       19          26  16500.0   \n",
              "3               10.0       102.0    5500.0       24          30  13950.0   \n",
              "4                8.0       115.0    5500.0       18          22  17450.0   \n",
              "\n",
              "  city-L/100km  horsepower-binned  diesel  gas  \n",
              "0    11.190476             Medium       0    1  \n",
              "1    11.190476             Medium       0    1  \n",
              "2    12.368421             Medium       0    1  \n",
              "3     9.791667             Medium       0    1  \n",
              "4    13.055556             Medium       0    1  \n",
              "\n",
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          },
          "metadata": {},
          "execution_count": 126
        }
      ],
      "source": [
        "path = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/Data%20files/automobileEDA.csv'\n",
        "df = pd.read_csv(path)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h2><b>1. Linear Regression and Multiple Linear Regression</b></h2>\n"
      ],
      "metadata": {
        "id": "ZtJlCo6zITcj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**<h4>Linear Regression</h4>**\n",
        "<p>One example of a Data  Model that we will be using is:</p>\n",
        "<b>Simple Linear Regression</b>\n",
        "\n",
        "<br>\n",
        "<p>Simple Linear Regression is a method to help us understand the relationship between two variables:</p>\n",
        "<ul>\n",
        "    <li>The predictor/independent variable (X)</li>\n",
        "    <li>The response/dependent variable (that we want to predict)(Y)</li>\n",
        "</ul>\n",
        "\n",
        "<p>The result of Linear Regression is a <b>linear function</b> that predicts the response (dependent) variable as a function of the predictor (independent) variable.</p>"
      ],
      "metadata": {
        "id": "fqdktUi4IaMi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "$$\n",
        " Y: Response \\ Variable\\\\\\\\\n",
        " X: Predictor \\ Variables\n",
        "$$"
      ],
      "metadata": {
        "id": "8ilcVlskI005"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        " <b>Linear Function</b>\n",
        "$$\n",
        "Yhat = a + b  X\n",
        "$$"
      ],
      "metadata": {
        "id": "qb78meqpJQnk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<ul>\n",
        "    <li>a refers to the <b>intercept</b> of the regression line, in other words: the value of Y when X is 0</li>\n",
        "    <li>b refers to the <b>slope</b> of the regression line, in other words: the value with which Y changes when X increases by 1 unit</li>\n",
        "</ul>\n"
      ],
      "metadata": {
        "id": "B8E4ChV2I2Zr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# load the modules for linear regression\n",
        "\n",
        "from sklearn.linear_model import LinearRegression"
      ],
      "metadata": {
        "id": "Hblrtz-pIUbl"
      },
      "execution_count": 127,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "# Create the linear regression object\n",
        "\n",
        "lm = LinearRegression()\n",
        "lm"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "G2vcnTl-JhNV",
        "outputId": "b42e1570-d49f-486a-8c57-980f2333a099"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
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              "<style>#sk-container-id-19 {color: black;background-color: white;}#sk-container-id-19 pre{padding: 0;}#sk-container-id-19 div.sk-toggleable {background-color: white;}#sk-container-id-19 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-19 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-19 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-19 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-19 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-19 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-19 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-19 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-19 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-19 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-19 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-19 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-19 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-19 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-19 div.sk-item {position: relative;z-index: 1;}#sk-container-id-19 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-19 div.sk-item::before, #sk-container-id-19 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-19 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-19 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-19 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-19 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-19 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-19 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-19 div.sk-label-container {text-align: center;}#sk-container-id-19 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-19 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-19\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" checked><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**How could \"highway-mpg\" help us predict car price?**\n",
        "\n",
        "For this example, we want to look at how highway-mpg can help us predict car price. Using simple linear regression, we will create a linear function with \"highway-mpg\" as the predictor variable and the \"price\" as the response variable."
      ],
      "metadata": {
        "id": "j53nVatBJeXa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df[['highway-mpg']]\n",
        "Y = df['price']"
      ],
      "metadata": {
        "id": "Oip9Fa0lJs0r"
      },
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit the linear model using highway-mpg\n",
        "\n",
        "lm.fit(X,Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "SXza7e_JJwn9",
        "outputId": "3db58a07-5b5c-40d6-f282-736f17e8eb39"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-20 {color: black;background-color: white;}#sk-container-id-20 pre{padding: 0;}#sk-container-id-20 div.sk-toggleable {background-color: white;}#sk-container-id-20 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-20 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-20 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-20 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-20 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-20 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-20 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-20 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-20 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-20 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-20 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-20 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-20 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-20 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-20 div.sk-item {position: relative;z-index: 1;}#sk-container-id-20 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-20 div.sk-item::before, #sk-container-id-20 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-20 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-20 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-20 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-20 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-20 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-20 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-20 div.sk-label-container {text-align: center;}#sk-container-id-20 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-20 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-20\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" checked><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Output Prediction\n",
        "\n",
        "Yhat=lm.predict(X)\n",
        "Yhat[0:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LDZqJY0gJ5DO",
        "outputId": "4eabd98a-eb72-449a-b52c-44ff6a63cca2"
      },
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([16236.50464347, 16236.50464347, 17058.23802179, 13771.3045085 ,\n",
              "       20345.17153508])"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# What is the value of the intercept (a)?\n",
        "\n",
        "lm.intercept_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TALV3uEvJ9Tl",
        "outputId": "bdb64106-2e3f-470d-ffc2-3f4c4fbc4c54"
      },
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "38423.3058581574"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# What is the value of the slope (b)?\n",
        "\n",
        "lm.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YBuT-PjLKFcB",
        "outputId": "d4f19c44-7b77-44ec-8109-918496fb860a"
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-821.73337832])"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### What is the final estimated linear model we get?"
      ],
      "metadata": {
        "id": "Rg7XsFMsKUvo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "$$\n",
        "Yhat = a + b  X\n",
        "$$\n"
      ],
      "metadata": {
        "id": "LlhpROFWKa16"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plugging in the actual values we get:\n",
        "\n",
        "**Price** = 38423.31 - 821.73 x **highway-mpg**"
      ],
      "metadata": {
        "id": "NUmKugQmKbh-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a linear regression object called \"lm1\"\n",
        "\n",
        "lm1 = LinearRegression()\n",
        "lm1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "738g3fpBKjMI",
        "outputId": "4b5dfd57-894c-4dae-9314-f542c4caecd5"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-21 {color: black;background-color: white;}#sk-container-id-21 pre{padding: 0;}#sk-container-id-21 div.sk-toggleable {background-color: white;}#sk-container-id-21 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-21 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-21 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-21 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-21 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-21 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-21 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-21 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-21 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-21 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-21 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-21 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-21 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-21 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-21 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-21 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-21 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-21 div.sk-item {position: relative;z-index: 1;}#sk-container-id-21 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-21 div.sk-item::before, #sk-container-id-21 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-21 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-21 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-21 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-21 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-21 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-21 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-21 div.sk-label-container {text-align: center;}#sk-container-id-21 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-21 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-21\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" checked><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model using \"engine-size\" as the independent variable and \"price\" as the dependent variable?\n",
        "\n",
        "\n",
        "lm1.fit(df[['engine-size']], df[['price']])\n",
        "lm1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "9Q8hpY9WK1lt",
        "outputId": "841ae26f-fa46-4b50-8f6f-8561a4976322"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-22 {color: black;background-color: white;}#sk-container-id-22 pre{padding: 0;}#sk-container-id-22 div.sk-toggleable {background-color: white;}#sk-container-id-22 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-22 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-22 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-22 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-22 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-22 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-22 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-22 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-22 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-22 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-22 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-22 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-22 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-22 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-22 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-22 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-22 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-22 div.sk-item {position: relative;z-index: 1;}#sk-container-id-22 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-22 div.sk-item::before, #sk-container-id-22 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-22 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-22 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-22 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-22 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-22 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-22 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-22 div.sk-label-container {text-align: center;}#sk-container-id-22 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-22 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-22\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" checked><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Find the slope and intercept of the model.\n",
        "\n",
        "lm1.coef_  #Slope"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1M4p_rwGK6kx",
        "outputId": "637bdcae-d513-4703-de8d-01b26a480350"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[166.86001569]])"
            ]
          },
          "metadata": {},
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lm1.intercept_  #Intercept"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yxvhaVrULN73",
        "outputId": "fd75c4f3-c583-49b3-9ba3-c82903028e42"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-7963.33890628])"
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# What is the equation of the predicted line? You can use x and yhat or \"engine-size\" or \"price\"\n",
        "\n",
        "Yhat=-7963.34 + 166.86*X   # Using X and Y"
      ],
      "metadata": {
        "id": "M7EKGjjZLSe2"
      },
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Price=-7963.34 + 166.86*Yhat"
      ],
      "metadata": {
        "id": "Lgc6AFt-Lhdz"
      },
      "execution_count": 139,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h4><b>Multiple Linear Regression</b></h4>\n",
        "\n",
        "<p>What if we want to predict car price using more than one variable?</p>\n",
        "\n",
        "<p>If we want to use more variables in our model to predict car price, we can use <b>Multiple Linear Regression</b>.\n",
        "Multiple Linear Regression is very similar to Simple Linear Regression, but this method is used to explain the relationship between one continuous response (dependent) variable and <b>two or more</b> predictor (independent) variables.\n",
        "Most of the real-world regression models involve multiple predictors. We will illustrate the structure by using four predictor variables, but these results can generalize to any integer:</p>\n"
      ],
      "metadata": {
        "id": "u-Ghi7TYMYKU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "$$\n",
        "Y: Response \\ Variable\\\\\\\\\n",
        "X_1 :Predictor\\ Variable \\ 1\\\\\\\\\n",
        "X_2: Predictor\\ Variable \\ 2\\\\\\\\\n",
        "X_3: Predictor\\ Variable \\ 3\\\\\\\\\n",
        "X_4: Predictor\\ Variable \\ 4\\\\\\\\\n",
        "$$"
      ],
      "metadata": {
        "id": "s91_Mo3BMjvP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "$$\n",
        "a: intercept\\\\\\\\\n",
        "b_1 :coefficients \\ of\\ Variable \\ 1\\\\\\\\\n",
        "b_2: coefficients \\ of\\ Variable \\ 2\\\\\\\\\n",
        "b_3: coefficients \\ of\\ Variable \\ 3\\\\\\\\\n",
        "b_4: coefficients \\ of\\ Variable \\ 4\\\\\\\\\n",
        "$$\n"
      ],
      "metadata": {
        "id": "F_lvelh0Mvck"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Equation is Given by:\n",
        "\n",
        "$$\n",
        "Yhat = a + b_1 X_1 + b_2 X_2 + b_3 X_3 + b_4 X_4\n",
        "$$"
      ],
      "metadata": {
        "id": "X7iAmFQdM0WL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<p>From the previous section  we know that other good predictors of price could be:</p>\n",
        "<ul>\n",
        "    <li>Horsepower</li>\n",
        "    <li>Curb-weight</li>\n",
        "    <li>Engine-size</li>\n",
        "    <li>Highway-mpg</li>\n",
        "</ul>\n",
        "Let's develop a model using these variables as the predictor variables.\n"
      ],
      "metadata": {
        "id": "sNVtb7J-M5y7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Z = df[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']]"
      ],
      "metadata": {
        "id": "pjhp4TZJNEvp"
      },
      "execution_count": 140,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit the linear model using the four above-mentioned variables.\n",
        "\n",
        "lm.fit(Z, df['price'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "r4SdjmYTNMJa",
        "outputId": "81235634-db95-4862-da6a-d1009e5fcf64"
      },
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-23 {color: black;background-color: white;}#sk-container-id-23 pre{padding: 0;}#sk-container-id-23 div.sk-toggleable {background-color: white;}#sk-container-id-23 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-23 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-23 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-23 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-23 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-23 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-23 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-23 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-23 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-23 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-23 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-23 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-23 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-23 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-23 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-23 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-23 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-23 div.sk-item {position: relative;z-index: 1;}#sk-container-id-23 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-23 div.sk-item::before, #sk-container-id-23 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-23 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-23 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-23 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-23 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-23 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-23 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-23 div.sk-label-container {text-align: center;}#sk-container-id-23 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-23 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-23\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" checked><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Value of the intercept(a)?\n",
        "\n",
        "lm.intercept_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RwbNu_jtNRqR",
        "outputId": "3977002a-63a2-4ea7-9771-4a9be7b2d0f3"
      },
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-15806.62462632922"
            ]
          },
          "metadata": {},
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Values of the coefficients (b1, b2, b3, b4)?\n",
        "\n",
        "lm.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KYhk5jvqNVal",
        "outputId": "9a53b29c-ba0a-4755-ae36-022f65edd7b2"
      },
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([53.49574423,  4.70770099, 81.53026382, 36.05748882])"
            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "What is the Final Estimated Linear Model that We Get?\n",
        "\n",
        "We should get a Final Linear Function with the Structure:\n",
        "\n",
        "$$\n",
        "Yhat = a + b_1 X_1 + b_2 X_2 + b_3 X_3 + b_4 X_4\n",
        "$$"
      ],
      "metadata": {
        "id": "6ynFzKY8NdPt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "What is the Linear Function we get in this example?\n",
        "\n",
        "<b>Price</b> = -15678.742628061467 + 52.65851272 x <b>horsepower</b> + 4.69878948 x <b>curb-weight</b> + 81.95906216 x <b>engine-size</b> + 33.58258185 x <b>highway-mpg</b>\n"
      ],
      "metadata": {
        "id": "c4z3IrV_NoUy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  Create and train a Multiple Linear Regression model \"lm2\" where the response variable is \"price\", and the predictor variable is \"normalized-losses\" and \"highway-mpg\".\n",
        "\n",
        "\n",
        "lm2 = LinearRegression()\n",
        "lm2.fit(df[['normalized-losses' , 'highway-mpg']],df['price'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "4YNNzksEN4S-",
        "outputId": "b22e6af5-d924-4aa2-ccda-7d11b05d07fb"
      },
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
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              "<style>#sk-container-id-24 {color: black;background-color: white;}#sk-container-id-24 pre{padding: 0;}#sk-container-id-24 div.sk-toggleable {background-color: white;}#sk-container-id-24 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-24 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-24 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-24 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-24 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-24 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-24 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-24 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-24 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-24 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-24 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-24 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-24 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-24 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-24 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-24 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-24 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-24 div.sk-item {position: relative;z-index: 1;}#sk-container-id-24 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-24 div.sk-item::before, #sk-container-id-24 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-24 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-24 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-24 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-24 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-24 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-24 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-24 div.sk-label-container {text-align: center;}#sk-container-id-24 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-24 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-24\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" checked><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Find the coefficient of the model.\n",
        "\n",
        "lm2.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1n6ktFHdOB6Q",
        "outputId": "5c6ebf3c-94e2-4116-c6cc-bf22480b027f"
      },
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([   1.49789586, -820.45434016])"
            ]
          },
          "metadata": {},
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**<h2>2. Model Evaluation Using Visualization</h2>**"
      ],
      "metadata": {
        "id": "qXKHaS1rONRh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that we've developed some models, how do we evaluate our models and choose the best one? One way to do this is by using a visualization.\n"
      ],
      "metadata": {
        "id": "YGC8aTleOXOm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# import the Visualization Package: Seaborn\n",
        "\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "kykN1Ec8OQkg"
      },
      "execution_count": 146,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**<h3>Regression Plot</h3>**\n",
        "\n",
        "<p>When it comes to simple linear regression, an excellent way to visualize the fit of our model is by using <b>regression plots</b>.</p>\n",
        "\n",
        "<p>This plot will show a combination of a scattered data points (a <b>scatterplot</b>), as well as the fitted <b>linear regression</b> line going through the data. This will give us a reasonable estimate of the relationship between the two variables, the strength of the correlation, as well as the direction (positive or negative correlation).</p>\n"
      ],
      "metadata": {
        "id": "VMtSavjIOk80"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        " Let's visualize **highway-mpg** as potential predictor variable of price:\n"
      ],
      "metadata": {
        "id": "xLLGlmlcO2bF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "width = 12\n",
        "height = 10\n",
        "plt.figure(figsize=(width, height))\n",
        "sns.regplot(x=\"highway-mpg\", y=\"price\", data=df)\n",
        "plt.ylim(0,)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 867
        },
        "id": "AyYy_kUjO3aA",
        "outputId": "f9ad6da1-7008-4763-941f-ffc55c43dccc"
      },
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 48156.46359617194)"
            ]
          },
          "metadata": {},
          "execution_count": 147
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 1 Axes>"
            ],
            "image/png": 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wDwAAAABAifMVugAAKBau62lHZ0K9qZxqwwEtaYrJNI1ClwUAAABMGuEfACRtae/Rxs0d6uhOynY8+S1DLQ1RrV/TotWt9YUuDwAAAJgUlv0DU8h1PbXtjWvzroNq2xuX63qFLgknYEt7j27Z1Kad+xOKBH1qqAwqEvRp5/4B3bKpTVvaewpdIgAAADApPPkHpghPjmcn1/W0cXOHktm8GmMhGcbQMv+QaakxZqorkdXGzR1auaiOLQAAAACYtXjyD0wBnhzPXjs6E+roTqomHBgJ/sMMw1B12K+O7qR2dCYKVCEAAAAweYR/YJKOfHIc8lsyTUMhv6XGWFDJrKONmzvYAlCkelM52Y6ngDX2P4dBy5TteupN5Wa4MgAAAGDqEP6BSeLJ8exWGw7IbxnKOe6Yr2cdV37TUG04MMOVAQAAAFOH8A9MEk+OZ7clTTG1NETVl7LleaNXZ3iep/6UrZaGqJY0xQpUIQAAADB5hH9gknhyPLuZpqH1a1oUDVrqSmSVth25rqe07agrkVU0aGn9mhaa/QEAAGBWI/wDk8ST49lvdWu9br1iqRbPq1Qqm1d3MqtUNq/F8yp16xVLmdYAAACAWY9Rf8AkDT85vmVTm7oSWVWH/QpaprKOq/6UzZPjWWJ1a71WLqrTjs6EelM51YYDWtIU488NAAAAJcHwjnxUiQlJJBKqqqpSPB5XLMYT3nK0pb1HGzd3qKM7Kdv15DcNtTREtX5NC0+OAQAAAEy58eRQnvwDU4QnxwAAAACKFeEfmEKmaWjp/KpClwEAAAAAo9DwDwAAAACAEkf4BwAAAACgxLHsH1PCdT32ugMAAABAkSL8Y9JGdbl3PPktutwDAAAAQDFh2T8mZUt7j27Z1Kad+xOKBH1qqAwqEvRp5/4B3bKpTVvaewpdIgAAAACUPcI/Jsx1PW3c3KFkNq/GWEghvyXTNBTyW2qMBZXMOtq4uUOu6xW6VAAAAAAoa4R/TNiOzoQ6upOqCQdkGKP39xuGoeqwXx3dSe3oTBSoQgAAAACARPjHJPSmcrIdTwFr7B+joGXKdj31pnIzXBkAAAAA4HCEf0xYbTggv2Uo57hjvp51XPlNQ7XhwAxXBgAAAAA4HOEfE7akKaaWhqj6UrY8b/S+fs/z1J+y1dIQ1ZKmWIEqPDbX9dS2N67Nuw6qbW+cvgQAAAAAShqj/jBhpmlo/ZoW3bKpTV2JrKrDfgUtU1nHVX/KVjRoaf2aFpmmcfyLzSBGEwIAAAAoNzz5x6Ssbq3XrVcs1eJ5lUpl8+pOZpXK5rV4XqVuvWJp0YVpRhMCAAAAKEc8+cekrW6t18pFddrRmVBvKqfacEBLmmJF98T/yNGEwxMKQqalxpiprkRWGzd3aOWiuqKrHQAAAAAmg/CPKWGahpbOryp0GW9qPKMJi/17AQAAAIDxYNk/ygajCQEAAACUK8I/ygajCQEAAACUK8I/ysZsHk0IAAAAAJNB+EdRc11PbXvj2rzroNr2xuW63vE/6RiGRxNGg5a6ElmlbUeu6yltO+pKZIt2NCEAAAAATBYN/1C0trT3aOPmDnV0J2U7nvyWoZaGqNavaZnwCMHh0YTD1427nvymocXzKid1XQAAAAAoZoZ35PpnTEgikVBVVZXi8bhiMZaNT9aW9h7dsqlNyWxeNeGAApapnOOqL2UrGrR06xVLJxXUXdcr+tGEAAAAAPBmxpNDefKPouO6njZu7lAym1djLDQyli9kWmqMmepKZLVxc4dWLqqbcGCfDaMJAQAAAGCqsOcfRWdHZ0Id3UnVhAMjwX+YYRiqDvvV0Z3Ujs5EgSoEAAAAgNmF8I+i05vKyXY8BayxfzyDlinb9dSbys1wZQAAAAAwOxH+UXRqwwH5LUM5xx3z9azjym8aqg0HZrgyAAAAAJidCP8oOkuaYmppiKovZevIfpSe56k/ZaulIaolTTRWBAAAAIATQfhH0TFNQ+vXtCgatNSVyCptO3JdT2nbUVciq2jQ0vo1LXTnBwAAAIATRPhHUVrdWq9br1iqxfMqlcrm1Z3MKpXNa/G8ykmP+QMAAACAcsOoPxSt1a31WrmoTjs6E+pN5VQbDmhJU4wn/gAAAAAwToR/FDXTNLR0flWhywAAAACAWY1l/wAAAAAAlDjCPwAAAAAAJY5l/yhqruux5x8AAAAAJonwj6K1pb1HGzd3qKM7Kdvx5LcMtTREtX5NC93+AQAAAGAcWPaPorSlvUe3bGrTzv0JRYI+NVQGFQn6tHP/gG7Z1KYt7T2FLhEAAAAAZg3CP4qO63rauLlDyWxejbGQQn5Lpmko5LfUGAsqmXW0cXOHXNcrdKkoMa7rqW1vXJt3HVTb3jg/YwAAACgZLPtH0dnRmVBHd1I14YAMY/T+fsMwVB32q6M7qR2dCcYAYsqwzQQAAACljCf/KDq9qZxsx1PAGvvHM2iZsl1PvancDFeGUsU2EwAAAJQ6wj+KTm04IL9lKOe4Y76edVz5TUO14cAMV4ZSxDYTAAAAlAPCP4rOkqaYWhqi6kvZ8rzRgcvzPPWnbLU0RLWkKVagClFKxrPNBAAAAJitCP8oOqZpaP2aFkWDlroSWaVtR67rKW076kpkFQ1aWr+mRaZpHP9iwHGwzQQAAADlgPCPorS6tV63XrFUi+dVKpXNqzuZVSqb1+J5lbr1iqU0YMOUYZsJAAAAygHd/lG0VrfWa+WiOu3oTKg3lVNtOKAlTTGe+GNKDW8z2bl/QI0xc9TS/+FtJovnVbLNBAAAALMaT/5R1EzT0NL5VVpz2hwtnV9F8MeUY5sJAAAAygHhH0DZY5sJAAAASh3L/gFAbDMBAABAaSP8A8DrhreZAAAAAKWGZf8AAAAAAJQ4wj8AAAAAACWO8A8AAAAAQIkj/AMAAAAAUOII/wAAAAAAlDjCPwAAAAAAJY7wDwAAAABAiSP8AwAAAABQ4gj/AAAAAACUOMI/AAAAAAAljvAPAAAAAECJI/wDAAAAAFDifIUuAACKhet62tGZUG8qp9pwQEuaYjJNo9BlAQAAAJNG+AcASVvae7Rxc4c6upOyHU9+y1BLQ1Tr17RodWt9ocsDAAAAJoVl/wDK3pb2Ht2yqU079ycUCfrUUBlUJOjTzv0DumVTm7a09xS6RAAAAGBSCP8Ayprretq4uUPJbF6NsZBCfkumaSjkt9QYCyqZdbRxc4dc1yt0qQAAAMCEEf5RllzXU9veuDbvOqi2vXGCXRnb0ZlQR3dSNeGADGP0/n7DMFQd9qujO6kdnYkCVQgAAABMHnv+UXbY243D9aZysh1PAWvse6FBy1Tc9dSbys1wZQAAAMDU4ck/ygp7u3Gk2nBAfstQznHHfD3ruPKbhmrDgRmuDAAAAJg6hH+UDfZ2YyxLmmJqaYiqL2XL80b/2Xuep/6UrZaGqJY0xQpUIQAAADB5hH+UDfZ2YyymaWj9mhZFg5a6ElmlbUeu6yltO+pKZBUNWlq/pkWmaRz/YgAAAECRIvyjbJzI3m6bvd1laXVrvW69YqkWz6tUKptXdzKrVDavxfMqdesVS+kFAQAAgFmPhn8oG4fv7Q6Z1lGvs7e7vK1urdfKRXXa0ZlQbyqn2nBAS5piPPEHAABASSD8o2wM7+3euX9AjTFz1NL/4b3di+dVsre7jJmmoaXzqwpdBgAAADDlWPaPssHebgAAAADlivCPssLebgAAAADliGX/KDvs7caxuK7HzwUAAABKEuEfZYm93TjSlvYebdzcoY7upGzHk98y1NIQ1fo1LawIAQAAwKzHsn8AZW9Le49u2dSmnfsTigR9aqgMKhL0aef+Ad2yqU1b2nsKXSIAAAAwKYR/AGXNdT1t3NyhZDavxlhIIb8l0zQU8ltqjAWVzDrauLlDrusVulQAAABgwgj/AMrajs6EOrqTqgkHRo1/lCTDMFQd9qujO6kdnYkCVQgAAABMHuEfQFnrTeVkO54C1tj/HAYtU7brqTeVm+HKAAAAgKlD+AdQ1mrDAfktQznHHfP1rOPKbxqqDQdmuDIAAABg6hD+AZS1JU0xtTRE1Zey5Xmj9/V7nqf+lK2WhqiWNMUKVCEAAAAweYR/AGXNNA2tX9OiaNBSVyKrtO3IdT2lbUddiayiQUvr17TINI3jXwwAAAAoUoR/AGVvdWu9br1iqRbPq1Qqm1d3MqtUNq/F8yp16xVLtbq1vtAlAgAAAJPiK3QBAFAMVrfWa+WiOu3oTKg3lVNtOKAlTTGe+AMAAKAkEP4B4HWmaWjp/KpClwEAAABMOZb9AwAAAABQ4gj/AAAAAACUOJb9A2XOdT32uQMAAAAljvAPlLEt7T3auLlDHd1J2Y4nv2WopSGq9Wta6HAPAAAAlBCW/QNlakt7j27Z1Kad+xOKBH1qqAwqEvRp5/4B3bKpTVvaewpdIgAAAIApQvgHypDretq4uUPJbF6NsZBCfkumaSjkt9QYCyqZdbRxc4dc1yt0qQAAAACmAOEfKEM7OhPq6E6qJhyQYYze328YhqrDfnV0J7WjM1GgCgEAAABMJcI/UIZ6UznZjqeANfY/AUHLlO166k3lZrgyAAAAANOB8A+UodpwQH7LUM5xx3w967jym4Zqw4EZrgwAAADAdCD8A2VoSVNMLQ1R9aVsed7off2e56k/ZaulIaolTbECVQgAAABgKhH+gTJkmobWr2lRNGipK5FV2nbkup7StqOuRFbRoKX1a1pkmsbxLwYAAACg6BH+gTK1urVet16xVIvnVSqVzas7mVUqm9fieZW69YqlWt1aX+gSAQAAAEwRX6ELAFA4q1vrtXJRnXZ0JtSbyqk2HNCSphhP/AEAAIASQ/gHypxpGlo6v6rQZQAAAACYRiz7BwAAAACgxBU0/G/cuFHLli1TLBZTLBbTqlWrdP/994+8nslkdP3116uurk7RaFQf+MAHdODAgVHX2L17ty6//HKFw2E1NDToL/7iL5TP50ed89hjj+m8885TMBhUa2ur7rrrrqNq+frXv65TTjlFoVBIF154obZt2zYt3zMAAAAAADOtoOF//vz5+vznP6/t27friSee0CWXXKL3vve92rFjhyTppptu0s9+9jP95Cc/0ebNm9XZ2an3v//9I5/vOI4uv/xy5XI5bdmyRXfffbfuuusuffaznx0555VXXtHll1+uiy++WE8//bRuvPFGXXfddXrwwQdHzvnRj36kDRs26G//9m/15JNP6uyzz9batWvV3d09c28GAAAAAADTxPCOHPJdYLW1tfqnf/onffCDH9ScOXN077336oMf/KAk6YUXXtDixYu1detWrVy5Uvfff7/e/e53q7OzU3PnzpUkffOb39TNN9+sgwcPKhAI6Oabb9Z9992n5557buRrfPjDH1Z/f78eeOABSdKFF16oCy64QLfffrskyXVdNTc364YbbtCnP/3pE6o7kUioqqpK8XhcsRiz0QEAAAAA02s8ObRo9vw7jqMf/vCHGhwc1KpVq7R9+3bZtq1LL7105JwzzjhDCxYs0NatWyVJW7du1dKlS0eCvyStXbtWiURiZPXA1q1bR11j+Jzha+RyOW3fvn3UOaZp6tJLLx05ZyzZbFaJRGLUBwAAAAAAxajg4b+trU3RaFTBYFCf+MQntGnTJp155pnq6upSIBBQdXX1qPPnzp2rrq4uSVJXV9eo4D/8+vBrb3ZOIpFQOp1WT0+PHMcZ85zha4zltttuU1VV1chHc3PzhL5/AAAAAACmW8HD/+mnn66nn35av/3tb7V+/XqtW7dOzz//fKHLOq7PfOYzisfjIx979uwpdEkAAAAAAIzJV+gCAoGAWltbJUnLly/X448/rq985Sv60Ic+pFwup/7+/lFP/w8cOKDGxkZJUmNj41Fd+YenARx+zpETAg4cOKBYLKaKigpZliXLssY8Z/gaYwkGgwoGgxP7pgEAAAAAmEEFf/J/JNd1lc1mtXz5cvn9fj388MMjr7344ovavXu3Vq1aJUlatWqV2traRnXlf+ihhxSLxXTmmWeOnHP4NYbPGb5GIBDQ8uXLR53juq4efvjhkXMAAAAAAJjNCvrk/zOf+Yze9a53acGCBRoYGNC9996rxx57TA8++KCqqqr0sY99TBs2bFBtba1isZhuuOEGrVq1SitXrpQkvfOd79SZZ56pq666Sl/4whfU1dWlv/7rv9b1118/8lT+E5/4hG6//Xb95V/+pa699lo98sgj+vGPf6z77rtvpI4NGzZo3bp1Ov/887VixQp9+ctf1uDgoK655pqCvC8AAAAAAEylgob/7u5uffSjH9X+/ftVVVWlZcuW6cEHH9Rll10mSfrSl74k0zT1gQ98QNlsVmvXrtU3vvGNkc+3LEs///nPtX79eq1atUqRSETr1q3T5z73uZFzFi5cqPvuu0833XSTvvKVr2j+/Pn6zne+o7Vr146c86EPfUgHDx7UZz/7WXV1demcc87RAw88cFQTQAAAAAAAZiPD8zyv0EWUgvHMVwQAAAAAYLLGk0OLbs8/AAAAAACYWoR/AAAAAABKHOEfAAAAAIASR/gHAAAAAKDEEf4BAAAAAChxhH8AAAAAAEoc4R8AAAAAgBJH+AcAAAAAoMQR/gEAAAAAKHGEfwAAAAAAShzhHwAAAACAEkf4BwAAAACgxBH+AQAAAAAocYR/AAAAAABKHOEfAAAAAIASR/gHAAAAAKDE+QpdAAAUC9f1tKMzod5UTrXhgJY0xWSaRqHLAgAAACaN8A8Akra09+gbj7Xrha4B2XlPfp+hMxor9advb9Xq1vpClwcAAABMCuEfQNnb0t6jm378tHoHc/K81w/mpN++0quXup/Wl/7gHG4AAAAAYFZjzz+Asua6nm67f6cODmTlep4s05DPNGSZhlzP08GBrG67f6dc1zv+xQAAAIAiRfgHUNba9sX1YldShiS/aco0DBmGIdMw5DdNGZJe7EqqbV+80KUCAAAAE0b4B1DWntrTr7zryrKGQv/hDMOQZRnKu66e2tNfmAIBAACAKUD4B1DWjOHV/Mda1e8dcR4AAAAwCxH+AZS1cxZUy2+ZclxP3hF3ADx5clxPfsvUOQuqC1MgAAAAMAUI/2UonraVsZ1ClwEUhaUnVem0uVF5kuy8K9cbugngep7svCtP0mlzo1p6UlWhSwUAAAAmjPBfhmzH1f54Rv2pXKFLAQrONA195l2LNacyKNM05Liu7Lwrx3VlmoYaKoP6zLsWyzSN418MAAAAKFKE/zLleZ56B3Pq7E8r77iFLgcoqNWt9frSH5yjCxfWqSYcVDTkV004qAsX1umLf3COVrfWF7pEAAAAYFJ8hS4AhZWxHe3tS6u+MqhokB8HlK/VrfVauahOOzoT6k3lVBsOaElTjCf+AAAAKAmkPcj1PHUnMkqFfKqPBAk7KFumaWjpfPb2AwAAoPSw7B8jkpm89vWnaQYIAAAAACWG8I9RbMdVZ39avYM5eR6DzQEAAACgFBD+Mab+VE6d8YxsmgECAAAAwKxH+McxZW1H+/rSSmTsQpcCAAAAAJgEwj/elOt56hnI6kAiI8dlGwAAAAAAzEaEf5yQwWxe+/rSSudoBggAAAAAsw3hHycs77raH0/rUDJLM0AAAAAAmEUI/xi3eNrWvv60cnmaAQIAAADAbED4x4Tk8q729acVT9MMEAAAAACKHeEfE+Z5ng4ls+qKZ5RnJCAAAAAAFC3CPyYtlctrX39ag9l8oUsBAAAAAIyB8I8p4bieDiQyOjiQlctIQAAAAAAoKoR/TKmBzFAzwIzNSEAAAAAAKBaEf0w523G1P55RfypX6FIAAAAAACL8Y5p4nqfewZw6+9M0AwQAAACAAiP8Y1plbEd7+9JK0gwQAAAAAAqG8I9p53qeuhMZdQ9kaAYIAAAAAAVA+MeMSWbyNAMEAAAAgAIg/GNG0QwQAAAAAGYe4R8zjmaAAAAAADCzCP8oGJoBAgAAAMDMIPyjoGgGCAAAAADTj/CPokAzQAAAAACYPoR/FA2aAQIAAADA9CD8o6gMNwPcH6cZIAAAAABMFcI/ilI652hff1qDNAMEAAAAgEkj/KNoOa6nAzQDBAAAAIBJI/yj6NEMEAAAAAAmh/CPWcF2XHX2p9U7mJPnsQoAAAAAAMaD8I9ZpT+V077+tHJ5mgECAAAAwInyFboAYLxyeVf7+tOqjQRUVeEvdDkoIa7raUdnQr2pnGrDAS1pisk0jUKXBQAAAEwa4R+zkud5OpTMKp1zVB8NyGexiAWTs6W9Rxs3d6ijOynb8eS3DLU0RLV+TYtWt9YXujwAAABgUkhMmNVSuaFmgKkcIwExcVvae3TLpjbt3J9QJOhTQ2VQkaBPO/cP6JZNbdrS3lPoEgEAAIBJIfxj1nNcT13xjHqSWZoBYtxc19PGzR1KZvNqjIUU8lsyTUMhv6XGWFDJrKONmzsYNwkAAIBZjfCPkpFI2zQDxLjt6EyoozupmnBAhjF6f79hGKoO+9XRndSOzkSBKgQAAAAmj/CPkjLcDDCetgtdCmaJ3lROtuMpcIy+EUHLlO166k3lZrgyAAAAYOoQ/lFyhpsBdsUzcliqjeOoDQfktwzlnLFXjGQdV37TUG04MMOVAQAAAFOH8F9mXjowoOfLZPlyKpfXvr600jmn0KWgiC1piqmlIaq+lH1UzwjP89SfstXSENWSpliBKgQAAAAmj/BfRjzP019tek7r7tim//ffL6q/DJYx511X++NpHaIZII7BNA2tX9OiaNBSVyKrtO3IdT2lbUddiayiQUvr17TINI3jXwwAAAAoUoT/MvJfz3Rq26u98iT9oq1LH73jcW16al9ZLI2P0wwQb2J1a71uvWKpFs+rVCqbV3cyq1Q2r8XzKnXrFUu1urW+0CUCAAAAk2J4PA6dEolEQlVVVYrH44rFinN58LZXevVXm9r0Undy1PFF9RHdcEmrzm6uLkxhM8gwDNVFA4qF/IUuBUXIdT3t6EyoN5VTbTigJU0xnvgDAACgaI0nhxL+p8hsCP+SZDuuNj7WoW9t7tDgEXvhLz59jj6xpkVzKoMFqm7mRII+1UeDsgh2AAAAAGap8eRQlv2XGb9l6soLF+jua1do7ZK5o1579MWDWnfnNt37290lvzx+MEszQAAAAADlgyf/U2S2PPmXpJ5kVom0LUl6vjOhrz3SrhcPDIw6Z35Nhf707S1auaiuECXOqOpwQDVhvwyDVQAAAAAAZg+W/RfAbA3/kuR6nu5v69J3fvWK4ocdl6SVi2p1/cWtOqm6YqbLnFFBv6WGyqD8FothAAAAAMwOM7Ls/3vf+57e8pa3qKmpSa+99pok6ctf/rL+8z//c6KXRIGYhqHLl83TPddeoPed06TDt8H/5uVeXXvX4/rur15R2i7dJfJZ29G+vrQGMvbxTwYAAACAWWZC4X/jxo3asGGDfvd3f1f9/f1ynKFQWF1drS9/+ctTWR9mUGXIr0+941R966rlWja/auS47Xj6/m936+o7HtejL3SrVBeLuJ6ngwNZdScycstg/CGO5rqe2vbGtXnXQbXtjfNzAAAAgJIxoWX/Z555pm699Va9733vU2VlpZ555hktWrRIzz33nN7+9rerp6dnOmotarN52f9YPM/Toy8e1Dc3d6gnmRv12jnNVbrhklO1sD4ynWUWlN8yNacyqJDfKnQpmCFb2nu0cXOHOrqTsh1PfstQS0NU69e0aHVrfaHLAwAAAI4y7cv+X3nlFZ177rlHHQ8GgxocHJzIJVFkDMPQJWc06O5rVujKFc3yW2/sBXh6T1x/fM8Tuv2RdiUz+QJWOX1sx1Vnf1p9g7njn4xZb0t7j27Z1Kad+xOKBH1qqAwqEvRp5/4B3bKpTVvay++GJgAAAErLhML/woUL9fTTTx91/IEHHtDixYsnWxOKSEXA0nVvW6TvrjtfKxfVjhx3Pek/ntqnj96xTb9o2y+3RLcC9KVy6uxPy3ZKe/RhOXNdTxs3dyiZzasxFlLIb8k0DYX8lhpjQSWzjjZu7mALAAAAAGY130Q+acOGDbr++uuVyWTkeZ62bdumH/zgB7rtttv0ne98Z6prRBGYXxPWrVcs1daOQ/r6Y+3q7M9IkvrTtv7ff+/Sz57dr09d0qrF84p7y8NEZF5vBlhfGVQ0OKG/MihiOzoT6uhOqiYcOGrco2EYqg771dGd1I7OhJYe1gsDAAAAmE0mlGSuu+46VVRU6K//+q+VSqV05ZVXqqmpSV/5ylf04Q9/eKprRBFZ1VKn5SfX6Cfb9+j7v9mtTH7oifiLXQO6/t6n9K6zGnXd2xaqJhwocKVTy/U8dScySoV8qo8EZZrG8T8Js0JvKifb8RQ4xpjHoGUq7nrqTbEFBAAAALPXhBr+HS6VSimZTKqhoWGqapqVSq3h34noTmT0rV++rEdfPDjqeCRo6erVp+h955wkqwRDMs0AS0vb3rg+/r0nFAn6xvwzTduOUtm8vnXV+Tz5BwAAQFGZkYZ/L730kiQpHA6PBP+XXnpJr7766kQuiVmoIRbS37z7TP3z7y8b1fl/MOvo64926I/veUJP7e4rYIXTw3Zc7Y9n1M+T4JKwpCmmloao+lL2UWMsPc9Tf8pWS0NUS5qK+6YeAAAA8GYmFP6vvvpqbdmy5ajjv/3tb3X11VdPtibMMucuqNG3r1quT17cokjwjSenrx5K6c9/8qw+97Pn1Z3IFLDCqed5nnoHc9ofTytPM8BZzTQNrV/TomjQUlciq7TtyHU9pW1HXYmsokFL69e0sNUDAAAAs9qElv3HYjE9+eSTam1tHXW8vb1d559/vvr7+6eqvlmjHJf9j6UvldN3//cV/eK5rlHHQz5TV164QH9wfrMCvgndcypalmmoPjo0Gg6z15b2Hm3c3KGO7qRs15PfNNTSENX6NS1a3Vpf6PIAAACAo4wnh04orRiGoYGBgaOOx+NxOY4zkUuiRNSEA/o/a0/X5cvm6auPtOvFrqGfk0ze1R2/flUP7OjS9W9v1aqWugJXOnUc19OBREaVIb/qo0d3jMfssLq1XisX1WlHZ0K9qZxqwwEtaYrxxB8AAAAlYUJP/t/znveooqJCP/jBD2RZQ8u8HcfRhz70IQ0ODur++++f8kKLHU/+j+Z6nh58rkv/8r+vqP+Ir3fhwlpdf3GL5teEp72OmeQzDfUkcxrI5gmPAAAAAKbVeHLohML/888/r4suukjV1dV629veJkn63//9XyUSCT3yyCM666yzJlb5LEb4P7ZkJq+7tryqnz69T+5hP21+y9DvL5+vj1x4sioCs79z/lO7+3Tvtj3ac2hQjicFfSbLxgEAAABMm2kP/5LU2dmp22+/Xc8884wqKiq0bNkyffKTn1Rtbe2Eip7tCP/H9/LBpG5/tF1P74mPOl4fDegTa1p08elzZu2S+ad29+mLD+1SKucoFvLLbxlyXE+JTF6VIZ9uvWIpNwAAAAAATKkZCf8YjfB/YjzP0+ZdB7XxsZd1MJkd9drZ86v0yUta1TInWpDaJsr1PN387216+WByaM+/3riB4UnqHczpzKaY7r5mBVsAAAAAAEyZaWn49+yzz+qss86SaZp69tln3/TcZcuWnehlUWYMw9DbT2/QhYvqdO9vd+vHT+yR7Qzdf3pmb1wf/952vfeck3T16pNVGfIXuNoT035gUHsODSoW8o8K/pJkSIoEfdrVNaDn9sW1rLm6IDUCAAAAKG8nHP7POeccdXV1qaGhQeecc44Mw9BYiwYMw6DjP46rwm/pY29dqN9Z0qivP9au37zcK0lyPWnTU/v0yAvduu6tC/WupY0yi3wrQDyTk+16illj1xmwDA1kXb10MKkz5sVKbtQhAAAAgOJ3wuH/lVde0Zw5c0b+NzAVTqqp0K1XLNVvXj6krz/aoX39aUlSPG3rnx/apZ8/u1+fekerFs8r3q0UVaGA/KYh2/EU9B19AyDnePIbhiJ+n/b1p1UbCaiqYnasagAAAABQGk74EeTJJ58swzBk27b+/u//Xq7r6uSTTx7zAxivlYvq9N115+u6ty5UyP/Gj+WLBwZ0/b1P6QsPvKjewVwBKzy21rkRNddFlMjY8jR6NYwnTwMZW811EbXOjcjzPB1KZtUVz8hxabcBAAAAYGaMe/2x3+/Xv//7v09HLShzAZ+pKy9coLuvWaFLzmgY9doDO7q07o5t+rfte5V33AJVODbTMHTlimaFA5Z6kjll8q5cz1Mm76onmVM4YOnKFc2jti+kcnnt60srnWOLDAAAAIDpN6HNx+973/v005/+dIpLAYbMqQzqry9frC/9wdlaVB8ZOT6Yc/SNxzr0J9/brid39xWwwqOdu6BGGy47TYvmRJXJ5XUolVMml9eiOVFtuOw0nbug5qjPybuu9sfTOpTMjtk/AwAAAACmyoRG/f3DP/yD/vmf/1nveMc7tHz5ckUikVGvf+pTn5qyAmcLRv1ND8f19F/PdOrOX7+qZDY/6rWLTqvX+jUtmhsLFai6o7mep/YDg4pncqoKBdQ6N3JCDQsDPlMNlSGaAQIAAAA4YePJoRMK/wsXLjz2BQ1DL7/88ngvOesR/qdXfyqn7/7qVf2ibf+oXfXB17cKfOj85lkfnA3DUF00oNgsGXEIAAAAoLCmPfwfbvjTjSIfxzbdCP8z48WuAX31kZe0c//AqOPzqkL607e3aHVL3az/WYwEfaqPBmWZs/v7AAAAADC9xpNDJ/yo9Lvf/a7OOusshUIhhUIhnXXWWfrOd74z0csBJ+T0xkp97Q/P1V+uPV014TeekO+PZ/Q3/7lDn/mPNu3pTRWwwskbzA41A8zYNAMEAAAAMDV8E/mkz372s/riF7+oG264QatWrZIkbd26VTfddJN2796tz33uc1NaJHA40zD0O2c16q2n1uuera/qP57cp+Gpedte7dOTdz+hDy6frz9auUDhwIR+xAsu77rq7E+rOhxQTdg/61czAAAAACisCS37nzNnjr761a/qD//wD0cd/8EPfqAbbrhBPT09U1bgbMGy/8J59dCgbn+kXU/u7h91vC4a0CcuWqRLzmiY1eE56LfUUBmU35rdPQ0AAAAATK1pX/Zv27bOP//8o44vX75c+Xx+jM8Aps8pdRH90weX6e/ec6YaKoMjxw8lc/q/v3hBN/7oGXV0JwtY4eRkbUf7+tIayJTODRsAAAAAM2tC4f+qq67Sxo0bjzr+7W9/Wx/5yEcmXRQwXoZh6KLT5uiuay7QVSsXyG+98aS/bV9cH//X7frKwy/N2hUPrufp4EBW3YmMXHdSPToBAAAAlKEJLfu/4YYbdM8996i5uVkrV66UJP32t7/V7t279dGPflR+/xuN2L74xS9OXbVFjGX/xaWzP62Nj3Xo1x2HRh2PhXy67m0L9a6z5s3abvo+09ScyqAqAlahSwEAAABQQNM+6u/iiy8+ofMMw9Ajjzwy3svPSoT/4rTtlV7d/mi79valRx0/bW5UN1zSqiVNVQWqbPJiFX7VRQKzup8BAAAAgImb9vCPoxH+i5ftuPr37Xv1vd/sVvqI8XnvPHOu/uSiRaqNBApU3eT4raFVACE/qwCmgut62tGZUG8qp9pwQEuaYjJn6QoRAAAAlD7CfwEQ/otfTzKrb//yZf3Pzu5Rx8MBS+tWnawrzj1JvlnYUd8wDFVX+FUzS29gFIst7T3auLlDHd1J2Y4nv2WopSGq9WtatLq1vtDlAQAAAEch/BcA4X/2eHZvv772SLs6Dg6OOn5ybVifvKRVy0+uKVBlkxP0W5oTDSrgm303MAptS3uPbtnUpmQ2r5pwQAHLVM5x1ZeyFQ1auvWKpUV5A4CVCgAAAOWN8F8AhP/ZxXE9/fzZTt3x61c1kBk9nvJtp9Zr/dtb1BgLFai6iTMMQ7WRgKoq/Mc/GZKGAvS6O7dp5/6EGmOhUT0UPM9TVyKrxfMqdfc1K4oqWLNSAQAAAOPJoTwiRFmyTEPvPeck3XPNCr1n2TwdHun+96UeXX3n47p7y6vKHtEjoNh5nqdDyaw6+9PK5d1ClzMr7OhMqKM7qZrw0c0TDcNQddivju6kdnQmClTh0YZXKuzcn1Ak6FNDZVCRoE879w/olk1t2tLeU+gSAQAAUGQI/yhrVWG/brrsNG38o/N05rw37pTl8q7u3vqarrnrCf3qpR7NtgUyGdvRvv604qnyXuFxInpTOdmOp8Ax+j0ELVO266k3lZvhysbmup42bu5QMptXYyykkN+SaRoK+S01xoJKZh1t3Nwh151dP7MAAACYXoR/QNJpcyv11T88R5/+ndNVE35jyXxXIqPP/tcO3fzvbdp9KFXACsfP8zwdGsxqH6sA3lRtOCC/ZSjnjP0eZR1XftNQbbg4GirOxpUKAAAAKDzCP/A60zD0ziWNuufaFfr95fNlHba/+4nX+vSxe57QNzd3KJXLv8lVik/29VUA/UXy5LrYLGmKqaUhqr6UfdQKD8/z1J+y1dIQ1ZKm4ujlMdtWKgAAAKA4EP6BI0SCPq1/e4u+89HlWr6geuS443r68RN7te6Ox/XQ8wdm1VYAz/PUO5jTvv60svnZ1cdgupmmofVrWhQNWupKZJW2Hbmup7TtqCuRVTRoaf2alqJp9jfbVioAAACgOBD+y1BNOKDqcECmURxhplidXBfRFz64TH/3e2dqbiw4cvzQYE633f+C/uyHT+ulAwMFrHD8srajzv4MvQCOsLq1XrdesVSL51Uqlc2rO5lVKpvX4nmVRTfmb7atVAAAAEBxYNTfFJlNo/6GOa6nRNpWPG3L5cfgTWVsRz98fI9++PieUfvnTUN697ImXfOWU2bdeL2KgKU50aB8x1g+Xo5c19OOzoR6UznVhgNa0hQrmif+hxvu9p/M5lXht2QYkudJadtRNOgruhsWAAAAmB7jyaGE/ykyG8P/MNf1lMgM3QRw6BD+pvbH0/rGYx36dfuhUcdjIZ+ufetCXb503qheAcXOMg3VRYOKBn2FLgXj9C+/7NDXH+vQQNqWJ8mQVFnh1/Vvb9EfX9RS6PIAAAAwAwj/BTCbw/8wbgKcuMdf7dXtj7RrT1961PHWhqg+dUmrzjqpqkCVTUw05FN9JFiUT7lxtOEn/wMZW+GAT6ZhyPU8pXKOKkM8+QcAACgXhP8CKIXwP8zzPCXSecXTtvIuI+KOxXZc/ceT+3TP1teUtkc30bt0cYM+ftEi1UWDx/js4uO3TM2pDCrktwpdCt6E63pad+c27dyfUGMsNGrcn+d56kpktXhepe6+ZgU3cwAAAErceHIom31xFMMwVBX2q7m2QnXRoHwmPyZj8VumPnRBs+659gJddubcUa/9z85uffSOx/XDx/fIPkZX9mJjO646+9PqHczNqkkG5WZHZ0Id3UnVhAOjgr809He3OuxXR3dSOzoTBaoQAAAAxYhUh2MyDENVFdwEOJ66aFCfedcZ+uqHz1HrnOjI8bTt6Nu/fFnX3f2EHn+1t4AVjk9/KqfOeGZUY0MUj95UTrbjKXCMRo1By5TteupN5Wa4MgAAABQz0hyO6/CbAPWVQfnpDj+ms06q0sY/Ok83XnqqYqE3Gujt6Uvr5n9v09/89Dntj6ff5ArFY2gkYFqJDCMBi01tOCC/ZSh3jBUlWceV3zRUGw7McGUAAAAoZqQ4nDDDMBQL+TW/pkJzuAkwJss09HtnN+nua1fo985u0uFbrn/dcUjX3PWE7vr1q8oc0SOgGLmep56BrA4kMjSALCJLmmJqaYiqL2UftT3D8zz1p2y1NES1pGl29x4BAADA1CK9YdwMw1BlyK/m2jA3AY6hqsKvGy89VRs/cp7OOiyE5fKu7vnNa7r6zsf1y5cOzoq99YPZvPb1pZXOje+Ghet6atsb1+ZdB9W2Ny6XGwhTwjQNrV/TomjQUlciq7TtyHU9pW1HXYmsokFL69e00OwPAAAAo9Dtf4qUUrf/iUhm8+pP5dgnPgbP8/Q/O7v17V++rEODo/dhL19QrU9e0qqT6yIFqm58qir8qo0c3WjuSFvae7Rxc4c6upOyHU9+y1BLQ1Tr17Qwgm6KjHqPXU9+k/cYAACg3DDqrwDKPfwPG8zm1Z+2lZ0Fy9pnWiqX1/e2vqZ/e3LfqGX0lmno/eeepI+uOlmRoO9NrlAcAr6hkYBB39gjAYdn0CezedWEAwpYpnKOq76UrWjQYgb9FHJdTzs6E+pN5VQbDmhJU4wn/gAAAGWE8F8AhP/RUrm8+lP2rNjbPtN2H0rp9kfb9cRrfaOO14T9+pOLFumyM+fKPM6T9UIzjKGGclVh/6jjzKAHAAAAZs54ciibtTEtwgGfmqor1FRdMSueZs+kBXVh/eMHlupzv7dEjbHQyPG+lK1/fOBFfeoHT2vXgYECVnh8nufp0GBWXfGM8od1nWcGPQAAAFCcCP+YViG/pbmxkObXhFUZ8h93r3i5MAxDbz21Xndefb7WrTpZAd8bfxWf35/Q+n99Ul98aJfiqeIetZfK5bWvP63BbF4SM+gBAACAYkX4x4wY3ifeXFOh6nCg6Je1z5Sg39K61aforqsv0NtOfWMfvCfp58/u10fv3KafPrWvqEftOa6nA4mMDg5kVVPhZwZ9CWBSAwAAQOlhz/8UYc//+Liup0TGViKdV95lQsCw7a/16fZH2vVab2rU8ZY5Ed1wSauWza8uTGEnyGca+sx/tOnFA0k1xoLs+Z+FmNQAAAAwe7DnH0XPNA1VhwNqrq1QfWVQ/mMsEy83y0+u0b98dLnWr1mkcOCNbvodBwd144+e0f+9b6cODmQLWOGby7ue3n/efFX4TWbQz0LDkxp27k/IMg1VBExZpqGd+xO6ZVObtrT3FLpEAAAATBBP/qcIT/4njzGBo/UO5vQv//uyHtxxYNTxkN/UVStP1geXzy/amyZP7e7Tjx7foz29KeU9MYN+Fhie1PDs3n7lHU85x5XnSYYhBSxTPsvQsvnVrNoAAAAoIoz6KwDC/9TJ2I76U7ZSuXyhSykKOzrj+toj7dp1IDnq+PyaCn3y4latWFhboMrenOt5evngoGRI82IVzKAvcm1747r6zm2Kp4eaTFqmIcOQPE8jPSeqKvy665oVWjq/qpClAgAA4HUs+8esFvJbaqwamhAQDfnKfkLAkqYqff3K87ThstMUC70xNnFvX1qf/o82/c1Pn1Nnf7qAFY7NNAy1NkTVOieqk2oqCP5F7lAyq0TGlud58lmGTMOQoaH/+ixDnjfUp+NQsni3nQAAAODYCP8oWgGfqYbKkJprKhSrKO8xgZZp6N3L5umea1fovec06fAc/euOQ7rmrsd1569fUaZIt0z0p3Lq7E8rf4wpACi8vpQt1/VkmkOh/3CGDJmmIdf11Ffk4ycBAAAwNsI/ip7PMlUfDWpBbbjsxwTGKvz6s3ecqm/90XItPemNpde24+l7v9mtq+98XJt3HVQx7ubJ2I729afZzlGkqiP+oYDveUf9/HieJ9cbujFQHfEXqEIAAABMBuEfs4ZlGqqNBLSgNqzaSEBWGS8jb2mI6ssfOlt/9buLVRcNjBzvHsjq73/2vP7i357Vq4cGC1jh2BzXU1c8o0PJbFHeoChn9ZGgYiGfTMOQ7XojNwFcz5PtejINQ7GQT/WRYKFLBQAAwAQQ/jHrDI8JXFAbVl20fMcEGoahdyxu0D3XrNCHL2iW77CbIU/u7tcf37Nd33isXcls8T1pj6dtdcYzyuXZBlAsljTFdGZTlSr8PoV8plzPU/71mwAhn6kKv09nNlVpSRMNTQEAAGaj8kxNKAmGYaiqwq/5NRWaUxlUwFeeP84VAUt/ctEifXfd+aM6/zuup3/bvk/r7timB57rkltkT9qzr28D6E/lCl0KNHRTbf2aFtVG/Ar5Lc2NhXRSdYXmxkIK+S3VRvxav6aFxo0AAACzFKP+pgij/opDKpdXf8ou2sZ3083zPG19+ZC+/miH9sczo147c16lbrjkVJ3eWFmg6o4t6Lc0J1q+N3CKyZb2Hm3c3KGO7qRs15PfNNTSENX6NS1a3Vpf6PIAAABwmPHkUML/FCH8F5eM7SietjVYhEveZ0Iu7+pHT+zRvb/drexhS+sNSe9a2qjr3rpQ1eHAsS9QAIZhqCbsL7q6ypHretrRmVBvKqfacEBLmmI88QcAAChChP8CIPwXp1zeVX86p8GsU5YN5g4kMvrm5pe1edfBUcejQZ+uecsp+r2zm4qucSKrAAAAAIATQ/gvAMJ/cbMdV/0pW8lsvixvAjy5u09fe6Rdrx1KjTq+qD6iG97RqrPnVxemsGNgFQAAAABwfIT/AiD8zw55x1U8bWsgky+6BnjTLe+4+unTnbp7y6sazI3uiXDx6XP0iTUtmlNZXGPcgn5L9dGAgj6r0KUAAAAARYfwXwCE/9nFcT3F07YSabvsbgL0Dub0L//7sh7ccWDU8ZDf1B9deLI+uHx+US25H57qUBP2yzCKa4sCAAAAUEiE/wIg/M9OruspkbEVT9ty3PL6q/B8Z0Jfe6RdLx4YGHV8fk2F/vTtLVq5qK5AlY3Nb5mqjwZVEWAVAAAAACAR/guC8D+7letNANfzdH9bl77zq1cUT9ujXlu5qFbXX9yqk6orClTd2KIhn+oiwaJrVAgAAADMtPHk0IKu7b3tttt0wQUXqLKyUg0NDXrf+96nF198cdQ5mUxG119/verq6hSNRvWBD3xABw6MXq68e/duXX755QqHw2poaNBf/MVfKJ8fPeLtscce03nnnadgMKjW1lbdddddR9Xz9a9/XaeccopCoZAuvPBCbdu2bcq/ZxQn0zRUHQ6ouSas2kigbIKlaRi6fNk83XPtBXrfOU06/Nv+zcu9uvaux/XdX72itO0c+yIzLJnJa29fSskyHeMIAAAATERBw//mzZt1/fXX6ze/+Y0eeugh2batd77znRocHBw556abbtLPfvYz/eQnP9HmzZvV2dmp97///SOvO46jyy+/XLlcTlu2bNHdd9+tu+66S5/97GdHznnllVd0+eWX6+KLL9bTTz+tG2+8Udddd50efPDBkXN+9KMfacOGDfrbv/1bPfnkkzr77LO1du1adXd3z8ybgaJQrjcBKkN+feodp+pbVy3XsvlVI8dtx9P3f7tbV9/xuB57sbtoJiU4rqfuREZd8Yxsxy10OQAAAEDRK6pl/wcPHlRDQ4M2b96siy66SPF4XHPmzNG9996rD37wg5KkF154QYsXL9bWrVu1cuVK3X///Xr3u9+tzs5OzZ07V5L0zW9+UzfffLMOHjyoQCCgm2++Wffdd5+ee+65ka/14Q9/WP39/XrggQckSRdeeKEuuOAC3X777ZIk13XV3NysG264QZ/+9KePWzvL/ktTOW4H8DxPj754UN/c3KGeZG7Ua+c0V+uGS1q1sD5SoOqONjwWsKqChoAAAAAoL7Nm2f+R4vG4JKm2tlaStH37dtm2rUsvvXTknDPOOEMLFizQ1q1bJUlbt27V0qVLR4K/JK1du1aJREI7duwYOefwawyfM3yNXC6n7du3jzrHNE1deumlI+ccKZvNKpFIjPpA6Tl8JUC57DM3DEOXnNGgu69ZoT9c0SzfYd/z03v69cf3PKHbH2lXMlMcy+49z1PvYE57+9LKFNH2BAAAAKCYFE34d11XN954o97ylrforLPOkiR1dXUpEAiourp61Llz585VV1fXyDmHB//h14dfe7NzEomE0um0enp65DjOmOcMX+NIt912m6qqqkY+mpubJ/aNY1YwTUNVYb8W1A7dBPCZRfNXZ9pUBCz98dsW6Y6rz9eFC2tHjrue9B9P7dNH79im+9v2H3NUout52tWV1OOv9mpXV3LaRyrajqvO/rS6BzJls0oDAAAAOFG+Qhcw7Prrr9dzzz2nX/3qV4Uu5YR85jOf0YYNG0b+fyKR4AZAGTCMoZsAsQqfEpm84ilbebe095zPrwnrtvcv1daOQ/r6Y+3q7M9IkvrTtv7pv3fpZ8/u1w2XtGrxvDeWGT21u0/3btujPYcGZbue/Kah5rqIrlzRrHMX1ExrvclMXumco5pIQLGQf1q/FgAAADBbFMXjy09+8pP6+c9/rkcffVTz588fOd7Y2KhcLqf+/v5R5x84cECNjY0j5xzZ/X/4/x/vnFgspoqKCtXX18uyrDHPGb7GkYLBoGKx2KgPlA/DMFRV4VdzbYXqouWxEmBVS53uWHeBrn3LKQr53vh+X+ga0PX3PqX/9+CL6kvl9NTuPn3xoV16+WBSFQGf6iIBVQR8evlgUl98aJee2t037bU6rqeegaz29aeVzbMVAAAAAChoYvE8T5/85Ce1adMmPfLII1q4cOGo15cvXy6/36+HH3545NiLL76o3bt3a9WqVZKkVatWqa2tbVRX/oceekixWExnnnnmyDmHX2P4nOFrBAIBLV++fNQ5ruvq4YcfHjkHGMvhNwHqK4PyW6V9EyDgM/VHK0/WXddcoLefNmfUa794rksfvWObvvw/L2kwm1d9NKCgz5RpGAr6TNVHA0rlHN27bc+0bwEYlrUddfZn1J/KHf9kAAAAoIQVtNv/n/7pn+ree+/Vf/7nf+r0008fOV5VVaWKigpJ0vr16/WLX/xCd911l2KxmG644QZJ0pYtWyQNjfo755xz1NTUpC984Qvq6urSVVddpeuuu0633nqrpKFRf2eddZauv/56XXvttXrkkUf0qU99Svfdd5/Wrl0raWjU37p16/Stb31LK1as0Je//GX9+Mc/1gsvvHBUL4Cx0O0fw5LZvPpTOeXypb0dQBpa3n/7ox16pWdw1HG/ZWhuZVDhwOidRZm8q0wur8+9d6lOa4zOZKkK+i3NiQYV8JX2DRoAAACUj/Hk0IKG/2ON5brzzjt19dVXS5IymYz+/M//XD/4wQ+UzWa1du1afeMb3xi1HP+1117T+vXr9dhjjykSiWjdunX6/Oc/L5/vjeDx2GOP6aabbtLzzz+v+fPn62/+5m9Gvsaw22+/Xf/0T/+krq4unXPOOfrqV7+qCy+88IS+F8I/jpTK5dWfsku+A73jevrPp/fpzi2vajA7+nuNBn2aEw2MrIhwPU+HUjl9+nfO0AWn1I51uWllGIZqwwFVhekFAAAAgNlv1oT/UkL4x7FkbEf9KVupXHGMxpsufamcvvjfu/TrjkOjjhuSaiMB1YT9yjlewZ78Hy7ktzSnDLZpAAAAoLSNJ4fymy8wzUJ+S41VIZ1UU6FoyHfMFS+zXU04oL9/7xKd0Vgpy3zje/QkHRrM6bXelA4NZtVcF1Hr3EjhCtXQDZl9fWnF03ZB6wAAAABmCuEfmCFBn6WGypDm11QoVuGXWYI3AUzD0B+/baHmVgYUDlg67B6AbMfTYNZRKptXZ3+6cEW+zvU8HUpmtT+eVt4p/f4MAAAAKG+Ef2CG+S1T9dGgmmvDqgkHRj0lLwXnLqjRn7/zdC2eF1Nt2K/gEQ32dnYN6GN3P6F/+d+Xlc4Vvh9COudoX39ag9nS3pYBAACA8sae/ynCnn9MlOd5SmTySqRt2SX0BNr1PLUfGFQ8k1My4+i/ntmnZ/bGR51THw3oE2tadPHpc4piO0Qk4NOBREZ9aVu14YCWNMVkltjNGQAAAJQOGv4VAOEfU6GUxwR6nqfNuw5q42Mv62AyO+q1s+dX6ZOXtKplTuGaAD61u0/3btujPb2Dcl0p4DPV0hDV+jUtWt1aX7C6AAAAgGMh/BcA4R9TKZ1z1J/Ozciy+MOf0FeFAmqdG5nWfgRp29G9v92tHz+xR7bzxj8/piH93tlNuuYtp6gyNLOj+J7a3acvPrRLqZyjoM+UYUimDGUdV9GgT7desZQbAAAAACg6hP8CIPxjOmTzjuIpW8lp2o8+8rT70KBs15PfNNRcF9GVK5p17oKaafmaw/b1p/WNRzu09eXRowGrKvz62FsX6l1nNc5IPwTX83Tzv7fpxa6EHNeT7bjyPMkwhvoz+C1DZzfX6O5rVrAFAAAAAEWFUX9AiQj6LDXEQmquDStW4Z/SffHDT7tfPphURcCnukhAFQGfXj6Y1Bcf2qWndvdN2dcay0nVFfq/V5ylW684SydVV4wcj6dtffGhXfrkvU9p5/7EtNYgSe0HBtXRnVQ65yiXd2UYhizLkGEYyuVdDWYd7dgX147O6a8FAAAAmC6Ef2AWGJ4QsGCKJgS4nqd7t+1RKueoPhpQ0GfKNAwFfabqowGlco7u3bZH7gwsDFq5qE7fXXe+rnvrQoX8b/yT9OKBAV1/71P6xwdeUO9gbtq+fn8qp2TWludJlmXINAwZGvqvZRnyPCmRsfVKT1IslAIAAMBsRfgHZhHLNFQTCWhBbVh10aD81sT+CrcfGNSeQ4OKhfwyNPpGgiFDlSG/9hwaVPuBwako+7gCPlNXXrhAd1+zQhefPmfUaw/uOKB1d2zTT7bvVX4apiHEM7ZcVzJNjflemKbkutL+eEb745lpqQEAAACYboR/YBYyDENVFX7Nr6lQQyykgG98f5XjmdzQHn9r7BUEAcuQ7XmKZ6bviftY5lQG9TfvPlNf+oOztag+MnJ8MOdo42Md+uPvbdeTU7wdIVbhHwr4no56su95nlxv6MZArMKvjO1oX3963I0YXddT2964Nu86qLa9cbkuKwgAAAAws3yFLgDAxBmGoWjQp2jQp1Qur3jaPqFgWhUKyG8ash1PQd/RNwByjie/YagqFJiOso/r7OZqfeuq5fqvZzp1569fHWl4+NqhlP7PT57VRafVa/2aFs2NhSb9tWoqAooGfUpm88q7nixTMiR5khzXk2lI0aBPNRVD74XjetofT6s6HFBt5Pjvz5b2Hm3c3KGO7qRsZ+iGCyMEAQAAMNN48g+UiHDAp3lVFWqqrlBFwHrTc1vnRtRcF1EiY8vTEU+75WkgY6u5LqLWuZFjXGH6WaahK849Sfdce4F+d2njqAX5v9zVo6vvfFzf2/qacvnJLcNvnRtRS0Olgj6fgj5TnufJcT15nqegz1TQ51NLQ+VR70V/Kqf98bScN3mKv6W9R7dsatPO/QlFgj41VAYVCfq0c/+AbtnUpi3tPZOqHQAAADhRhH+gxIT8luZVVWheVYVC/rFvApiGoStXNCscsNSTzCmRsTWQtZXI2OpJ5hQOWLpyRbPMKZwuMFHV4YD+zztP1zc+cp4Wz6scOZ7Nu7pzy6u65q7H9ev2ngk34xt+L6rDPgV9luqiQc2NhVQXDSros1Qd9h3zvUjnHO3rSytjH73awnU9bdzcoWQ2r8ZYSCG/JdM0FPJbaowFlcw62ri5gy0AAAAAmBGEf6BEVQQsNVVXqLEqpOAYNwHOXVCj9yxrUt711J3IqiueVXciq7zr6T3LmnTugpoCVH1spzdW6mt/eK7+cu3pqgn7R47vj2f0N/+5Q5/5jzbt6U1N6NrnLqjRhstOU8uciHJ5V4lMXrm8q5Y5EW247LQ3fS/yrqvO/rR6B3OjbkDs6EyoozupmnDgqBGNhmGoOuxXR3eSEYIAph19RwAAEnv+gZIXDvgUDgz1BOgdzI0sk39qd59+9mynLENqiAVlGoZcz1PGdvWzZzt16txo0d0AMA1Dv3NWo956ar3u2fqq/uPJfRr+HXbbq3168u4n9MHl8/VHKxcoHJjIP29HPt0/8ZUP/amcBrN5zakMKuS31JvKyXY8BY4xkSFomYq7nnpTE2+q6LqednQm1JvKqTYc0JKmmMxJjoEEUFroOwIAGGZ4DK6eEolEQlVVVYrH44rFYoUuBzimwWxevcmcbvzx03r5YFL10cCoEXeePPUkc1o0J6p//MDSolj6fyyv9Azq9kfb9dTu/lHH66IBfeKiRbrkjIajnrqP5andffriQ7uUyjmKhfzyW0PNEBMZW+GAddyn/0eqDge0tzelT/zrdkWCvjG3X6RtR6lsXt+66nwtnV91wtcexi/0AI5nuO9IMptXTTiggGUq57jqS9mKBi3desVS/r0AgFluPDmUZf9AmYkEfepP29rXl1JVhX/M2faVIb/2HBpU+4HBAlV5YhbWR/T/PrhMf/ueM9VQGRw5fiiZ0//9xQu68UfPqKM7+abXcD1P927bo1TOUX00oKDPlGkYCvpM1UcDSuUc3bttj9xx3CftT+VUHfZrYX1EfSl7zBGC/SlbLQ1RLWka/81CGgkCOB76jgAAjkT4B8pQbyonx5UiAZ98lnnU0/GAZcj2PMUzE1+SPl1cz9OurqQef7VXu7qS8iStOW2O7rrmAl21coH81hvfS9u+uD7+r9v1lf95SYm0Peb12g8Mas+hQcVCU3sjJO96ev95J6nCb2p/PK3+VE7xdE79qZy6EhlFg5bWr2kZ9zJ9fqEHcCLoOwIAOBJ7/oEyVBsOyG8ZyjnuUHg0JNczRkbc5RxPfsNQVej4c+xn0lO7+3Tvtj3ac2hQtuvJbxpqrovoyhXNOndBja55y0KtXdKobzzWoS0dhyRJrif95zOdevTFbn3srQv1u0vnyToscMczOdmup5g1dggPWIYGJngj5NwFNbp86Tx9f9tu9fWlJWOoi0BlhV8fuXDBhJbbjucX+olsJwBQGmai7wgAYHbhyT9QhpY0xdTSEB1Zkm4YhizTkN8a+u9AJq/mushRs+0LaXhf/ssHk6oI+FQXCagi4NPLB5P64kO79NTuPklSU3WF/uF9Z+nz71+q+TUVI5+fyOT1pf95SX/6/Se1ozM+crwqFJDfHNrjP5bJ3Ag5sqliU1WF5lWFZBmGvv/b3RNann8iv9Db/EIPlL3Db/KOJeu48puGasPFdZMXADB9CP9AGTJNQ+vXtCgatNSVyCptO3JdT5m8q4PJnGIhS+vXLFLQd3SjukKYyL78FQtr9d115+tP3rZQIf8b/9S91J3UDT94Wp+//wX1DubUOjei5rqIEhlbno7Ymy9PAxl7QjdCDq95TmVQsZBf4YClSNCvuZNYns8v9DgexrpBOvom7+Em23cEADA7Ef6BMrW6tV63XrFUi+dVKpXNqzuZVSqb1+J5lbrt/ct06ZmNaq4Na24spOAY3epn0kT35fstUx9esUB3X7NC7zijYdRr//38AX30jm36t+179aHz5yscsNSTzCmTd4dGHuZd9SRzCgcsXbmiedxTD45Vs+d5yrueKkO+Ce235Rd6vJkt7T1ad+c2ffx7T+j//PgZffx7T2jdndtoAlmGjnWTN2076kpkJ9x3BAAwe7HnHyhjq1vrtXJR3ZvOio8EfYoEfUrnHPWnc0rnnBmvc7L78udUBvVXly/We86ep6890q6Og0M3CVI5R9/c/LIW1IZ1+bJ52v5av/YcGtSAN7TUf9Gc6Eg/gSmt2ZMsQ6+vtMiO67rDv9DfsqlNXYmsqsN+BS1TWcdV/+vju/iFvjwda6zb8BQIxrqVn+GbvMNjQeOv90pZPK+SsaAAUIYI/0CZM03jhBrDVQQsVQQqlLEdxdO2BrP5GahuyOH78oO+o0Ptie7LXza/Wt/8o+X6+bOduuPXr2ogM/Q97O5N6du/fEVvba3Xp95xmvy+oWu1zo2M+4n/eGr2GZKddzWQsVUZ8p/wtfmFHkc6cgrEcDPIkGmpMWaqK5HVxs0dWrmojhtDZeZEbvICAMoD4R/AuIT8lkJ+S9m8o/7UzNwEGN6X//LBpOqjgdHL6F/fl79oTvSE9uVbpqH3nnOS3n5ag+749Sv6+bP7R3b6/6q9R7995ZDWnDZHv7fspBmpuaUhooMDWaVzjuqiwVGTCN4Mv9CP5rpeWb8XTIHAmznRm7wAgNJG+AcwIUGfpbmxmbkJYBqGrlzRrC8+tEs9yZwqQ34FLEM5ZyhET2RfflXYr5suO02XLxvaCjC89952PP3Pzm498kK3TqmP6E/XLNJ5J9dOe83JbF4Z21V9ZUDhwIn908wv9EO2tPeMrIKwHU9+y1BLQ7SsVkEw1g0AABwPDf8ATMrQTYCQmqorTji0TsS5C2q04bLTtGhOVJlcXodSOWVyeS2aE9WGy06b0L58STptbqWuWX2yasL+Ua0EXU96+eCg/uqnO/TAc/tnpOa866ornlFPMntUMz+MbXif+879CUWCPjVUBhUJ+kb2uZdLozumQAAAgOPhyT+AKRHyW2qsspSxh1YCpHJTvxLg3AU1Oru5Wu0HBhXP5Ca9L18aGsn3g8f3SpIW1ofVN2irL22PvJ7Nu/rCg7v0Sk9KH111siLB8f2zOZGaE2lb6ddHBIYKPGmhmLHP/Q3DUyB27h9QY8wctfR/eArE4nmVTIEAAKCMEf6BMjfVe6WHbwJk847iKVuDOWdKn2KbhqHTGqNTdr3DR/L5TFNzKoOKVfh1cCCrlP3GZIOfbN+rh1/o1scvWqRLFzccta96qmu2HVed/WlVhwNDqxImcYOjVLHP/Q1MgQAAAMdD+AfK2HTulQ76LDXELOUdV/G0rYFMXm4RLmUfayRf0GfqpOqQkjlHBweyyrtDdfcO5nTb/S/oZ8906oZLWnXq3Mppr68/lVPadjQnGlTAx06tw7HPfTSmQAAAgDdD+AfK1EzNBPdZpuqiQdWEAxrI5BVP28q7Y+9LLoRjjeQzDEOVQZ8s01BvMivb9WQ7QzcBnutMaP33n9S7lzXpmrecoqqKEx/TNxFZ29G+/rRqwwFVhaf3a80mh+9zD5lHb48ox33uTIEAAADHwmMkoAwduVc65LdkmsbQkv1YUMmso42bO+S6U7hc3zRUFfarubZCDbGQgkWyl314JF8iY8vT6O/Xk6fBbF6Lm6p059UX6K2H3QxxPem/nunUuju26b+e6ZQzhe/VWDzP06HBrDr707KP0dSt3Azvc+9L2UdtLRne597SEC27fe7DUyDWnDZHS+dXEfwBAIAkwj9QlsazV3qqGYahaNCnk6orpn1CwIkYHskXDlg6OJBTPGMrmc0rnrF1cCA3MpKvqbpCn3vvEv3jB5aquaZi5PMTmby+/D8vaf33n9Rz++LTXm/GdrS3L614yj7+ySVueJ97NGipK5FV2nbkup7StqOuRJZ97gAAAIch/ANl6ET2StszsFd6qDlgSPNrwqoMFa6p3bkLavSeZU1yPE8HE1l1xjM6mMjK8Ty9Z1nTqJF8F5xSq++sO18fv2iRKg5bvdDendSnfvi0brv/BR1KZqe13sNXAeTy5b0KYHif++J5lUpl8+pOZpXK5rV4XuWUbV0BAAAoBez5B8pQse2VDviGuuzXRgJKpG0lMva0L6M/3FO7+/SzZztlGVJNxC/Xk0xDclxPP3u2U6fOjY66AeC3TH3ogmZdurhB3/7fV/TQ8wdGXnvo+QP6dXuPrlp5st5/3knyH+MGy1TI0AtAEvvcAQAATgRP/oEyVKx7pS3TUE0koAW1YdVXBqc1OA9zPU/3btuj/pStbH5oMkEibSueHvr//am87t22Z8xJBXXRoD7zrjP01Q+fo9aGN0b5pXKOvvXLl3Xd3U/o8Vd7p7X+4VUA+8p8FQD73AEAAN4c4R8oQ8W+V9owDMVCfjXXhjVnmm8CtB8YVEf3gLJ5R9m8K8MwZJmGDMNQNu8qm8+ro3tA7QcGj3mNs06q0saPnKcbLz1VsdAbC6r29KV187+36bP/uUNd8cy0fQ/SGxMBir0XgOt6atsb1+ZdB9W2Nz6lTSUxhPcYAACMhWX/QJmaLTPBK0N+RYM+DWTz6h+c+jGBfemcktm8XE/yvR76JcmQZJhS3vWUzObVl37z/geWaej3zm7SmtPm6M5fv6qfP9up4cz1q/YebXu1Vx++oFl/eEHztE06GF4FkLLzmhMNyjfBmyau603LEvot7T0jP2+248lvGWppiBbVz9tsx3sMAACOxfCOXPOLCUkkEqqqqlI8HlcsVl5jpTC7TVfQmw6e5ymRySuemrqbAA89f0D/eP8LMk3JMo8Oy47rynWlm991hi47c+4JX/elAwP62iPteu6IiQlzY0Gtf3uL3tZaP60NDk3DUF00oMrQ+HoBTFd43NLeo1s2tSmZzasmHFDAMpVzXPWlbEWDFs35pgDvMQAA5Wc8OZRl/0CZm017pQ3DUFWFX821FaqLBGVNQa1VIb9MU3JdydMR/Q/kyXUl0xw6bzxOnVupr3z4HN3yu2eoLvJG48QDiaz+7r+e11/+e5t2H0pNuv5jcT1PBweyOpDInHDzxOHwuHN/QpGgTw2VQUWCPu3cP6BbNrVpS3vPxGpxPW3c3KFkNq/GWEghvyXTNIamPcSCSmYdbdzcwfL0SeA9BgAAx0P4BzCruK6n5/Yl9PTefvWnbFVX+GVO4gl6dTigaNAvw5Acx5PreUOh3/PkOJ4MQ4oG/aqewOQDwzB06eK5uvvaC/Sh8+fLd9jNiu2v9elj9zyhjY91aDCbn3D9xzOYzWtvX0qp3Jt/jekMjzs6E+roTqomHDhqtYNhGKoO+9XRndSOI1ZJ4MTxHgMAgONhzz+AWWNLe4++8Vi7XugakJ335PcZOqOxUp+4qEVnnlSlePro6QXH0zo3opaGqF7sSshxPdnO0DJ/wxgaQWiZQ8veW+dGJlx3OODTx9e06F1L5+n2R9r1xGt9koZGCf5k+149/EK3/uRtC3XpmXMndSPjWBzXU1c8o8qQX3WRwJirO8YTHpfOrxrX1+9N5WQ7ngLH6EEQtEzFXU+9qTfvq4Bj4z0GAADHw5N/ALPClvYe3fTjp/Wblw+pbzCngaytvsGcfvPyIf35vz2jF/Yn1FxTocqQf1x76U3D0JUrmlVV4VfIb6k+GlRDLKj6aFAhv6WqCr+uXNE8JaF8QW1Y//iBpfrc7y1RYyw0crx3MKfPP/Ci/uyHT2vXgYFJf51jGcjY2tefHnMVwImER3uC4bE2HJDfMpRzxu7TkHVc+U1DtRNYXYEhvMcAAOB4CP8Aip7rerrt/p3qTmSVdyXHk1xv6L95V+pOZHXb/TtlGobmVAY1v6ZC0dCJL2w6d0GNNlx2mhbNicp1PWXyrlzX06I5UW247DSdu6BmcvV7nnZ1JfX4q7166cCgVrfW6c6rz9fVq09WwPfGP8M7OhNa/69P6ksP7VI8ffyRfYdfd1dXUu4JrHqwHVdd8Yy6ExnlDwuKh4dHT57SOUcDGVvpnCNP3qTC45KmmFoaoupLHb0yw/M89adstTREtaSJZqkTxXsMAACOh2X/AIpe2764du4f0LGirSdp5/4Bte2L6+zmavktUw2VIVVVOOpP2Se0p/7cBTU6u7la7QcGFc/kVBUKqHVuZNJP/J/a3ad7t+3RnkODsl8fp9hcF9GVK5r10VWn6J1LGvXNxzr0y5d6Rr6Xnz27X4/tOqhr33KK3r2saczGhm923RO5WZHM5pXKOaqJBFRV4R8Jj8/u7Vfe8YZuAnivb3+wTPksQ8vmV08oPJqmofVrWnTLpjZ1JTKq8FsyDMnzpLTtKBr0af2alqJuNlnsRr/HWVWH/QpaprKOq/7Xu/3zHgMAUN548g+g6G3f3af8cRrN5V1P23f3jToW9FmaGwupqbpCFQHruF/HNAyd1hjVBafU6rTG6JQE/y8+tEsvH0yqIuBTXSSgioBPLx9M6osP7dJTu/vUGAvp735vif7pg8u0oDY88rkDmby+8nC7PvGv2/Xs3v5xX/dEuJ6nQ8ms9vWnZbuuLjq1XoNZR6mcI0ka3gGQyjkazDq66NT6CYfH1a31+siFC5R3PXX2p7W3L63O/rTyrqePXLiAEXRTYHVrvW69YqkWz6tUKptXdzKrVDavxfMqGfMHAAB48g+g+O3vT0/qvJDf0ryqCqVzjnpTOWVtZyrLG5Prebp32x6lco7qowEZGgrNQZ+h+mhAPcmc7t22R2c3V8s0DC0/uUbf+ehybXpqn+7e+tpIAO84OKgbf/SM3nFGgz6+ZpFqI4FxXfdEZG1He3vT+p+d3arwm3I9Kee4cl5vfBgOWLJMU798qUcfe+uiCd0A2NLeo+//drcsQ2qqrpBpGHI9T6mco+//dreWNFURTqfA6tZ6rVxUpx2dCfWmcqoNB7SkKcYTfwAAQPgHUD4qApZOClRoMJtXXyqnXH7s5mhTof3AoPYcGlQs5B8J6MMMGaoM+bXn0KDaDwzqtMaoJMlnmfr985v1jsVz9e1fvqz/fv7AyOc8/EK3ft3Ro3ctmafdPclxXfdEvHQgqVcOJlUXDSrst5RzPOVdVz7TVMhvKpN3J9zt//AxgvOqKkY1ZKyq8NSVyGrj5g6tXFRHSJ0CpmmM+88IAACUPpb9Ayh6TdUVU3peJOjT/Jqw5lQG5T9Gd/vJimdyQ3vxrbHDbMAyZHue4pmju+fXRgL69LvO0Fc/fI5ObXgjwGdsV5ue3qdDKVs5Z+zVC2923ROp12cayruefJahaNCnioAlwzAm1e2fGfQAAACFR/gHUPTOW1Aj/3GeCPtNQ+eNsyt/Zciv+TUV03IToCoUkN80ZDtj9yrIOZ78hqGq0LG75591UpW+8ZHztOGyUxU7bHqB60n740N79Y8c7XYi1z2Rel3Xk+14cl7vtTCZbv/TOUYQAAAAJ4bwD6DoLT2pSmfMq9SxtrAbhnTGvEotPWn8S50NY2ipfHNtWHNjIYX8x28MeCJa50bUXBdRImPLO2JOgSdPAxlbzXURtc6NvOl1LNPQu5c16Z5rV+i95zTp8HsggzlHrx1KqSeZlet547ruidTreZ7yjqtc3lHfYG7Co+KYQQ8AAFB4hH8ARc80DX3mXYvVUBmUzxj6h8vQ0H99htRQGdRn3rV40vvFI0Gfmqor1FRdoXBgci1RTMPQlSuaFQ5Y6knmlMm7cj1PmbyrnmRO4YClK1c0n3BTvliFX3/2jlP1rT9aroX1b0wF8CT1pmy9ciilzv6MKvzmuK57rHoTGVsDWVuJjK3ugaxCflPrVp08ofeYGfQAAACFR/gHMCusbq3XdW9dqGjIr5E+d4YUDfl13VsXTmmn+JDfUmNVSCfVVCga8h21T/1EnbugRhsuO02L5kSVyeV1KJVTJpfXojlRbbjsNJ07zm0KktTSENV3Pnq+PnJh86h+Ao7raTDnKBz0qyYysSfo5y6o0XuWNSnveupOZNUVz6o7kVXe9fSeZU1aNCeqrnhG+WM8wT+W4Rn00aClrkRWaduR63pK2466Ellm0AMAAMwAwzvyMQwmJJFIqKqqSvF4XLEYT6+AqbalvUe3bGpTMptXhd+SYUieJ6VtR9Ggb1rnmNuOq3jaVjKTlzuBfzJdz1P7gUHFMzlVhQJqnRsZ95P5sSSztr7+SIf+Z2e3nMPqMg3pinNP0rrVpygaPPEVDE/t7tMXH9qlwWxeIb81Mo4vY7uKBK2RGxamYagmElBVhX9c9W5p79HGzR3q6E4ONUM0DbU0RLV+TQtj/gAAACZgPDmU8D9FCP/A9HFdT+vu3Kad+xNqjIVGPYn3vKFRcYvnVerua1ZM69Njx/WUSA8thR9uhFcMdvem9I1H27Xt1b5Rx2vCfv3x2xbpnUvmHvdmg+t5uvnf2/TywaTqo4FRYwQ9eepJ5rRoTlT/+IGlI9cK+i3VRwMK+k68T4LresygBwAAmCLjyaEs+wdQ9A4fFSdJ6ZyjgYytdG5o3N1MjYqzzKEn3gtqw6qLTt+YwPFaUBvWbe9fqv/vvUs0ryo0crwvZesLD76oT/3gKb3YNfCm12g/MKg9hwYVC/lHBX9JMjTUFHHPoUG1HxgcOZ61HXX2Z3QomT1qL/+xDM+gX3PaHC2dX0XwBwAAmCGT62gFADNgeFRcLu+qsz+tbN6V53lD8+d9puqjwRkdFWcYhqoq/Kqq8CuZzSuetpW1nRn52m9W01ta63XBKbX60RN7dO9vdyubH9qb//z+Af3p95/Uu5Y26rq3LlT1GF3145mcbNdTzBo7jAcsQwOep3hm9HvseZ7iaVupnKO6aGDSjRIBAAAwPYrjsRWAkuO6ntr2xrV510G17Y3LncQy+dpwQK7nam9/WoM5R3nXk+NJ+deb3O3tT8t13YKMiosGfTrp9QkBkXHsr58uAZ+pq1aerLuuuUAXnfbGPnpP0i/auvTROx7Xpqf2HbVtoSoUkN80ZDtj/znlHE9+w1BVaOz32HZcdcUz6k6MvyEgAAAApl/hf1MFUHJGNXZzPPmtyTV2W9xYqWzeHQmshz+b9jS0Fz+bd7W4sXJqvoEJCPkthfyWbMdVIm1rYILNAafK3FhIf/eeJXrytT597dF2vXYoJUlKZvP62iPtuu/Z/brhHa06e361JKl1bkTNdZFj7vkfyNhaNCeq1rmRN/26yWxeqZyj2mhAsdD4GgICAABg+vDkH8CUGu7Kv3N/QpGgTw2VQUWCPu3cP6BbNrVpS3vPuK+5Y39iZAm7NBT4hz+GZfOuduyf3j3/J8JvmaqLBoumL8B5J9foX65arj99e4sigTca873cM6ibfvSM/r+fP6+DA1mZhqErVzQrHLDUk8wpkbE1kB1qbtiTzCkcsHTliuYTmlLgep56BrLq7E8rl2cVAAAAQDEg/AOYMq7raePmDiWzeTXGQkPj4kxDIb+lxlhQyayjjZs7xr0F4Ond/cof53Pyrqend/dPovqpZZpDfQGaa8NqrAqpInDiHfGnms8y9cHl83X3tSv0O0saR7326IsHte7Obbr3t7u1pKlK71nWpLzrqTuRVVc8q+5EVnnX03uWNencBTXj+roZ29G+/rR6B3Mn3BAQAAAA04Nl/wCmzOFd+Y0jnhAbhjGqK//S+VUnfF3P8zScHQ1Dox/5G5LnDX8UZ8AMB3wKB3zK5h3FU7aS2XxB6qiNBPSXv3O63r1snr72SLtePDA0ASBju/rOr17RT5/eJ8/zZBlSQywo0zDkep4ytqufPdupU+dGx30DwPM89adyGszmVR8NFvQmCAAAQDnjyT+AKTPclT9wjKXuQcucUFf+yoo39o57noY2/b/+cXjeP/y8YhT0WWqIhdRcG1aswn/UDZKZcmZTTF//yLn688tOU9Vh71lPMqdDg7Zsx1PIZyka9CkW8mtOZUCpnKN7t+2ZcB8D23G1P55W90DmqGaDAAAAmH6EfwBTpjYckN8ylDtGt/es48pvGuPuyl8bDejw+wlvPOl/45hlDp03GVM5oeDN+K2h8YQLasOqDgdkFWDWvWkYunzZPN1z7QW64tyTdPh9iMGco9d6U+pJZuV6ngwZqgz5tefQoNoPDE7q6yYzee3tSymRsSf5HQAAAGA8WPYPYMosaYqppSGqnfsH1BgzRz3ZHlr+bWvxvEotaYqN67r1kaCqK/zqT9sa676CZUrVFX7VR4ITrn2qJxScCMs0VBsJqLrCr4FMXvG0rbw7sw3yKkN+3XBJq06uq9Dtj3SM9FbwJPWmbCUyec2JBhQOWBrwPMUz41u1MRbHHWoImMo6qo8G5CtwU0QAAIBywG9cAKaMaRpav6ZF0aClrkRWaduR63pK2466EllFg5bWr2mROc4n3UuaYjqzqUqRgF9hvymfKZmG5DOlsN9UJODXmU1V476pMGw6JhSMh2kaqgr71VxbodpI4IQ66k+1M+ZWqabCp/pIQL7D/nzyrqf9iaz29mdkuJ6qQpNbXXG4VC6vff3pgvVAAAAAKCeEfwBTanVrvW69YqkWz6tUKptXdzKrVDavxfMqdesVSyf0FH34pkJtxK+KgE+NVRVqrgmrsapCFQGfaiP+Cd1UkKZvQsFEDDVFDKi5NqyqGe4J0Do3ogX1UTmepwW1FYqFRi8My+Zd9abzevD5/Upmpi6sO66n7kRG3Ql6AQAAAEwnwyvW9tizTCKRUFVVleLxuGKxiT19BEqJ63ra0ZlQbyqn2nBAS5piEwrnhxu1NN/15DcnvzS/bW9cH//eE4oEfQr5j+5En7YdpbJ5feuq88c1oWAq5B1XfSlbAzO0P/6p3X269Rc7FU/b8jzJ9UYPVhhWXeHXH79todae1TilqxR8pqn6yoDCAXakAQAAnIjx5FB+wwIwLUzTmPKwvLq1XisX1U3pTYUTmVAQn8CEgqngs0zNqQyqqsKv/lRuxpfHD7+twzcAhh/M96dt/dN/79LPnt2vGy5p1eJ5U3PDM++66opnVBnyqy4SmPTNIgAAALyB8A+grB0+oSBkHv3kf6ITCqZSwGeqIRZSzHbUn7KVyk39TQDX814f5SedUhdRLu/J8VxZhqmAz1BPMqtw0KfeZE6Z/FBTwhe6BnT9vU/pXWc16rq3LVTNFL1HAxlbGdtRfTSoisDRfyYAAAAYP8I/gFljOjryT9eEgukQ8ltqrLKUzQ/dBBicwpUA7QcGtefQoGIhv0zDUMhv6PC2MLGKgDK5vD777iV6aGeXHn3x4Mhr9z/XpV++dFBXrz5F7zvnpCkZXWg7rvbH04pV+FUbZhUAAADAZNHwD8CsMF0d+adrQsF0CvoszY2FdFJNhaLBqbmHG8/khvooWGN/nwHLkO15sizpb959pv7595dpYX1k5PXBrKOvP9qhP/nedj29p39KapKkRNrWvv60MrYzZdcEAAAoR4R/AEVvujvyT8eEgpkQ9FlqiIU0vyY86ZsAVaGA/KYh2xn7Pcw5nvyGMTLq79wFNfr2Vcv1yYtbFAm+sTT/lZ5BbfjxM/rcz55XdyIzqZqG2Y6rzv60epJZ0aMWAABgYlj2D6Do7ehMqKM7qZpw4Kjxd0Pj8fzq6E5qR2diwk0Gp6OZ4EwZ7glQnXfVn85NaBRf69yImusievlgUvXRgAwdtv1BngYythbNiap17htP+y3T0PvPm6+Lz2jQd//3Fd3/XNdIc8DHdh3Ub14+pCsvXKA/OL9ZAd/k7zUn0rbSOUdzKoNjTmYAAADAsfHkH0DRO5GO/PYUdOQfnlCw5rQ5Wjq/alYE/8MFfKYaKie2EsA0DF25olnhgKWe15v6uZ6nTN5VTzKncMDSlSuaxxztVxMO6P+sPV23X3muTm+sHDmeybu649ev6tq7H9fWjkOT/v6kN1YBHGIVAAAAwLgQ/gEUvcM78nuep3TO0UBm6Cmw53lF0ZG/mAyvBDippkKRcdwEOHdBjTZcdpoWzYkqk8vrUCqnTC6vRXOi2nDZaTp3Qc2bfv7ieTF9/cpz9RfvPE3VFf6R4539Gf3VT5/TZ/6jTXv7UhP+vg4XT9va20cvAAAAgBNleDw6mRKJREJVVVWKx+OKxQrfFRw4Ua7rFf1Sd9f1tO7ObXpmT1yO675+E0AyDClgmbJMU2c3V+nua1YUXe3FIJt31Dd44iMCXc9T+4FBxTM5VYUCap0bGfOJ/5tJZvK6a+ur+ulT+3R4Kwa/ZeiDy+frjy48ecrG+FVV+FUbOXpLCAAAQKkbTw5lzz9QxqZjdN50ME1DF51ar60dh+S4nnyWIcuUXE9K5RxZpquLTq0n+B9D0Dc0IjBjD40IPN5NANMwdFpjdFJfMxry6ZMXt+rypfP0tUde0tN74pIk2/H0g2179NDzB/SJNS26+PQ5kw7t8bStFL0AAAAA3hTL/oEyNV2j86aD63r65Us9igQthV9/Wuy4Q6+FA5YiQUu/fKlnwt3+y0XIb6mxampHBB7PwvqI/vn3z9Zn371Yc6LBkeM9yZz+4b6d2vDjZ9RxMDnpr3P4RAB+DgAAAI5G+AfK0HSPzptqw93+GypDWjgnopNrI5pfU6GTayNaOCeiOZWhkW7/OL7hEYHNtWFVhvzTvlzeMAy9/fQG3XXtBfrIhQvkt974es/sjevj39uurz78kgYy9qS/ViJta19/WukcvQAAAAAOR/gHytB4RucVg8O7/RsyVBGwVBnyqyJgyZAxZd3+y43fMjWnMqjmmgpVhwPj3tc/XhV+Sx9760Ldse4CrVpUN3Lc9f5/9u48To66zB/4p6q6qu+eM3PlzkwSQkgCJJxCEg5FVBRERWEFOVQQ8GB1WWG9dveHx66ucnqwAh4o4BoVXHWRkEQMkIOEhBASZnJNMlfm6vuo4/v7o6c708kkme7pnu6e+bxfzGuY6prqb3eleur5Hs8D/G5rB67/6UY8t60T5hg7nXTTQqefswCIiIiIhmPwTzQJjVfpvHwZnu1/JMz2PzY2RUa1W8OMaheq3RqUAudOmFrlxP+76jTcd9VpmFrpTG/3R3V87/nduOPJLdjZOfaOJ84CICIiIjqCwT/RJFRuwfTCJh+a6zwYiOjH1HYXQmAwoqO5zoOFTay0MRayLKHSlewEqHHbYZML+yfi3Dk1+O8bluGWC2bDoR55rl3dQdz+5BZ8+89voT88tg6o1CyAw0HOAiAiIqLJjcE/0SRUbsG0LEu4bUUzPHYFXYE4oroJyxKI6ia6AnF47ApuW9HMbP95IkkSKlwqplc7C94JoNlkXHvODDxx49m4aP6UjMf+sqMbN/x0A57ZfBDGcTqqRisY03FwIDrqcodEREREE40kjr7zp5xkU1+RqBSksv2H4iYqXSrsioy4aWEwosNjV3DfVYtKqtwfcFRpQktAlUuzNOFEI4RAIGbAH9FhWGMLwk/m9fZBPLC6FXt6wxnbZ9a4cOfFLThzRtWIv2cJgdbuMPyxBCocGlrq3cfNYeBx2FDrtrOziIiIiMpeNnEog/88YfBP5agcg2nLEtjREUB/JIFql4aFTb68BXGFPPZEMF6dAKYl8IfXO/DY3/chFM8cqV8+rxa3rWhGvc+R3rblwACe3NCO9r5w+t/x9Bo3rj17Os44TmeBTZZR69Xg0san5CHRaPAziIiIssXgvwgY/FO54s1mUkZHiCmgKqXfEVIs49UJMBhJ4NGX9uJP27sw/A+VfWipwDXLpmNHhx/fe343IgkTPocKVZGgmwKBmA6XpuCud847bgcAkJwFUOO2FzzJIdHJ8DOIiIhyweC/CBj8E5WvI0sgDFS5NGiKjIRpYaCEl0CUgvHqBNjVFcT9q9/Gzs5gxvbGCgdcmoK+UBxTvHZIOBLACwj0hhKYM8WDb1+96IRlDBVZQo3HDo+dswCoOPgZREREucomDmXCPyKa1CxL4JG1bQjFDTT4HHCoCmRZgkNV0OCzIxQ38cjaNmaKH4EkSahwJhMD1noLlxhwfoMXD3zsDPzTZfNR5VLT2zv9MbQdDiNmWNCNzPMjQYLXoaK9L4zW7vDRh8xgWgI9gRi6/DHoY0wsSJQtfgYREdF4YfBPRAVhWQLbD/qxdvdhbD/oL9kb1x0dAbT1hFDl0iAdNTosSRIqXSraekLY0TH2uvMTlSRJ8DmOdAKoSv7/tMiShHef1oAnbjobH1o6FcNn6cd0C/v6IzgcyiznpykSdCHgj42uXGAkYeDgQBT+iJ7v5hMdFz+DiIhovHCOIxHlXTmtXe2PJKCbAtpxAla7IsNvCfRHxlZvfjJIdQJ47TYE48nlAPkeSffYbfjMyha8Z1EjvvPnXXir68hSgIGIjkDMwBSPBq/dhoQpoEoSKhzaqI8vhEBfOI5gXMcUrx12m5LX9hMdjZ9BREQ0XjjyTzTJ5XuEPrV2dWdnAG67DXVeO9x2G3Z2BnHPqu1Y39qbp5bnR7VLg6pISJgWBASiCRPBmI5owoSAQNy0oMoSql2jDyDHS6nOrkh1AkyrcmJKgWYCzKpx44GPnY45te6MWQCmJdAViKN9MIqBSALTa9xoqXdnffyEYeHQQBR9R80mIMq34Z9BIynlzyAiIiovHPknmsTyPUJ/9NrV1BRWh6ygwSejKxDHI2vbcO6cmpKpKLCwyYfmOg+2HRyEYYpkJ4AAJAnQFBk2RcLiaZVY2FRaiTwLObsiXxUgJCm57t7rUBGM6RjM80wARZZx+0XN+M//24WBsI6YceTYMd1CTLfgsMkIxQz4nOoJjnR8/qiOSMJEjYdlAakwUp9BOzuDaPDJGVP/hRAYjOhY0Ogtuc8gIiIqPxz5J5qkCjFCX45rV2VZwvK5tQjHTUQSJgAgNVAdSZgIx00sn1tbMp0VQGFnV6xv7cUNj23Ap3++CV98+nV8+uebcMNjG8Y8Y8PrUDG92oU6nwOaLX9/es6YUYUvvms+Fk6tQI0rWepvuL+39eH6n27Ac9s6YOY4gq+bFrr8MXQHYjCYEJDyTJYl3LaiGR67gq5AHFHdhGUJRHUTXYE4PHYFt61oLqnPICIiKk8M/okmoUJllx7N2lV9jGtX8z3V3bIE1r3dC5emwKUl13en4rvkNhvWvd1bMlO/C5kZfDyWbHjsNkyrcqE+j50AZ8yowrevXoT/d9Vi/PuVp+HOi1owrcqZfjwQM/C959/G7U++hh0d/pyfJxw30D4QxWAkAVbJpXw6v6UW9121CAsavYjEDfSE4ojEDSxo9LLMHxER5Q3nMBJNQtmM0C+aVjHq4w5fu+qQj02UNta1q4WY6p56L+p9DthtMmK6BcOyYJNlOFQZMcPK6b0olEKdu/FesuG22+C22xBJGBiM6Ijp5piOJ0sS5jV4AABnzQLet6QR/7P5IH7+ygFEh469uzuEO3+1FZctrMcnL5yDanf2/w6FEOgPJxCMGZjitcOhMiEg5cf5LbU4d05NXpbcEBERjYQj/0STUKFG6FNrVwci+jEjo6m1q811npzWrhZqVHr4eyFJEpyaAq9DhVNTIElSXmYr5FOhzl2xlmy4NBuaKp1orHDCqeUvkFYVGR89ewaeuOksXLqgLuOxv+zoxvU/3YBnNrXnPI1fNy10DEbRE4zlvJygUEo1ESSdnCxLWDStAivmTcGiaRUM/ImIKK848k80CRVqhD61dvWeVdvRFYij0qXCrsiImxYGI3rOa1cLOSpd6NkK+Vao9ha73JhTU+DUnIjpJgYjOiIJIy/HrfXYcc97FuB9ixvxwOpWtB0OA0jmc3hk7R787/Yu3HFxC5bOrMrp+KGYgWjCRJVbg8+RW1LBfCqnMptEREQ0vjjyTzQJFXKEvhBrVws5Kl3I96IQCtXeUik35lAVNFQ4MLXKCY89f/3Ti6dV4of/sBSfu6QFXseR4+7vj+BLv9mGr/9hB7oCsZyObVoCvcE4Ov1RJIziJQQstzKbRERENL448k80CRVqhD4l32tXh49KCwjEEsPW5WvymEalC/1e5Fuh2ltq5cbsNgV1PgWVhoXBaALhuDnmJHuKLOEDp0/Fynl1+O+/78Uft3UidcR1b/fi1b39+NjZ03HNsumw57CWP5owcUiPosqlosKpHtNRVUjlWGaTiIiIxpckmLI4LwKBACoqKuD3++HzlcYIIdHJZEwRtgRUuTSnCG8/6Menf74JkiTBH9URN0wIAUhSMkiscKoQQuBHH1+Wc1K+cnkvUgrR3tTIcShuQJYkAAKABEsIeOy2omYd14c6N0JxI2+Z9nd3B3H/C2/jzc5gxvYGnwO3X9SM85trcg7gNZuMWs/4JQRMXSNuu23E54zqJiJxY0zXCBEREZWebOJQjvwTTWLlkl16YZMPNR4NOzoCkADYFBmSDAgBRBMGIgkDC5t8YxqVLpf3IqUQ7T2/pRbvaK7BU5sOwhiWJM4mS7js1PqidoKoiowpXjuqXCr8UR2B2Ng7AebVe3H/x87AX9/sxo/W7cFARAcAdAVi+Mrvd+CsWVW4/aIWzKh2ZX3shJFMCOhzqqh2aQX/d1TsnA1ERERU+hj8E01yqezSZSMVQ4lhP+dp/lK5vRf5bu9P1rWlA3+bfOStNSyBpzYdxKxaNz65vDlvz5cLmyKjxmNHpUtLdgJEdVhj6ASQJQnvWtiA81tq8fOX9+O3Ww6ls/dv3DeAW57YhKvPnIqPnzcTLi37P5mBqI5I3EStV8vp90er3BJXEhER0fhjwj8iKnk7OgLoCyXQWOGAw6bAtAR004JpCThsyQRxfaFE3svQTSaGYeGhNW0wLQG7TYKqKLApClRFgd0mwbQEHlrTBqOICe2GU2QJ1W4N06td8OVhfb3HbsNtK5vx6PVLsXRGZXp7quPjhp9uxPNvduc028CwLHT5Y+gOxHIuLXgy5Za4koiIiMYfg38iKnmpKc02WUZyLFoMDfYn/1+Vc6ttT0c8u60TwagOmyJBljL/NMiSDJsiIRjV8ey2ziK1cGSKLKHWY8e0PFUHmFnjxnc+tBhfv+JU1Hnt6e194QS++ae38Llfb0VrTyinY4fjBg4OROEfIUAfq1QiSI9dQVcgjqhuwrIEorqJrkC85BJXEhER0fhj8E9EJa/apcESFjr8UcQMC5IkQZEkSJKEmJHcblkWpzSPwaHBCCwAx4sNZSnZ1XJoMDKezRo1VZFR50uWCHRqY0uyJ0kSls+bgsdvPAvXnzsTqnLkTXmjI4Bbf7EZ3//r2/BH9ayPbQmBvnAchwajiOnmmNp5tEKU2SQiIqKJg2v+iajkLWjwwhSAbgpIAKyhkf9USGZCwBTJ/Sg3UytdkAFYYuQOAEsk3++pldknvxtPdpuCxgonogkTfeE4EmNYpuBQFXziHbNw2Wn1eHhNG/7e2gcg+V784fUOrNnVg5sumI33LmqEkuWIeiohoNehotqtZf37x1NuiSuJiIho/HDkn4hK3s6uICzryER/4Ni8f5YlsLMrePSv0ihdsbgRXqcKwxSwRGbAbAkLhingdaq4YnFjkVqYHaemYFqVC3U+B9TjZMAfrcYKJ/7tA6fh21cvwvQqZ3p7IGbg+399G7f98jW8ccif07GDMR0HByIIxLKfRXA8qUSQK+ZNwaJpFQz8iYiICACDfyIqA32h5BpmGUemn6e+ZCn5QRbVTfSF4kVtZzmz2WTcvrIZiiwhYQgYlpUM+i0LCUNAkSXcvrIZNlt5/dnw2G2YVuVEjcc+lDMid2fNqsajNyzDp5fPgVM9srSgtSeEz/56K775p7dy+jdoWgK9wTg6BqOIG/ldCkBERESUUl53cUQ0KQ1EdFiWwPFiN1lOjvyn6rRTbj65vBl3v3s+KlwqLEtANwUsS6DCpeLud88vepm/XEmShAqnimlVTlS7NchjqAygKjKuOWs6nrjpLFy6oC7jseff7MYNj23EUxvboeeQ1T+mmzg0EEVvKJ6e6UJERESUL5LId8rhSSoQCKCiogJ+vx8+H0spEeXTi7t6cMsTm9L114eHbqkPMEWW8OgNy3DR/Lpjfp+yYxgWnt3WiUODEUytdOGKxY1lN+J/IqYlMBhJIBAzTpp13xICrd1h+GMJVDg0tNS7MzoP3jjkx/0vtKL1cGYFgOlVTtxxcQvOmlWdUxttsowqtwqvQ83p94mIiGhyyCYOZcI/Iip51S5txIB/OGloPxo7m03GVWdOLXYzCkaRJdR47KhwquiPJBCKGSPut+XAAJ7c0I72vjB0S0CVJUyvcePas6fjjBlVAIDTplbgkX84E3/c3omfvrQXgaFjtQ9Ecff/bMc7WmrwmZXNaKxwjvgcx2NYFg4H4wjGDNR4NNhtY6tgQERERDRxhnKIaEJTZCm95j/VESDhyJr/fGVLp+QSiu0H/Vi7+zC2H/RP2CnoNkVGndeBaVUueOyZfeFbDgzge8/vxp7DITg1G2rcGpyaDXsOh/C953djy4GB9L6KLOH9S5rwxE1n44oljRkdVX9v7cONj2/C4+v3IZ5Dab+YbqJjMIY+LgUgIiKiMeLIPxGVvMGoDoeqwLIELCGgDKu7nswFIMGhKhjMoe46ZVrf2otH1rahrScE3RRQFQnNdR7ctqJ5wtaJ12wy6nwOVBrW0HIAHU9uaEckYaLWo0EaCuftNgm1Hg29oQSe3NCOJdMrM5YAVDhVfOHSeXjfokY8sLoVb3QEACTL+v3s5f34y44u3LayGRe21ELKIu+AEAL+qI5w3ES1Rzumo4KIiIhoNDjyT0Qlr9qlwa0pqPM54NSSgU9qqbZTs6HO54BbUzjtf4zWt/binlXbsbMzALfdhjqvHW67DTs7g7hn1Xasb+0tdhMLKtUJEIgaONgfgc+hpgP/FAkSvA4V7X1htHaHRzzO3HovfvDR0/Hly09BtfvIv8nuQBxf/8Ob+KffbMP+vpF/90QMy0JPIIYufyynhIJEREQ0uTH4J6KSt7DJh+Y6D+KGhZk1TsysdmNaVfL7zBon4oaF5joPFjYx2WauLEvgkbVtCMUNNPgccKhKekZFg8+OUNzEI2vbJsXU82DcgCUAl5Z8D46mKRJ0IeCPJY57DEmS8M5T6/HEjWfhI8umZSxL2XxgELf8bDMeWdOGcHzkfAMnEkkYODgQxUA4cdKEhUREREQpDP6JqOTJsoTbVjTDY1fQHYgjbpiwhEDcMNEdiMNjV3DbiuYRAzUanR0dAbT1hFDl0o6Zki5JEipdKtp6QtgxNJV9Iqt2aVAVCYYloCoyVJuc8Z4kTAFVklDhOPlME7fdhltXNOO/r1+GZTOr0ttNS+CZzQdx/U834P92dMHKMogXQmAgksDBgSiiiexzCRAREdHkw+CfiAoi30njzm+pxXXnzIBhCXQMRnFwIIqOwSgMS+C6c2ZM2PXo46U/koBuCmiKDAGBaMJEMKYjmjAhIGBXZOiWQH/k+KPdE0VqpslARIcQArIkQbPJsCkyBIBgTMf0Gjda6t2jPuaMGhe+ffUi/Ov7F6LB50hvH4jo+Nafd+Gzv9qK3d3BrNuqmxY6/VH0BGPpUphEREREI2HWICLKu0IkjVvf2otfvnoAigQ0VTohSxIsIRBJmPjlqwewsKmCHQBjkBrtHozq8Ed1xA0TQgCSBNhtCiqcKlRZmhR5FVIzTe5ZtR1dgTgqXSrsioyEaWEgnIDHbsO1Z0/PSPY3GpIk4YK5tThrVhWe2tSOJze0I2Ek1+6/2RnAbb94De9d3Iib3zEbFS41q2OHYgYicRNVbg0Vzux+l4iIiCYHjvwTUV4VImnc8PXojRVOVLo0+JwqKl0aGisck2o9eqEsbPKhxqOh0x9FNGFAliTYFAmyJCGaMNDpj6LGo02avArnt9TivqsWYUGjF5G4gZ5QHJG4gVObfPj21YvxviVNcKhKTse2qwquP28WHv/EWbhw7pEOKwHguW2duP6xDfj91kNZj+RbQqAvFMfBgQhiOZQVJCIioomNI/9ElDfDg/R6rx1xQyCcMGCTZdR7NXQHE3hkbRvOnVOT1fr8bNajL5pWke+XNfmk3mIx7OdJ2K9yfkstzp1Tgx0dAfRHEqh2JTs/Uv92myqdCMZ0DIR1GFb22fcbKhz4xvsXYvP+ATywuhUH+iMAgGDMwA9eaMVz2zpx58UtWDytMqvjJgwLHYNReOw2VLs12BT28xMRERGDfyLKo1SQbrfJ2N8fQdywhk0dl+Fz5hakD1+PPhK7IsM/SdajF8qOjgD6Qgk0VjiGpv0fOXdOVYHPqaIvlBhzB4tlieMG06VIlqUTvl6vQ4Vbs6WXS+SSfX/pzCo8ev1SrNpyCE+8vB+RoQR+bYfD+PxTr+OSU+rw6RVzUOuxZ3XcUNxAJGGi0qWiwqke03FGREREkwuDfyLKm/5IAuG4iahuwBSATZYgSclB46huIWHE4dRsWQfpqfXoCdOCQz52qnXctCbNevRCSXWw1HntqHJpiOkWDMuCTZbhUGUIAfSE4mPqYClELoiUYnYqyLKEarcGj92G/nACkUT25ftsiowPL5uOSxbU48fr9uD/3uxOP/bCWz1Y39aHj587A1cvnQY1i5F8Swj0hxMIxgzUeDS4NP7ZJyIimqx4F0BEeVPpVBHVTZhDJdJSI40SAFVOZiaPJkxUZpmQLJV9fWdnEA2+zLJrQggMRnQsaPROmvXohZDRwaIqcGoKgCMdLTHDHFMHSyoXRChuoMqlQRtKoJfKBXHfVYvGlAyyUJ0K2dBsMhoqHIgkDPSFEtDN7JcCVLs1/PPlp+B9ixvxwOpWvN0TAgBEdRM//tte/O8bXbjjohacPbs6q+PqpoUufwwuLbkUQLNxKQAREdFkw7/+RJRX6bj86EFX6ajHs5DKvu6xK+gKxBHVTViWQFQ30RWIw2NXcNuK5pKePl7qji5vN1yqg6W5zpNTB8vwXBANPgccqgJZluBQFTT47GNK2FiIBJNj5dJsmFblRI3bnnVFgJTTplbg4evOxF3vnAuf40g//cGBKP75t9vxL797Ax2D0ayPG0kYODQYRW8oztKAREREkwyDfyLKm8GongzsJAmGKWAJAYHkd8NM1kt3qAoGo3rWxz5e9vUFjd4xjRpTUiE7WLJJ2JiNQnYqjJUkSahwqZhW5YTHkdskO0WW8L7FTfjZTWfjA0uaMPytX9/Whxsf34jH/r4368z+QggEojra+yMYjCRyylNARERE5YfT/okob6pdGtyaAo/ddqRWvJUc7XeoyVrxQoicp46fLPs6jU2qgyU1hd5vCaiyhAWN3jFNoS9UwsZyqAJhU2TUeR3wOUz0hROI51CCz+dU8blL5+J9ixtx/+q3sf1QspNENwV+/soB/GVHN25b2Yzlc2uzSuqXygcQiBqocqvwOrJbjkNERETlhcE/EeXN8LX5M2uciOsinTTOrkroDiTGvDb/ZNnXaWwK0cEyPJ+AXZYRSwxLJqjJOSdsLKcqEA5VwdRhpQHjuonVu3rQHYih3ufAxfProCgnfo+b6zz4/jWnY/VbPfjhuj3oCyVfV08wjm88+ybOmFGJOy9uwawad1ZtMywLh4NxBGIGatwaHOqxSTWJiIio/DH4J6K8SU0dv2fVdnQHEqh0JcugxU0L3YEE1+aXiXx3sKQ6hbYdHIRhCiTMI2UENUWGTZGweFpl1p1ChepUKCSvQ8WvXt2PB9e0IRQ1YCG5/u7BF1tx3dkz8JGzpp/w9yVJwiUL6nFecw1+8coB/GbzQRhDyxq2HBjELU9swlVnTMUN58+Cx57dn/i4bqJjMAqPPZkU0JZFVQEiIiIqffzLTkR5xbX5dDRZlrB8bi3CcTNdwz4VV0YSJsJxE8vn1mbdKZTqVOgJxrD3cBj7+8M4OBDF/v4w9h4O43AwlnOSwkL5ybo2fOcvuxGMGpBlQFMAWQaCMQM/+dsePL2xfVTHcWk2fGr5HDx6wzKcPasqvd0SwP+8dgg3/HQD/vxGF6wc1vOH4gYODkSZD4CIiGiC4cg/EeUd1+bTcJYlsO7tXrg0BaYlEDcsGJaAJElwaQoUWca6t3tx8wVzsvo3kupUeLmtD6YlYFMkKHIyAI4kTChDj5fKvzvDsPDQmjaYloBmkyBLyR4QWQjIkoWECfxywwFcfea0ky4BSJlR7cI3P7gI69v68PCaNnT6YwCAgYiO7/xlF57b1oE7L56L+Q3erNqaygcQjBmo9diHSj8SERFROePIPxEVRGrq+Ip5U7BoWkXJBGA0/lKJ+Y4klBMQQ98BwOuw5ZztP9Wp4BoKTk0r+Vhymw3r3u4tSrb/kTy7rRPBqA6bciTwB5JT+RVZgU0GwjEDq3f1ZHVcSZLwjpZaPPaJs3DjO2bBbjty7Dc7g/jML1/Df/7fLgzmkPtANy10+qPoCcRgpN5cIiIiKksc+SeigrAswZF/ApBMzBdOmIjEDQgAiizDJgFCAHHDQk8gBpfdlnO2/3qfA3abjJg+bM2/KiNmWEXP9j/cocEILCD52iEgBNK5DyQpWdrPtEROQToAaDYZHz93Jt51aj0eWduGdbt7ASS7WP53exfW7e7Fje+YhfcvaYKS5bUYihuIJExUuTT4nLasqgoQERFRaWDwT0R5t761N10uTjcFVEVCc51nTOXiqHxVOlXEdBOWEFBtMiQkA0dJAiQF0A0LMd1EpTO7UnPDs/1LkjQ0Nf3I9PRSyvYPAFMrXZABmJaAJYaCfwASku+FPPQ1r96HpkpnzqUB630OfP2KhXht/wAeeLEV+/siAJIB/AOrW/HHbZ2485IWLJlWmdVxLSHQF44jENO5FICIiKgMcdo/EeXV+tZe3LNqO3Z2BuC221DntcNtt2FnZxD3rNqO9a29xW4iFUE6b9zRM/DFUY9nYXi2/5GUWrb/KxY3wqkpMKxkXgIASI2fWwIwLMCpKbhicWO6NGCt1571KH3KmTOr8JOPL8VnVjbDPSxQ39Mbxheeeh3/9tybOByMZ33c1FKALn8MCYNLAai8WJbA9oN+rN19GNsP+ktmWRAR0XjgyD8R5Y1lCTyytg2huIEGnyM9NdghK2jwyegKxPHI2jacO6eGSwAmkcGoDqeqIKoL6JaATU4GvQKAYQkocnLUfjCqZ3XcVLb/nZ1BNPjkjKnoQggMRnQsaPSWTLZ/WZYwxWtHKJ4ciR8p5JjitWdcGz5HslxmMvledu8PANgUGR9aOg0Xn1KHR/+2F3/e0ZV+7MVdh/Hynj78wzkz8aGl06DZshsPiCQMRHUTPocNVS6N1zSVPM5KI6LJjiP/RJQ3qTXYVS7tmDXBkiSh0qXmlNiNylu1S4PbrmCK1w6nKsMSAsbQ1HenKqPWa4dbU7IeoZdlCbetaIbHrqArEEdUN2FZAlHdRFcgDo9dwW0rmksmKN3REUBct1Dr1nB0Mn9FAmrdGuK6dcz1oQx1GjRVOmFXTz7V3hICu7tC2LivH7u7QrCEQLVbwz+9ez4e/NgZmF9/JPN/TLfw6Et7cfMTm/DKnr6sX5MQAv6ojvaBCPxZdt4QjSfOSiMi4sg/EeXR8DXYIym1Ndg0PoaP0M+sdiFuiHRiPrtNQncwkfMI/fkttbjvqkXp0Ty/JaDKEhY0ektuNC91fdT7HKj32XE4lEDCtKApMqZ4NAASekLx414fqaUA/qiOgXAC1ghrJbYcGMCTG9rR3heGPvReTK9x49qzp+OMGVU4tcmHh647A3/a3oVHX9qbDtgPDUZxz6o3cO6caty+sgVTq5xZvTbTEugLxRGIMh8AlR7OSiMiSmLwT0R5M3wNtkM+9ua/1NZg0/hIjdDfs2o7uoMJVLqSU9njpoXuYGLMI/Tnt9Ti3Dk1JV9dInV9DEZ1+KM64oaZzvYfSZiocKqjuj4qnCo8dhv6wnGEYkZ6+5YDA/je87sRSZjwOVT4FAm6KbDncAjfe3437nrnPJwxowqyJOG9ixuxfF4tHl+/H7/feiidg+CVPf3YvH8jPrJsOq49Zwaco5hpMFwqH4BLs6HKrcJuYycAFV82s9JKoTIIEVGhcNo/EeVNaoR3IKJDHDUqmVqD3VznKZk12DR+UiP0Cxq9iMQN9ITiiMQNLGj04r6rFo15hF6WJSyaVoEV86Zg0bSKkgv8geT1UePR0OmPIpowIEsSbIoEWZIQTRjo9EdR49FGdX0osoQ6rwNNlU6oSnIpxZMb2hFJmKj1aLDbZMiSBLtNRq1HQyRh4skN7RmzBbwOFXde3IIffXwpFg8LeHRT4JevHsAnfroRL77Vc8y1PBqRhIFDA1H0BGPQj5OQkWi8jGZWms5ZaUQ0CXDkn4jyZvgIb1cgjkqXCrsiI25aGIzoJbcGm8ZXuYzQj4vUSxbDfs4h6bhDVTCtyomX2/rQ3heGz6GmSykeeSoJXoeK9r4wWrvDmNfgyXi8eYoH//WRJXhx12H8cG0bekPJAOhwKI5/++NOPLutA3dePBeza91Zty8UMxCOJ5MCVrq0nCsXEI0FZ6URESVx5J+I8qrQI7xU3sphhH64fJYF29ERQF8ogcYKB5yqclTiQwUNFQ70hRJZJ8SUJAm6JWAJwH6cjP2aIkEXAv7YyCObkiTh4lPq8MSNZ+Pas6dDHZaRcGu7H5/82SY8uLo1Y5nBaKWTAvZHMBhJ5DSTgGgsOCuNiCiJI/9ElHcc4aWJIN9lwVJTj+u8dlS5NMR0K5340KHKEAInTPh3ItUuDZpNhgCgKhLCcROGZUGRZNhVCQlTQJUkVDhOPLLp1BTccuEcvPu0Bjy8pg2v7OkHAFgC+O2WQ1j9Vg8+eeFsXHZaA2Qpu+vZEgL94QQCUQOVbhU+h5r16yTKBWelERElceSfiAqi3EZ4iYYrRFmw4VOPRzKWqcepkc2eYAz7+yI4NBhBlz+GDn8EBwei6A/HMb3GjZb60U3dn1blwn1XLcL/u/I0NFU60tsHozr+4/924/Ynt2BnZ24lOw3LQm8wjvb+CKIJM6djEGWLs9KIiDjyT0RElGF4WbB6rx1xQyCcMGCTZdR7NXQHEzmVBUsF6K+3+2FaFhKmlc72rykyFFnGkukVOU09lmUJy+fW4uW2PpiWgE2RoMjJEnxR3YIiAWfNrMp6tP685hosnVmFZza345evHEDMSHZc7OoK4vYnt+Dy0xpwy4WzUZVDh8XwygDV7uTMBaJC4qw0Iprs+JeWiIhomFRZMLtNxv7+CPb3h3FwIIr9/WHs749As0npsmDZSAXokYSByNCIdyr5eCRhIpIwsHxubU6BiGUJrHu7Fy5NgUtLJjQzreRafremwKXZsPnAQEa2/9HSbDKuO2cmHr/xLFw0f0rGY396owvX/3QDfvvaQZg55kOIJAwcGoyiLxQfU04FotHgrDQimswY/BMREQ3TH0kgHDdxOBhHVLeSJfnkoZJ8enLKejhhZr02PxWgu+2ZAToAuDQFbruCdW/35hQApzos6n0OzK51Y2a1G9OqnJhZ7cbsWjfqKxw42B9BTyB+TJ3z0arzOfCV952K7354cUbm/3DcxIMvtuFTP9+Mre2DOR07nRRwIIJATM/pGERERHRiDP6JiGjc5DN7/nCGYWHVa4fw4Oq3seq1QzCM3GvLVzpVRHUTpiWgDgX9kpT8rspScip9wkSlM7uEdakAvc7rwIxqBzyaDXabDI9mw4xqB6Z4HTnNKAAy65hLkgSnpsDrUOHUFEiSBLsiwxCAbglMq3LCqR1b7my0zphRhR9/fCnuuKgZbvuR4+ztDeOup1/Hvz33Jg4H4zkd27QEeoNxHBxgPgAiIqJ8K2rwv27dOlxxxRVoamqCJEn43e9+l/G4EAJf/epX0djYCKfTiUsvvRRvv/12xj79/f247rrr4PP5UFlZiZtvvhmhUChjn23btuHCCy+Ew+HA9OnT8Z3vfOeYtjzzzDM45ZRT4HA4sGjRIvzv//5v3l8vEdFktr61Fzc8tgGf/vkmfPHp1/Hpn2/CDY9tyCl53nA/WdeGZff9FV98Ziu++/xufPGZrVh231/xk3VtOR8zPTh+9CC5dNTjWUgF6IeDcezsCmMgqiOcMDEQ1bGzK4zeYBy6JXLO9j/aZIKqIqOxwokpXjuUHKc8K7KED545DT+76Wy857SGjLfpxV2HccNPN+CXr+5HIsdOmISRzAfQ5Y/lfAwiIiLKVNTgPxwOY8mSJXjooYdGfPw73/kO7r//fvzwhz/Eq6++CrfbjcsuuwyxWCy9z3XXXYcdO3bg+eefx3PPPYd169bhU5/6VPrxQCCAd73rXZg5cyY2b96M//iP/8DXv/51/PjHP07vs379enzsYx/DzTffjC1btuDKK6/ElVdeiTfeeKNwL56IaBIpRPZ8IBn4f/vPu+CP6JBlCZoiQZYl+CM6vv3nXTl1AAxGdThUBbIkQTcFDMuCaSW/66aALElwqAoGo9lNT692aQjH9eP+3mBURzimjynbfzZ1zL0OFdOqXPDYc8/9W+XS8MXL5uPBa8/A/AZvenvMsPDfL+3DTU9sxCt7+nI+fiofQG8onnNOASIiIkqSxNF3CUUiSRJWrVqFK6+8EkDyZqWpqQn/+I//iC9+8YsAAL/fj/r6ejz++OP46Ec/ip07d+LUU0/Fxo0bsWzZMgDAn//8Z7znPe/BwYMH0dTUhEceeQT33nsvurq6oGnJG6p//ud/xu9+9zu89dZbAIBrrrkG4XAYzz33XLo95557Lk4//XT88Ic/HFX7A4EAKioq4Pf74fNln6mZiGiisiyBGx7bgJ2dATT4HBlrzoUQ6ArEsaDRiyduPDur5FuGYWHZfX+FP6JDs0mQpSP92ZawkDAEKlwqNt1zKWxZZJLfftCPT/98E+KGhcGonhF0KrKESqcKu03Gjz6+DIumVYz6uLGYgVO+/peT7vfW1y+Dw5F9QJ7qYAnFzRHrmJ+onFkkYaA3mIBh5T7KbgmBv7zRhZ/8be8xHRznzqnG7StbMLXKmfPxZUlClUuDz2nLOW8BERHRRJNNHFqya/737t2Lrq4uXHrppeltFRUVOOecc/Dyyy8DAF5++WVUVlamA38AuPTSSyHLMl599dX0PsuXL08H/gBw2WWXYdeuXRgYGEjvM/x5Uvuknmck8XgcgUAg44uIiI6VWute5dKOCdokSUKlS81prfuz2zoRjOqwKZmBPwDIkgybIiEY1fHsts6sjruwyYcaj4aBSAIQAqqSnFGgKhIgBAYiCdR4tKxL8v34pb153e9oY6lj7tJsmFblhC/LPAbDyZKEyxc14mc3nY0PnjkVw/txXtnTj5ue2IhH/7YHUT23tfyWEOgLx3FwIIpQ3Mi5nURERJNV7nP9CqyrqwsAUF9fn7G9vr4+/VhXVxfq6uoyHrfZbKiurs7YZ/bs2cccI/VYVVUVurq6Tvg8I/nmN7+Jb3zjGzm8MiKiyWV4MrqR2BUZ/hzWuh8ajMACYDvOILAsAebQfjmTkkGtBEAAsCSR/J8c7O8P53W/kYyljrksS6j12OGx23A4GId+nPwBJ+Nx2HDHRS1476JGPLD6bWxt9wMAdFPgyQ3teP7NHty6Yg5Wzp+S0wi+blroCcQQUBVUuzU41NyTFxIREU0mJTvyX+q+/OUvw+/3p7/a29uL3SQiopKUTTK6bEytdEEGYAlAQMASAqaV/J78OZmfb2qlK6vj7ugIoC+UQGOFA05VgSUEjKHjOlUFDRUO9IUSWc9UmFl9pDyeJCXblv6SRt4vF2OtY+5QFUyrcqIyh9wDw82udeO7H16Cr75vAaZ47Onth0Nx/Nsfd+Kup1/HnsOhExzhxGK6iY7BZFLAuMHKAERERCdTssF/Q0MDAKC7uztje3d3d/qxhoYG9PT0ZDxuGAb6+/sz9hnpGMOf43j7pB4fid1uh8/ny/giIqJjDU9GZwkL0YSJYExHNGHCEtaIyehG44rFjfA6VeimQFy3kDAsJMzk97ieTM7ndaq4YnFjVsdNzVSodGqYVePGzGo3plU5MbPajVk1blQ5tZyy8t+6fA5sQ4G4EMiI/lPZd2yyhFuXz8nquIUgSRKq3RqaKp3QhvIlWEJgd1cIG/f1Y3dXCNYoUgZJkoSV8+vw+E1n4bpzZiSXTgx5/aAfn/r5ZjywuhXBWHbJE4eLJAwcGoiiJxjLebYCERHRZFCywf/s2bPR0NCAF154Ib0tEAjg1VdfxXnnnQcAOO+88zA4OIjNmzen91m9ejUsy8I555yT3mfdunXQ9SM3Fs8//zzmz5+Pqqqq9D7Dnye1T+p5iIgod7Is4bYVzVBkYHd3CPv6Qmjvj2BfXwi7u0OwycBtK5qzHqG22WS897RkJ63Akdn4w///vac1ZJXsD8icqSBJEpyaAq9DhVNTIElSzjMVNE3BNcumpX8W4shXyjXLpkHTSmcau0NVMLXSibe7g/jn/9mOr/5+O771p7fw1d9vx93/sx1bDgyM6jhOVcHNF8zGTz9xFs6bU5Pebglg1ZZDuP6nG/HHbZ2j6lA4nlDMwMEBVgagE7Msge0H/Vi7+zC2H/TD4r8VIppEirrmPxQKobW1Nf3z3r17sXXrVlRXV2PGjBn4/Oc/j3//93/H3LlzMXv2bHzlK19BU1NTuiLAggUL8O53vxuf/OQn8cMf/hC6ruOOO+7ARz/6UTQ1NQEArr32WnzjG9/AzTffjLvvvhtvvPEGfvCDH+C//uu/0s/7uc99DitWrMB3v/tdvPe978Wvf/1rbNq0KaMcIBER5YuUMc0911tvyxI4MBCFW5MR1S0Mv4eXJcCpyjgwEIVliaw6FlIzFXZ2BtHgk4+pTjAY0bGg0Zv1TAUA+H8fXAwAeGrTQRjDGmyTJVyzbFr68VLyclsfvvmntxCM6fA6VNjkZAnEPYdD+N7zu3HXO+fhjBlVozrW1Eon/t9Vp+HVvX146MU2HByIAgD8UR3ffX43ntveic9e3IIFjbnNphNCIBDVEYoZqHCqqHCqWXcq0cS1vrUXj6xtQ1tPCLqZTObZXOfBbSuaT5gQk4hooihqqb81a9bgoosuOmb7DTfcgMcffxxCCHzta1/Dj3/8YwwODuKCCy7Aww8/jHnz5qX37e/vxx133IFnn30Wsizj6quvxv333w+Px5PeZ9u2bbj99tuxceNG1NbW4s4778Tdd9+d8ZzPPPMM/uVf/gX79u3D3Llz8Z3vfAfvec97Rv1aWOqPiGhkw0v91XvtiBsChmXBJsuw2yR0BxM5lfpLleRz223QbBL8EQO6aUFVZFS4bIgbApG4kXVJPmBsZfNGI5Ew8cN1e7C/P4yZ1W7cunxOSY34p4xUptGwLJiWgBACvaEE5kzx4NtXL4KcZfK+hGHhN5sP4hev7kdMz5yu/+6FDbjlwtmodo8t74AiS6hwqvA52Akw2R25pg1UuTRoioyEaWEgT9c0EVGxZBOHFjX4n0gY/BMRjWx4kG5XZcQSVjr4d2gyYrqVU5C+dvdhfPHp11HntUOSgMGong7+K50qhAB6QnH854eXYMW8KVm3e31rLx5e04ZdXUEkTAuaImN+gxefWTl5RgmHn7vhWfUtIWCYAlHdRCxh4F8/sAjzGjwnONLxHQ7G8cO1bXhx1+GM7W5NwQ3nz8KVpzfBdpxKEaOlyBIqnRp8TltOFQaovI3UiZUihEBXIJ5TByQRUSnIJg4t2VJ/RETjzbJETiXS6MRSCfQSpoXOoczsQiQz3NttCmo8uSXQS63N7wrEMBjVM9Z5d/pjqHSqsNvkrNfmZ0pWDsBQRYHcFymUp+FlGoUQiOnDOm5UGYCCYFyHP5bduRtuiteOr7zvVLx/ySAeWN2KPb3JUofhhImH17Thf7d34o6LW3DmKJcWjMS0BPrCcfijenImADsBJpUdHQG09YRQ5dKOOe+SJKHSpaKtJ4QdHYGsZwkREZUTBv9EROBa0EKqdmmwhMChofXdiixBkpOJ7mK6iUMDUVQ41ayD9IVNPthVGR3+GIBk4vyUZLCXwOxaV05r8483RfitrhDuWbV90kwRTnWwDEYT8Ed1xA1rWMeNDJ9ThUORUTuslF+ulkyvxI8+vhS/39qBx9fvQyhuAAD29UXwxWe2Yfm8Wty2ohn1PkfOz2FY1pFOAJcKn4OdAJPB8E6skdgVGf4cOiCJiMpNyWb7JyIaL6lAb2dnAG67DXVeO9x2G3Z2BnHPqu1Y39pb7CaWtQUNXphCwLQEFBmQJQkSJMiSBEVOBuqmEFjQ4M3quJYl0BOMp38WwDFj8z3BeNbZvC1L4JG1bQjFDTT4HHCoCmRZgkNV0OCzIxQ38cjatpLNEp7PbOYLm3yo8Wjo9McQ1U3IkgSbnDx3Ud1Elz+GWq8dF82vQ43bPuZAWpElfPDMqfjZTWfhPYsaMjp01u3uxSce24ifv7IfCWNsJf0My0JfKI72/igCMR1cATmxDa/gMZJcK3gQEZUbBv9ENKmVe6BXDnZ2BaFIgE2RYFjJ9eJCiOS6cSu5XZGS+2Xj2W2diCXMoQ6FIyP/EpI/KzIQS5h4dltnVsfNZopwqVnf2osbHtuAT/98E7749Ov49M834YbHNuSnAyt1CUhH/TykwqViaqUTdnXsiQsrXRq++K75eOi6M7Cg8UinUNyw8Njf9+HGxzfi7629Yw7aDctCbzCOgwPR9EwDmnhSFTwGIsd29KQqeDTXeXKaJUREVE4Y/BPRpFbOgV656I8kIEsymiqccKryUNCfDP6danK7LMtZT7k9NBiBhWSZPE2RodnkjO82WYIY2i/b9p5sinAuOQoKrRAzWHZ0BNAXSqCxwgmnZksn+rOEgFOzobHCib5QIn19aDYZUyudeZkFAACnNPjwwMfOwD9dNh9VLjW9vdMfw1d+vwNfXvUG2vuzO78j0U0LPYEYDg5EEEmwE2CikWUJt61ohseuoCsQR1Q3YVnJhJVdgTg8dgW3rWhmjhcimvAY/BPRpFaugV45SU251WwyZtW4MbPajWlVTsysdmNWjRuqTc5pyu3UShdkANZQMj5LHPlK/pwcpJ5a6cqpveU0RbhQM1hS10elU8XMaieqXBo8DhuqXBpmVjtR6VRHvD5SswAceZgFIEsS3n1aA5646WxcfeZUDI/PNuztx81PbMKP1+1BNGGO+bkShoUufwwdg1HE9LEfj0rH+S21uO+qRVjQ6EUkbqAnFEckbmBBo3fS5PAgImLCPyKa1IYHeg752EClFAO9cpOacruzM4gGnx1OTQGQfK9TU24XNHqznnJ7xeJGfOO5HRiM6Mfdp9Kl4orFjWNor3xMWbBc2ztcvitLDJ/BAgDRhJmRlT/XbOap66M7EMNAJAFzWN9BXyieTIZ4nIoKmk1GU6UT/oiO/khizFP0PXYbbr+oBe9Z1IgHX2zFlgODAADDEvj1xnY8v7Mbty6fg4tPqRvzrIOYbqJjMAqXZkOVW4XdNvZODCq+81tqce6cGlZ1IaJJi8E/EU1q4xHoTXapKbf3rNqOrkAclS4VdkVG3LQwGNFznnJrs8lYOqMSL7x1+Lj7LJ1RCZstu0luhWpvyvrWXjy8phVvdQWhGwKqTcIpDV58ZmVLzqOP6XKKhoVOf/SYrPw1bntOM1iOrqgwnCmA3lFUVKhwqXBqCnpD8byMps+udeM/P7QYa3f34odr29JJH/tCCfy//30Lf3i9E5+9uAXNdZ4xP1ckYSCSMOC225L/DtgJUPZkWWI5PyKatDjtn4gmNa4FHR+FmHJrGBY2D43+Hs/mA4MwcsgMX6gpwutbe/GFp7fi1b39GIzoCCcMDEZ0vLq3H194emvOifmS5RQtdPijiOrWUVn5k9sty8p6BotlCRweVlFBGvaVcngUFRVSswDylQtAkiSsnD8Fj914Fv7h3BlQlSPH3H7Ij0//YjN+8MLbCESPPyskG+G4gUMDUXQHYlwOQEREZUsSrG+TF4FAABUVFfD7/fD5OEJIVG7Wt/bikbVtaOsJQbcEVFlCc50Ht61o5lrQPMrndPdVrx3CF5/ZClmWoMgSTEukR7tTP1uWwH9++HRcdebUorfXsgQ+8NBL2NERgCQBNlmGhGTSfMNKjtQvbPLh97dfkPVzGIaFZff9Ff6IDs0mQZaO9O1bwkLCEKhwqdh0z6VZzYRIvceSNJRbQSTbKyH5PstScls277FuWjgczM8sgJSOwSgeXtOG9W19Gdt9DhtuuXA2Lj+tEUoeO/BcWnImQD5yGhAREY1FNnEop/0TEYFrQcdLPqfcprP9S4CE5Eh3xnNJAiayz/afcYw8tnf7IT92dYUgAVDlI0tMUj/rpoVdXSFsP+THkumVWR07WU4x1QkCQBaQhgJz00p2hiiShJ1dwaxeT+o91mQJNklKBv9DHSzJ4wvopsjqPVaV/OYCAICmSif+/crT8OrePjz0YhsODkQBAIGYge89/zae29aJOy9uwcKm/JzL1HIAdgIQEVE5YfBPRDSEa0HLy/Bs/5IkjglMc832Xyhb2geTSfgUacSykooiwTAtbGkfzDr4T5ZTlDC1Kll6L26YEFbyfXCoCmo8GiIJM+s1/8PfY5skQTpqzr8pRM7vcSoXwOFQHPE8zQI4Z3YNzphehf957SB+/sp+xPTkko/d3SHc+autuGxhPT554RxUu/OTwJOdAEREVE645p+IiMrSFYsb4XWqMEyBuG4hYVhImMnvcd2CYQp4ndln+y8UKTXAPVSa0LAs6KaVnPKPofn0w/fLQrqcoiJjVq0rs5xirQuqkls5xeHvsWmZsIRILqcQyZ/H+h5rNhlTK52odmt5yQWQOubHzp6BJ248G5ecUpfx2F92dOP6n27AM5vaYRynlGMuIgkDHYNRdPljiBvMCUBERKWJwT8REZUlm03Ge09rgADSXxj2/wLAe09ryDrbf6GcPqMSqiJDNwViugXdFDAskfGzqsg4fUZl1sdOVa0YiOiAAJyaAq8jObIOAQxGdDTXebKuWmGzybh9ZTMkCUiYQHyogyX5PTmz4PaVzWN+jytdGpoqHdDyeK6meO24970L8P1rlqB5iju9PZIw8cjaPfjkzzZj8/6BvD1f8thHEgMmckg0SUREVEilcUdERESUJcsSODAQhV0ZecTYrsg4MBA9aSb68bJoagWqXCqO1xoBoMqlYtHU7JeeFLJqxcKmClQ4VRz9mxKACqeat3X0dpuCqZVOVGY5O+FkFk+rxA//YSk+d0kLvI4jqx3390fwpd9sw9f+sANdgWNLGY5FOG7g4EAEPewEICKiEsLgn4iIytKOjgDe7PDDAqDIgE0GFGnouwxYEHizw48dHYFiNxVAsrMiGDdOuE8wbuTcWVGI8oSWJfDI2jZYQsCpyVAkCbIEKJIEpybDEkOP56mDRZIkVLs1NFU6oSr5u0VRZAkfOH0qfnbj2Xjf4saMjoy/vd2LGx/biJ+9vC9vuQdSQqlOgGAMeh6XGRAREeWCCf+IiKgs9YbjCMQMWEJkZM8HhrLQWxYCMQO94fgJjjJ+nt3WiVjChCInExMOj5floSSFsYSJZ7d15lyaMN9VK5IdLAGE4yYEAFkGJMgQEIgbAgnDxJsdAezoCOQ1WaZDVTCtyon+cAL+qJ6341a4VNz1znl43+JG3P9CK97sTHYMxQ0Lj6/fjz+/0Y3PrGzGO1pqTpiDwBICrd1h+GMJVDg0tNS7IZ9g/1DMQDhuwmO3ocqlwpbHjg0iIqLRYvBPRERlaTCsw7IEZHnk7PmyJMGyBAbD+Qsex2J42TwIwMKR6gQyAEjIumzeSPJZtaIvFEcgpsO0kqUDk8nzk70W8lBFhUBMR18o/x0skiShxmOH227D4WA8ryPn8+q9uP9jp+Ovb3bjR+v2JHMlAOgKxPDVP+zAsplVuOPiFsyoPraKwZYDA3hyQzva+8LQLQFVljC9xo1rz56OM2ZUHfc5hRAIxnSE4ga8DhsqnewEICKi8cW/OkREVJaqXCpkORngi6NW0guIdMdAlUstUgszpcrmGZaAbgkYQwn/DHPoZyv3snmFMhBJBv4CmTMVMPSzAGBaIh08F4JDTeYC8Dnzex5lScK7FjbgZzedjQ8vnQZl2OyITfsHcMsTm/CjtW2IJI4s1dhyYADfe3439hwOwanZUOPW4NRs2HM4hO89vxtbDpw8gaAQAoGojvaBKPpCcZglkpOCiIgmPgb/RERUlmo8dvgcKiRJgmEmy88JJL8bpoAkSfA5VNR47MVuKoBk2TyHpsC0jgTOANKBtWkBDk0pmdKEAOBz2Y4J+o9mieR+hSTLEmo9djRWOGGT83vr4rbbcNvKZjx6/VIsHVZpwbAEntp0ENf/dCOef7MbpmXhyQ3tiCRM1Ho02G0yZEmC3Saj1qMhkjDx5IZ2WGJ0wbwQAv6ojvb+CPrDiZJJTElERBMXg38iIho3liWw/aAfa3cfxvaD/jEFPAubfDi1yQe3XYFmS3YAJAwLhimg2SS47QpObfJlXd6uUGRZQp33SEeENOwrpc5rz3l9fiGMdsnEeC2tcGrJXAAeR/47G2bWuPGdDy3G199/Kup9R85TfziBb/7pLdz6i9ewpycInyM5AyGmWwgnDMSSayHgdaho7wujtTuc1fNaQmAwksCB/ggG2AlAE0A+P+eJKL+45p+IiMbF+tZePLK2DW09oaGa9hKa6zy4bUVzTpnoU+Xtbn/yNUQT1rCJ/wLRhIDdlXt5u0LY0RFAXLdQ49YwEEkck/CvyqUhrltjTp5nWSJvCf+Co0y2N9r98iHZieKAx27gcPD40+azTcoHJPMMLJ87BWfPqsavN7bj1xvb06X62g4ng/q4KSCsZELJVM4GTVFQ6VahCwF/LJHT67KEwEAkgUBMR4VTTZZXPEl7iUpNvj/niSi/GPwTEVHBrW/txT2rtiMUN1Dl0qApMhKmhZ2dQdyzanvOpeh2dPjhj+o4OvwTAPxRHTs6/CVzw9kfSUA3BTx2G2K6iZhuQUBAggSHKsNjtyGim+iP5BY8Avm/8RajjD1Hu18+uTQbplUp6A3FET6qhGKuSflSHKqCT5w/C5ctrMcja/bgpdbe9GORRLIcoCwlS0oCEuKGiW6/Ca9DRYVDG9PrMi2RrnJQ6dTgc9rYCUBloVCf80SUP5z2T0REBZWqFR+KG2jwOeBQFciyBIeqoMFnRyhu5lQr3jAsPLSmDUIAmgLYbTI0RR76nsyk/9CaNhhGadRXr3ZpsISFDn8UMcOCTZGgKTJsioSYkdxuWRaqXbkFj6kb7zc7/FBkCS5NgSJLeLMjgHtWbcf6YQHsaHntoxsjGO1++abIEup9DtR67elR/Xwk5UtprHDiXz+wEN++ehGmVzkzHrMEoJvJEXtZBkwBmEKgeYo7L6/NtAT6wnG090cRiOkQo8wlQFQMwz/n6312CAGEEwaEAOp9Ws6f80c/B5cTEI0NR/6JiKigdnQE0NYTQpVLG7EkX6VLRVtPKOvp7s9u60QwqsOmSFBSSeBSh5cUCFgIRnU8u60TV505NU+vJncLGrwwBdI5CVLBqgTAJgskDAFTJPfLVurGuz+cgGkJBGKxYVPSZehm8vFz59RktQQgcNSI+lj3KxSfQ4VTVdDtj2Uk5ZOG/kHYbRJqPRp6Qwk8uaEdS6ZXnnQJwHBnzarG3Zedgi/+5nXEjupMMi3ARPI8KlJyecC8Bk/eXpthWegNxuGP6KhwqfDaOROASk/qc95uU7C/L4q4YaY/g+w2BRXO3D7nU9a39uLhNa14qysI3RBQbRJOafDiMytbOJuAKAsc+SciooJKTXfXjlPT3K7I0C2R9XT3Q4MRWEhOv05l+TetI1n/k9uT+5WCnV1BKJIERZaGMv6LYe1OjmIrkoSdXcGsj72jI4A3O/yI6iZihgVZkmCTkx0MMcNCVDfwZocfOzoCWR1XSYfPxycN7VdsqiJjIKLjUH8kWQXiqDZJkHJOygcAYd2ATZGP+0oFgEjCQl8kln3jR0E3k50ABwc4E4BKT38kgXDCRE8ghphuJj+DlKHPID25PZzIbVnT+tZefOHprXh1bz8GIzrCCQODER2v7u3HF57emtOsJqLJisE/EREVVLVLg6pISJgjT7+PmxZUWcp6uvvUShdkJKdHJwwr+WVa6f83LQFpaL9c5XOaaX8kAVmSMLXKCYeqpEsSWkIka9lXOSHLUk43x73hOAIxA5ZIrm+XJQmSlPyuyhIsIRCIGegNx7M67ukzKqHZZMhIdrIMJ0vJmwjNJuP0YSXyiqk/koApAJemjDg6rilSzkn5vA4VccOEBEBVUuv9MyVMC/e/0IqN+/pzaD1gmgLPv9mNX7yyP1le0Dz231uqE6C9P5rMd8FOACoBlU4VMd2EJUQ66JcgpTsBLCEQ001UOtWsjmtZAt/8004cDsZhCQFFTnZsKkOfa4eDcXzzTzu5BIBolDjtn4iICmphkw/NdR7s7AyiwSdnBGVCCAxGdCxo9GZdku+KxY34yu+3IxRPJmAbXjbPEskvj13BFYsbc2p3vpPnpTpBNEXGrFoXYgkLhmXBJstwaDJiugVVzm3N/2BYh2UJyLI04tIKWZJgWSLrknyLplZgXr0nOWNAAKpy5NimKQAJmFfvwaKpuVcnyKfUe2xYAnabnJySPywoSJgCqiTlnpRv6FAShgIQScASgDHsOboDcdz9P9vxjpYafGZlMxornMc5WKanN7bjlxsOIBwzkjNaADz4YiuuO3sGPnLW9GP2NywLfaGh5QBOlYkBqejS/VACmTVMxVGPZ2H7IT92dYWSnW7ykb8fqZ9108KurhC2H/JjyfTKnNtONFlw5J+IiAoqVZLPY1fQFYgjqpuwLIGobqIrEIfHnltJPlmWMMV7pB67GPaVMsVrz6nMXSp53s7OANx2G+q8drjttnTW6lymmaY6QQYix47WpjpBmus8WXeCAECVS4UsJwN8cVTtAwGR7hiocmU36ibLEr58+QJM8dqhKBKESB5LCAFFkVDntePLly8omXKKw99jALApMlQlGTAICARjOqbXuNFSn31SvmBMh92mQJGTwb5pWUOjjckZJkffUP29tQ83Pr4Jj/99H2K6ecJjP72xHT/52x4EYwZkOTmzQJaBYMzAT/62B09vbD/u7xqWhb5wHAf6I/BHdI6AUlEMRnU41WSSUT21/Eokv+tWcsTeqSkYzLIs6Jb2QRiWBUUZuWNTUSQYloUt7YN5fDVEExeDfyIiKrjzW2px31WLsKDRi0jcQE8ojkjcwIJG7xjK/AUQ1y3UujUoR8WeigTUujXEdSvrde6Fqk6Q6gRRZGB3dwj7+kJo749gX18Iu7tDsMnIqRMEAGo89uQ6d0lKLyVI5RMwTAFJkuBzqKjx2E9+sKOc31KL//rI6Thndg2qXHZ4HCqqXHacM7sG3/vI6SWVbGukjiYgNUquw6UpuPbs6Vkl+0upcGhwacmSjBCAYQHG0HcJgM9pQ5XTlpHtP2FY+Nkr+3Hj4xux7u3DI07RN02BX244AFMkg35JkgCRDHRUJVlF4JcbDoy4BCDjOEPVAQ70R9AXisM4zjIbokKodmlw2xVM8drhVOXkZ89QJ4BTlVHrtcOtKVnPbJKGzyYYiThqPyI6IU77JyKicXF+Sy3OnVODHR0B9EcSqHZpWNjky3nUOJVIsN7nQH2FHf6IAd20oCoyKlw2QEjoCcWzXkNfqOoEx5Iw/PBjuXdd2OTDqU0+bDs4CMMUSJgWhJXKtJ0sJ3hqky+nWQVA/s9dIaU6mlJLNvxWMg/Cwqk+3Hj+LDTXeTKWAoxWS70blS4Vb/ckkwUqEtLZzC0B+KMG5ta58dB1Z2L1W4fxo3V70B9O/tvrDsTx9T+8iaUzKnHHxS2YWXOkg2D1rh6EYwYUCTBMZMzcSFUQCMcMrN7Vg3eeWn/SdlpCwB/VEYgZcNsVVDo1aDaO9VBhDV/eNbPahbgh0sua7DYJ3cFETsu7Tp9RCVWRYZgWZFlkJPIUSCZ5VZXSyTtCVOoY/BMR0biRZWmMAfMRwxMJOlQFVe7MEaWoYeaUSHA01Qn8OVQnSM0oMC2BeXWeEW+OcynHBxwZ8b5n1XaE4gaqVQ3SUHAa1U147LacZxUMb39rTwiHBiOYWunCggZvSQb/wIk7K0xLoDcURzjH8oSp0Nw8akQyXWVSkvDOU+txfnMNfv7KfvzPa4fSnQ2bDwzilp9txgfPmIrrz5sJt92G7kAM1vADH/VcqefpDmRXRUAIgVDMQChmwKXZUOlS4VCVrI5BNFrDP4O6gwlUulS4NRvipoXuYCLn5V3D847ohpWsuDH02WaYFgRKK+8IUalj8E9ERGWpUIkEMzoV5GODpVyrEwyfUSDLMpwaABw5/lhnFBw94q0PjXgvaPTlnKQw5Sfr2vDQmjYEo3o6Gd03ntuB21c245PLm3M+biEdr6NJkSXU+xwIxnT0hRKwRpmFrLU7jJ5gAhKOP0ujJ5hAa3cY8xo8cNttuHVFM95zWiMefLEVm/YPAEhOz39m80G88FYPPnXhbEwZ5VKMOq9jVPuNJJIwEEkYcKgKKl0qXBpv/yj/jjfrZkGjN+fPoFTekS88vRX94QRMy0rPuJFlCTVuraTyjhCVOn76ExFRWRo+0tQViKPSpcKuyIibFgYjes4jTYXqVCjUjILhCjE9/yfr2vDtP++CaSVLeNlS09wjOr79510AULIdACfidahwqgoOh+KIJk6ckA8ABiMJBGP6CZceB2M6Bo86fzNqXPj21Yvw99Y+PLymDV1DI/j94QS+9eddmF07uuSD06pHVzXgRGK6iS6/Cc0mo9KlJfMXEOVRIT6DUnlHHl7Thl1dQSRMC5oiY36DF59ZObaOTaLJhp/6RERUtgo10lSIToVCzSgYqf35WlphGBYeWpNcqqDZksUUhQBkCdBsQMIQeGhNG248fzZsZbiu3KbIaKxwwh/VMRA+8SyAwaiOk+TcgykwYjZzSZJwwdxanDWrCk9taseTG9qRMJIJ+fb2hk/aTqcq57xMYSQJw0JPIIYBRUalS4XXkV0VCKITyednUEo55R0ZD5Yl+F5QThj8ExFRWSvUSNN9Vy06ZqTplDGMNBVqRkEhPbutE8GoDkUGdDNZuitVwluSAEUGglEdz27rxFVnTi12c3NW4VTh0hQcDsaPW5YvGB9dibIT7WdXFVx/3iy869QGPLK2DX97+8QlI+WhtqmKhArH2DqFRqKbFg4H4xiM6KhwqfDabcckuSQqFYXoVChH61t7jyzvMgVURUJznWfMy7tocii/bnoiIqKjpG4KV8ybgkXTKvI4ApIsmZf8L1nTfSxtPFKGLobBSAL+aAKDkQS6ArGcZxQU0qHBCKyhcnapBPmp2HD49kODkeI1Mk9URUZTpRM1bvuIAfBoS4mNZr+GCge+8f6F+I8PLcaMEabzKzJQ5VQxe4oLkIDpNW601I9ueUAudNNCbzCO9v4o/FF9xJKERFR861t7cc+q7djZGYDbbkOd1w633YadnUHcs2o71reeuEORiME/ERHRUVI3WG91BVHl0jCtyokql4a3ukJjusE6v6UW150zA4Yl0DEYxcGBKDoGozAsgevOmVFyozaNFc50d4eUnPU/9MORTgAxtN9EUeFSMbXSCftRmfHdo1wfP9r9AGDpzCo8ev0yvH9JI4Z3N5gWMBDV0d4fhd0m49qzp0MehxF5w7LQF2InAFEpSlWMCcUNNPgccKgKZFmCQ1XQ4LMjFDfxyNo2WDmUMqXJg8E/ERHRMIW8wVrf2otfvnoAigQ0VToxvcqFpkonFEnCL189UHKjNnOmuNNB6dFxYOpnaWi/iUSzyZha6US1W0vPAggnRrfmfrT7pdgUGZ+/dB6+esUCVB9VrjJhCgxEdOzqCkI3rayOOxYZnQARdgIQlYLhFWOOnp0kSVJGxRii42HwT0RE48ayBLYf9GPt7sPYftBfkiMUhbrBGt6p0FjhRKVLg8+potKlobHCUZKjNoGYAdewEXAhjnyluDQFgVj+ktGVkkqXhqZKBzSbfGSqw8nkOELvc6iYVe2ES5UzZgHEDQs//tte3PzEJmzY25/TsXNlWBb6wuwEICoFo6kYo4+xYgxNfEz4R0RE46JckhQVqiRfNp0KpZLUqtqlocKlwmnIGIhkZrtXJCQrIdiUMVcoKGV2m4KplU4sm1k5qv3n13uyfo4tBwZw3//uHJpqf2R1xfBQ++BAFP/82+04v7kGn1nZjKbK8VtqkeoE8Ed1VDhVeB22kspNQTQZjFfFGJrYOPJPREQFV05JiobfYI0k1xuschy1SVUogCRjfr0H0yqdqPfaMa3Sifn1HkiSjOY6T0lVKCgESZLQGxzdeTnYH83q2JYQ+NHaNvSH9XQCxRNN8F/f1ocbH9+In/5973ErExRKeibAQAQD4QTMEpqlQjTRpT6PB0aYhZOqGDMZPo9pbBj8ExFRQZVbkqJC3WAVqlOhkIZXKOgJ6XBoCmo9dji05M+lWKGgUF5v949qv7c6g1kdd3d3CHt7I+lR/qO/A4BNAlqmHJlRoJsCv3jlAD7x2Eas3X143Kfjm5bAQCSB9v4I+kJxGOOYj4BossqsGBNHVDdhWQJR3URXID6pPo8pdwz+iYiooMotSVGhbrDKddTm/JZa3HfVIpzS4IU/ouPgYHL99ykNXtx31aKSWrJRSE77sdNsR2LXsru1erMzAP0kHV+GAC47rR73vucU1HiOdA71BOP4xrNv4ou/2Ya9veGsnjcfLCHgj+poH4iiJxhDwpi8nQDlkM+Eyl/q83hBoxeRuIGeUByRuIEFjZPr85hyxzX/RERUUIVaQ19IqRusVI4CvyWgyhIWNHpzzlGQ6lS4Z9V2dAXiyfXyioy4aWEwUg6j6AICAsn/BDLHpie+yxbW48fr9px0vwvnZvdvwzRGN3XfMi1csqAe5zXX4BevHMBvNh+EMRRgbjkwiE/+bBOuOmMqbjh/FjxZlBvMByEEQjEDoZgBt92GCqcKhzq6zpKJoFzymdDEcH5LLc6dU4MdHQH0RxKodmlY2OQr4b8d5cmyxIR8jxn8ExFRQZVrkqJC3GAVolOh0FL5GkJxA1UuDZoiI2FaeKsrhHtWbZ80o002WYYsJdfkH48EQJGzG/nfPsoZL9s7AvgwAJdmw6eWz8HlpzXgoRdbsWHfAIBku/7ntUNY/VYPbrlwDi5bWA85x8oDYxGOGwjHDThUBRVOFe5x7ogYb8e7PlL5TCbL9UHjS5alkkkMOxFN5A69if2JTERERZea7r6zM4gGn5wx9T813X1Bo7fkprsDhbnBKqdRm6PzNaTOnUNW0OCT0RWI45G1bTh3Tk1Jtj+fBqM6vHYVwbg+YgeABMDjsCEY07M6biQxupH/o/ebXu3CNz+4COvb+vDwmjZ0+mMAgIGIjv/4yy48t60Dn714LuY3eLNqT77EdBMx3YSqyPA5VfgctmOW/ZQ7Xh9EE89E79Djmn8iIiooJik6VqpTYcW8KVg0raJkX3u55WsopGqXBrddQWOFAy5VgiINjfRLgEuV0FjpgNduQ8sUL9TjLHEZSVOFI+f9JEnCO1pq8dgnzsKN75gFu+3I8+7sDOIzv3wN//l/uzBYxCU1ummhLxTHgf4I+idYhYDh1wckIJowEYzpiCZMYKgU5mS5PogmgnJLUJwLBv9ERFRwTFJUnsqxPGGhpGawxA2B2bUezK71YGaNK/3/CUOguc6DM2ZUYlqVExVOdVTHfdeC+jHvp9lkfPzcmXj8xrOwYt6U9HYB4H+3d+H6n27Eqi2Hihp4m5bAYCSBA/0RHA7GJ0RywNT1kTAt7OuNYH9/GAcHotjfH8a+3gh005o01wfRRDAZOrw57Z+IiMZFOU13p6RyzddQCMMTNnYHE6h0qXBrNsRNC93BxDEzWGo8drjtNhwOxqGfoBReaJTT/kezX73Pga9dcSpeOzCAB1a3Yn9fJPm7cQMPrG7FH7d34s6LW7BkWuWonrMQhBAIxnQEYzpcmg2VrvJNDljt0mAJgUMDUQCAIkuQZECI5LKHQwNRVDjVSXF9EE0E5ZigOFsc+ScionFTLtPdKalcyxMWSrYzWByqctJZAG93h0b13KPdDwDOnFGFn3x8KT6zshlu7UhgvedwGF946nX8+x934nAwPurjFUokYaBjMIpDg1GE4kaxm5O1BQ1emELAtAQUGZAlCRIkyJIERU7OdjCFwIIi5V0gouwM7/AeyUTo8ObIPxEREY2o/MsT5l+2M1gkSTrhLAALo5v+Ptr9UmyKjA8tnYaLT6nDo3/biz/v6Eo/tvqtHqxv68U/nDMTH1o6DZqtuGNBcd1Ej25iYCg5oNduK4t/Uzu7glAkwKZIMCzAJgtISC63MKzkdkVK7sfM7ESlr5wTFI8WR/6JiIjouJiv4Vi5zGBJzQLwHTULoDc0uumjo93vaNVuDf/07vl46NozML/+yAh0TLfw6Et7ccvPNuGVPX05HTvfUskB2wciGAgnSj6pVn8kAVmS0VThhFOVYQkBwxKwhIBTTW6XZbmspwgTTSaTIUExR/6JiIjohJivIT8kSUKtxw63ZkNvKDULYLQB7tgC4QWNPjx03Rn40/YuPPrSXvijyZKEBweiuGfVGzhvTg0+c1EzplY6x/Q8+WBaAgORBAIxHRVOFRVOtSTLBKamCGs2GbNq3IjpFgzLgk2W4VBlxAwLqmmV9RRhoskm1eH9yNo2tPWE4LcEVFnCgkYvblvRXPYd3gz+iYiI6KRSo900dk5NwdRKJ/rCCSSM0QX1o93vRGRJwnsXN2L5vFo8vn4/fr/1EFKD6y/v6cOm/f34yLLpuPacGXCWQBI+0xLoDycQiBqodCeXA5RSJ0DmFGE7nJoCIPm+TZQpwkST0UTu8Oa0fyIiIqJxJssSpnjtWDqjalT7z6vz5O25vQ4Vd17cgh99fCkWD+vQ0U2BX756ADc+thFrdh0+JsljsRiWhd5gHAcHSisx4GSYIkw0WU3UBMUM/omIiIiK5LyW2pPejMkAFhagPF/zFA/+6yNLcO97FqDGc2Rqek8wjn997k384zPbsLc3nPfnzZVuWugJxHBwIIJIojQ6AZgTg4jKiSRKpVu3zAUCAVRUVMDv98Pn4/QuIiIiOjnLEvjAQy/hjUOB467qn1fnxsP/sBRyAae8RxMmfvHqfjyz6SCMYYn2ZAm48oyp+MR5s+BxlNZqUbuqoNKpwm0vfrssS0zIKcJEVPqyiUMZ/OcJg38iIiLKxfrWXnzh6a3oC8VhWsnUfhKSgXelS8U971mAM0a5PGCsDg5E8OCLbdiwtz9je5VLxS0XzMZlpzUUtBMiF6oio9KlwlNiOQGIiMYDg/8iYPBPREREuVrf2ouH17Tira4gdFPAJgOzat249uwZ4xb4pwgh8PKePjz0Yhs6/bGMx05p8OKzl7TglIbC3uuYpsDqXT3oDsRQ73Pg4vl1UJQTB/Y2WUaFU4XXYeOoOxFNGgz+i4DBPxER0eRQqCneRx/3lAYv+iMJhIuU5C5hWHh6UzuefPUAYoaV3i4BuPy0Btx84WxUFaCM3dMb2/HLDQcQjhmwkMx54HbYcN3ZM/CRs6af9PcVWYLPocLnVKGwE4CIJjgG/0XA4J+IiGjiW9/am67/rJsCqiKhuc5T0PrPobgxtCSgOLdsPYEYfrh2D9bsPpyx3W1XcOP5s/CB06fmLch+emM7fvK3PTAFYJMBSQKEAAwLUCTgkxfOGVUHAJAsbehx2FDpVGFTkmkVuTafiCYaBv9FwOCfiIhoYlvf2ot7Vm1HKG6gyqVBU2QkTAsDER0eu1LQ7O6GaaE3lMgpy70lBFq7w/DHEqhwaGipd+e0bn/LgQE8sLoV+/oiGdtn17px58UtOH16ZdbHHM40BT74w/UIxgyoCgBIRxIgQEA3Aa/Dht/eev5JlwAMJ0kS3HYFOzsCePSlvePacUNEVGgM/ouAwT8REdHEZVkCNzy2ATs7A2jwOTISywkh0BWIY0GjF0/ceHZBR5IDMR39oQSsUd6+bTkwgCc3tKO9LwzdElBlCdNr3Lj27Ok55RIwLYHfbz2Ex9bvQzhuZjy2ct4U3LpiDup8jqyPCwDPv9mNb//pLUgSYAlkVD9IJUAUArj78lPwzlPrszr2lgMD+N7zuxFJmKh2a3DYlHHruCEiKqRs4tCTlZYlIiIimvR2dATQ1hNClUs7JqO8JEmodKlo6wlhR0egoO3wOVRMrXLCkRwaP6FUwLvncAhOzYYatwanZsOewyF87/nd2HJgIOvnV2QJHzxzGn5209m4/LSGjMfW7D6MTzy2Eb98dT8Sw3IEjFZ3IAYLgHlU4A8kfzYFYA3tlw1LCDy5oR2RhIlajwZFkmBYAqoio8FnRyhu4pG1bbCKtKyCiGi8MPgnIiI6DssS2H7Qj7W7D2P7QT+Dg0msP5KAbgpoysi3TnZFhm4J9EcSBW+LqshoqnSi2n1sR0TK0QGv3SZDliTYbTJqPRoiCRNPbmgf9QyCo1W5NHzpsvl46NozML/Bm94eMyz890v7cNMTG/FyW19Wx6zzjm7GwGj3S2ntDqO9LwyfQ4WUXEMAIQQM04JuCngdNrR2BwvecUNE+ce/09mxFbsBREREpagYid0of/Kd2K3apUFVJCRMCw752FH3uGlBlSVUFyD7/fFUujQ4NQWHg/FjRtpHCnhTJEjwOlS094XR2h3GvAZPzm1Y0OjDQ9eegT+/0YVH/7YXg1EdANAxGMO9v3sD58yuxu0XNWNaleukx5pW7RzVc452vxR/LAHdEvCNkCdACAFFAuKmwIGBCBMAEpUR/p3OHoN/IiKioxwvsdvOziDuWbWd64NLXCFuCBc2+dBc58HOziAafPIxa/4HIzoWNHqxsGl88/7YbQqmVjrRH07APxR4AycOeAFAUyQEhYA/NvaZCrIk4T2LGrF87hQ8vn4ffrf1EFKDb6/u7cdrBwbw4aXTcN05M+HUjr9cIRw34FRlRPXjLxlwqnLWpQ8rHBpUWYJuCthtx74fCVPAJgEKJBzoj8DrsKFiWIUAIio9/DudG36qERERDWNZAo+sbUMobqDB54BDVSDLEhyqwvXBZSB1Q7izMwC33YY6rx1uuy19Q7i+tTen48qyhNtWNMNjV9AViCOqm7AsgahuoisQh8eu4LYVzUUZNZYkCTUeO5oqnVCHAtbhAe9IEqaAKkmocORvpoLHYcMdF7fgxx9fitOnV6S362ZyCcINj23A6rd6cLxc0xUODV67DVVO9ZgbVBlAlVOF127Lus0t9W5Mr3EjENMhjsomICAQjOmYXuNGS70blhDwR3W0D0TRE4zllLuAiAqLf6dzx+CfiIhomFJJ7EbZK/QN4fkttbjvqkVY0OhFJG6gJxRHJG5gQaO3JEaZHGpyFoDXoWYV8ObbnCkefPfDS/DV9y3AFI89vb03lMC//3En7nr6dbQdDh3ze6k2QwJmT3Gh3mtHjVtFvdeO2VNcgISc2ixLEq49ezpcmoLeUAIxw4IlBGJGsnyiS1Nw7dnTM8ofCiEQihk4OBBBdyCGuGGe4BmIaDzx73TuOO2fiIhomNEkdvOPU2I3yk42N4SLplUc5ygndn5LLc6dU5PXfAL5JMsSpnjtcNsV/MM5M/Cf/7cLvaEEvA4VmiIhYSYD/5EC3nySJAkr59fhnDk1ePLVA3h6U3t6FsLrB/349M834wOnT8Unzp8Jr0NNtn0oSP/e87vRH9bhdajwKjYkTIH+8NjafMaMKtz1znnpsodBkZz5MGeK56RlD8NxA+G4AZdmQ6VLHVWlBSIqHP6dzh2DfyIiomFKMbEbjc543RDKspRz58F4cWk2vP/0qdBsMn76931ZB7z54lQV3HzBbLx7YQMeWtOKV/b0AwAsAazacgir3+rBLRfMxuWLGiBL0piC9JM5Y0YVlkyvRGt3GP5YAhUODS317lF3JkQSBiIJA05NQZVLYycAUZHw73TuGPwTERENU6qJ3ejkeEOYSZElvHdxEy5oqcUre/oxGM0+4M2XqVVO3HfVIryypw8PvdiGQ4NRAIA/quO7z+/Gc9s68dlLWrCg0TfmIP1EZEkaU3UDAIgmTEQTUTjUZCfAiZIYElH+8e907rjmn4iIaJhSTuxGJ5a6IRyI6McklUvdEDbXeSbdDWGFS8MlC+qwfN4UzGvwjHvgP9y5c2rw3zcswy0XzIbDduQ2dFd3ELc/uQXf+fMu9IcT6SD9rFnVRW/z8cR0E53+KA4NRhFJZFeBgIhyx7/TuZPE8VKuUlYCgQAqKirg9/vh802umwoiookoo1ycJaDKrB9cDo6UfzJR6VJhV2TETQuDER0eu1ISifmKyR/V0R9OHDfj/ng6HIzjh2vb8OKuwxnb3ZqCG86fhStPbyqrcnuaTUaVS4Pbzom1ROOBf6eTsolDGfznCYN/IqKJx7JEySZ2o+PjDeGJJQwLh0NxxPXSyGD/evsgHljdij294YztM2tcuPPiFpw5DrkJ8klVZFS5NXjYCUBUcPw7zeC/KBj8ExERlQ7eEJ5YahnEYPTYJRLFYFoCv9/agcfX70MonjmFfvm8Wty2ohn1PkeRWpcbVZFR6VLhsduOqT5BRJQvDP6LgME/ERERlZuYbuJwMA7dtIrdFADAYCSBR1/aiz9t78LwG1S7Tca158zANcumQ7OVz1IAINkJUOFS4WUnABEVAIP/ImDwT0REROVICIG+cAKBqF7spqS91RXAA6tbsbMzmLG9scKBz6xsxvnNNWUXSNvkZCeAz8FOACLKHwb/RcDgn4iIiMpZNJGcBWBYpTELwBICf9nRjUf/tgcDkcyOibNnVeH2i1owvdpVpNblzibLqHCq8DnZCUBEY8fgvwgY/BMREVG5My2BvlD8mHX3xRSKG3hi/T6s2nII1rC7Vpss4UNLp+Hj586EU1OK18AcsROAiPKBwX8RMPgnIiKiiSIY09EXSsAqodvEvb1hPPhiK7YcGMzYXuPRcOvyObj4lLqyDKIVWUp2AjhUJqUkoqwx+C8CBv9EREQ0kRhmsiRgNFEaJQGBZH6CdW/34pE1begJxjMeWzS1Ap+9uAXNdZ4itW5sFFmCz6HC51ShsBOAiEaJwX8RMPgnIiKiicgf0dEfSZREScCUqG7iVxsO4KmN7dDNI+2SJeCKJU248fxZ8DnVIrYwd7IkweuwocKpwqaUV2UDIhp/DP6LgME/ERERTVQJw0JPMIaEURrJAFM6BqN4eE0b1rf1ZWz3OWy45cLZuPy0xrIdRZeGdQKo7AQgouNg8F8EDP6JiIhoIhNCYCCiYzCSKHZTjrFhbz8efLEVBweiGdvn1nnw2UtasLCpokgtGztJkuC2K6h0atBs7AQgokwM/ouAwT8RERFNBjE9WRJQN0trFkDCsPA/rx3Ez1/Zj5ie2bbLFtbjkxfOQbVbK1Lr8sNjt6HCpcJuK7/qBkRUGAz+i4DBPxEREU0WQgj0hRMIRPViN+UYvaE4frR2D154qydju0tTcMN5M3HVGVPLfi29S7Oh0qXCobITgGiyY/BfBAz+iYiIaLKJJkz0hkpvFgAAbDs4iAdWt6LtcDhj+8xqF+64uAVLZ1YVqWX549SSywGcGjsBiCYrBv9FwOCfiIiIJiPLSs4CCMZKbxaAaQk8t60DP/37PgRjRsZjF86txW0rm9HgcxSpdfljVxVUuVS4NFuxm0JE44zBfxEw+CciIqLJLJIw0BtMwLBKbxaAP6Ljp3/fi+e2dWL4ja9mk/Gxs6bjo2dNh30CTKHXbDIqXRo8dnYCEE0WDP6LgME/ERERTXamJdAXiiMUN06+cxHs7g7i/hda8WZnIGN7g8+Bz6xsxjtaaiBJ5VkacDhVkVHhUuG12ybE6yGi42PwXwQM/omIiIiSQnEDfaE4TKv0bjMtIfDXN7vxo3V7MBDJXKqwbGYV7rioBTNqXEVqXX7ZZBkVThVehw2yzE4AoomIwX8RMPgnIiIiOsIwLfSGEogkSnMWQDhu4Gcv78dvtxzK6KRQZAlXnzkV1583c8KsoVdkCT6HCp9ThcJOAKIJhcF/ETD4JyIiIjpWIKajP5SAVaK3nPv7wnhwdSs2HxjM2F7j1vCp5XNw6YK6CTN1XpIkeOw2VDhVaLbyLndIREkM/ouAwT8RERHRyHTTQm8ojmjCLHZTRiSEwN9ae/HImjZ0B+IZj53W5MOdF7dgbr23SK0rDJeW7ARgmUCi8sbgvwgY/BMRERGdmD+qoz+cQKnefsZ0E7/e0I5fbTwA3TzSRlkC3ru4ETe9YzYqnGoRW5h/mi2ZF8DD5IBEZYnBfxEw+CciIiI6uYRh4XAojrhemrMAAKDTH8XDa9rw99a+jO0+hw03XTAb713UOOHWzqeSA/qc7AQgKicM/ouAwT8RERHR6A2EExiM6iU7CwAANu7rx4OrW9E+EM3Y3lLnwWcvbsFpUyuK1LLCUWQJlU6NnQBEZYLBfxEw+CciIiLKTkw3cTgYh25axW7Kcemmhd++dgg/e3k/okfNVrh0QR0+vXwOajz2IrWucNgJQFQeGPwXAYN/IiIiouwJIdAXTiAQ1YvdlBPqDcXx43V78NedPRnbnaqC68+biQ+eORWqMvEy6LMTgKi0MfgvAgb/RERERLmLJpKzAAyrdGcBAMAbh/y4/4VWtB4OZWyfXuXEHRe34KxZ1UVqWWGxE4CoNDH4LwIG/0RERERjY1oCfaE4QnGj2E05IdMS+OP2Tvz0pb0IxDLbekFLLT6zshkNFY4ita6wFFmCz6HC51QnXNJDonLE4L8IGPwTERER5UcobqA3GIdV4rep/qiOx/6+D89t64A1rKmaTcZHz5qOj501HXZVKV4DC0iWJHgdNlQ4Vdgm4HIHonLB4L8IGPwTERER5Y9hJksCRhOlWxIw5e3uIB5Y3Yo3OgIZ2+t9dnxmZQsuaKmZsFPlJUmCx57sBNBs7AQgGm8M/ouAwT8RERFR/vkjOvojiZIuCQgkExf+dWcPfrxuD/rCiYzHls6swp0XtWBGjatIrRsf7qFOAMcEne1AVIoY/BcBg38iIiKiwogbJnoCpV0SMCWSMPDzl/fjf147BGPYWgBFlvDBM6bi+vNmwm23FbGFhefUFFQ6NTg1dgIQFRqD/yJg8E9ERERUOEII9IcT8Jd4ScCUA/0RPPRiKzbuG8jYXu3W8KkLZ+PSU+shT9ClACl2VUGlU53wnR1ExcTgvwgY/BMREREVXrmUBASSHRZ/b+3Dw2va0BWIZTy2sMmHOy9uwbx6b5FaN35URUalS4XHzjKBRPnG4L8IGPwTERERjY9yKQmYEtdNPLWpHU9uaEfCONJpIQF43+JG3HTBbFQ41eI1cJzYZBkVThU+JzsBiPKFwX8RMPgnIiIiGl/BmI6+UKLkSwKmdAVi+OGaNqx7uzdju9dhw03vmIX3LW6CIk/8oFiRJfgcKnxOdVK8XqJCYvBfBAz+iYiIiMafblo4HIwjppd+ScCUzfsH8ODqVuzvj2Rsb57ixmcvnotF0yqK1LLxJUsSvI5khQCbwjKBRLlg8F8EDP6JiIiIimcwksBARC/5koAphmlh1ZZDeOLl/YgkMjsuLl1Qh08tn4Naj71IrRtfkiTBM1QmULOxE4AoGwz+i4DBPxEREVFxlVNJwJT+cAI/+dse/GVHd8Z2p6rg4+fOwNVLp0GdRKPiHrsNFS4VdhvLBBKNBoP/ImDwT0RERFR8Qgj0hRMIlElJwJQdHX7c/0Ir3u4JZWyfVuXEnRe34KxZ1UVqWXG4NBsqXSocKjsBiE6EwX8RMPgnIiIiKh2RhIHeYKIsSgKmmJbAn97oxKN/24tALLOSwTuaa3DbymY0VTqL1LrisKsKKp0q3HZbsZtCVJIY/BcBg38iIiKi0mJaAoeDcUQS5VESMCUQ1fHY3/fh2W0dsIbdqauKhI+eNR0fO3vGpBsR12zJMoEeO8sEEg3H4L8IGPwTERERlSZ/VEd/OFE2yQBTWntCeGD129h+KJCxvc5rx20rm7F8bu2kC4RVRYbPqcLnYCcAEcDgvygY/BMRERGVrnJMBggkcxi88FYPfrR2D/rCiYzHzpxRiTsubsGsGneRWlc8iiyhwqnC51Ahy+wEoMmLwX8RMPgnIiIiKm3lmgwQSOYw+MUrB/CbzQdhDFsLoMgSrjqjCdefNwueSbguXpEl+BwqfE4VCjsBaBJi8F8EDP6JiIiIykM4bqA3FIdpld9t8IH+CB5+sRUb9g1kbK9yqfjU8jl456n1kCfhdHhZkuB12FDhVGGbRKURiRj8FwGDfyIiIqLyYZgWDofiiCbMYjcla0IIrG/rw8Nr2tDpj2U8dmqjF5+9ZC7m1XuL1LrikoZ1AqjsBKBJgMF/ETD4JyIiIio//oiO/kj5JQMEgLhu4ulNB/HkhgOIG0dyGUgA3rOoEbdcMBsVLrV4DSwiSZLgtiuodGrQbOwEoImLwX8RMPgnIiIiKk8Jw0JPMIaEUV7JAFO6AjH8cG0b1u3uzdjusdtw4ztm4f1Lmib1eniP3YYKlwq7bXKVR6TJgcF/ETD4JyIiIipfQgj0hxPwl2EywJTX9g/ggdWt2N8fydg+Z4obd17cgiXTKovTsBLh0myodKlwqOwEoImDwX8RMPgnIiIiKn/RhInDwTgMqzxnARimhVVbO/Cz9fsQPiqfwcWn1OHTy+dgitdepNaVBoeqoMqlwamxE4DKH4P/ImDwT0RERDQxWJZAbyiOUNwodlNy1h9O4Cd/24O/7OjO2O5QZfzDOTPxoaXTJv1aeLuqoMqlwqVNvhKJNHEw+C8CBv9EREREE0sobqCvTEsCprzZEcD9q9/G7u5QxvZpVU58ZmUzzp1TU6SWlQ67qqDSqcJtZycAlR8G/0XA4J+IiIho4jFMC72hBCKJ8p0FYFoCf3qjC//90t5jchqcN6cGn7moGVMrnUVqXenQbDKqXBo7AaisMPgvAgb/RERERBNXMKajL5SAVca3zsGYjsf+vg9/eL0DwyczqIqEjyybjmvPmQEnk+GxE4DKCoP/ImDwT0RERDSxTYRZAADQdjiEB1a3YttBf8b2Oq8dt65oxop5tZCkyVsaMEWzyah0afCwE4BKGIP/ImDwT0RERDQ5BGI6+st8FoAQAi/uOowfrm1DbyiR8djp0ytx58UtmF3rLlLrSgs7AaiUMfgvAgb/RERERJOHYVo4HIojelQ5vXITTZj4xav78cymgzCGrQWQJeDKM6biE+fNgsfBoBdgJwCVJgb/RcDgn4iIiGjymQizAADg4EAED73Yhlf39mdsr3Sq+OSFs3HZaQ2QuRQAADsBqLQw+C8CBv9EREREk1PCSM4CiOvlPQsAAF5u68NDa1rRMRjL2H5Kgxd3XtyCBY28z01hJwCVAgb/RcDgn4iIiGhyGwgnMBjVUe631wnDwtOb2vHkqwcQM6yMxy4/rQG3XDgbVS6tSK0rPaoiw+dU4XPYmCiRxh2D/yJg8E9EREREMd3E4WAcummdfOcS1xOI4Ydr92DN7sMZ2912BTeePwsfOH0qFJnBbooiS6hwqvA6VL4vNG4Y/BcBg38iIiIiApKZ9PvCCQSierGbkhdbDgzgwRfbsLc3nLF9dq0bd17cgtOnVxanYSVKliR4HTZUOFXYFLnYzaEJjsF/ETD4JyIiIqLhIgkDvcEEDKv8ZwGYlsDvth7C4+v3IRzPzG2wct4U3LpiDup8jiK1rjRJkgS3XUGFU4XdphS7OTRBMfgvAgb/RERERHQ00xLoC8URihvFbkpeDEQSePRve/GnN7oytjtsMq47dwY+vHQ6NBtHu4/m1JKdAC6NyQEpvxj8FwGDfyIiIiI6nmBMR98EKAmYsrMzgPtXt2JXVzBje1OlA7evbMF5zTVFallpUxUZFS4VXjuTA1J+MPgvAgb/RERERHQiumnhcDCO2AQoCQgAlhD48xtdePRvezF4VH6Dc2ZX4/aLmjGtylWk1pU2RZbgc6jwOZkckMaGwX8RMPgnIiIiotEYjCQwECn/koApoZiBx9fvw++2HoI17CWpioQPL52G686ZCafGNe8jkSQJHnsyOSCXS1AuGPwXAYN/IiIiIhqtiVQSMGXP4RAefLEVW9v9GdtrPRpuXdGMi+ZP4VT3E3BpyU4AdpRQNhj8FwGDfyIiIiLKxkQrCQgkX9Pa3YfxyJo9OByKZzy2ZFoF7ri4Bc1TPEVqXXnQbDIqnCo8zAtAo8DgvwgY/BMRERFRLiIJA4eDcZjWxLktj+omnnz1AJ7e1A7dPPK6ZAl4/5Im3PiOWfA61CK2sPTZZBk+pw0+hwqZeQHoOBj8FwGDfyIiIiLKlWkJHA7GEUlMjJKAKYcGonhoTSte2dOfsb3CqeKWC2bj8kUNkDm6fUKyJMHrSC4JsCnMC0CZGPwXAYN/IiIiIhorf0RHfyQxYZIBpryypw8PvdiGQ4PRjO3z67347CUtWNDI++eTYXJAGgmD/yJg8E9ERERE+RA3TPQEJlYyQABIGBZ+s/kgfvHKfsSMzNf27oUNuOXC2ah2a0VqXXlxD3UCOFQmB5zssolD2WV0lIceegizZs2Cw+HAOeecgw0bNhS7SUREREQ0idhtCqZVOeFzTqw18ZpNxrXnzMATN52Ni+ZPyXjszzu6cMNPN+A3mw/CmGCdHoUQjhvoGIyi0x9FNGEWuzlUJhj8D/PUU0/hrrvuwte+9jW89tprWLJkCS677DL09PQUu2lERERENIlIkoRajx0NFQ4oEyzZ2xSvHV9536n4r48swZxad3p7OGHi4TVt+NTPN+O1AwNFbGH5iCZMdPqjODQYRTg+sfJFUP5x2v8w55xzDs466yw8+OCDAADLsjB9+nTceeed+Od//ucT/i6n/RMRERFRIRimhd5QYsIlAwSSiQ5/v7UDj6/fh9BRwevyebW4bUUz6n2OIrWu/KiKjEoXywROJtnEobZxalPJSyQS2Lx5M7785S+nt8myjEsvvRQvv/zyMfvH43HE40dql/r9fgDJN5+IiIiIKJ9cEmAIHQPhBKwJNnb3zrlenD11Pn728n48/2YPUq9uzfYDWL/zID68bBquPmMqVCa5G5X+waEygQ4VXoeNZQInuFT8OZoxfQb/Q3p7e2GaJurr6zO219fX46233jpm/29+85v4xje+ccz26dOnF6yNRERERESTzbeGvojo+ILBICoqKk64D4P/HH35y1/GXXfdlf7Zsiz09/ejpqYmL1NsAoEApk+fjvb2di4jKDM8d+WN56988dyVL5678sVzV954/soXz135yve5E0IgGAyiqanppPsy+B9SW1sLRVHQ3d2dsb27uxsNDQ3H7G+322G32zO2VVZW5r1dPp+PF3SZ4rkrbzx/5Yvnrnzx3JUvnrvyxvNXvnjuylc+z93JRvxTuHBmiKZpWLp0KV544YX0Nsuy8MILL+C8884rYsuIiIiIiIiIxoYj/8PcdddduOGGG7Bs2TKcffbZ+P73v49wOIwbb7yx2E0jIiIiIiIiyhmD/2GuueYaHD58GF/96lfR1dWF008/HX/+85+PSQI4Hux2O772ta8ds7SASh/PXXnj+StfPHfli+eufPHclTeev/LFc1e+innuJDGamgBEREREREREVLa45p+IiIiIiIhogmPwT0RERERERDTBMfgnIiIiIiIimuAY/BMRERERERFNcAz+i2zdunW44oor0NTUBEmS8Lvf/S7j8U984hOQJCnj693vfndxGksZvvnNb+Kss86C1+tFXV0drrzySuzatStjn1gshttvvx01NTXweDy4+uqr0d3dXaQWU8pozt3KlSuPufZuvfXWIrWYUh555BEsXrwYPp8PPp8P5513Hv70pz+lH+c1V7pOdu54zZWPb33rW5AkCZ///OfT23jtlY+Rzh+vv9L09a9//Zjzcsopp6Qf53VXuk527op1zTH4L7JwOIwlS5bgoYceOu4+7373u9HZ2Zn++tWvfjWOLaTjWbt2LW6//Xa88soreP7556HrOt71rnchHA6n9/nCF76AZ599Fs888wzWrl2Ljo4OfPCDHyxiqwkY3bkDgE9+8pMZ1953vvOdIrWYUqZNm4Zvfetb2Lx5MzZt2oSLL74YH/jAB7Bjxw4AvOZK2cnOHcBrrhxs3LgRP/rRj7B48eKM7bz2ysPxzh/A669ULVy4MOO8vPTSS+nHeN2VthOdO6BI15ygkgFArFq1KmPbDTfcID7wgQ8UpT2UnZ6eHgFArF27VgghxODgoFBVVTzzzDPpfXbu3CkAiJdffrlYzaQRHH3uhBBixYoV4nOf+1zxGkWjVlVVJR599FFec2Uode6E4DVXDoLBoJg7d654/vnnM84Xr73ycLzzJwSvv1L1ta99TSxZsmTEx3jdlbYTnTshinfNceS/DKxZswZ1dXWYP38+brvtNvT19RW7STQCv98PAKiurgYAbN68Gbqu49JLL03vc8opp2DGjBl4+eWXi9JGGtnR5y7ll7/8JWpra3Haaafhy1/+MiKRSDGaR8dhmiZ+/etfIxwO47zzzuM1V0aOPncpvOZK2+233473vve9GdcYwL935eJ45y+F119pevvtt9HU1IQ5c+bguuuuw4EDBwDwuisHxzt3KcW45mwFfwYak3e/+9344Ac/iNmzZ6OtrQ333HMPLr/8crz88stQFKXYzaMhlmXh85//PN7xjnfgtNNOAwB0dXVB0zRUVlZm7FtfX4+urq4itJJGMtK5A4Brr70WM2fORFNTE7Zt24a7774bu3btwm9/+9sitpYAYPv27TjvvPMQi8Xg8XiwatUqnHrqqdi6dSuvuRJ3vHMH8Jordb/+9a/x2muvYePGjcc8xr93pe9E5w/g9VeqzjnnHDz++OOYP38+Ojs78Y1vfAMXXngh3njjDV53Je5E587r9RbtmmPwX+I++tGPpv9/0aJFWLx4MZqbm7FmzRpccsklRWwZDXf77bfjjTfeOGYtD5W+4527T33qU+n/X7RoERobG3HJJZegra0Nzc3N491MGmb+/PnYunUr/H4/fvOb3+CGG27A2rVri90sGoXjnbtTTz2V11wJa29vx+c+9zk8//zzcDgcxW4OZWk054/XX2m6/PLL0/+/ePFinHPOOZg5cyaefvppOJ3OIraMTuZE5+7mm28u2jXHaf9lZs6cOaitrUVra2uxm0JD7rjjDjz33HN48cUXMW3atPT2hoYGJBIJDA4OZuzf3d2NhoaGcW4ljeR4524k55xzDgDw2isBmqahpaUFS5cuxTe/+U0sWbIEP/jBD3jNlYHjnbuR8JorHZs3b0ZPTw/OPPNM2Gw22Gw2rF27Fvfffz9sNhvq6+t57ZWwk50/0zSP+R1ef6WpsrIS8+bNQ2trK//mlZnh524k43XNMfgvMwcPHkRfXx8aGxuL3ZRJTwiBO+64A6tWrcLq1asxe/bsjMeXLl0KVVXxwgsvpLft2rULBw4cyFjjSuPvZOduJFu3bgUAXnslyLIsxONxXnNlKHXuRsJrrnRccskl2L59O7Zu3Zr+WrZsGa677rr0//PaK10nO38jLSPl9VeaQqEQ2tra0NjYyL95ZWb4uRvJeF1znPZfZKFQKKOHZ+/evdi6dSuqq6tRXV2Nb3zjG7j66qvR0NCAtrY2/NM//RNaWlpw2WWXFbHVBCSniz/55JP4/e9/D6/Xm15fVVFRAafTiYqKCtx888246667UF1dDZ/PhzvvvBPnnXcezj333CK3fnI72blra2vDk08+ife85z2oqanBtm3b8IUvfAHLly8fsTwSjZ8vf/nLuPzyyzFjxgwEg0E8+eSTWLNmDf7yl7/wmitxJzp3vOZKm9frzciJAgButxs1NTXp7bz2StfJzh+vv9L1xS9+EVdccQVmzpyJjo4OfO1rX4OiKPjYxz7Gv3kl7kTnrqjX3LjXF6AML774ogBwzNcNN9wgIpGIeNe73iWmTJkiVFUVM2fOFJ/85CdFV1dXsZtNQox43gCIxx57LL1PNBoVn/nMZ0RVVZVwuVziqquuEp2dncVrNAkhTn7uDhw4IJYvXy6qq6uF3W4XLS0t4ktf+pLw+/3FbTiJm266ScycOVNomiamTJkiLrnkEvF///d/6cd5zZWuE507XnPl5+gyVbz2ysvw88frr3Rdc801orGxUWiaJqZOnSquueYa0dramn6c113pOtG5K+Y1JwkhRGG7F4iIiIiIiIiomLjmn4iIiIiIiGiCY/BPRERERERENMEx+CciIiIiIiKa4Bj8ExEREREREU1wDP6JiIiIiIiIJjgG/0REREREREQTHIN/IiIiIiIiogmOwT8REdEEsnLlSnz+858/7uOSJOF3v/vdqI+3Zs0aSJKEwcHBMbeNiIiIisdW7AYQERHR+Ons7ERVVVWxm0FERETjjME/ERHRJNLQ0FDsJhAREVERcNo/ERHRBGNZFv7pn/4J1dXVaGhowNe//vX0Y0dP+1+/fj1OP/10OBwOLFu2DL/73e8gSRK2bt2acczNmzdj2bJlcLlcOP/887Fr1y4AgN/vh6Io2LRpU/q5q6urce6556Z/9xe/+AWmT5+e/vnuu+/GvHnz4HK5MGfOHHzlK1+BrusAgH379kGW5fTxUr7//e9j5syZsCxrxNf8iU98AldeeSXuu+8+1NfXo7KyEv/6r/8KwzDwpS99CdXV1Zg2bRoee+yx9O/s27cPkiTh17/+Nc4//3w4HA6cdtppWLt2bcax//CHP2Du3LlwOBy46KKL8MQTT3ApBBERlR0G/0RERBPME088AbfbjVdffRXf+c538K//+q94/vnnj9kvEAjgiiuuwKJFi/Daa6/h3/7t33D33XePeMx7770X3/3ud7Fp0ybYbDbcdNNNAICKigqcfvrpWLNmDQBg+/btkCQJW7ZsQSgUAgCsXbsWK1asSB/L6/Xi8ccfx5tvvokf/OAH+MlPfoL/+q//AgDMmjULl156aUaQDgCPPfYYPvGJT0CWj3/rsnr1anR0dGDdunX43ve+h6997Wt43/veh6qqKrz66qu49dZb8elPfxoHDx7M+L0vfelL+Md//Eds2bIF5513Hq644gr09fUBAPbu3YsPfehDuPLKK/H666/j05/+NO69994Tvf1ERESlSRAREdGEsWLFCnHBBRdkbDvrrLPE3XffLYQQAoBYtWqVEEKIRx55RNTU1IhoNJre9yc/+YkAILZs2SKEEOLFF18UAMRf//rX9D5//OMfBYD07911113ive99rxBCiO9///vimmuuEUuWLBF/+tOfhBBCtLS0iB//+MfHbfN//Md/iKVLl6Z/fuqpp0RVVZWIxWJCCCE2b94sJEkSe/fuPe4xbrjhBjFz5kxhmmZ62/z588WFF16Y/tkwDOF2u8WvfvUrIYQQe/fuFQDEt771rfQ+uq6LadOmiW9/+9tCCCHuvvtucdppp2U817333isAiIGBgeO2h4iIqNRw5J+IiGiCWbx4ccbPjY2N6OnpOWa/Xbt2YfHixXA4HOltZ5999kmP2djYCADpY65YsQIvvfQSTNPE2rVrsXLlSqxcuRJr1qxBR0cHWltbsXLlyvTvP/XUU3jHO96BhoYGeDwe/Mu//AsOHDiQfvzKK6+EoihYtWoVAODxxx/HRRddhFmzZuHAgQPweDzpr/vuuy/9ewsXLsyYGVBfX49Fixalf1YUBTU1Nce8F+edd176/202G5YtW4adO3em36OzzjorY//jvUdERESljME/ERHRBKOqasbPkiQdd618LseUJAkA0sdcvnw5gsEgXnvtNaxbty4j+F+7di2ampowd+5cAMDLL7+M6667Du95z3vw3HPPYcuWLbj33nuRSCTSx9c0Dddffz0ee+wxJBIJPPnkk+llBk1NTdi6dWv669Zbbz3h6y7Ee0FERFSOGPwTERFNUvPnz8f27dsRj8fT2zZu3Jj1cSorK7F48WI8+OCDUFUVp5xyCpYvX44tW7bgueeey1jvv379esycORP33nsvli1bhrlz52L//v3HHPOWW27BX//6Vzz88MMwDAMf/OAHASRH5ltaWtJf1dXVObzyTK+88kr6/w3DwObNm7FgwQIAyffo6OSDubxHRERExcbgn4iIaJK69tprYVkWPvWpT2Hnzp34y1/+gv/8z/8EcGR0f7RWrlyJX/7yl+lAv7q6GgsWLMBTTz2VEfzPnTsXBw4cwK9//Wu0tbXh/vvvT0/vH27BggU499xzcffdd+NjH/sYnE7nGF7piT300ENYtWoV3nrrLdx+++0YGBhIzzT49Kc/jbfeegt33303du/ejaeffhqPP/44gOzfIyIiomJi8E9ERDRJ+Xw+PPvss9i6dStOP/103HvvvfjqV78KABl5AEZjxYoVME0zY23/ypUrj9n2/ve/H1/4whdwxx134PTTT8f69evxla98ZcRj3nzzzUgkEulAvFC+9a1v4Vvf+haWLFmCl156CX/4wx9QW1sLAJg9ezZ+85vf4Le//S0WL16MRx55JJ3t3263F7RdRERE+SQJIUSxG0FERET/v507VFEgCsMw/Ie9A5MIgl6BxfuwaLdPM4jF4kyyipgFMXgdZtFqGbBMEgQvYNN2NwyznH2ePoevvpzD/A2HwyGm02m8Xq9ab9s/sVqt4nQ6xe12q+X8siyj1+vF5XKJwWDw8XdFUcRut4vH41HLLgCow1fTAwCA5uz3++j3+9HpdOJ6vcZ8Po/JZNJo+L/f7yjLMjabTeR53tiOH9vtNobDYbRarTifz7FeryPLsqZnAcCviH8A+MeqqorlchlVVUW73Y7xeBxFUTS6KcuyOB6PMRqNan/y/4n7/R55nsfz+Yxutxuz2SwWi0XTswDgVzz7BwAAgMT54R8AAAAkTvwDAABA4sQ/AAAAJE78AwAAQOLEPwAAACRO/AMAAEDixD8AAAAkTvwDAABA4sQ/AAAAJO4b1pZDS8NFfhgAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<p>We can see from this plot that price is negatively correlated to highway-mpg since the regression slope is negative.\n",
        "\n",
        "One thing to keep in mind when looking at a regression plot is to pay attention to how scattered the data points are around the regression line. This will give you a good indication of the variance of the data and whether a linear model would be the best fit or not. If the data is too far off from the line, this linear model might not be the best model for this data.\n",
        "\n",
        "Let's compare this plot to the regression plot of \"peak-rpm\".</p>\n"
      ],
      "metadata": {
        "id": "fOpGrtM-O9fu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(width, height))\n",
        "sns.regplot(x=\"peak-rpm\", y=\"price\", data=df)\n",
        "plt.ylim(0,)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 867
        },
        "id": "VLE4dkPGPEz_",
        "outputId": "56273252-af6d-4e6b-e8f1-d6cc653e5b36"
      },
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 47414.1)"
            ]
          },
          "metadata": {},
          "execution_count": 148
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<p>Comparing the regression plot of \"peak-rpm\" and \"highway-mpg\", we see that the points for \"highway-mpg\" are much closer to the generated line and, on average, decrease. The points for \"peak-rpm\" have more spread around the predicted line and it is much harder to determine if the points are decreasing or increasing as the \"peak-rpm\" increases.</p>\n"
      ],
      "metadata": {
        "id": "acWWTD0AO-NI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Given the regression plots above, is \"peak-rpm\" or \"highway-mpg\" more strongly correlated with \"price\"? Use the method \".corr()\" to verify your answer."
      ],
      "metadata": {
        "id": "Gn4V1r02PMqg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# The variable \"highway-mpg\" has a stronger correlation with \"price\", it is approximate -0.704692  compared to \"peak-rpm\" which is approximate -0.101616. You can verify it using the following command:\n",
        "\n",
        "df[[\"peak-rpm\",\"highway-mpg\",\"price\"]].corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "6agkSPeGPKLM",
        "outputId": "ec8ce0e0-3461-4096-9f93-cd02cb1e8f19"
      },
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             peak-rpm  highway-mpg     price\n",
              "peak-rpm     1.000000    -0.058598 -0.101616\n",
              "highway-mpg -0.058598     1.000000 -0.704692\n",
              "price       -0.101616    -0.704692  1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c47cd6ab-b07c-4887-a76c-e0efa5a7f3c2\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>peak-rpm</th>\n",
              "      <th>highway-mpg</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>peak-rpm</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.058598</td>\n",
              "      <td>-0.101616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>highway-mpg</th>\n",
              "      <td>-0.058598</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.704692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>price</th>\n",
              "      <td>-0.101616</td>\n",
              "      <td>-0.704692</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c47cd6ab-b07c-4887-a76c-e0efa5a7f3c2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c47cd6ab-b07c-4887-a76c-e0efa5a7f3c2 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c47cd6ab-b07c-4887-a76c-e0efa5a7f3c2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ecfe32e1-9ed2-4956-bc9b-f576fe73ba2f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ecfe32e1-9ed2-4956-bc9b-f576fe73ba2f')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ecfe32e1-9ed2-4956-bc9b-f576fe73ba2f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**<h3>Residual Plot</h3>**\n",
        "\n",
        "<p>A good way to visualize the variance of the data is to use a residual plot.</p>\n",
        "\n",
        "<p>What is a <b>residual</b>?</p>\n",
        "\n",
        "<p>The difference between the observed value (y) and the predicted value (Yhat) is called the residual (e). When we look at a regression plot, the residual is the distance from the data point to the fitted regression line.</p>\n",
        "\n",
        "<p>So what is a <b>residual plot</b>?</p>\n",
        "\n",
        "<p>A residual plot is a graph that shows the residuals on the vertical y-axis and the independent variable on the horizontal x-axis.</p>\n",
        "\n",
        "<p>What do we pay attention to when looking at a residual plot?</p>\n",
        "\n",
        "<p>We look at the spread of the residuals:</p>\n",
        "\n",
        "<p>- If the points in a residual plot are <b>randomly spread out around the x-axis</b>, then a <b>linear model is appropriate</b> for the data.\n",
        "\n",
        "Why is that? Randomly spread out residuals means that the variance is constant, and thus the linear model is a good fit for this data.</p>\n"
      ],
      "metadata": {
        "id": "uZvgDfpkPUxn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "width = 12\n",
        "height = 10\n",
        "plt.figure(figsize=(width, height))\n",
        "sns.residplot(x=df['highway-mpg'], y=df['price'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        },
        "id": "S1Pz5hJ1PceI",
        "outputId": "24d2a8b3-bc00-4383-eb55-f2308e47ac8a"
      },
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<i>Q. What is this plot telling us?</i>\n",
        "\n",
        "<p>:- We can see from this residual plot that the residuals are not randomly spread around the x-axis, leading us to believe that maybe a non-linear model is more appropriate for this data.</p>"
      ],
      "metadata": {
        "id": "TFP9Vzq4PUkj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**<h3>Multiple Linear Regression</h3>**\n",
        "\n",
        "<p>How do we visualize a model for Multiple Linear Regression? This gets a bit more complicated because you can't visualize it with regression or residual plot.</p>\n",
        "\n",
        "<p>One way to look at the fit of the model is by looking at the <b>distribution plot</b>. We can look at the distribution of the fitted values that result from the model and compare it to the distribution of the actual values.</p>\n"
      ],
      "metadata": {
        "id": "2ElXgchuPkD1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make a Prediction\n",
        "\n",
        "Y_hat = lm.predict(Z)"
      ],
      "metadata": {
        "id": "bu5D2W-cP6y5"
      },
      "execution_count": 151,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(width, height))\n",
        "\n",
        "\n",
        "ax1 = sns.distplot(df['price'], hist=False, color=\"r\", label=\"Actual Value\")\n",
        "sns.distplot(Y_hat, hist=False, color=\"b\", label=\"Fitted Values\" , ax=ax1)\n",
        "\n",
        "\n",
        "plt.title('Actual vs Fitted Values for Price')\n",
        "plt.xlabel('Price (in dollars)')\n",
        "plt.ylabel('Proportion of Cars')\n",
        "\n",
        "plt.show()\n",
        "plt.close()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "xxiZ5yRjQDoy",
        "outputId": "f7ced6e6-8bed-4e5d-98ca-bb0021d2d638"
      },
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-152-7377bca648c1>:4: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  ax1 = sns.distplot(df['price'], hist=False, color=\"r\", label=\"Actual Value\")\n",
            "<ipython-input-152-7377bca648c1>:5: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(Y_hat, hist=False, color=\"b\", label=\"Fitted Values\" , ax=ax1)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 1 Axes>"
            ],
            "image/png": 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855ecnKyioiJt3rxZrVu3Ln5/3759yszMVHJyskv9eVpFfjabNm2qtWvXqm/fvm5dAgIAqJhqM6X8XO6880599913mjFjhn7++Wdde+21uvzyyyu9Zk8y07QaNmyoJk2aaOTIkUpPT3djxQDgGSdOnNCcOXN0xRVX6JprrinzuvPOO3X06FHNnz9fknT11Vdr7dq1ZdaZSioeraxZs6YklRusmzZtqhUrVigvL6/4vY8++kg7d+4sdZ9ztLvkCOj333+v7777rtK/1tjYWPXv31+zZs3SjBkzFBYWpqFDh5a659ChQ6W+rlWrlpo1a+aRI5PO9vtUs2bNMu8HBwfr6quv1gcffFBusDpw4EBxe+DAgdq9e7dmz55d/N7x48c1ceJEl2p0jmb/85//1Jo1a8qcvR0cHFxmlPr9999XRkbGOftu2rSpNmzYUKrutWvXavny5aXuu+6661RYWKgnnniiTB8FBQXFv09HjhwpU4tzN/vK/PkNHDhQkvTCCy+Uet85Ojxo0CCX+/SkivxsXnfddcrIyNAbb7xR5p4TJ04oJyfH43UCQCCrNiPcZ5Oenq5JkyYpPT29+LiS+++/X59++qkmTZqkp556yuU+u3XrpsmTJ6tly5bas2ePHnvsMfXq1Uvr169XVFSUu38JAOA28+fP19GjRzVkyJByP+/evbvi4uI0bdo0XX/99frb3/6m2bNn69prr9XYsWPVuXNnHT58WPPnz9drr72mDh06qGnTpoqNjdVrr72mqKgo1axZU926dVNqaqpuvfVWzZ49W5dffrmuu+46bd26VVOnTi0+9srpiiuu0Jw5czRs2DANGjRIaWlpeu2119SmTRsdO3as0r/e66+/XjfeeKNeeeUV9e/fv3ijLac2bdro4osvVufOnVWnTh2tWrVKs2fPLnU0lrt07txZkvTwww9r+PDhCg0N1eDBg1WzZk117txZixYt0nPPPaeGDRsqNTVV3bp109NPP60lS5aoW7duuu2229SmTRsdPnxYq1ev1qJFi3T48GFJ0m233aaXXnpJN998s3788UclJCRoypQpqlGjhks1pqam6sILL9S8efMkqUzgvuKKK/T4449rzJgxuvDCC7Vu3TpNmzatzDrk8owdO1bPPfec+vfvr1tuuUX79+/Xa6+9prZt2yo7O7v4vj59+mjcuHGaMGGC1qxZo8suu0yhoaHavHmz3n//fb344ou65ppr9M477+iVV17RsGHD1LRpUx09elRvvPGGoqOji8OzKzp06KBRo0Zp4sSJyszMVJ8+ffTDDz/onXfe0dChQ4s3GPQVFfnZvOmmmzRr1iz98Y9/1JIlS9SzZ08VFhZqw4YNmjVrVvF53wAAD7Fre3RPkmTNnTu3+OuPPvrIkmTVrFmz1CskJMS67rrrLMuyrN9+++2Mx7s4Xw8++OAZn3nkyBErOjraevPNNz39ywOAKhk8eLAVERFh5eTknPGe0aNHW6GhocVHUR06dMi68847rUaNGllhYWFW48aNrVGjRpU6qmrevHlWmzZtrJCQkDLHQT377LNWo0aNrPDwcKtnz57WqlWryhwRVVRUZD311FNWcnKyFR4ebnXq1Mn66KOPyhwZZVkVOxbMKTs724qMjLQkWVOnTi3z+b/+9S/rggsusGJjY63IyEirVatW1pNPPmnl5eWdtV/nsWDvv//+Ge8pr84nnnjCatSokRUUFFTqiLANGzZYvXv3Lq615PFP+/bts+644w4rMTHRCg0NtRo0aGD17dvXmjhxYqm+d+zYYQ0ZMsSqUaOGVa9ePevuu+8uPkrrXMeClfTyyy9bkqwLLrigzGcnT5607rvvPishIcGKjIy0evbsaX333Xdl/jzLOxbMsixr6tSpVpMmTaywsDCrY8eO1meffVbun7FlWdbEiROtzp07W5GRkVZUVJTVvn1764EHHrB2795tWZZlrV692hoxYoSVlJRkhYeHW/Hx8dYVV1xhrVq16py/xvKOBbMsy8rPz7cee+wxKzU11QoNDbUSExOt8ePHWydPnix1X3JyskvHyZ3peb93tn7LO2KvIj+beXl51jPPPGO1bdvWCg8Pt2rXrm117tzZeuyxx6ysrKwK/xoAAK5zWFY5u5f4OYfDoblz5xZPG5w5c6ZGjhypX375pdQGPZKZOtigQQPl5eWVu66sJOeOuWfStWtX9evXTxMmTKjyrwEAAAAA4N8CYkp5p06dVFhYqP3796tXr17l3hMWFlalY72OHTumrVu36qabbqp0HwAAAACA6qPaBO5jx45py5YtxV+npaVpzZo1qlOnjlq0aKGRI0fq5ptv1rPPPqtOnTrpwIEDWrx4sc4777xKbYJy//33a/DgwUpOTtbu3bv1yCOPKDg4WCNGjHDnLwsAAAAA4KeqzZTypUuXlruZyahRozR58mTl5+frX//6l959911lZGSoXr166t69ux577DG1b9/e5ecNHz5cX331lQ4dOqS4uDhddNFFevLJJ8tsAgQAAAAACEzVJnADAAAAAOBLAuYcbgAAAAAAvInADQAAAACAB/j1pmlFRUXavXu3oqKi5HA47C4HAAAAAFDNWZalo0ePqmHDhgoKOvsYtl8H7t27dysxMdHuMgAAAAAAAWbnzp1q3LjxWe/x68AdFRUlyfxCo6Ojba4GAAAAAFDdZWdnKzExsTiPno1fB27nNPLo6GgCNwAAAADAayqyrJlN0wAAAAAA8AACNwAAAAAAHkDgBgAAAADAAwjcAAAAAAB4AIEbAAAAAAAPIHADAAAAAOABBG4AAAAAADyAwA0AAAAAgAcQuAEAAAAA8AACNwAAAAAAHkDgBgAAAADAAwjcAAAAAAB4AIEbAAAAAAAPIHADAAAAAOABBG4AAAAAADyAwA0AAAAAgAcQuAEAAAAA8AACNwAAAAAAHkDgBgAAAADAAwjcAAAAAAB4AIEbAAAAAAAPIHADAAAAAOABBG4AAAAAADyAwA0AAAAAgAcQuAEAAAAA8AACNwAAAAAAHkDgBgAAAADAAwjcAAAAAAB4AIEbAAAAAAAPIHADAAAAAOABBG4AAAAAADyAwA0AAAAAgAeE2F0AAFRKRob0xhuSZUl//asUE2N3RQAAAEApjHAD8C8//CCNHCmlpEiPPSY9/rjUooU0aZJUVGR3dQAAAEAxAjcA/1BYKF11ldStmzR9ulRQIPXqJbVsKe3fL40dK/XoIa1aZXelAAAAgCQCNwB/8fzz0ty5UmiodPPN0o8/Sl99Jf38s/Tf/0pRUWb0u1cvacMGu6sFAAAACNwA/MDGjdLf/27ar70mvfOOdP755uuwMOm++6RNm6Q+faSTJ6VbbjEj4gAAAICNCNwAfFthoZkunpsr9e8vjRlT/n0NGkjvvivVqiV9+630yiverRMAAAD4HQI3AN/2v/+ZAB0VJU2cKDkcZ743KUn6979Ne/x4aft2r5QIAAAAlIfADcB3bd4sPfywaf/3vyZQn8u4cVLv3lJOjnT77ebYMAAAAMAGBG4AvsmypNtuk06ckPr1M+2KCAqS3nxTioiQvvhCmjzZo2UCAAAAZ0LgBuCbVq+Wli2TwsOlN944+1Ty32ve3JzPLUl//auUne2ZGgEAAICzIHAD8E1vv22uw4ZJKSmuf/+995ozujMzpSlT3FkZAAAAUCEEbgC+58QJafp00x47tnJ9hIRId9xh2i+/zFpuAAAAeB2BG4DvmTvXjEwnJUl9+1a+n5tvlmrWlH77TVq61F3VAQAAABVC4Abge5zTyceMMZugVVZMjHTTTabNudwAAADwMgI3AN+yfbu0eLFpjx5d9f7+/GdznTtXysioen8AAABABRG4AfgW5zFefftWbrO032vfXurVSyoslCZOrHp/AAAAQAURuAH4jqIiadIk067sZmnlcW6eNnGilJfnvn4BAACAsyBwA/AdX34ppadLsbHmODB3GTZMatBA2rtX+vBD9/ULAAAAnAWBG4DvcG6WdsMNUmSk+/oNC5Nuv920X37Zff0CAAAAZ0HgBuAbTpwwG5tJZndyd7v9dik4WPrqK2nbNvf3DwAAAPwOgRuAb/j6a+nkSalxY6lzZ/f336iR1Lu3aTOtHAAAAF5A4AbgG774wlwvvVRyODzzjKuuMlfnSDoAAADgQQRuAL6hZOD2lKFDzXX5cmnfPs89BwAAABCBG4Av2LdPWrvWtPv189xzGjeWunaVLEuaN89zzwEAAABE4AbgCxYvNtdOnaS4OM8+y3ncGNPKAQAA4GEEbgD288Z0cifnOu7Fi6WsLM8/DwAAAAGLwA3AXpYlff65aXsjcLdsKbVuLeXnSx9/7PnnAQAAIGARuAHY67ffpN27pYgI6aKLvPNMppUDAADACwjcAOzlnE7eq5cJ3d7gnFa+cKF04oR3ngkAAICAQ+AGYC9vrt92Ov98KSlJysk5/XwAAADAzQjcAOyTlyctXWra3gzcDsfpM7mZVg4AAAAPIXADsM+KFWaUOS5OOu887z7buY57/nypoMC7zwYAAEBAIHADsE/J6eRBXv6fo4sukmJjpcOHpR9/9O6zAQAAEBAI3ADsY8f6baeQEOmSS0x78WLvPx8AAADVHoEbgD1ycqSVK027b197anA+d9Eie54PAACAao3ADcAeq1dLRUVSo0ZSYqI9NfTrZ67Ll0vHj9tTAwAAAKotAjcAezhHty+4wL4amjeXGjc2u6UvX25fHQAAAKiWCNwA7PHDD+batat9NTgcp0e5mVYOAAAANyNwA7CHM3DbOcItnV7HzcZpAAAAcDMCNwDvO3hQSksz7S5d7K3FGbhXrzZHhAEAAABuQuAG4H3O9dstW0oxMfbWkpAgtWkjWZa0ZIm9tQAAAKBaIXAD8D5f2DCtJNZxAwAAwAMI3AC8zxc2TCuJ87gBAADgAbYG7pSUFDkcjjKvO+64w86yAHiSZfnOhmlOffpIwcHSli3Sjh12VwMAAIBqwtbAvXLlSu3Zs6f49cUXX0iSrr32WjvLAuBJ6enSgQNSSIjUoYPd1RgxMadH29mtHAAAAG5ia+COi4tTgwYNil8fffSRmjZtqj59+thZFgBPco5ud+ggRUTYW0tJznXcBG4AAAC4ic+s4c7Ly9PUqVM1duxYORyOcu/Jzc1VdnZ2qRcAP+Nr08mdSq7jtix7awEAAEC14DOB+8MPP1RmZqZGjx59xnsmTJigmJiY4ldiYqL3CgTgHs4dyn1lwzSnHj3MiPv+/dKmTXZXAwAAgGrAZwL3W2+9pQEDBqhhw4ZnvGf8+PHKysoqfu3cudOLFQKossJCadUq0/a1Ee7w8NP/CPDNN/bWAgAAgGrBJwL3jh07tGjRIt16661nvS88PFzR0dGlXgD8yIYNUk6OVLOm1KqV3dWUddFF5rp8ub11AAAAoFrwicA9adIkxcfHa9CgQXaXAsCTnOu3u3Qxx3D5mp49zZURbgAAALiB7YG7qKhIkyZN0qhRoxQSEmJ3OQA8yRm4fW39ttOFF5rr5s1mLTcAAABQBbYH7kWLFik9PV1jx461uxQAnubcMM3X1m871a4ttW1r2kwrBwAAQBXZHrgvu+wyWZalFi1a2F0KAE/Ky5N+/tm0u3Sxt5azYR03AAAA3MT2wA0gQGzaJOXnS1FRUkqK3dWcGeu4AQAA4CYEbgDesX69ubZrJzkc9tZyNs4R7tWrpePH7a0FAAAAfo3ADcA7nIG7fXt76ziXlBQpIcGMxjvXnAMAAACVQOAG4B0lR7h9mcPBOm4AAAC4BYEbgHesW2euvh64JdZxAwAAwC0I3AA8LydH2rbNtP0hcDtHuL/9VioqsrcWAAAA+C0CNwDP+/VXc61fX4qLs7eWiujQQapZU8rKkn75xe5qAAAA4KcI3AA8z1/WbzuFhEjdu5s267gBAABQSQRuAJ7nT+u3nVjHDQAAgCoicAPwPH8b4ZbYqRwAAABVRuAG4Hn+cgZ3Sd27S0FB0vbt0p49dlcDAAAAP0TgBuBZhw6dDqxt2thbiyuiok7X+8MP9tYCAAAAv0TgBuBZztHtlBQTYv3JBReYK4EbAAAAlUDgBuBZ/rh+26lbN3P9/nt76wAAAIBfInAD8Cx/DtzOEe6VK6WiIntrAQAAgN8hcAPwLH/cMM2pXTspMlLKzpY2bbK7GgAAAPgZAjcAz7Es/zyD2ykkROrc2bSZVg4AAAAXEbgBeE5GhpSVJQUHSy1b2l1N5bBxGgAAACqJwA3Ac5zTyVu0kMLD7a2lstg4DQAAAJVE4AbgOf68ftvJOcK9dq108qS9tQAAAMCvELgBeI4/r992Sk6W4uOlggJpzRq7qwEAAIAfIXAD8Bx/PhLMyeE4PcrNtHIAAAC4gMANwDOKiqTffjPttm3traWqnOu42TgNAAAALiBwA/CMXbukEyek0FCpSRO7q6kaRrgBAABQCQRuAJ6xaZO5Nm1qzrP2Z127muvWrdKhQ/bWAgAAAL9B4AbgGc7A3aKFvXW4Q+3ap38dK1faWwsAAAD8BoEbgGds3Giu1SFwS0wrBwAAgMv8fJ4nAJ/lwRHuH36QPvzQLBPPyDBXh0O67TZp3DipVi23P9JsnDZ1KhunAQAAoMII3AA8wwOBu7BQeuop6dFHzSbov3f//dKECdK990p33CHFxrrt0aVHuC3LJHwAAADgLByWZVl2F1FZ2dnZiomJUVZWlqKjo+0uB4BTbq5Uo4ZJxbt3SwkJVe5y717pxhulxYvN10OHSt27S40aSY0bm/3Mnn5a2rLFfB4TI82aJV12WZUfbeTmSlFRUn6+lJYmpaS4qWMAAAD4E1dyKGu4Abjftm0mbNeqJTVoUOXuliyROnY0YbtGDWnyZGnuXOnBB00Iv/hi6ZZbzLHf06ZJbdpIWVnSlVdKS5dW+fFGeLh03nmmvWqVmzoFAABAdUbgBuB+JaeTV3Hq9aZN0hVXSPv2Se3amaw7alT594aESDfcIP30kzRokHTypPne5curVMJpnTub648/uqlDAAAAVGcEbgDu56b12/n5ZgT7+HEziv3991Lr1uf+vrAwafZsM508J0caMMBNe5116WKujHADAACgAgjcANzPGbhbtqxSN088YY69jo2Vpkwx08krKiLCTDu/+GLp6FGpf39p3boqlVN6hNt/t78AAACAlxC4AbifG0a4v/tOevJJ037tNbMxmqtq1JAWLJB69pQyM6Wbbzaj5pXWrp0ZPj9yxGycBgAAAJwFgRuA+1UxcB89aqaSFxWZ6/XXV76UWrWkDz6Q6tSR1qyR/vOfyvelsLDTG6exjhsAAADnQOAG4F7Z2eYML0lq3rxSXdx7r9noPClJeumlqpdUv770wgum/dhjZjfzSnNOK2cdNwAAAM6BwA3AvTZvNtf69c1h2C5at0566y2zufm771aqi3LdeKPZPC0vzxwhVlhYyY6cG6cxwg0AAIBzIHADcK8qTif/97/N9dprpT593FSTTIB//XUzxfy776SXX65kR2ycBgAAgAoicANwryoE7h07pPfeM+0HHnBjTackJp4O9OPHV3Lfs7ZtpfBwswvbtm3uLA8AAADVDIEbgHtVIXA//7yZ6t237+mBZHcbN07q3duc7V2pUF9y4zTWcQMAAOAsCNwA3KuSgfvQIemNN0z7wQfdXFMJQUGnp5PPni2tX1+JTljHDQAAgAogcANwH8uqdOB++WUz6typk9SvnwdqK6FdO+maa0z78ccr0UHJddwAAADAGRC4AbjP/v3mWDCHQ2ratMLfdvy49P/+n2k/8ID5dk/75z/NtVKj3CVHuNk4DQAAAGdA4AbgPhs3mmtKitlYrILefls6eFBKTT098uxp7dubZ1mW9MQTLn5zmzbm15eVJW3d6pH6AAAA4P8I3ADcpxLTyQsKpGefNe3775dCQjxQ1xk4R7nff1/65RcXvjE0VOrY0bTZOA0AAABnQOAG4D6VCNxLlkjbt0t16khjxnimrDNp3166+upKjnKzjhsAAADnQOAG4D7OwN2yZYW/ZcYMc73uOiky0gM1nYNzlHvWLBdHuZ3ruBnhBgAAwBkQuAG4j4sj3Hl50pw5pn399R6q6RzOO+/0KPeECS58o3OEe/VqqajII7UBAADAvxG4AbhHYeHpDcSaN6/Qt3z+uZSZKSUkSL16ea60cxk/3lzff186cKCC39SmjRQRYXZl37bNY7UBAADAfxG4AbjH7t1myDokREpMrNC3lJxOHhzswdrOoXNnqWtXU/7bb1fwm0JCzPC4ZEa5AQAAgN8hcANwD+cob0pKhdLziRPSvHmmbdd08pL+9Cdzff11F2aIn3++uRK4AQAAUA4CNwD3SEsz1yZNKnT7J59Ix45JyclS9+4erKuCrr9eio01v4zPPqvgNxG4AQAAcBYEbgDu4RzhTk2t0O0lp5M7HB6qyQU1akijR5v2q69W8JtKBm7L8kRZAAAA8GMEbgDu4QzcFRjhPnpU+vhj0x4+3IM1ueiPfzTXjz+W0tMr8A3t2pm13IcOSTt3erQ2AAAA+B8CNwD3cCFwL1hg1nA3ayZ16uThulzQsqV0ySVmDffEiRX4hvBwE7olppUDAACgDAI3APdwIXA7p5MPH+4b08lLcm6e9uabZtfyc2IdNwAAAM6AwA2g6nJypH37TPscgTszU/r0U9P2penkTldeac4F37dP+vDDCnwDgRsAAABnQOAGUHXOHcpr1zZbfZ/F559L+flS69ZS27aeL81VoaHSrbea9uuvV+AbCNwAAAA4AwI3gKpzYTr5F1+Ya//+Hqynim65xVyXLJEyMs5x83nnSUFB0p495gUAAACcQuAGUHUVDNyWdTpwX3qph2uqguRk6aKLTL0zZ57j5po1zW5rkvTTTx6vDQAAAP6DwA2g6ioYuLdskXbsMNO2+/TxQl1VcMMN5jptWgVudk4rJ3ADAACgBAI3gKqrYOB2jm5feKEZGPZl115rjthevVrasOEcN7OOGwAAAOUgcAOoOhcDty9PJ3eqV+/0OvPp089xM4EbAAAA5SBwA6gayzq9S/lZAndBgdmETPKPwC1JI0ea6/Tp5pd5Rh07muv27dLhwx6uCgAAAP6CwA2gavbulU6eNDt1Jyae8bZVq6SsLHNyWOfOXqyvCoYMMVPft26VfvjhLDfGxkpNm5o267gBAABwCoEbQNU4p5MnJZnd0M7AOZ38kkuk4GAv1OUGNWtKQ4eaNtPKAQAA4CoCN4CqqYbrt0ty7lY+c6aZFn9GBG4AAAD8DoEbQNVUIHAfPSp9951p9+vnhZrc6NJLzQZq+/ZJX355lhsJ3AAAAPgdAjeAqqlA4F62zIwOp6aeXursL0JDpeuuM+2zTivv1MlcN22SsrM9XhcAAAB8H4EbQNVUIHAvWmSu/jad3Mk5rXzuXCkv7ww3xcWd3jRu7Vqv1AUAAADfRuAGUDUVCNz+un7bqUcPKSHBDFw7jzYrF9PKAQAAUAKBG0DlnTgh7d5t2mcI3BkZ0q+/Sg6H2aHcHwUFSVdeadoffniWGwncAAAAKIHADaDytm831+hoqU6dcm9ZutRcO3c+4y1+wXk82Lx5UlHRGW4icAMAAKAEAjeAyis5ndzhKPcW5+7kF13kpZo85A9/MP+usGeP9MMPZ7jJGbh/+82M/gMAACCgEbgBVF4F1m+vWGGu3bt7oR4PCguTBg0y7blzz3BTQoIUHy8VFkrr1nmtNgAAAPgmAjeAyktLM9czBO7jx09v2N2jh5dq8iDntPK5cyXLKucGh4Np5QAAAChG4AZQec4R7tTUcj/+8Udz/nbDhqdPzPJnAwaYke7Nm82s8XIRuAEAAHAKgRtA5Z1jSrlz/Xb37mdc4u1XoqKkfv1M+4y7lRO4AQAAcAqBG0DlWNY5R7idgbs6TCd3GjbMXM+4jtsZuNetk/LyvFITAAAAfBOBG0DlHD4s5eSYdnJymY8tq/psmFbS4MFmtH7VKmnnznJuSEmRYmNN2P71Vy9XBwAAAF9C4AZQOTt2mGv9+lJERLkf790rhYSYM7iri/r1pZ49TXvevHJuYOM0AAAAnELgBlA5zsBdzui2dHp0u1MnKTLSSzV5iXO3ctZxAwAA4GwI3AAq5xyBu+SGadWNM3AvXSplZZVzA4EbAAAAInADqKwKBu7qtGGaU9OmUqtWUmGhtGhROTc4A/eaNeYmAAAABCQCN4DKOUvgPnFC+ukn066OI9ySOZNbkj75pJwPmzeXatUyvxEbN3q1LgAAAPgOAjeAytm+3VzLCdyrV0sFBWaDsZQUr1blNQMHmuvChWZH9lKCgqSOHU2baeUAAAABi8ANoHLOMsJdcv22w+HFmryoVy+pZk1pzx5p7dpybmAdNwAAQMCzPXBnZGToxhtvVN26dRUZGan27dtr1apVdpcF4GyOHTPncEvlBm7nDuXVcf22U3i41LevaS9cWM4NzsDtnFsPAACAgGNr4D5y5Ih69uyp0NBQLVy4UL/++queffZZ1a5d286yAJyLc3Q7Jsa8SrCs6r1hWklnXcddcoS7qMhrNQEAAMB3hNj58GeeeUaJiYmaNGlS8XupqalnvD83N1e5ubnFX2dnZ3u0PgBncJbp5Lt2Sbt3S8HBUufOXq7Ly5yB+7vvpCNHpFL/VtiqlRkGz86W0tLM1uYAAAAIKLaOcM+fP19dunTRtddeq/j4eHXq1ElvvPHGGe+fMGGCYmJiil+JiYlerBZAsQqs3+7Qwaxxrs6Sk6U2bczJX1988bsPQ0Ol884zbdZxAwAABCRbA/e2bdv06quvqnnz5vrss8/0pz/9SXfddZfeeeedcu8fP368srKyil87d+70csUAJJ01cDuzZdeuXqzHRhWeVg4AAICAY+uU8qKiInXp0kVPPfWUJKlTp05av369XnvtNY0aNarM/eHh4QoPD/d2mQB+7yyBe80ac+3UyXvl2GngQOnZZ6VPPzVLtYNK/jMmgRsAACCg2TrCnZCQoDZt2pR6r3Xr1kpPT7epIgAVUoHA7TyGurq76CKpVi1p375yNiQvGbjLHNYNAACA6s7WwN2zZ09t3Lix1HubNm1Scjl/iQfgQ84QuPfuNcEzKEhq396GumwQFib162faZY4Ha9dOCgmRDh40u8kBAAAgoNgauO+9916tWLFCTz31lLZs2aLp06dr4sSJuuOOO+wsC8DZ5OVJe/aY9u8Ct3N0u0ULqUYN75ZlpzOu446IkNq2NW2mlQMAAAQcWwN3165dNXfuXL333ntq166dnnjiCb3wwgsaOXKknWUBOJudO8306IgIKT6+1EeBNp3cyRm4v/9eOnz4dx+yjhsAACBg2Rq4JemKK67QunXrdPLkSf3222+67bbb7C4JwNk4p5MnJUkOR6mPAjVwJyZKrVubTdOWLPndhwRuAACAgGV74AbgZ9gwrVyXXmquZc7jJnADAAAELAI3ANecIXDn5EibNpk2gbuEDh3MTIDdu82OcgAAAAgYBG4ArjlD4F63ziztTkiQ6te3oS6b9eljNiTfts28itWsKbVqZdplzg0DAABAdUbgBuCaMwTuQJ5OLklRUVL37qa9aNHvPmRaOQAAQEAicANwzRkCt3PwNlADt8Q6bgAAAJRG4AZQcUVF5lgwiRHucjgD9+LFUmFhiQ8I3AAAAAGJwA2g4vbskfLzpeBgqVGj4rcLCqSffzbtQA7cXbtK0dHSkSO/W67t/E1JSzMfAgAAICAQuAFUnHM6eaNGZoewUzZvlk6eNPuDNW1qU20+ICRE+sMfTLvUtPLYWKlJE9Nm4zQAAICAQeAGUHHn2DDtvPPM4HcgYx03AAAAnAjcACqOHcrPyRm4ly+Xjh8v8QGBGwAAIOAQuAFUHIH7nJo3lxITpbw86euvS3xA4AYAAAg4BG4AFVdO4Las08uSO3WyoSYf43CcYVq58zdn0ybp6FGv1wUAAADvI3ADqLhyAvfevdKBA1JQkNSunU11+ZhyA3d8vNS4sfkXirVrbakLAAAA3kXgBlAxliWlp5t2icDtnE7eqpUUGen9snxR377m+vPP0r59JT5gWjkAAEBAIXADqJjMTOnYMdNOTCx+2xm4O3TwekU+Ky7u9O/H0qUlPiBwAwAABBQCN4CK2bnTXOvWlWrUKH7711/NtX17G2ryYZdcYq5fflniTQI3AABAQCFwA6gYZ+AuMbotSb/8Yq5t2ni5Hh/3hz+Y65IlJd50Bu5ff5VOnvR6TQAAAPAuAjeAiikncBcWSr/9ZtoE7tJ69zYbyW3eLO3aderNhg3N5mmFhdK6dbbWBwAAAM8jcAOomHIC944dZqA2PFxq0sSmunxUTIzUubNpF49yOxxMKwcAAAggBG4AFVNO4Hau327VSgoOtqEmH8c6bgAAgMBG4AZQMWcJ3EwnL59zHfeXX5pT1SQRuAEAAAIIgRtAxRC4XXbRRVJIiDm+PC3t1JudOpnrzz9L+fm21QYAAADPI3ADODfLOr3zF4G7wmrWlLp1M+3iddypqWaBd17e6d9AAAAAVEsEbgDnduCAlJtrNv1q1EiSyeAE7nMrs46bjdMAAAACBoEbwLk5p5PXry+FhRW/lZMjhYZKTZvaWJuPK3keN+u4AQAAAguBG8C5nWX9dosWJnSjfD16mGPT9uyRNm489SaBGwAAICAQuAGcWzmB+5dfzJXp5GcXESFdeKFpF6/jdgbuNWukwkI7ygIAAIAXELgBnBs7lFdJmXXczZubHdWOH5c2bbKtLgAAAHgWgRvAuRG4q8S5jnvpUqmoSFJwsNSxo3mTaeUAAADVFoEbwLn9LnCzQ7lrunY1A9oHD0rr15960zmt/KefbKsLAAAAnkXgBnBuvwvcu3dL2dlmoLZFCxvr8hNhYdJFF5n2smWn3mTjNAAAgGqPwA3g7AoLpYwM0z4VuJ2j282bF58ShnPo08dcly499UbJwF18XhgAAACqEwI3gLPbu9eE7uBgKSFBEtPJK+Pii8112bJT67hbtzZbmGdlSVu22FkaAAAAPITADeDsnNPJGzY0oVsE7sro0kWqUUM6dOjUkWqhoac3Tlu50s7SAAAA4CEEbgBnxw7lbhEaenodd/G08q5dzZXADQAAUC0RuAGcXTk7lP/yi3mLwO2aktPKJRG4AQAAqjkCN4Cz+13g3rdPOnJECgpih3JXOTdOK17H7Qzcq1dLBQW21QUAAADPIHADOLvfBW7ndPImTaTISJtq8lPOddwHD576fWzRQoqKkk6ckH77ze7yAAAA4GYEbgBnd4bAzXRy14WFST17mvbSpTLTBDp3Nm8wrRwAAKDaIXADOLvfBW7nQGzr1jbV4+ec67jZOA0AAKD6I3ADOLO8PHMOt1QcuDdtMl+2amVTTX6u5MZpliUCNwAAQDVG4AZwZrt3m1QYFibFxUk6HbjZMK1yunQxa9+L13E7A/fPP0u5ubbWBgAAAPcicAM4M+d08saNpaAgnTghpaebt5o3t68sf1ZmHXdyslS3rpSfL61da2dpAAAAcDMCN4Az+9367a1bzZexsVK9evaUVB2UWsftcJwe5V61yqaKAAAA4AkEbgBn9rvAXXI6ucNhU03VAOu4AQAAAgOBG8CZnSFwM528arp2Neu4Dxw4tes7gRsAAKBaInADOLOzjHCj8sLCpAsvNO2lS2V2UpNM+j52zK6yAAAA4GYEbgBn9rvAvXmz+ZLAXXWl1nEnJEiNGklFRdLq1TZWBQAAAHcicAM4s127zJURbrcrGbhZxw0AAFA9EbgBlO/kSbPIWJIaN1ZWlrR/v/mSNdxVd8Z13OxUDgAAUG0QuAGUb/duc42IkOrUKZ5O3qCBFBVlX1nVRXj46XXcy5aJEW4AAIBqiMANoHwZGebauLHkcDCd3AP69DHXUhunbd0qHTpkV0kAAABwIwI3gPI51283aiSJI8E8odQ67tjap/814/vv7SoJAAAAbkTgBlA+Z+Bu3FgSO5R7wgUXmBn7+/dLGzZI6t7dfEDgBgAAqBYI3ADKV3JKudih3BPKrOPu1s18sWKFbTUBAADAfQjcAMpXYkq5ZTGl3FNKncftHOH+4QdzJjcAAAD8GoEbQPlKTCk/cEDKzpYcDqlpU3vLqm5KbpxmtWtvzgrLzDz9LxwAAADwWwRuAOUrMaXcmf2Sk82aY7iPcx33vn3Sxm2hUufO5gPWcQMAAPg9AjeAsgoKpD17TLtE4GY6uftFREg9eph2qWnlrOMGAADwewRuAGXt2ycVFkrBwVJ8PDuUe5hzHXepjdMY4QYAAPB7BG4AZTnXbzdsKAUHs0O5h5U6j7vbqRHun3+Wjh+3qyQAAAC4AYEbQFkcCeZVF1xgjgjbu1fadLyx+YeOwkLpxx/tLg0AAABVQOAGUFaJI8GKiqQtW8yXrOH2DNZxAwAAVE8EbgBllTgSbNcu6eRJKTTU7FIOzyh1HjfruAEAAKoFAjeAsso5EqxpUykkxL6SqruSG6cVr+NmhBsAAMCvEbgBlFViSjlHgnlHt25mHfeePdLmmC5mh/iMjNN/FgAAAPA7BG4AZZWYUs6RYN4REXF66fbSH2pI7dubL5hWDgAA4LcI3ABKs6xSU8qdgZsRbs8rtY7bmb4J3AAAAH6LwA2gtEOHpNxc027YUFu3mmbTpvaVFChKncd9wamN01jHDQAA4LcI3ABKc04nj49XUUiY0tLMlwRuz+ve/fQ67i0Jvcybq1ZJ+fn2FgYAAIBKIXADKK3EdPLdu81gd0iIlJhob1mBoNQ67h2pUu3a0okT0po1ttYFAACAyiFwAyitxA7l27aZZnIyR4J5S58+5rr0qyDpwgvNF8uX21cQAAAAKo3ADaC0EjuUO9dvN2liXzmBptQ67gt7mi8I3AAAAH6JwA2gtBJTyp0j3Kzf9p7u3aWwMGn3bmlLSl/z5vLlZvd4AAAA+BUCN4DSSkwpZ4Tb+yIjT6/jXpbZUQoNNbuobd9uZ1kAAACoBAI3gNLKmVLOCLd3FU8r/zZMOv988wXTygEAAPwOgRtAaeVMKWeE27uKN05byjpuAAAAf0bgBnBadrZ5ScqOaqSDB83bjHB7l3Mdd0aGtLXpZeZNAjcAAIDfIXADOM05uh0To237a0mS4uKkqCgbawpANWpI3bqZ9tLcHqaxfr2UmWlbTQAAAHAdgRvAaSWmk7Nhmr2c67iX/RRtphhYlrRiha01AQAAwDUEbgCnldgwjSPB7FXuedzffGNXOQAAAKgEAjeA0zgSzGd0725OBNu1S9rW8nLzJuu4AQAA/AqBG8Bp5exQzgi3PUqt4y7sbRrffy/l59tXFAAAAFxC4AZwWjlncDPCbZ/iaeWbGkqxsdKJE9KaNTZWBAAAAFcQuAGcdipwFzRorB07zFuMcNuneOO0rxyyelxovmBaOQAAgN8gcAM47dSU8vSgFBUWSuHhUkKCzTUFsB49zDrunTultDaDzJsEbgAAAL9B4AZg5OZKBw5IkrblNpJkppMH8b8StqlRQ7rgAtNeGtzXNJYvN0eEAQAAwOfZ+lfpRx99VA6Ho9SrVatWdpYEBK49e8w1PFxb90dJYv22Lyhex72rqRQWZv6cnAvsAQAA4NNsH7tq27at9uzZU/z6hnNmAXs4dyhv2FDb0hySWL/tC4rXcX8TIqurc7h7qV3lAAAAwAW2B+6QkBA1aNCg+FWvXj27SwICkzNwcwa3T3Gu405Pl7Z3HGreXLbM1poAAABQMbYH7s2bN6thw4Zq0qSJRo4cqfT09DPem5ubq+zs7FIvAG6ye7e5lgjcjHDbr2bNEuu4Iy43jWXLWMcNAADgB2wN3N26ddPkyZP16aef6tVXX1VaWpp69eqlo0ePlnv/hAkTFBMTU/xKTEz0csVANXZqhNtKaKht28xbjHD7huJ13HtaSCEhZtvy7dvtLAkAAAAVYGvgHjBggK699lqdd9556t+/vz755BNlZmZq1qxZ5d4/fvx4ZWVlFb927tzp5YqBauxU4D5Uu5mck0dSU22sB8X69DHXpd+ESl27nvpiqW31AAAAoGJsn1JeUmxsrFq0aKEtW7aU+3l4eLiio6NLvQC4yakp5duCmkmSGjaUIiPtLAhOF15oBrZZxw0AAOBffCpwHzt2TFu3blVCQoLdpQCB59QI99b8JEms3/YlpdZxRw4wDQI3AACAz7M1cN9///1atmyZtm/frm+//VbDhg1TcHCwRowYYWdZQOCxrOLAve1YnCTWb/sa5zruL3e3koKDzRruHTvsLAkAAADnYGvg3rVrl0aMGKGWLVvquuuuU926dbVixQrFxcXZWRYQeDIzpRMnJElbD8RIYoTb1/Tta66LvwqVdX5n8wWj3AAAAD4txM6Hz5gxw87HA3ByHglWp462pZv/WWCE27dceKEUEWH+qDYMuE6tV/5gAvfNN9tdGgAAAM7Ap9ZwA7DJqenkatRIaWmmSeD2LRERUq9epv1FSInzuAEAAOCzCNwAigN3foNE7dpl3kpJsa8clK9fP3NdlN5CCgqStm5V8R8YAAAAfA6BG0Bx4N4V205FRVJ4uFS/vs01oQxn4F76TajyO3YxXzDKDQAA4LMI3ACK13Bvj2glyYxuB/G/Dj6nY0epTh3p6FFpZYsbzZsEbgAAAJ/FX6kBFI9wpylFEtPJfVVQ0OndyhcFXWoaS5faVg8AAADOjsANoDhwb89rJInA7csuPZWzv9jWzCTwzZulnTvtLQoAAADlInADOB24j9WVROD2Zc513CtWhejo+X3MF4sW2VcQAAAAzojADQS6ggJp3z5J0vZDUZKk1FQ7C8LZpKaaI9sKCqSvmow2bxK4AQAAfBKBGwh0e/dKliWFhChtV6gkRrh9XfHxYEWXnGosMn+GAAAA8CkEbiDQnZpOntcgSRkZDkkEbl9XHLh/ayjVqCHt3y+tW2dvUQAAACiDwA0EulNHgu2s10mWJUVGSvHxNteEs/rDHySHQ1r/S5D2XHCleZNp5QAAAD6HwA0EOueRYLXaSzKj2w6HjfXgnOrVkzp1Mu3FDW8yjS++sK8gAAAAlIvADQQ65w7lYS0kMZ3cXzinlX9xtJtpfPWVlJtrX0EAAAAog8ANBDpn4LaSJRG4/cVll5nrZz/UVlFcfen4cWnFCnuLAgAAQCkEbiDQnVrDvT03QRJHgvmLiy6SataU9u1zaG3nseZNppUDAAD4FAI3EOica7iz6khihNtfhIdLl5w6FezTmlebBhunAQAA+BQCNxDonFPKD9SQROD2J5dfbq4Ld7Y1jZUrpSNH7CsIAAAApRC4gUB29Kh09KhyFabd+0MlEbj9yYAB5vrtjxHKbNZFKiqSli61tSYAAACcRuAGAtmp9dvpNdtIMmuC69WzsyC4IjVVatlSKiyUFjcbZ95kWjkAAIDPIHADgcy5frvO+ZI4g9sfFU8rL7jUNNg4DQAAwGcQuIFA5ly/XbOdJKaT+yPntPJPf02UFRwibd4sbd1qb1EAAACQROAGApvzSLCQZpI4Eswf9e4tRURIGbuDtL7TTebNTz6xtygAAABIInADgc05wl2UJIkRbn8UGSn94Q+m/Wn8zaZB4AYAAPAJBG4gkDnXcB+vL4nA7a+c08oXHuxiGkuWSMeP21cQAAAAJBG4gcDmHOHOjJVE4PZXzo3Tvvmppo4mtpFyc03oBgAAgK0I3EAg271bJxShvZkRkljD7a+aN5eaNpXy8x36su1fzJsff2xvUQAAACBwAwGrqEjas0c7lCxJioqSate2uSZUWvHxYDo1v/yTTyTLsq8gAAAAELiBgLV/v1RQoO2OJpI4g9vfOddxf7wuUVZYuLRjh/Tbb/YWBQAAEOAI3ECgcq7fjmovienk/u6SS6QaNaRdGUH6qfOt5k12KwcAALAVgRsIVM4zuGu0kcSGaf4uMlK67DLTnh/DedwAAAC+gMANBCrnkWBBTSURuKuDK68013k7OprG119LWVm21QMAABDoCNxAoHJOKS9oJInAXR1ccYUUFCSt+S1cO1IvlgoKpEWL7C4LAAAgYBG4gUDlDNw5cZII3NVBvXpSz56mPT/51PFgTCsHAACwDYEbCFSnzuDen1NLkpScbHM9cAvntPL5WX1M45NPzBFwAAAA8DoCNxCoMjK0U4mSpFq1OIO7uhgyxFyXrqujzFqNpb17pR9+sLcoAACAAEXgBgJVRoZ2yAxrJyVxBnd10by51Lq1VFDg0MJ2fzNvzp1rb1EAAAABisANBKITJ6QjR4oDN9PJq5fiaeVBpxpz50qWZV9BAAAAAYrADQSiU2dw7whpJonAXd04A/cn65OUF1pT2rxZ+vVXe4sCAAAIQC4H7tWrV2vdunXFX8+bN09Dhw7VQw89pLy8PLcWB8BDTu1QviOypSQCd3VzwQVS/fpSdrZDyzrebd788ENbawIAAAhELgfucePGadOmTZKkbdu2afjw4apRo4bef/99PfDAA24vEIAHnArc6UGpkgjc1U1QkDR4sGnPr3WDabCOGwAAwOtcDtybNm1Sx44dJUnvv/++evfurenTp2vy5Mn64IMP3F0fAE9wTikvaCjJbJqG6sU5rfzDja1U5AiWfvxRSk+3tygAAIAA43LgtixLRafOdF20aJEGDhwoSUpMTNTBgwfdWx0Az8jIUKGCtOtEXUmMcFdH/fpJ0dHSrt3BWnHe7eZNppUDAAB4lcuBu0uXLvrXv/6lKVOmaNmyZRo0aJAkKS0tTfXr13d7gQA8ICNDu9VQBUXBCgmREhLsLgjuFhFxepR7ZvRtpkHgBgAA8CqXA/cLL7yg1atX684779TDDz+sZs3MLsezZ8/WhRde6PYCAXhARobSZeaRJyZKwcE21wOPuP56c31/Y3sVKkj66ivp0CF7iwIAAAggIa7cXFhYqMzMTH311VeqXbt2qc/+85//KJi/tQP+Yfdu7VB3Sazfrs4uvVSKjZX27A/RN01Hq8/Wt6UFC6TRo+0uDQAAICC4NMIdHBysyy67TJmZmWU+i4iIUGhoqLvqAuAplnUqcJuF26zfrr7CwqRhw0x7Zu0/mga7lQMAAHiNy1PK27Vrp23btnmiFgDecOiQlJtL4A4Qzmnls7d2UoGCpc8+k7Kz7S0KAAAgQLgcuP/1r3/p/vvv10cffaQ9e/YoOzu71AuAj3MeCRbaXBKBu7q75BKpbl3pwJEQLW18k5SbK82fb3dZAAAAAcHlwD1w4ECtXbtWQ4YMUePGjVW7dm3Vrl1bsbGxZdZ1A/BBGRmSpPTgFEms4a7uQkOlq6827Zlxd55qzLSvIAAAgADi0qZpkrRkyRJP1AHAWzIyZEnakd9QEiPcgeD666WJE6U52zroFYUo9LPPpMOHpTp17C4NAACgWnM5cPfp08cTdQDwlowMHVYd5RRGSmKEOxD06SPVry/t2xeiRSm3acD2V83mabfcYndpAAAA1ZrLU8qdjh8/rg0bNujnn38u9QLg40rsUF6/vhQRYXM98LjgYOmaa0x7Zp1Tu5XPmGFfQQAAAAHC5RHuAwcOaMyYMVq4cGG5nxcWFla5KAAelJHBDuUB6PrrpZdfluZuaqdXFaHIL7+U9u+X4uPtLg0AAKDacnmE+5577lFmZqa+//57RUZG6tNPP9U777yj5s2baz473wK+LyND6TLzyJlOHjh69jT/wJJ9LEgfNr1fKiqSZs+2uywAAIBqzeXA/eWXX+q5555Tly5dFBQUpOTkZN14443697//rQkTJniiRgDuxAh3QAoKkkaPNu23g241DaaVAwAAeJTLgTsnJ0fxp6Yg1q5dWwcOHJAktW/fXqtXr3ZvdQDcKy9POnCAwB2gRo0y18VbkrRDSdLXX0u7dtlbFAAAQDXmcuBu2bKlNm7cKEnq0KGDXn/9dWVkZOi1115TQkKC2wsE4EZ79kiSdjhSJBG4A01qqnTJJZJlOfRO0j/Nm++/b29RAAAA1ZjLgfvuu+/WnlN/aX/kkUe0cOFCJSUl6X//+5+eeuoptxcIwI0yMiSdDtys4Q48Y8aY6+Sca1Qkh/Tee/YWBAAAUI05LMuyqtKB83iwpKQk1atXz111VUh2drZiYmKUlZWl6Ohorz4b8Evvv6/j141STR2XJB0+LNWubXNN8Krjx6WEBCk7W/oyqJ/+ULRY2rBBatnS7tIAAAD8gis51KUR7uzsbBUVFZV6r0aNGurYsaPCwsJcrxSAd+3eXbxDeVSUFBtrbznwvho1pOHDTXtSwkOm8e679hUEAABQjVU4cM+dO1ddunTRyZMny3x24sQJde3aVQsWLHBrcQDc7Hc7lDscNtcDW4wda66zD/RWlqKlKVPMMWEAAABwqwoH7ldffVUPPPCAatSoUeazmjVr6sEHH9RLL73k1uIAuBlHgkHSBRdIrVtLJ/JCNCtytLRzp7Rkid1lAQAAVDsVDtzr16/XxRdffMbPe/furXXr1rmjJgCesnt3ceBmw7TA5XCcHuV+O/pu03jnHfsKAgAAqKYqHLiPHDmigoKCM36en5+vI0eOuKUoAB6SkVG8hpsR7sB2001SSIi0Yl8TrVEH6YMPpKNH7S4LAACgWqlw4E5JSdGqVavO+PmqVauUzN/gAd9lWUwpR7H69aWrrzbt/0X/3Wxf/sEH9hYFAABQzVQ4cF911VV6+OGHtW/fvjKf7d27V3//+991tfNvbwB8T1aWdPw4gRvF7j41m3z68aE6oHpMKwcAAHCzCp/DffToUfXo0UPp6em68cYb1fLUma0bNmzQtGnTlJiYqBUrVigqKsqjBZfEOdyAC375RQXtOihCJ1WoEO3aJTVqZHdRsJNlSd26SStXSk/oH/q7/iWlpUkpKXaXBgAA4LM8cg53VFSUli9frhtvvFEzZ87Uvffeq3vvvVczZ87UjTfeqG+++carYRuAizIytEcJKlSIQkKkBg3sLgh2czhOj3K/HHaP8hQqTZ1qb1EAAADVSIVHuEuyLEsHDx6UZVmKi4uTw6bDfBnhBlwwaZKWj31TF2m5UlLMQCaQl2cGtPfskaZqpEY2/V7avJlD2gEAAM7AIyPcJTkcDsXFxSk+Pt62sA3ARRkZ2qlESRwJhtPCwqQ//cm0Xwz6q6ytW6WlS22tCQAAoLqoVOAG4Id27y4+Eiwx0eZa4FPGjTPBe2VRZ61Qd+mNN+wuCQAAoFogcAOBosQZ3Ixwo6T4eGnkSNN+UXeb48EOHrS3KAAAgGqAwA0ECqaU4yycm6fN1jXakddAmjLF3oIAAACqgQoF7jp16ujgqdGOsWPH6ujRox4tCoAHMMKNs+jQQerbVypUiCZovDRxojk3DAAAAJVWocCdl5en7OxsSdI777yjkydPerQoAG6Wny/t28cabpzVI4+Y69saqx0bjkvLl9tbEAAAgJ8LqchNPXr00NChQ9W5c2dZlqW77rpLkZGR5d779ttvu7VAAG6wd69yrEgdVl1JjHCjfL16mVHuxYvDNEHj9drEidJFF9ldFgAAgN+q0Aj31KlTNXDgQB07dkwOh0NZWVk6cuRIuS8APqjE+u3oaCkmxuZ64LNKjXLP+l7if9cBAAAqrUIj3PXr19fTTz8tSUpNTdWUKVNUt25djxYGwI1Yv40KMqPclhnlzr1Xr02dKv3lL3aXBQAA4Jdc3qU8LS2NsA34mxKBm/XbOJdHHnFIOjXK/dICNk8DAACopEodC7Zs2TINHjxYzZo1U7NmzTRkyBB9/fXX7q4NgLswwg0X9Ool9e2Tr3yFacKmq6RvvrG7JAAAAL/kcuCeOnWq+vXrpxo1auiuu+4q3kCtb9++mj59uidqBFBVu3dzBjdc8sgToZLMKHfa0zNtrgYAAMA/uRy4n3zySf373//WzJkziwP3zJkz9fTTT+uJJ57wRI0AqooRbrioVy/p0u7ZyleYHlh4sbRnj90lAQAA+B2XA/e2bds0ePDgMu8PGTJEaWlpbikKgJuxhhuV8OzEaAWpULOta7R0/Gd2lwMAAOB3XA7ciYmJWrx4cZn3Fy1apET+Jg/4HsuStSuDKeVwWfv20h8v3SpJuntaVxWcyLe5IgAAAP9SoWPBSrrvvvt01113ac2aNbrwwgslScuXL9fkyZP14osvur1AAFWUna0Dx2soVxFyOCw1auSwuyL4kcffSdF7jY7o54K2evOuH/XHNzrbXRIAAIDfcDlw/+lPf1KDBg307LPPatasWZKk1q1ba+bMmbryyivdXiCAKioxnTwhwaGwMJvrgV+pmxCmxy77Qnd9Nkh/n9xU1/9bql3b7qoAAAD8g8uBW5KGDRumYcOGubsWAJ7A+m1U0Z9e76jXU9brl4J2euwvB/XC1Hp2lwQAAOAXKnUOtyc8/fTTcjgcuueee+wuBahe2KEcVRSS3EgvXPSBJOml6bW1fr3NBQEAAPgJnwjcK1eu1Ouvv67zzjvP7lKA6ieDDdNQdf2e6KOhmqtCK1hjbi5QQYHdFQEAAPg+2wP3sWPHNHLkSL3xxhuqzcJAwP0Y4YY79Omjl1u9pFgd0aqfQvSf/9hdEAAAgO+zPXDfcccdGjRokPr163fOe3Nzc5WdnV3qBeAcdu9mDTeqzuFQw7+N1Iu6W5L06KMWU8sBAADOwdbAPWPGDK1evVoTJkyo0P0TJkxQTExM8Ytzv4EKYIQb7nLDDbqp3qe6QguUl+fQ6NFSPkdzAwAAnJHLu5QXFhZq8uTJWrx4sfbv36+ioqJSn3/55ZcV6mfnzp26++679cUXXygiIqJC3zN+/Hj99a9/Lf46Ozub0A2cQ+6uA9qrBEkEblRRRIQcf/6TXn98nNoG99GPP0br3/+WHn7Y7sIAAAB8k8OyLMuVb7jzzjs1efJkDRo0SAkJCXI4HKU+f/755yvUz4cffqhhw4YpODi4+L3CwkI5HA4FBQUpNze31Gflyc7OVkxMjLKyshQdHe3KLwMIDAUF2hbWSk2tLYqIsHT8uEO/+5EFXLN3r5SUpCn51+tmTVFoqLRqlcSelwAAIFC4kkNdHuGeMWOGZs2apYEDB1a6QEnq27ev1q1bV+q9MWPGqFWrVnrwwQfPGbYBVMDevUq3Gksy67cJ26iyBg2kESN047vvanajuzU/o4uuu05auVKKirK7OAAAAN/i8hrusLAwNWvWrMoPjoqKUrt27Uq9atasqbp166pdu3ZV7h+Afrd+m7QNN7nnHjkkvbX3CjVqUKCNG6Vx4yTX5ksBAABUfy4H7vvuu08vvviiXJyJDsAOnMENT+jUSerTR/UK92lmvzcVHCy99540caLdhQEAAPgWl6eUf/PNN1qyZIkWLlyotm3bKjQ0tNTnc+bMqXQxS5curfT3AihHiRFu9heEW91zj7RsmXp+/JCefnys/vZwmO66S+raVTr/fLuLAwAA8A0uB+7Y2FgNGzbME7UAcLeMDKWrjyRGuOFmgwdLTZpI27bpvtpv66vBf9SCBdK110qrV0sxMXYXCAAAYD+XA/ekSZM8UQcAT2BKOTwlOFi66y6znvt/L2ry17fr/C5B2rZNGjlSmjfP3AIAABDIXF7D7XTgwAF98803+uabb3TgwAF31gTATayM3dqhZEkEbnjAmDFma/ING1Rn1eeaPVuKiJA+/lgaP97u4gAAAOzncuDOycnR2LFjlZCQoN69e6t3795q2LChbrnlFh0/ftwTNQKopKyd2Tomc1YTa7jhdtHR0i23mPbzz6tLF8k5Ceo//5EmT7atMgAAAJ/gcuD+61//qmXLlmnBggXKzMxUZmam5s2bp2XLlum+++7zRI0AKik9w8zprRtbqBo1bC4G1dNdd0lBQdLnn0u//KLhw6V//MN8NG6ctHy5veUBAADYyeXA/cEHH+itt97SgAEDFB0drejoaA0cOFBvvPGGZs+e7YkaAVRGdrZ2nqgrSUpKtrkWVF+pqdKVV5r2//4nSXr0Uenqq6W8POmqq6QdO+wrDwAAwE4uB+7jx4+rfv36Zd6Pj49nSjngS0ocCZaUwu5V8KB77jHXd9+VDh5UUJD0zjtSx47S/v3SFVdIWVl2FggAAGAPlwN3jx499Mgjj+jkyZPF7504cUKPPfaYevTo4dbiAFQBZ3DDW3r1kjp1kk6elCZOlCTVrCnNny81bCitXy9dc42Un29znQAAAF7mcuB+8cUXtXz5cjVu3Fh9+/ZV3759lZiYqG+//VYvvviiJ2oEUBklR7jZoRye5HBI995r2i+9ZOaSy/xDz0cfmfC9aJH0xz9KlmVjnQAAAF7mcuBu166dNm/erAkTJqhjx47q2LGjnn76aW3evFlt27b1RI0AKoMzuOFN118vNWgg7dkjldjPo1MnaeZMs6/a229LEybYWCMAAICXhVTmm2rUqKHbbrvN3bUAcCdGuOFNYWHSn/8s/fOf0vPPSyNGmJFvSYMGSf/v/0l33CE9/LCUkiLdcIO95QIAAHhDhQL3/PnzNWDAAIWGhmr+/PlnvXfIkCFuKQxA1RTu2qNdaiyJNdzwknHjpCeflFatkr79VurZs/ijP/9ZSkuT/vtfacwYqXFjqXdvG2sFAADwAodlnXtFXVBQkPbu3av4+HgFBZ15FrrD4VBhYaFbCzyb7OxsxcTEKCsrS9HR0V57LuAPdnW8QolrP1JwUJFy84IUzEbl8IZbb5Xeesvskvb++6U+KiqSrrtO+uADqXZt6bvvpJYtbaoTAACgklzJoRVaw11UVKT4+Pji9ple3gzbAM5uZ4b58W5cP5+wDe+5+25znTOnzAHcQUHSlClS9+7SkSPSwIHSgQM21AgAAOAlLm+a9u677yo3N7fM+3l5eXr33XfdUhSAKiooUPqhmpJYvw0va99e6tvXDGe/9FKZjyMjpXnzpCZNpG3bpCFDpBMnbKgTAADAC1wO3GPGjFFWVlaZ948ePaoxY8a4pSgAVbRvn9KtU+u3m4TaXAwCzj33mOsbb0jHjpX5OD5e+uQTM618xQpp1CiOCwMAANWTy4Hbsiw5Tu08W9KuXbsUExPjlqIAVFHJHcqTXf4xB6pm4ECpeXMpK0t6551yb2nZUvrwQyk01Cz1fvJJ75YIAADgDRU+FqxTp05yOBxyOBzq27evQkJOf2thYaHS0tJ0+eWXe6RIAC7iDG7YKSjIrOW+807pxRelP/3JvPc7vXtLr7wi3Xab9I9/mNnoV15pQ70AAAAeUuHAPXToUEnSmjVr1L9/f9WqVav4s7CwMKWkpOjqq692e4EAKiEjQ+m6UBKBGzYZNcocur15s5k/fsUV5d52663S2rVmufeNN5qdy9u183KtAAAAHlLhwP3II4+osLBQKSkpuuyyy5SQkODJugBURYkp5ZzBDVvUqmWGrv/7X+mFF84YuCXpueekX36RliwxI9w//CDVreu9UgEAADzFpcWdwcHBGjdunE6ePOmpegC4wfEdB3RI9SQxwg0b3XmnmUq+eLG0bt0Zb3Ou405NNTuXDx8uccokAACoDlzeTaldu3batm2bJ2oB4CY7t+VLkqIi8sRehrBNcrJ01VWm/eKLZ721bl1zXFiNGtKiRdJTT3mhPgAAAA9zOXD/61//0v3336+PPvpIe/bsUXZ2dqkXAPul7zarRRLr56mcQwUA73EeETZ1qnTgwFlvbd9eevVV0370UWnZMo9WBgAA4HEuB+6BAwdq7dq1GjJkiBo3bqzatWurdu3aio2NVe3atT1RIwAXpe+PkMR0cviACy+UunaVcnOl118/5+0332z2Wysqkm644ZwZHQAAwKdVeNM0pyVLlniiDgDucvSodubGSZKSmoXbXAwCnsNhRrlHjpRefln629+k8LP/d/nyy9L330sbNpgA/vHH5Z4qBgAA4PNcDtx9+vTxRB0A3KXEDuVJTUNtLgaQdM01Jmjv3i3NmiXddNNZb69Z09x2wQXSp59K//mP9OCDXqoVAADAjSo1ZpCZmalnn31Wt956q2699VY9//zzysrKcndtACqDI8Hga8LCpDvuMO0XXpAs65zf0r699P/+n2n//e/mrG4AAAB/43LgXrVqlZo2barnn39ehw8f1uHDh/Xcc8+padOmWr16tSdqBOCKkiPcrOGGr7j9djOVfPVqacWKCn3LLbeYTc4LCqTRo6X8fM+WCAAA4G4uB+57771XQ4YM0fbt2zVnzhzNmTNHaWlpuuKKK3SPczdaALaxdmVop8zQNoEbPqNePWnECNN++eUKfYvDIb3yijkybM0aacIEz5UHAADgCZUa4X7wwQcVEnJ6+XdISIgeeOABrVq1yq3FAXDdwa1ZOqlIOVSkRo3srgYowTmtfNYsad++Cn1L/frSSy+Z9hNPMLUcAAD4F5cDd3R0tNLT08u8v3PnTkVFRbmlKACVl77NzLutH33iXJtBA97VpYvUrZuZG/7mmxX+tuuvl4YNM1PLx4xhajkAAPAfLgfu66+/XrfccotmzpypnTt3aufOnZoxY4ZuvfVWjXBOFwRgm/RdwZKkpPq5NlcClMM5yv3aayZBV4BzanmdOtJPP0lPP+3B+gAAANzI5cD93//+V1dddZVuvvlmpaSkKCUlRaNHj9Y111yjZ555xhM1AnDBzgMRkqQkdiiHL7r2WikuTtq1S5o/v8Lf1qDB6V3Ln3hC2rjRQ/UBAAC4kcuBOywsTC+++KKOHDmiNWvWaM2aNTp8+LCef/55hTN/FbBXYaHSs2MlSUnNwuytBShPRIR0662m7VycXUEjRkgDB5op5XffXaHTxQAAAGxVqXO4JalGjRqKjY1VbGysatSo4c6aAFTWvn1KtxpLkhJbRNpcDHAGf/yjFBQkLVki/fJLhb/N4TDHeIeFSZ995tIAOQAAgC1cDtwFBQX6xz/+oZiYmOIp5TExMfr73/+ufHayAexV8gzu1GCbiwHOIClJGjLEtF95xaVvbd5cuu8+0773XunECTfXBgAA4EYuB+6//OUvmjhxov7973/rp59+0k8//aR///vfeuutt3TXXXd5okYAFZXBGdzwE3feaa7vvisdPerStz70kNSokZSWJv33vx6oDQAAwE0cluXaKriYmBjNmDFDAwYMKPX+J598ohEjRigrK8utBZ5Ndna2YmJilJWVpejoaK89F/BVeS++qoh7xslSkPbtk+Lj7a4IOAPLklq2lDZvll5/Xbr9dpe+feZMafhwsyR8wwYpOdlDdQIAAPyOKznU5RHu8PBwpaSklHk/NTVVYWFs0gTYKWPjMVkKUnhwvuLi7K4GOAuH43TIfv11l7/9uuukiy+WTp48PcUcAADA17gcuO+880498cQTys09fcZvbm6unnzySd3pnCIIwBbpW80+ComxR+Vw2FwMcC6jR5sd0Favln780aVvdTik//1PCg6WPvhA+vprz5QIAABQFS4H7p9++kkfffSRGjdurH79+qlfv35q3LixFixYoLVr1+qqq64qfgHwrvRd5kc6uX7uOe4EfEC9etLVV5t2JUa527c/fcLYQw9xTBgAAPA9Ia5+Q2xsrK52/gXplMTERLcVBKDy0vdHSJKSEkke8BPjxknvvSdNn252QHNxP45//EN65x3pm2+khQvNOd0AAAC+wuXAPWnSJE/UAcAN0rNMWElqxn4K8BO9e0utWpmdz6ZPN2d0u6BRI+kvf5H+8x8zyn355eaIbwAAAF9Q6b+WHDhwQN98842++eYbHThwwJ01AaiM7Gyl5ydIkpLa1LS5GKCCfr95WiXmhT/4oBkYX7tWev99N9cHAABQBS4H7pycHI0dO1YJCQnq3bu3evfurYYNG+qWW27R8ePHPVEjgIrIyFC6zOHbSS0ibS4GcMGoUVJ4uLRmjbRypcvfXreudP/9pv2Pf0j5+e4tDwAAoLJcDtx//etftWzZMi1YsECZmZnKzMzUvHnztGzZMt3H2SyAbaydu7RD5jDipCSbiwFcUaeOOedLqtTmaZJ0zz1SXJw51nvyZLdVBgAAUCUuB+4PPvhAb731lgYMGKDo6GhFR0dr4MCBeuONNzR79mxP1AigAo5s3K8c1ZIksY8h/M64ceY6Y4Z09KjL3x4VJT38sGk/9ph04oQbawMAAKgklwP38ePHVb9+/TLvx8fHM6UcsFH6bzmSpLiIbEUyoxz+5sILpZYtpePHpVmzKtXFuHHmH5syMqS333ZzfQAAAJXgcuDu0aOHHnnkEZ08ebL4vRMnTuixxx5Tjx493FocgIpL32oWribVPmZzJUAlOBzS2LGmXcm0HBFhNlCTzK7lrOUGAAB2czlwv/DCC1q+fLkaN26svn37qm/fvkpMTNS3336rF1980RM1AqiA9F3mxzk5IdfmSoBKuukmKThY+vZbc0xYJYwdK8XHSzt2mNnpAAAAdnI5cLdv316bN2/WhAkT1LFjR3Xs2FFPP/20Nm/erLZt23qiRgAVkH4gQpKUlOSwuRKgkhISpIEDTbuSo9yRkdK995r2009LRUVuqg0AAKASQly5OT8/X61atdJHH32k2267zVM1AaiE9KwYSVJS83CbKwGqYOxYacEC6d13pSeflEJDXe7iT3+SJkyQfv1Vmj9fGjrU/WUCAABUhEsj3KGhoaXWbgPwEcePKz2vgSQpqV20zcUAVTBokJkTvm+ftHBhpbqIiZHuuMO0J0yQLMuN9QEAALjA5Snld9xxh5555hkVFBR4oh4AlZGRcfoM7lY1bC4GqILQUOnmm027CluN33OP2UTthx+kJUvcUxoAAICrHJbl2r/9Dxs2TIsXL1atWrXUvn171axZs9Tnc+bMcWuBZ5Odna2YmBhlZWUpOppRPQSuvM+XKqJ/b1kK0t69Ujkn9wH+49dfpbZtzQZqu3ZJDRpUqpu//EV66SWpb19p0SI31wgAAAKWKznU5RHu2NhYXX311erfv78aNmyomJiYUi8A3pex/ogsBSk8KE9xcXZXA1RRmzZS9+5SYaE0dWqlu7n/fikkRFq8WFq50o31AQAAVJBLm6ZJ0qRJkzxRB4AqSP8tR5KUWPOwgoIqNxoI+JSxY6UVK8y08vvuM+d0uyg5WRoxQpoyRXrhBWnaNPeXCQAAcDYVHuEuKirSM888o549e6pr1676v//7P504ccKTtQGooPS0QklSct0cmysB3OS668wi7N9+k1avrnQ3d99trrNmSbt3u6k2AACACqpw4H7yySf10EMPqVatWmrUqJFefPFF3eHcBhaArdIzgiVJSQn5NlcCuElMjHTllab97ruV7qZzZ6lnT6mgQHrtNTfVBgAAUEEVDtzvvvuuXnnlFX322Wf68MMPtWDBAk2bNk1FRUWerA9ABaQfjJQkJaW4vC0D4Ltuuslc33tPyq/8PyY5R7lfe03iZEsAAOBNFf7beXp6ugYOHFj8db9+/eRwOLSbOXqA7dKzYyVJSS0i7C0EcKfLLpPi4qQDB6TPP690N8OGSY0bm25mznRjfQAAAOdQ4cBdUFCgiIjSf5kPDQ1VfhVGHQC4QW6u0vPMRmlJ7TkpANVIaKh0ww2mXYVp5SEhknMF1IsvSq4dhgkAAFB5FT6HOygoSAMGDFB4eHjxewsWLNAll1xS6ixuzuEGvMvalqaopnHKUS1t3GCpRUvXd3MGfNaPP0pdukjh4dK+fWZtdyUcOiQlJkonTkhffSX16uXmOgEAQMDwyDnco0aNUnx8fKkzt2+88cYyZ3ED8K4jv+1VjmpJkhKTCNuoZs4/X2rdWsrNlWbPrnQ3detKN95o2v/7n5tqAwAAOIcKj3D7Ika4AWnNhIXq9NAAxYUe0f682naXA7jf009L48dLffpIS5dWupv166X27aXgYGnbNikpyX0lAgCAwOGREW4Avil94wlJUlLUEZsrATxk5EjJ4ZCWLZO2b690N+3aSZdcIhUWckQYAADwDgI34OfSt5uj+ZLrHbe5EsBDEhOliy827WnTqtTVn/9srpMmVemkMQAAgAohcAN+Ln13iCQpqWGBzZUAHuQ8k3vKlCptMz5kiNSggbR3rzR/vptqAwAAOAMCN+Dn0g+ZUwKSUoNtrgTwoKuvNjuVb9worV1b6W5CQ6WxY0379dfdVBsAAMAZELgBP5d+1GyUltQq0uZKAA+KjpYGDTLt996rUle33mqWhH/xhdk8DQAAwFMI3IA/y8/XjvwESVLSebH21gJ42ogR5jpjhlRUVOluUlOlyy4z7TfecENdAAAAZ0DgBvxYXvpe7dGpwN2hjs3VAB42aJAUFSWlp0vffVelrsaNM9e335by8txQGwAAQDkI3IAfy1hzQJaCFK5cxdXnxxnVXGSkNHSoac+YUaWurrjCbJ62f780b17VSwMAACgPf0MH/Fj6+ixJUmLEfgXx04xA4JxWPmuWVFD5nflDQ6VbbjHtiRPdUBcAAEA5+Cs64MfSN+VKkpKis2yuBPCSfv2kunXN0PSSJVXqyrl52qJF0pYtbqoPAACgBAI34MfSd5iNo5LjjttcCeAloaHSNdeYdhWnlaekSP37m/abb1atLAAAgPIQuAE/lr4nTJKU1LjyOzYDfsc5rfyDD6Tc3Cp1deut5vruu1WaoQ4AAFAuAjfgx9IP15QkJTUJsbkSwIt69ZIaNpSysqRPP61SV4MHmxnqe/ZIn3/upvoAAABOIXADfiw9p64kKalVDZsrAbwoKEi6/nrTfu+9KnUVFiaNHGnakyZVsS4AAIDfIXADfsoqKNSO/IaSpKROdW2uBvAy57TyBQuk41Xbw2DsWHOdP186dKiKdQEAAJRA4Ab81JHf9ipHtSRJiZ3q2VwN4GVduphdz44flxYurFJXHTpInTpJeXnS9OnuKQ8AAEAicAN+K331QUlSXNBBRdYKtrkawMscjtO7lb//fpW7GzPGXJlWDgAA3InADfip9F+OSpKSahy0uRLAJtdea64ffSSdOFGlrm64wazn/uknae1aN9QGAAAgAjfgt9I3m+OQkmOzba4EsEnXrlJSkpSTU+XdyuvWlYYMMe3Jk6teGgAAgETgBvxWerq5JtWv2jnEgN/y0LTyqVPNem4AAICqInADfip9X7gkM8AHBCzntPIFC6o8rfyyy6SEBOngQenjj91QGwAACHgEbsBPpWdGS5KSWkTYXAlgo27dpMRE6dgx6bPPqtRVSIh0882mzbRyAADgDrYG7ldffVXnnXeeoqOjFR0drR49emhhFY93AQLFjhNxkqSkdtE2VwLYqOS08tmzq9ydM3AvXMiZ3AAAoOpsDdyNGzfW008/rR9//FGrVq3SJZdcoiuvvFK//PKLnWUBPi/vaK72FNWXJCWdzxncCHDOaeXz50snT1apqzZtzJnc+fluWRYOAAACnK2Be/DgwRo4cKCaN2+uFi1a6Mknn1StWrW0YsUKO8sCfF7Gj3tlKUjhOqm4lnXsLgewV7duUuPG0tGj0uefV7m7G28016lTq9wVAAAIcD6zhruwsFAzZsxQTk6OevToUe49ubm5ys7OLvUCAlH6msOSpMTQvQoKdthcDWCzoCDp6qtN2w3D0sOHmy6XL5e2batydwAAIIDZHrjXrVunWrVqKTw8XH/84x81d+5ctWnTptx7J0yYoJiYmOJXYmKil6sFfEP6hhxJUlKtwzZXAviIkruVV/FMr4YNpb59TXv69CrWBQAAAprtgbtly5Zas2aNvv/+e/3pT3/SqFGj9Ouvv5Z77/jx45WVlVX82rlzp5erBXxD+tYCSVJy3RybKwF8RI8eUoMGUlaWtGRJlbsrOa3csqrcHQAACFC2B+6wsDA1a9ZMnTt31oQJE9ShQwe9+OKL5d4bHh5evKO58wUEovQM86Ob1LDA5koAHxEUJA0datpz5lS5u2HDpMhIaeNG6ccfq9wdAAAIULYH7t8rKipSbm6u3WUAPi39YA1JUlJqsM2VAD5k2DBznTdPKiysUldRUafzO5unAQCAyrI1cI8fP15fffWVtm/frnXr1mn8+PFaunSpRo4caWdZgM9Lz46VJCW1jLS3EMCXXHyxFBsr7dsnffddlbtzTit/7z2pgMkkAACgEmwN3Pv379fNN9+sli1bqm/fvlq5cqU+++wzXXrppXaWBfg0y5J25DaQJCV1qG1zNYAPCQuTrrjCtOfOrXJ3l14qxcVJ+/dLixZVuTsAABCAbA3cb731lrZv367c3Fzt379fixYtImwD53Ak/ahyVEuSlNilvs3VAD7mqqvMdc6cKu92FhpqjgiTmFYOAAAqx+fWcAM4u/RV+yVJcY4DioyPsrkawMf07292O9u+XVq7tsrdOaeVz50rHTtW5e4AAECAIXADfiZ9XaYkKSliv72FAL6oRg3p8stN2w27lXftKjVvLh0/7pZZ6gAAIMAQuAE/k77xpCQpKTrL5koAH+WcVu6GhOxwlD6TGwAAwBUEbsDPpO8w61KT40/YXAngowYNkkJCpPXrpU2bqtyd8+CMRYukPXuq3B0AAAggBG7Az6TvCZUkJSUW2VwJ4KNq15YuucS03TDK3bSp1KOHVFQkzZhR5e4AAEAAIXADfib9sNmhPKlpmM2VAD5s2DBzddPCa6aVAwCAyiBwA35mR049SVJSm1o2VwL4sCuvNNfvv5f27q1yd9ddZ2apr14t/fprlbsDAAABgsAN+JG8XEt7CuMkSUmd6tpcDeDDEhKkCy4w7Y8+qnJ39epJAwaY9rRpVe4OAAAECAI34Ecy1h+RpSCF66TizkuwuxzAtw0ZYq7z57ulO+e08mnTzHpuAACAcyFwA34kffVBSVJi8G4FRYbbXA3g45yB+4svzEHaVTR4sBQVJe3YIS1fXuXuAABAACBwA35kxy/HJElJNQ7ZXAngB9q1k1JSpJMnzZleVRQZKV1zjWlPmVLl7gAAQAAgcAN+ZMeWPElSSp0smysB/IDD4bFp5bNnS7m5bukSAABUYwRuwI9sTw+WJKUk5NlcCeAnnIF7wQK3LLzu00dq2FA6ckT69NMqdwcAAKo5AjfgR7bvi5QkpaTaXAjgL3r3lmJipP37pR9+qHJ3wcHSiBGmzW7lAADgXAjcgB/Znl1bkpTcIsLmSgA/ERp6+jyvefPc0uUNN5jrggVSdrZbugQAANUUgRvwE4WF0s6T5gzulA4xNlcD+BE3r+Pu1Elq1crsxTZ3rlu6BAAA1RSBG/ATe3YWKF9hClG+GnbmDG6gwgYMkEJCpF9/lbZsqXJ3Doc0cqRpM60cAACcDYEb8BPbV506g1u7FNK4gc3VAH4kNtbsdiaZeeBu4FzHvXixtHevW7oEAADVEIEb8BM71mZKkpIj9klB/OgCLnHztPKmTaXu3c3G5zNnuqVLAABQDfG3dsBPbN9wQpKUEptpbyGAPxo82Fy//lo6fNgtXTo3T2NaOQAAOBMCN+AntqeZa0qDk/YWAvij1FSpfXuz++DChW7p8vrrzTFhK1dKmze7pUsAAFDNELgBP7F9T7gkKSXF3joAv+XmaeXx8dKll5r29Olu6RIAAFQzBG7AT+zIjJYkJbcIt7kSwE85A/fChVJenlu6LDmt3LLc0iUAAKhGCNyAHygqknaciJfEGdxApXXpIjVoIB09Ki1b5pYuhw6VIiPNlPIff3RLlwAAoBohcAN+YO8eS3lWmIJVoMad69tdDuCfgoJOb57mpmnlUVHSlVeaNpunAQCA3yNwA35gx5ojkqRGylBIaqLN1QB+rOQ6bjfNAXdOK58xw+zJBgAA4ETgBvzA9tXmGKOUsN1SWJjN1QB+rG9fMwc8PV1au9YtXfbvL9WpI+3dKy1Z4pYuAQBANUHgBvzA9t9OncEdc8TmSgA/FxkpXXaZabtpWnlYmHTddabNtHIAAFASgRvwA9vTiiRJKfEnbK4EqAaci67dFLil09PKP/hAOsGPKQAAOIXADfiBHbvNNPLkJM4dAqps0CDJ4TDbiu/a5ZYue/aUkpLMBugff+yWLgEAQDVA4Ab8wPbDUZKklBas3waqLD5e6tHDtD/6yC1dBgVJI0aYNtPKAQCAE4Eb8HGWJe3IqSdJSmkfZXM1QDXh3K183jy3dTlypLl+8ol0hO0WAACACNyAz9u/XzppRShIhZzBDbiL8zzuJUuknBy3dNm+vXnl5Zm13AAAAARuwMdt/8WEgYbarbBmSTZXA1QTrVtLTZpIubnSokVu69a5eRrTygEAgETgBnze9h8PSZJSQnZJtWrZXA1QTTgc0hVXmLab1nFLpwP3smXSzp1u6xYAAPgpAjfg43b8aka4U6IO21wJUM04A/fHH0tFRW7pMilJ6t3b7L3AKDcAACBwAz5u+9YCSVJy3HGbKwGqmd69zayRPXukn35yW7c33WSuU6aY4A0AAAIXgRvwcdt3hUqSUhILba4EqGbCw6XLLjPtBQvc1u0115iuf/3VrTkeAAD4IQI34OO2HzLrtlOah9pcCVANOXcrd+M67tjY06eOTZnitm4BAIAfInADPsyypB3H6kqSUtqxYRrgdgMGmA3UfvxR2r3bbd3efLO5vveeVFDgtm4BAICfIXADPuzgQel4UaQkKfH8OJurAaqh+vWlCy4w7U8+cVu3/ftLcXHSvn3SF1+4rVsAAOBnCNyAD9u+MVeS1FAZCm/OGdyARzh3K3fjOu7QUGn4cNNmWjkAAIGLwA34sB0/maPAUoLSpbp1ba4GqKacgXvRIunECbd169yt/MMPpaNH3dYtAADwIwRuwIelrTsmSUqudcisMwXgfh06SI0bS8ePS0uXuq3bLl2kli1Nhv/gA7d1CwAA/AiBG/Bh2zaZ3Zaa1GN4DPAYh+P0KLcbdyt3OEqfyQ0AAAIPgRvwYWm7QiRJTRLzba4EqOZKBm7Lclu3N95orkuWSDt3uq1bAADgJwjcgA/bdiBKkpTaLNjmSoBq7pJLpMhIKT1dWrfObd0mJ0sXX2wy/Lvvuq1bAADgJwjcgI8qLJR2HK0jSWrSvqbN1QDVXGSk1LevabtxWrkkjR5trpMnu3XwHAAA+AECN+Cjdu+W8qwwhShfjTvWs7scoPobPNhc3Ry4r75aqllT2rJF+vZbt3YNAAB8HIEb8FFpW8yGacnaoeBmqTZXAwSAQYPMdcUK6cABt3Vbq5Z03XWmPWmS27oFAAB+gMAN+Khtq45Ikpo4tksJCfYWAwSCRo2kTp3MvO+FC93atXNa+axZUk6OW7sGAAA+jMAN+CjnGdyp0YekIH5UAa9w7la+YIFbu73oIqlJE+noUWnuXLd2DQAAfBh/iwd81LbNhZKkJvUZDgO8xhm4P/tMystzW7dBQdKoUabNtHIAAAIHgRvwUdt2hUqSUpOLbK4ECCBdukj165uh6K+/dmvXzsD95ZfSjh1u7RoAAPgoAjfgo9IORUuSmrQKs7kSIIAEBZ3ePM3Nu5UnJ5vjviXO5AYAIFAQuAEfdOKEtOdEbUlSk04xNlcDBJiS67jdfHB2yTO5i5i8AgBAtUfgBnzQ9u3mGq0s1T4v0dZagIDTr58UFiZt3Spt3OjWrq++WoqKkrZtk776yq1dAwAAH0TgBnzQtt9OSpKaaJscTTiDG/CqqCjp4otN283TymvUkIYPN+0333Rr1wAAwAcRuAEflPajOYM7NWSXVLu2zdUAAWjwYHN1c+CWpNtuM9fZs6XDh93ePQAA8CEEbsAHbfvlhCSpSe0jNlcCBCjnxmnffCMdce/PYZcuUocOUm6uNHWqW7sGAAA+hsAN+KC0NLNRU2rDkzZXAgSo1FSpbVupsFD69FO3du1wnB7lfuMNt+/LBgAAfAiBG/BB23ZHSpKaNOVHFLCNc7dyD0wrv+EGKSJCWr9e+uEHt3cPAAB8BH+bB3yMZUlpmbGSpNQ2kfYWAwQyZ+BeuFAqKHBr17VrS9dea9psngYAQPVF4AZ8zKFD0tGCGpKklM51ba4GCGA9ekh16pg13N995/bundPK33tPOnrU7d0DAAAfQOAGfMy2rWZBZyPtUkTLZJurAQJYcLA0cKBpL1jg9u4vukhq2VLKyZFmznR79wAAwAcQuAEfk7bumCQpVWlSSoq9xQCBzoPruB0O6dZbTfuNN9zePQAA8AEEbsDHbPspS5LUJHKPFMkabsBW/fubke7ffpO2bnV79zffLIWGmo3T1q51e/cAAMBmBG7Ax6RtzJMkpdZlUSdgu9hYqVcv0/74Y7d3Hx8vDRtm2q++6vbuAQCAzQjcgI/ZtsP8WDZJzLe5EgCSpMGDzdUD08ol6c9/NtepU6WsLI88AgAA2ITADfiYtP01JUmpzUNsrgSApNPruJculbKz3d59795SmzZm87QpU9zePQAAsBGBG/AhBQXSjqN1JElNzqtlczUAJEktWkjNm0v5+dIXX7i9e4fj9Cj3K69IluX2RwAAAJsQuAEfsmuXVGgFK1wnldCpgd3lAHDy4G7lknTTTVLNmmZvtmXLPPIIAABgAwI34EO2bSmSJKVou4KaptpcDYBizsD98cdSUZHbu4+ONqFbMqPcAACgeiBwAz5k20+ZkqQmjjSpcWN7iwFwWq9eJhUfOCCtXOmRR/zpT+Y6d660e7dHHgEAALyMwA34kG0/H5MkpUYdNGf/AvANoaHS5Zeb9oIFHnnEeedJF11k9nJ44w2PPAIAAHgZgRvwIVs2mqmqzeofs7kSAGV4eB23dHrztIkTzR5tAADAvxG4AR+yZWe4JKlZaqHNlQAoY8AAs6X42rXSzp0eecRVV0nx8WZK+YcfeuQRAADAiwjcgI+wLGnLoVhJUrM2YfYWA6CsevWkHj1M++OPPfKI8HDp9ttN+8UXPfIIAADgRQRuwEccOCAdzY+UQ0VK7RRrdzkAyjN4sLl6aB23ZKaVh4ZKy5d7bH82AADgJQRuwEds2WKuidqpiJbJ9hYDoHzOddyLF0s5OR55REKCNHy4ab/wgkceAQAAvITADfiILb/kSpKaaYvUvLnN1QAoV9u2UnKylJsrffmlxx5zzz3mOmuWlJHhsccAAAAPI3ADPmLLj1mSpOZh6VKdOjZXA6BcDodXdis//3ypd29zRNjLL3vsMQAAwMMI3ICP2PLrqRHu+GybKwFwViUDt2V57DHOUe7XX5eOH/fYYwAAgAcRuAEfsWV7iCSpWUqBzZUAOKuLL5Zq1jRnd/30k8ceM2SIlJoqHT4sTZnisccAAAAPInADPmLL/mhJHAkG+LyICOnSS03bg9PKg4Olu+4y7RdekIqKPPYoAADgIQRuwAccPiwdya0pSWrSubbN1QA4J+e0cg8eDyZJY8dKUVHShg3S55979FEAAMADCNyAD3AeCdZIu1Sjbaq9xQA4t0GDzAZqq1Z5dBvx6GjplltM+9//9thjAACAh9gauCdMmKCuXbsqKipK8fHxGjp0qDZu3GhnSYAttvyWL+nUkWDNmtlcDYBzatBA6tbNtOfP9+ij7r1XCgmRliyRVq706KMAAICb2Rq4ly1bpjvuuEMrVqzQF198ofz8fF122WXKycmxsyzA67asypQkNQvZIcXH21sMgIq58kpz9XDgTkqSbrjBtJ95xqOPAgAAbmZr4P700081evRotW3bVh06dNDkyZOVnp6uH3/80c6yAK/b8supI8Hissw0VQC+zxm4v/xSOnrUo4964AFznTNHYiIYAAD+w6fWcGdlZUmS6tSpU+7nubm5ys7OLvUCqoPN24IlSc2S822uBECFtWolNW8u5eVJn37q0Ue1bSsNHmyO/f7vfz36KAAA4EY+E7iLiop0zz33qGfPnmrXrl2590yYMEExMTHFr8TERC9XCXjGln21JEnNWofaXAmACnM4To9yz5vn8cc9+KC5vvuuOQIcAAD4Pp8J3HfccYfWr1+vGTNmnPGe8ePHKysrq/i1c+dOL1YIeEZmpnTwZJQkqWnnWFtrAeAiZ+D++GMp37MzVHr2NK+8PHMuNwAA8H0+EbjvvPNOffTRR1qyZIkaN258xvvCw8MVHR1d6gX4u61bzbW+9iqqXbK9xQBwTY8eUr165l/Ovv7a44/7v/8z19deM48EAAC+zdbAbVmW7rzzTs2dO1dffvmlUlM5fxiBZ8uGAkkcCQb4peBg6YorTNsL08oHDjTruY8elV55xeOPAwAAVWRr4L7jjjs0depUTZ8+XVFRUdq7d6/27t2rEydO2FkW4FVbfjSbBTYLTpMSEmyuBoDLSq7jtiyPPioo6PQo93PPSceOefRxAACgimwN3K+++qqysrJ08cUXKyEhofg1c+ZMO8sCvGrLevMPTM3qZpq/TQPwL5deKkVESDt2SOvWefxxw4ebyTCHDjHKDQCAr7N9Snl5r9GjR9tZFuBVW7aaH8NmSXk2VwKgUmrWNKFb8sq08pAQ6eGHTfu//5Vycjz+SAAAUEkMpwE227K3piSpWasQmysBUGnOaeUffuiVx40cKTVpIh04YDZQAwAAvonADdjo2DFp7/EYSVLTTuy6D/itwYPNkpDVq83Ucg8LDZUeesi0//Mf6fhxjz8SAABUAoEbsJHzSLC6OqjaHZLsLQZA5cXHSxddZNpeGuW++WYpOVnat0+aONErjwQAAC4icAM22rKpUBJHggHVwlVXmeucOV55XMlR7meekTjgAwAA30PgBmxUfCRY0DapcWObqwFQJUOHmus330j793vlkaNHS4mJ0t690ptveuWRAADABQRuwEab1546Eqz2YSk42OZqAFRJcrJ0/vlSUZE0f75XHhkWdnqU+6mnWMsNAICvIXADNtqw2fwItkxiLihQLTinlc+d67VHjh0rpaSYUe6XXvLaYwEAQAUQuAEbbdwdJUlq2ZofRaBaGDbMXBctkrKzvfLIsDDp0UdN++mnpawsrzwWAABUAH/LB2xy6JB08EQtSVKLzlE2VwPALVq3llq2lPLypE8+8dpjb7zRPPrIEem557z2WAAAcA4EbsAmGzeaa2PtVK02HAkGVAsOx+lRbi9OKw8Olh5/3LSfe046cMBrjwYAAGdB4AZssvG3IklSK20wI2IAqgfnOu5PPpFOnvTqY88/Xzp2zBwTBgAA7EfgBmyy4Qez0LJl0BYpiRFuoNro0sUc83fsmFnL7SVBQdK//mXaL70kZWR47dEAAOAMCNyATTb+nCtJalk/kyPBgOqk5LTyOXO8+ujLL5cuukjKzZWeeMKrjwYAAOUgcAM22bgtVJLUqlmBzZUAcDvntPJ586T8fK891uEw53FL0ptvShs2eO3RAACgHARuwAb5+dKWA7GSpJYdI+0tBoD79eolxcdLhw9LS5Z4/dGDB0uFhdL48V59NAAA+B0CN2CDtDSpwApWpI6rcZcGdpcDwN2Cg0+Pcr//vtcf//TTZk33hx9Ky5d7/fEAAOAUAjdgA+c0z5baqKA2rewtBoBnXHONuc6d69Vp5ZLUpo10yy2m/be/SZbl1ccDAIBTCNyADTauOSHJBG61aGFzNQA8ok8fqV496dAhaelSrz/+0UelGjWk777z6pHgAACgBAI3YIONq45KklrVypCio22uBoBHhITYOq28YUPpvvtM+//+z+uD7AAAQARu4P+3d9/hUVVbH8e/k54ASZASehNCL6Gj0hQEBAs2VK6CXrvYr+3qK2ADbFdRxC42UFFBRUEBQQEBAQm9S1V6SwKBlDnvH8s0aQlmcmaS3+d5zjNnZk4yK+QwmXX23mu5YvVqm99Zv0aqy5GIiE9dcYXdTpgAGUXfkeCBB6BCBVi3Dt58s8hfXkREpMRTwi3igjXbSgNQv5H6b4sUa126QLlysGcP/PRTkb98mTI2tRzs9sCBIg9BRESkRFPCLVLE9u6FPamlAIhvE+NyNCLiUyEh0Lev7bswrRzgppugYUPL+Z94wpUQRERESiwl3CJFbM0au63GVko3P9PdYETE97KmlX/5pSvTykND4X//s/1XXsl5DxIRERHfU8ItUsTWrMwEoAGroYFagokUe127whlnwO7d8PPProTQowf06WP5/n33uRKCiIhIiaSEW6SIrf41CYD6wRugenWXoxERnwsNdX1aOcALL1go330Hkye7FoaIiEiJooRbpIitWXoUgPpxByBI/wVFSoSsaeVffOHKtHKA+Hi46y7bv/detQkTEREpCvq0L1LE1mwMBaBBXXc+dIuIC849F8qXt2nl06e7Fsb//Z+1CVuzBkaNci0MERGREkMJt0gRSk+H9butMnn9hCiXoxGRIhMamjPKPW6ca2HExMDTT9v+kCGW/4uIiIjvKOEWKUIbN0KGE0Ikh6nWupLb4YhIUbr6arudMAGOHHEtjBtugObN4eBBePxx18IQEREpEZRwixSh1avttj5rCGqkCuUiJcrZZ0O1apCUZJXLXBIcDC+/bPtvvglLl7oWioiISLGnhFukCK1ZfBiwhJv4eJejEZEiFRQEV11l+y5OKwfo3NlmuHu9cM894DiuhiMiIlJsKeEWKUJrFqUA0KDMn1C6tMvRiEiRu+Yau500yUa6XfTssxAeDjNm2Cx3ERERKXxKuEWK0MqVdtugxmF3AxERd7RoAfXr2xrur75yNZRateCBB2z/P/9xdVm5iIhIsaWEW6SIOA6s2BYNQJOmHpejERFXeDw5xdNcnlYO8NBDUKWKFXT83//cjkZERKT4UcItUkS2bYOkoxGEkE5821i3wxERt2Ql3FOnwp49roZSujSMGGH7Tz9t71MiIiJSeJRwixSR5cvtNp61hDWu524wIuKe+Hho2RIyMuDzz92OhmuugQ4d4NAhm1ouIiIihUcJt0gRWZ6YAUATlkPjxi5HIyKuyhrl/vhjd+PAiqePGmW3n34K06e7HZGIiEjxoYRbpIismGcViZtErLdFkyJScl19ta3nnj0bfv/d7WhISIDbb7f9QYMgLc3deERERIoLJdwiRWT5Mmt027jWYfugLSIlV9Wq0K2b7X/0kbux/OXJJ6FiRVi9Gl56ye1oREREigcl3CJFwOuFlVvLANCkRYjL0YiIX7juOrv94ANrY+Cy2Fh47jnbf+IJ2LrV1XBERESKBSXcIkVg40ZIzQgjnCOceVac2+GIiD/o2xdKlYING2DuXLejAeDaa+Gcc6yA2v33ux2NiIhI4FPCLVIEsiqUN2QVwc1UME1EsGT78stt/4MP3I3lLx6PFVALDobx461zmYiIiJw+JdwiRWD5b1aBSBXKRSSPrGnln34KR464G8tfmjWzwmlgt0ePuhuPiIhIIFPCLVIEVsxLBqBJ6c1QvrzL0YiI3+jSBapXhwMHYNIkt6PJNnQoxMXB2rXwv/+5HY2IiEjgUsItUgSWr7Sq5I3PTHU5EhHxK0FB8K9/2f7777sbSy4xMfD887b/5JOwZYu78YiIiAQqJdwiPpaeDqv/jAagSctwl6MREb+TNa188mTYtcvdWHLp3x86doTDh+G++9yORkREJDAp4RbxsfXrId0bQmmSqdG+itvhiIi/adAA2raFzEwYN87taLLlLqD2xRfw/fduRyQiIhJ4lHCL+FhWhfJGrCSoWRN3gxER/5Q1yv3ee37RkztL06Zw1122f+edKqAmIiJSUEq4RXxs+QJbt92E5dCokcvRiIhfuvpqCA+HJUvgt9/cjiaPIUOgcmVYtw6efdbtaERERAKLEm4RH1vx6yEAmpT9E6KjXY5GRPzSGWfAZZfZ/ttvuxvL30RH51Qqf/ppWyYjIiIi+aOEW8THlq8OBqBxXc3FFJGTuPFGux07Fg4dcjeWv7nySjj/fJtSfscdfjXrXURExK8p4RbxoSNHYN2uGACatI5wORoR8WudO8OZZ0JSEnz+udvR5JFVQC08HH74AT77zO2IREREAoMSbhEfWrMGvE4QZdlH5fY13Q5HRPxZUBD8+9+2/9Zb7sZyHHXrwqOP2v4998DBg66GIyIiEhCUcIv40PJlNu+yCcvxNFWFchE5hYEDrQ/XnDmwapXb0RzjwQchPh527IDHHnM7GhEREf+nhFvEh5bPt3WYjVlpvXZFRE6mcmXo08f233nH3ViOIzwcRo+2/VGjYMECd+MRERHxd0q4RXxoxcK/WoJV3AmRkS5HIyIBIat42vvvQ1qau7Ecx7nnwr/+ZYXTbrkFMjLcjkhERMR/KeEW8aHE1eEANG2gT6Qikk89e0KVKrBnD3z9tdvRHNfzz0NsLCxeDK+95nY0IiIi/ksJt4iP7N0LWw9Y3+0W7VWhXETyKSQErr/e9t94w91YTiAuDoYPt/3HHoM//nA3HhEREX+lhFvERxYvttszWU90q3ruBiMigeXGG60X17RpsHat29Ec1003Qfv2kJwM997rdjQiIiL+SQm3iI8sXuQFIIHF0LSpy9GISECpVSuneJqfztkOCoLXX7ei6uPHw+TJbkckIiLif5Rwi/jI4lnJACSELrc+OiIiBXHHHXb73nuQkuJuLCfQvDncfbft33EHpKa6G4+IiIi/UcIt4iNZU8oTzky2ISARkYLo3h3q1YOkJPj4Y7ejOaEhQ6BaNdi4EZ5+2u1oRERE/IsSbhEfOHQI1vxZBoCE9uEuRyMiASkoCG6/3fZffdX6cPmhMmVg5Ejbf/ZZWLXK3XhERET8iRJuER9YuhQcgqjEdiqdU9ftcEQkUA0cCFFRsHw5zJrldjQndMkltuQ8PR1uu81vrw2IiIgUOSXcIj6w+Df7tJnAYkhIcDkaEQlYsbHQv7/tjxrlaign4/HAK69AZCT89BN8+KHbEYmIiPgHJdwiPvDbLCtwlOBZAo0buxyNiAS0rOJpX34Jf/7pbiwnUasWDB5s+/ffD/v2uRqOiIiIX1DCLeIDixdkAJBQYy+Eaw23iPwDzZvDOedARga88Ybb0ZzUfffZNcY9e+Dhh92ORkRExH1KuEUKWXo6LN/8V8G01qpOLiKFYNAgu339dThyxN1YTiI0FEaPtv233oI5c9yNR0RExG1KuEUK2cqVkJYZQgwHqNOpmtvhiEhxcOmlUKMG7NoFH33kdjQn1bEj3HCD7d98M6SluRuPiIiIm5RwixSyrP7bLUjE01IF00SkEISGwt132/4LL4DX6248p/Dcc1Chgl2AHDHC7WhERETco4RbpJAtnnMYgAQSbe2liEhhuPFGiI6G1ath8mS3ozmpM86Al1+2/aeespBFRERKIiXcIoVs8TxbX5kQ9yeUKeNyNCJSbERH2xxtgOefdzeWfLjqKujVy6aU33yz3w/Ki4iI+IQSbpFC5PVC4rpSACS0cFyORkSKnbvugpAQmDkTFi1yO5qT8njgtdcgKgpmzYJ33nE7IhERkaKnhFukEP3+OyQfDSecIzToVNHtcESkuKleHfr1s/0XXnA3lnyoVcumlAM88ABs3+5qOCIiIkVOCbdIIcoqmNaUZYS21vptEfGB+++3288+gy1b3I0lH+68E1q1goMHc+q+iYiIlBRKuEUK0W9zjwKQwGJIUIVyEfGBhAQ491zIzISXXnI7mlMKCYG334bgYBg/Hr75xu2IREREio4SbpFCtHjOIQASYjdZTxwREV944AG7feMN2L3b3VjyoUWLnIH522+HpCRXwxERESkySrhFConjwG8rIwBIaJLucjQiUqz16GHztA8fDohRboDBg6FOHdi2DR57zO1oREREioYSbpFCsnEj7E6JIoyjJHRSOzAR8SGPJydrfeUV2L/f3XjyISoKXn/d9l99FebNczceERGRoqCEW6SQZH14TGAx4W2auRuMiBR/F10ETZtCcjKMHOl2NPnSvTtce63NCLrpJuvRLSIiUpwp4RYpJHN/tk+O7ZkHbdu6HI2IFHtBQTmj3C+9FDALo198EcqXh+XL4dln3Y5GRETEt5RwixSSeTOPANC+3DqoUsXlaESkRLjsMmjQAA4cgFGj3I4mX8qXh//9z/afeMISbxERkeJKCbdIIUhNhcR1pQBo387lYESk5AgOhkcftf0XX4RDh9yNJ5/694c+fSA9Ha6/HjIy3I5IRETEN5RwixSC336DDG8wceygZtc6bocjIiXJVVfBmWfCnj0werTb0eSLx2MdzWJjYeFCeP55tyMSERHxDSXcIoVg3lwHsPXbng7tXY5GREqUkJCcUe4RI6yIWgCoUiWno9ngwbBypavhiIiI+IQSbpFCMO/HwwC0D/oVWrZ0ORoRKXGuvRbi422U+8UX3Y4m3667Di64wKqVa2q5iIgUR0q4RQrBvPkeANrX3QuRkS5HIyIlTkgIPPWU7T//POze7W48+eTxwJtvQkwM/PprQF0rEBERyRcl3CL/0LZtsG1fFEFk0rpLabfDEZGS6rLLoFUrSEmBZ55xO5p8q1o1p2r544/D6tXuxiMiIlKYXE24f/75Zy688EKqVKmCx+Nh4sSJboYjclrmz7fbpiyjdCdNJxcRlwQFwbBhtv/aa7B5s7vxFMDAgdCzJxw9alPLMzPdjkhERKRwuJpwHzp0iObNmzMqQHqHihzPvDm26LA986C9CqaJiIu6dYNzz7VF0UOHuh1NvmVNLY+OhnnzcoqpiYiIBDpXE+5evXrx1FNP0bdvXzfDEPlHsgumlV4BddQSTERc5PHkTCd///2AKv1dvXrOGu7HHoM1a9yNR0REpDAE1Bruo0ePkpSUlGcTcVN6OixcGQVA+9YZ9mFXRMRN7dpB377g9cIjj7gdTYHccAOcfz4cOWL7mlouIiKBLqAS7mHDhhETE5O9Va9e3e2QpIRbuhSOpIcQy37iz63mdjgiIuaZZyA4GL7+GqZOdTuafPN44K23oEwZ+OUXePlltyMSERH5ZwIq4X7kkUc4ePBg9rZ161a3Q5ISbt48u23HfILO0vptEfETDRrAoEG2f889AdXgukYN62wG8OijATUrXkRE5BgBlXCHh4cTHR2dZxNx07yZqQC0Zz60aeNyNCIiuQweDOXKWcb6+utuR1MgN91kVcuPHIH+/a0GnIiISCAKqIRbxN/Mne0FoH3N7VZeV0TEX5QtC089ZfuPPw5797obTwF4PPDuu3a9IDERhgxxOyIREZHT42rCnZKSQmJiIomJiQBs3LiRxMREtmzZ4mZYIvny55+wYUcpPHhp1ync7XBERI51003QrBns328j3gGkcmVrFQYwYgTMnu1uPCIiIqfD1YR74cKFJCQkkJCQAMB9991HQkICjz/+uJthieTLzJl224JEynZu5mosIiLHFRycU3ls9GhYtszdeAro0kthwAAruH7ttaDmJCIiEmhcTbi7dOmC4zjHbGPGjHEzLJF8mTHN+tV0ZQZ06OByNCIiJ9ClC1x+uWWtgwbZbQAZORJq1oRNm+Duu92ORkREpGC0hlvkNM34war4dI1ZDA0buhyNiMhJPP88REXBzz/De++5HU2BREfDhx/auu4xY+DTT92OSEREJP+UcIuchq1bYcMfkQSRSceuIfZJUETEX9WsCU8+afsPPAA7d7obTwF17GgtwgBuvhk2bnQ3HhERkfxSwi1yGmbMsNtWLCLm/HbuBiMikh933QUtW1oBtXvvdTuaAhs82FbvJCXBNddAerrbEYmIiJyaEm6R0zBzeq712126uBuMiEh+hITAW29BUBCMGweTJ7sdUYGEhMDYsRATA/PmwdChbkckIiJyakq4RU7DjB9saKVLTCI0aOBuMCIi+dWyJdxzj+3fdhscOuRqOAVVq1ZOq7BnnsnpFiEiIuKvlHCLFNCmTbBpRwTBZHDOuWFavy0igWXoUFvTvXkzPPaY29EU2JVXwr//DY4D/fvDrl1uRyQiInJiSrhFCihr/XYbFlDmfLUDE5EAU7q09eQG69Gd9aYWQF5+2SYX/fkn/OtfkJnpdkQiIiLHp4RbpIDy9N/W+m0RCUS9esFNN9kw8YABcPCg2xEVSKlS8Pnn1uls6lR46im3IxIRETk+JdwiBeA4MHOard/uGpsI9eu7G5CIyOl68UWoU8f6HN51l9vRFFjjxvD667Y/dChMm+ZuPCIiIsejhFukAH7/HbbuiiCUNM4+L0Lrt0UkcJUuDR9+aFXLP/gAvvjC7YgK7Npr4cYb7WLoNdfAH3+4HZGIiEheSrhFCiBrqWM75hPV7Sx3gxER+afOOgseesj2b7kFtm93N57TMHIkNG8Ou3fDVVepP7eIiPgXJdwiBZC1frsLM6FrV3eDEREpDEOGQIsWsHevrecOsApkkZEwfjxER8Ps2XD//W5HJCIikkMJt0g+OQ7MnJ4BQNeySyA+3uWIREQKQVgYfPyxZa4BWoGsXj2bHQ/wyivw/vvuxiMiIpJFCbdIPq1YAX/uCSecI3Q4L0rrt0Wk+GjUKG8FsqlT3Y3nNFx0EQwebPu33AILF7obj4iICCjhFsm3SZPs9jymE9ntbHeDEREpbNddl1OBrH//gKxA9vjjcOGFcPQoXHop7NrldkQiIlLSKeEWyadJX9l08j5MgvPOczkaEREfGDnS1nPv3g39+gVcBbKgIJtaHh9v3c6uvDLgfgQRESlmlHCL5MOePTD312AAetdeBXXruhyRiIgPREbC559bBbI5c+DBB92OqMBiYmDiRChTBn76CQYNskF7ERERNyjhFsmHKVPA6/XQjCXUuDjB7XBERHznzDNhzBjbf+kleOstN6M5LQ0bwtixVmrjzTftxxAREXGDEm6RfJj0jQ2P9GES9O7tcjQiIj7Wty888YTt3347zJjhbjynoU8feOEF27///pw6HCIiIkVJCbfIKaSnw5TvrC9tn4jp0LGjyxGJiBSBxx6Da66BjAy47DJYu9btiArsnnvg5pttSvnVV8PSpW5HJCIiJY0SbpFTmDMHDqaEUJ7dtD0/FsLD3Q5JRMT3PB545x1o3x7277ch43373I6qQDweePVVOPdcSEmxH2H7drejEhGRkkQJt8gpZE1DvIDvCO7Ty91gRESKUkSEVSCrUQPWrYOLL4bDh92OqkBCQ60OXFbl8gsugKQkt6MSEZGSQgm3yCnkaQfWSwm3iJQwcXF25TEmBmbPhiuuCLheW2XLwuTJULEiJCbaDPm0NLejEhGRkkAJt8hJrFsHa9aHEEI65zfZDtWquR2SiEjRa9rUku7ISPjuOxgwADIz3Y6qQOrUsdBLlYJp0+CGG8DrdTsqEREp7pRwi5zEt9/abSd+JubCTu4GIyLipnPOgS++gJAQGDcO7rwz4Bpct2pl08tDQuDjj+GRR9yOSEREijsl3CInMekbG/5QOzAREWxZzUcfWTWy0aPhoYcCLunu2RPeftv2n302p3WYiIiILyjhFjmBgwfhp59sv0/0LGjXzt2ARET8Qb9+lmwDPPec9d4KsKR7wAAYNsz2//MfePNNd+MREZHiK8TtAET81cSJkJEZRANWUa93vM1BFBERuOUWS7Jvuw1GjoSjR+G11yAocK7jP/QQHDgAI0bArbdCmTLWq1uOw+uFzZvh999t27ABtmyBPXtg717bDh60nu1er63v93jsHzU62rbYWKt2X7Mm1Kpli+qbNoUzznD7pxMR8SllECInMHas3V7DWOsjIyIiOW691dqG/fvf8MYbcOSI9e0ODnY7snzxeGyUOynJBuyvvdYKql10kduRuczrhVWrYO5cK+memAhLllgj84I6cgR27z75MTVrQosWtsC+c2ebTRYefhqBi4j4J4/jBNg8sFySkpKIiYnh4MGDREdHux2OFCM7d0KVKg5er4d1IQ2pu+sX6ysjIiJ5jRtn2WpmJvTta2u8o6LcjirfvF4YOBA+/NDyvK+/hvPPdzuqIuQ4llBPmwazZlnrt337jj0uLAxq14Yzz7TR6Vq1oEIFKFfOtpgYa3oeHGyb40Bysl3ROHjQRsG3bIFNm2y0fO1a2/+7yEg4+2w491zr+96woV0dERHxIwXJQ5VwixzHK6/AXXdBW+Yzv89T8M03bockIuK/vvzS5mOnpUHbtvaeWbGi21HlW0aGLU3/8ssSknQfOGCNyadMgR9+gB078j4fGQnt29uoc4sWttWvX/hLqw4cyBlFnzcPZsyAXbvyHhMfbxdyLr0U2rRR8i0ifkEJt8g/1KGDw7x5Hl7mLu76qB307+92SCIi/m3WLLjkEhsdrV3bml43aOB2VPmWlgZXXglffWVJ91dfQY8ebkdViPbtsx/q889h6lRIT895rlQp6NrVpnR37AgtW9podVFzHJvO/uOPdkFg2jT7xWSJj7fpCNddB1WrFn18IiJ/UcIt8g/8/rvNmAsikz/Cz6TS7mVW+EVERE5u7VqrebFhgxXJ+uILmxocINLSbKR74kRLuidOtDZiAWvvXvshxo+H6dNtKD9Lw4bQp4/9gGef7Z/rppOSLPGeMMFmTRw+bI8HBUH37nD77dayM0DqBohI8VGQPDRwyomKFJFx4+z2PKZTqU9rJdsiIvkVH2/Ftjp0sOnC3bvDM8/YQukAEBYGn35qM5iPHrUB+2+/dTuqAkpJgTFjbE58XBzceCN8/70l202bwtChsGIFrFxpjcjPPdc/k22w6ub9+sEnn9i093fftRF4r9d+posvhnr14PnnYf9+t6MVETkujXCL5OI40Lixw6pVHt5jIAM/6w1XXOF2WCIigSU1Fe64A957z+736QMffBAwxSfT0y3PmzDBli1/9JHd91ter61/fv99m1WQNRIM0Ly5/R27/HJbh10crF8Pb71lW1aiHRVlFxfuv9/aj4mI+JCmlIucpsRESEiAcI6wM6oOMbvXB1S1XRERv/LOO5Z4Hz1qVa0/+cTaPgWA9HRbLjx2rNXpeuMNuOkmt6P6m7VrLcn+8EPYujXn8Xr1bJ1zv362X1wdPmy/oFdegaVL7bGQEPjXv+DBB23avIiID2hKuchpyuq9fSHfEHNxFyXbIiL/xL//bVPM69SxFlBnnQWPPmoJuJ8LDbU89tZbbfbTzTfbzGXXHTwIb75p/5b169uU/a1brS3XLbfAL7/AmjXw2GPFO9mGnFHtxESrtt61q02dHzMGGje2gqfr17sdpYiUcEq4Rf7i9cK4cTbh4xrG+vn8QRGRAJGQAIsWwTXX2BvtM89Y67DERLcjO6WgIHjtNXj4Ybv/wAO2X+RL0r1eq9jdvz9UqmSJ9dy5Vizsggts4fmOHfD667Z+vqS1zvJ4rF7Ajz9ae7FLLrGrJGPHWqX8m26yHuAiIi7QlHKRv0yfDt26QQwH2BFdn4hdW/y3kIyISCD64gsbMt6zx6b+Pvgg/Pe/1pbKz40YkZN4X3ON1e/y+Z+I9ettyvj77+edMt64sc13/9e/LAGXYy1ebKP8331n98PC7Nz773+tmJyIyD+gKeUip2HUKLvtz8dE9O2lZFtEpLBddplVyO7b16b+PvOMjUB++qmNSPqxhx6ymcohITZw2rOnFWIvdMnJVmyuUyebEv7UU5Zsx8ZaG6xff4Vly+A//1GyfTIJCVZifs4c6NLFer6NHGnLGx55xPqSi4gUAY1wi2AzzWrXdvB6PaykIQ0n/y/Am6+KiPgxx7H+0PfdZ2u7wZKiF16Ali1dDOzUpk616wbJyTbQ/N13hVAU2+uFWbMs0f78czh0yB4PCrL2XtdfDxddBBER/zj+EslxbBrbo4/aBQuwNe8PPwx33w2Rke7GJyIBRyPcIgU0ejR4vR7OYxoNqx+ytWAiIuIbHo+Ncq9caX2hIyJg5kxo1crW3/rx+u7u3eHnn6FyZRusb9vWllMXmONY8nf//VCzpl1weP99S7bj42HYMLsaPHkyXHmlku1/wuOxNWPz5sFXX0GzZlZ87pFHrPDcBx8ETK94EQk8GuGWEu/IEahWDfbuhQlcwiVPtrZ1XyIiUjQ2b7b33bFjcxKfvn1tBLJtW3djO4EtW6y9+LJltjz4zTdhwIBTfJHjwJIl1h7ts89g48ac56KjrVjnwIEls/BZUfJ64eOPbcQ7a218ixbw3HOWmIuInIL6cIsUwPvv2+ebGmxmQ1A8IVs3QpUqboclIlLyrF4NTz4J48blrOlu0wYGDbJk1M9qa6SkwLXX2ux4sMHqESOseHg2x7ER+wkTbK362rU5z0VF2VTxq66CHj00il3UUlNtXfczz0BSkj3Ws6f9Eps1czc2EfFrSrhF8slx7LPcokUwjId5+NJ1VkVXRETcs3IlPPusJd5pafZYhQpWHvyqq6BdO78ZAfZ6YfBgq20Gljd/9HoK5ZfPhEmTbPvjj5wvCA+H3r3tAkLv3gFRob3Y27PHLvS89poV8/N47Er8k09C1apuRycifkgJt0g+zZtnM/fCOcJWqlPh+4+tQI2IiLhv9254+21LhLZty3m8Vi1LWPv0seQ7NNS1EAE4coRPR2zi+qfOJDUjlOpsYTxX0I6/CnRFRdni78svtxFtfWbxT+vX27ruzz+3+5GRVtjvwQf1OxORPJRwi+TTv/5ly7gGMIYxdZ6EdeusKqyIiPiPjAyYMsXWPk+cmFPFG6BMGSs41r07tG8PTZv6dmp2Rob9rViyxK7azpsHv/0G6eksowmX8QXriCeUNF44ZyKDHimD59yumi4eSObNs7Zrc+bY/QoVYMgQuOkm9y/uiIhfUMItkg87d0L16pCeDgtpRasR/ewqtoiI+K/Dh60X1+efw7RpVvEyt+Bg69eVkGDVvmvVsq1mTTjjDEt8TzYdPSPDKljv3WuV0TZtsqJuv/9uZclXrcqZ5p5bhQrQtStJZ/Xk31Ou4PMppQG44gp44w0oW7aw/gGkSGS1rnvoIbvAAnY+jRgBF1/sN0saRMQdSrhF8uHBB60gaXvmMje0s01XrFjR7bBERCS/vF4rSDZ1KsyYYQU59uw5+deEhloP5uhoS5q8XkuushLt5ORTv26pUpbUt21ro+odOkDt2tlJmOPAyy/DAw/Yt61WDT780AbiJcCkp1sJ+qFDbYkDwDnn/PUBor27sYmIa5Rwi5zCjh1Qp44VKJ1Eb3pfFW3FeUREJHA5jl08/e03m/K9caONUG/aZO2fMjPz/73KlLFMOWt0vGZNaNTIpqzXrJmv5Ue//gr9+9vSYI/HBkuHDrU2YhJgkpJsdPvFF62fKNj0hWHD4Mwz3Y1NRIqcEm6RU7jnHht9aOeZz1ynPZ6ZM6FzZ7fDEhERX/F6rY/XwYN5R7I9Hkueg4Js5LtsWbstpLW6KSn2N+edd+x+QgK8+661fZYAtG0b/N//WU9Rx7Hz5PbbrY98+fJuRyciRUQJt8hJbNsGdevC0aPwA93p3uGQFUbReiwREfGRL7+0mlv79tky84cesrxNtdQC1NKltjbt++/tfnQ03HuvXV2JjXUzMhEpAgXJQ1WOWUqcZ56xZLtj0By6Mc2uSivZFhERH7r0UmsvfvnlNrP9mWdslHv2bLcjk9PSrJlVzv/hB2je3KacDx1qSxCGDIEDB1wOUET8hRJuKVE2b7aWrgBPev+Lp2VL6NXL3aBERKREiIuD8eNttLtSJVizBjp2hAEDYPt2t6OT09K9u9UM+OwzK6R38KASbxHJQwm3lChPPmkFR88LnklnftbotoiIFLm+fW20+8Yb7f4HH1jHqWeftRlYEmCCgqyA2tKlSrxF5Bhawy0lxvr10KCBTeX7hQ50aJxsfxzzUWlWRETEF379Fe66C+bPt/t168ITT0C/fvrzFLC8XvjiC0u4V6ywx6KjbRH/XXdBjRruxici/5jWcIv8jePY37jMTOgVOpUOzINHH9WnGRERcVXbtvDLL1b0ulIluzh8zTW2vvurr+zvlwSY3CPe48dDkya2xvuFF6wnab9+OVdYRKTY0wi3lAiffWZ/38KCM1ia2Zj69RxYtcpKxYqIiPiBlBRrWfncczYjGaB1a6to3rev/mQFLK/XCqy9+CJMn57zeIcOcN99cMklEBLiWngiUnAa4RbJ5cABuPtu2/9v2PPUZy088og+uYiIiF8pXdomX23cCP/9L5QqBQsX2mBpvXowcqQl5RJggoLgggtg2jRITISBAyEsDObOtV9u3bqWjO/Z43akIuIDGuGWYu/222H0aKgfu4MlB2oS3rS+VRTV1WQREfFju3bBq6/Ca6/B3r32WEyMTTkfOBDatFHdz4C1Y4f9YnP/ckND4eKL4YYb4PzzNTAg4scKkocq4ZZibe5cOPtsWwM309OVzs5MmDkTOnd2OzQREZF8OXzYKpn/73+wdm3O440bW0uxyy+H2rXdi0/+gdRU+OgjeOMNWLQo5/GqVe2Xe/31NgIuIn5FCbcI1v6rZUtYvhyuj/uWd3f2sYXcn3zidmgiIiIF5vXCjBnw3ntWBPvIkZznmjaFiy6yrVUrDY4GpCVL7Jf70Uc5o95gzdqvv97Wepct61p4IpJDCbcIMHiwtVYpX+YIq5OrUi7qCKxeDdWrux2aiIjIP3LwIHz6KYwbB7NmWReOLNHRcNZZlqedc45VPPe3j0mOY+vRDxywn+XoUbtQnpZmm8djS5+ztogIW9OetcXGFuOVYUePwjffwLvvwvff25UWsB+4Wzeb0nDxxVC+vLtxipRgSrilxJsyxeqTOA6Mjb2dqw+Mhqeesmo0IiIixci+ffDdd/D115afJSUde0z16jYFvXFjm35erZrNWq5WDcqVs+XD+eX12kzoQ4dsuntysiXOBdkOHsx7keB0lC1rOWeFClClCtSsmbPVqWOF5sLD/9lruG7bNltPMG6cTdnLEhwMXbpY8t23L8TFuRaiSEmkhFtKtC1bICHBPoDc0nwery/pYH95V6ywS+QiIiLFVGamtX+ePdtGvn/5Bf7449RfFx5uo+DR0VZAO/foclqaJdaHDtmWmlp48YaGWiG4iAh73bCwnOTf67UtMzMnwT90yAaA8yM42P78N2hgrbBbtrStdu0ALTa3erWtJfjiC1i8OOdxj8emMlxwAfTsCc2bB+gPKBI4lHBLiXX0KHTqBL/+Cq3qJzN7bRwRTip89ZUtbBMRESlh9u+3a84rVsDKlXZh+o8/bPB0xw6bDXa6IiNtinfZsjbNu6BbZGTBc8OMDPuZ9uyB3btt27YNNm/O2datO/5IP9jrtm5t0+7PPhvat/e/KfentGGDJd6ffw4LFuR9rlIlS7x79oTu3eGMM9yJUaQYU8ItJdagQTBqFJSN9fJb5DnU2j7XqnyOGeN2aCIiIn4nI8OmhCcl5dympVkSnjW6HBYGUVGWWGfdliplyXJQkNs/wfE5DmzfboPCq1bZqP+iRbBsmf18uQUF2aBwt27WjeuccwJsQtzmzfDttzB5Mvz4o01HyBIUBO3aQY8eNgW9XbsA++FE/JMSbimR3n/f+pICTDpnOL1nP2KtNH77DcqUcTU2ERERcV9amo30z58Pc+bYtnFj3mMiImy23Pnn2wBx06YBNEP76FFbTzBlim25132DrR3o0MHao3bpYsP7SsBFCkwJt5Q4EybAFVfYlfhHeyfy1LcJVs3zl1+gTRu3wxMRERE/tX27tVubOhV++AH+/DPv85UqWeLdp4/N0g6oj5zbtlniPX06zJxpawhyCwuzpLtTJ5tj366dpqCL5IMSbilRpkyx5dnp6TDgkoO8+31VglIPwfDh8NBDbocnIiIiAcJxbJ17VvI9c2beInFhYXDuudaV66KLrDp6wHAcW9w+c2bOtn37scfVr2+j4B06WDLeuLEau4v8jRJuKTF+/tmWJR05AldcksbYdW0JWbHE/hpOneq/i8tERETE7x09atPOJ0+2+qvr1uV9vm1bS74vuQQaNgygqedgCfj69ZZ4z54Nc+ce+wOCLctr29aS76wkvFy5Ig9XxJ8o4ZYSYd48W1+VnAy9e2XyZUoPwmZNh8qVrWJn1apuhygiIiLFhONYEbaJEy35nj8/7/N161ryffHFNjs7IAeF9+61D1hz59rt/PmQknLscXXrWqn31q2hVSvrt6bP4lKCKOGWYu+LL+Daa22aV9euDt+WvZbILz+2q7CzZlm5UREREREf+fNP+OYbS76nT89b/bxCBVvzfdFFtv67VCn34vxHMjOtylxWAj53LqxZc/xj4+NzEvDWrSEhQUVrpdhSwi3FluPY0uz//tfu9+oFn9V5mNKjRkBoqM35Ou88d4MUERGREiU5Gb7/3ka/v/0WDhzIeS483FqOXXQRXHihTcQLaHv3wsKF1mdt0SLb37Ll2OM8nuMn4aVLF33MIoVMCbcUS2lpcMstOS2177wTXowbQchjD9sDH30E/fu7Fp+IiIhIerpNtvv6a9v+3nasbVtLvnv3hmbNikm5md278ybgixbB1q3HHufxQIMGNgU9IQFatLBNa8IlwCjhlmJn7Vq47jpbShQUBCNf9nLHpgfhhRfsgBEj4MEH3Q1SREREJBfHsRnZWcn339d9V6hgE/O6d7etenV34vSJXbvyJuALF8Iffxz/2OrVc5LvrES8Vq0Aq0InJYkSbik2vF4YNcq6e6WmQkwMfPJhOj0/GQhjx9pBI0bAAw/oTVlERET82o4dMGmSJd8zZhxbjyw+Pif57tLFPvcUKzt2WPK9eDEkJtrt778f/9iYGKvJk3skvFEj680m4jIl3FIsbN4MN9wAP/5o97t3h3deTqH6XX1h2jQICYF337XqaSIiIiIBJC3NRrynTrXt119toCGLxwNNm1rF87POso5cZ55ZDMcXDh6EpUtzEvDERFi+3Obm/11oqPUFzz0a3rx5MbwyIf5OCbcEtIMHrTDaSy9Zf+2oKHjuObit/WI8/a+xnhylSsHnn0PPnm6HKyIiIvKPHThgLbGzEvDjtcSOibEcs2VLyzcbN7Yl0VFRRRysr6Wl2ee9rAQ8a8tdjS632rXzjoS3aAHVqhXDqxPiL5RwS0BKT4c33oChQ2HPHnusc2d46w0v9b550UqTp6dbec+vvoI2bdwNWERERMRHtm+3Llxz5ti2eHHe1mNZPB6oWRMaNoQ6dSz3rF3blkBXrWrrxItFYTbHsWrouZPwxYuPXyEdIDbWqtLl3po0CeAebeJPlHBLQElOtpnhL70EmzbZYw0a2NLsCxO24bl+oDW4BLj4Ynj7bShf3qVoRURERIpeejqsXAm//WbbkiWwalXOIMWJBAdDXBxUqgRly9ooeXR03tus/YgIWyIdGnri25CQk29Fntzv22f/GLmT8FWrICPj2GM9HpuX36yZTUXPSsRr1SomVyWkqCjhloCwZQu8+iq8+aZNIweoWNFGuG+8KoWQF5+F55+3amlRUZaR33ijpgeJiIiI/GX3bssvV6+2FmRZ2+bNVii8qD/pBwWdOBkPC7M23GXK2Pb3/ehoG1OpWNFG5rNuo6ML+PEva0r6kiW2Pjxr27Hj+MeXLm0L5nOPhjdtqrXhckJKuMVvJSXBF19Yy+wZM3L+CNSvD/feC9f1zyTyk/fg//4v503xnHNsVLt+ffcCFxEREQkwGRmWdG/fbh+rDh7M2ZKSjt0/etRy1fT0499mZOTdikpYWE7yXa2adRGrUcNus/arVLER+JPatQuWLcubhK9YYT/48dSsmZN8N2pkW/36xXDRvBSUEm7xK3v2wJQp8M03tqWm5jx37rmWaF9w1gGCxrxrQ94bN9qTZ54Jzz4LfftqVFtERETEjziOVVVPTz82ET/elp5ueW1Kim3Jybbl3j940D437t5tufGuXXDoUP7iCQqyMj+1akHduvYxMvdWrtwJPk5mZFiFutxJ+NKlJ14b7vHYIvmsBLxRo5zqdaVLn+4/pwQYJdziqrQ0WLDAKm1OnmwFP3K3uahf3zp59b/Godb+xfDOO/D++znvqGecYSPct9+uXosiIiIiJdjhw5aA794NO3fCtm2WC2/datuWLfbY8QrK5RYdffxEvG5dKy53zBLu/futPdmSJTYKvnKl3e7de+IXqVnTEvAGDewb160L9erZMHxIyD/+txD/oYRbitSff8KiRbBwIcyaZQn2kSN5j2nWDPr0gUsudmgd9Buez8dbW68NG3IOatIE7roL+vfXVB0RERERyRev10bDt2yxiZLr19tHzKztjz9O/vXh4TZonZWE5670Xru2JevZdu/OScBzbzt3nvgFQkPtG+VOwrP2a9bMx1x48TdKuMUnUlKs/kTW+8ry5ZZoH6/+RIUK0KkTnHeuQ++Gv1NjzVQb8p45M+8bUmQk9O4Nt90GXbtq6riIiIiIFKrUVPj997xJeFZSvmnTqdejn3HGsUl41lazpn2cZe9eq163YgWsXWsvkPUiJ1ojDjbynbUQ/USbpqr7nYBLuEeNGsVzzz3Hjh07aN68Oa+88gpt27Y95dcp4S48Xq+tmfnzz5ztjz/sPSLrDepEhR2Dgqz3Y6smRzmr1p90illCg50/4Un8q09iVgnyLFlJ9hVXwAUX6E1ERERERFyRkWFT07OS8N9/tyQ8q9r7yWaQZylXzoq2Va1qt7n3q1b2UjFzO+X3ryNy61+J+Lp1OQn536eFHk9MjPV1q1w5723WfoUKVt69XLm/sn/xtYBKuD/99FOuu+46Xn/9ddq1a8dLL73E+PHjWbNmDRUrVjzp1yrhPlZ6ul3FO3zYik/s33/ibc8eq1r555+WTOen2mTcGWk0ittHo5g/aBi2gQRPIs2TZlFq0wr7pscTHg4dOkCXLjaK3batNXoUEREREfFjycmWeOdOwnNvKSn5/15RUTm5cfnyUKG8Q/mIFMqyn+i0PcSk7iA6+Q9iDmwmeu9GYnauJTp5G6VJIZJUQsg89YtERlrinXuLjbV58Vk92LK2vz+WdT88XLNOTyGgEu527drRpk0bXn31VQC8Xi/Vq1fnzjvv5OGHHz7p1wZKwj1jhk3FzqrSmJl54gqOJ3vuyJGcZDrrNvd+auo/a9Hg8VjLhcqVc67O1alja1mybste2R2mTTvxN6la1dalNG8OCQnQooUNf6v4mYiIiIgUI45j401ZM0Nz3+be373bBsX+qZBgLxEhGUQGpxHpOUKkc5hI7yEiMg4RmZls90klgiOEkEEwmYSQke/9nPuZ1A77g55RP9sgWUSEJeF/38+6DQ21qfHBwTlN17P2/357vMeCgvJukZFw3XX//B/MhwqSh7paLi8tLY1FixbxyCOPZD8WFBREt27dmDt37jHHHz16lKO51kAc/GuqclJSku+D/QfefhvGji361y1d2i5o5d5iYnL2zzgj72yUihVPXbMhqXlzy+orVbKMPOu2Th1b1FKq1LFfdORI/qbLiIiIiIgEkJCQnKXWJ+I4NlK+Zw/s22fT1Pfsybk9eDCnLVpysvVFz7rNneZkZEJKJtigethfW6xPfq4eaZM5K+1rn3zvUypbFi65xJ3Xzqes/DM/Y9euJtx79uwhMzOTuLi4PI/HxcWxevXqY44fNmwYQ4cOPebx6tWr+yzGQJbV53DbNrcjERERERGRQPE9EOPWi+/fb6OEASA5OZmYU8QaUA3hHnnkEe67777s+16vl3379lGuXDk8Wmcgx5GUlET16tXZunWrXy87EP+g80UKQueLFITOFykInS9SEDpfip7jOCQnJ1OlSpVTHutqwl2+fHmCg4PZ+be+dTt37qRSpUrHHB8eHk54eHiex2JjY30ZohQT0dHRegOSfNP5IgWh80UKQueLFITOFykInS9F61Qj21mCfBzHSYWFhdGqVSumT5+e/ZjX62X69Ol06NDBxchERERERERE/hnXp5Tfd999DBgwgNatW9O2bVteeuklDh06xPXXX+92aCIiIiIiIiKnzfWEu1+/fuzevZvHH3+cHTt20KJFC6ZMmXJMITWR0xEeHs7gwYOPWYogcjw6X6QgdL5IQeh8kYLQ+SIFofPFv7neh1tERERERESkOHJ1DbeIiIiIiIhIcaWEW0RERERERMQHlHCLiIiIiIiI+IASbhEREREREREfUMItxdqoUaOoVasWERERtGvXjl9//dXtkKSQ/fzzz1x44YVUqVIFj8fDxIkT8zzvOA6PP/44lStXJjIykm7durFu3bo8x+zbt4/+/fsTHR1NbGws//73v0lJSclzzNKlS+nYsSMRERFUr16dZ5999phYxo8fT4MGDYiIiKBp06Z89913hf7zyukbNmwYbdq0oUyZMlSsWJFLLrmENWvW5DnmyJEj3HHHHZQrV47SpUtz2WWXsXPnzjzHbNmyhd69exMVFUXFihV54IEHyMjIyHPMzJkzadmyJeHh4dStW5cxY8YcE4/en/zb6NGjadasGdHR0URHR9OhQwcmT56c/bzOFTmZ4cOH4/F4uOeee7If0zkjWYYMGYLH48mzNWjQIPt5nSvFjCNSTH3yySdOWFiY8+677zorVqxwbrrpJic2NtbZuXOn26FJIfruu++cRx991Pnyyy8dwJkwYUKe54cPH+7ExMQ4EydOdJYsWeJcdNFFTu3atZ3U1NTsY3r27Ok0b97cmTdvnjNr1iynbt26ztVXX539/MGDB524uDinf//+zvLly51x48Y5kZGRzhtvvJF9zJw5c5zg4GDn2WefdVauXOk89thjTmhoqLNs2TKf/xtI/vTo0cN57733nOXLlzuJiYnOBRdc4NSoUcNJSUnJPubWW291qlev7kyfPt1ZuHCh0759e+ess87Kfj4jI8Np0qSJ061bN2fx4sXOd99955QvX9555JFHso/5/fffnaioKOe+++5zVq5c6bzyyitOcHCwM2XKlOxj9P7k/77++mvn22+/ddauXeusWbPG+e9//+uEhoY6y5cvdxxH54qc2K+//urUqlXLadasmXP33XdnP65zRrIMHjzYady4sbN9+/bsbffu3dnP61wpXpRwS7HVtm1b54477si+n5mZ6VSpUsUZNmyYi1GJL/094fZ6vU6lSpWc5557LvuxAwcOOOHh4c64ceMcx3GclStXOoCzYMGC7GMmT57seDwe548//nAcx3Fee+01p2zZss7Ro0ezj3nooYec+vXrZ9+/8sornd69e+eJp127ds4tt9xSqD+jFJ5du3Y5gPPTTz85jmPnRmhoqDN+/PjsY1atWuUAzty5cx3HsQs8QUFBzo4dO7KPGT16tBMdHZ19fjz44INO48aN87xWv379nB49emTf1/tTYCpbtqzz9ttv61yRE0pOTnbq1avnTJ061encuXN2wq1zRnIbPHiw07x58+M+p3Ol+NGUcimW0tLSWLRoEd26dct+LCgoiG7dujF37lwXI5OitHHjRnbs2JHnPIiJiaFdu3bZ58HcuXOJjY2ldevW2cd069aNoKAg5s+fn31Mp06dCAsLyz6mR48erFmzhv3792cfk/t1so7R+ea/Dh48CMAZZ5wBwKJFi0hPT8/ze2zQoAE1atTIc740bdqUuLi47GN69OhBUlISK1asyD7mZOeC3p8CT2ZmJp988gmHDh2iQ4cOOlfkhO644w569+59zO9V54z83bp166hSpQp16tShf//+bNmyBdC5Uhwp4ZZiac+ePWRmZuZ5IwKIi4tjx44dLkUlRS3rd32y82DHjh1UrFgxz/MhISGcccYZeY453vfI/RonOkbnm3/yer3cc889nH322TRp0gSw32FYWBixsbF5jv37+XK650JSUhKpqal6fwogy5Yto3Tp0oSHh3PrrbcyYcIEGjVqpHNFjuuTTz7ht99+Y9iwYcc8p3NGcmvXrh1jxoxhypQpjB49mo0bN9KxY0eSk5N1rhRDIW4HICIiUtTuuOMOli9fzuzZs90ORfxY/fr1SUxM5ODBg3z++ecMGDCAn376ye2wxA9t3bqVu+++m6lTpxIREeF2OOLnevXqlb3frFkz2rVrR82aNfnss8+IjIx0MTLxBY1wS7FUvnx5goODj6nouHPnTipVquRSVFLUsn7XJzsPKlWqxK5du/I8n5GRwb59+/Icc7zvkfs1TnSMzjf/M2jQICZNmsSMGTOoVq1a9uOVKlUiLS2NAwcO5Dn+7+fL6Z4L0dHRREZG6v0pgISFhVG3bl1atWrFsGHDaN68OS+//LLOFTnGokWL2LVrFy1btiQkJISQkBB++uknRo4cSUhICHFxcTpn5IRiY2OJj49n/fr1en8phpRwS7EUFhZGq1atmD59evZjXq+X6dOn06FDBxcjk6JUu3ZtKlWqlOc8SEpKYv78+dnnQYcOHThw4ACLFi3KPubHH3/E6/XSrl277GN+/vln0tPTs4+ZOnUq9evXp2zZstnH5H6drGN0vvkPx3EYNGgQEyZM4Mcff6R27dp5nm/VqhWhoaF5fo9r1qxhy5Ytec6XZcuW5blIM3XqVKKjo2nUqFH2MSc7F/T+FLi8Xi9Hjx7VuSLHOO+881i2bBmJiYnZW+vWrenfv3/2vs4ZOZGUlBQ2bNhA5cqV9f5SHLldtU3EVz755BMnPDzcGTNmjLNy5Urn5ptvdmJjY/NUdJTAl5yc7CxevNhZvHixAzgvvviis3jxYmfz5s2O41hbsNjYWOerr75yli5d6lx88cXHbQuWkJDgzJ8/35k9e7ZTr169PG3BDhw44MTFxTnXXnuts3z5cueTTz5xoqKijmkLFhIS4jz//PPOqlWrnMGDB6stmJ+57bbbnJiYGGfmzJl5WrEcPnw4+5hbb73VqVGjhvPjjz86CxcudDp06OB06NAh+/msViznn3++k5iY6EyZMsWpUKHCcVuxPPDAA86qVaucUaNGHbcVi96f/NvDDz/s/PTTT87GjRudpUuXOg8//LDj8XicH374wXEcnStyarmrlDuOzhnJcf/99zszZ850Nm7c6MyZM8fp1q2bU758eWfXrl2O4+hcKW6UcEux9sorrzg1atRwwsLCnLZt2zrz5s1zOyQpZDNmzHCAY7YBAwY4jmOtwf7v//7PiYuLc8LDw53zzjvPWbNmTZ7vsXfvXufqq692Spcu7URHRzvXX3+9k5ycnOeYJUuWOOecc44THh7uVK1a1Rk+fPgxsXz22WdOfHy8ExYW5jRu3Nj59ttvffZzS8Ed7zwBnPfeey/7mNTUVOf22293ypYt60RFRTl9+/Z1tm/fnuf7bNq0yenVq5cTGRnplC9f3rn//vud9PT0PMfMmDHDadGihRMWFubUqVMnz2tk0fuTf7vhhhucmjVrOmFhYU6FChWc8847LzvZdhydK3Jqf0+4dc5Iln79+jmVK1d2wsLCnKpVqzr9+vVz1q9fn/28zpXixeM4juPO2LqIiIiIiIhI8aU13CIiIiIiIiI+oIRbRERERERExAeUcIuIiIiIiIj4gBJuERERERERER9Qwi0iIiIiIiLiA0q4RURERERERHxACbeIiIiIiIiIDyjhFhEREREREfEBJdwiIiI+UKtWLV566SWfv06nTp0YO3Zs9n2Px8PEiRML/XUK+n0HDhzIJZdckn2/S5cu3HPPPYUeV25TpkyhRYsWeL1en76OiIhIfinhFhEROYmBAwfi8XjweDyEhYVRt25dnnjiCTIyMk76dQsWLODmm2/2aWxff/01O3fu5Kqrrsp+bPv27fTq1cunr+uvevbsSWhoKB9//LHboYiIiABKuEVERE6pZ8+ebN++nXXr1nH//fczZMgQnnvuueMem5aWBkCFChWIioryaVwjR47k+uuvJygo5895pUqVCA8P9+nrusFxnFNe5AC7QDJy5MgiiEhEROTUlHCLiIicQnh4OJUqVaJmzZrcdtttdOvWja+//hrImTr99NNPU6VKFerXrw8cO6X8wIED3HLLLcTFxREREUGTJk2YNGlS9vOzZ8+mY8eOREZGUr16de666y4OHTp0wph2797Njz/+yIUXXpjn8dxTvzdt2oTH4+HLL7+ka9euREVF0bx5c+bOnXvSn3fdunV06tSJiIgIGjVqxNSpU485ZtmyZZx77rlERkZSrlw5br75ZlJSUk76fXP78MMPad26NWXKlKFSpUpcc8017Nq1K/v5mTNn4vF4mDx5Mq1atSI8PJzZs2ezZMkSunbtSpkyZYiOjqZVq1YsXLgw++suvPBCFi5cyIYNG/Idi4iIiK8o4RYRESmgyMjI7JFsgOnTp7NmzRqmTp2aJ4nO4vV66dWrF3PmzOGjjz5i5cqVDB8+nODgYAA2bNhAz549ueyyy1i6dCmffvops2fPZtCgQSeMYfbs2URFRdGwYcNTxvvoo4/yn//8h8TEROLj47n66qtPOFrs9Xq59NJLCQsLY/78+bz++us89NBDeY45dOgQPXr0oGzZsixYsIDx48czbdq0k8b7d+np6Tz55JMsWbKEiRMnsmnTJgYOHHjMcQ8//DDDhw9n1apVNGvWjP79+1OtWjUWLFjAokWLePjhhwkNDc0+vkaNGsTFxTFr1qx8xyIiIuIrIW4HICIiEigcx2H69Ol8//333HnnndmPlypVirfffpuwsLDjft20adP49ddfWbVqFfHx8QDUqVMn+/lhw4bRv3//7KJi9erVY+TIkXTu3JnRo0cTERFxzPfcvHkzcXFxeaaTn8h//vMfevfuDcDQoUNp3Lgx69evp0GDBseNdfXq1Xz//fdUqVIFgGeeeSbPuvCxY8dy5MgRPvjgA0qVKgXAq6++yoUXXsiIESOIi4s7ZUw33HBD9n6dOnUYOXIkbdq0ISUlhdKlS2c/98QTT9C9e/fs+1u2bOGBBx7Ijr1evXrHfO8qVaqwefPmU8YgIiLiaxrhFhEROYVJkyZRunRpIiIi6NWrF/369WPIkCHZzzdt2vSEyTZAYmIi1apVy062/27JkiWMGTOG0qVLZ289evTA6/WycePG435NamrqcRPx42nWrFn2fuXKlQHyTN/ObdWqVVSvXj072Qbo0KHDMcc0b948O9kGOPvss/F6vaxZsyZfMS1atIgLL7yQGjVqUKZMGTp37gxYQp1b69at89y/7777uPHGG+nWrRvDhw8/7tTxyMhIDh8+nK84REREfEkJt4iIyCl07dqVxMRE1q1bR2pqKu+//36eZDP3/vFERkae9PmUlBRuueUWEhMTs7clS5awbt06zjzzzON+Tfny5dm/f3++4s895drj8QC42jora0p6dHQ0H3/8MQsWLGDChAkAeabqw7H/tkOGDGHFihX07t2bH3/8kUaNGmV/bZZ9+/ZRoUIF3/4QIiIi+aAp5SIiIqdQqlQp6tate9pf36xZM7Zt28batWuPO8rdsmVLVq5cWaDXSEhIYMeOHezfv5+yZcuedmx/17BhQ7Zu3cr27duzR8PnzZt3zDFjxozh0KFD2QnxnDlzCAoKyi4adzKrV69m7969DB8+nOrVqwPkKXx2KvHx8cTHx3Pvvfdy9dVX895779G3b18Ajhw5woYNG0hISMj39xMREfEVjXCLiIj4WOfOnenUqROXXXYZU6dOZePGjUyePJkpU6YA8NBDD/HLL78waNCg7JH0r7766qRFyBISEihfvjxz5swp1Fi7detGfHw8AwYMYMmSJcyaNYtHH300zzH9+/cnIiKCAQMGsHz5cmbMmMGdd97Jtddem6/12zVq1CAsLIxXXnmF33//na+//ponn3zylF+XmprKoEGDmDlzJps3b2bOnDksWLAgT+G4efPmER4efsw0eBERETco4RYRESkCX3zxBW3atOHqq6+mUaNGPPjgg2RmZgI2Av7TTz+xdu1aOnbsSEJCAo8//nieddR/FxwczPXXX8/HH39cqHEGBQUxYcIEUlNTadu2LTfeeCNPP/10nmOioqL4/vvv2bdvH23atOHyyy/nvPPO49VXX83Xa1SoUIExY8Ywfvx4GjVqxPDhw3n++edP+XXBwcHs3buX6667jvj4eK688kp69erF0KFDs48ZN24c/fv393kPdBERkfzwOI7juB2EiIiIFNyOHTto3Lgxv/32GzVr1nQ7HNft2bOH+vXrs3DhQmrXru12OCIiIhrhFhERCVSVKlXinXfeOaayd0m1adMmXnvtNSXbIiLiNzTCLSIiIiIiIuIDGuEWERERERER8QEl3CIiIiIiIiI+oIRbRERERERExAeUcIuIiIiIiIj4gBJuERERERERER9Qwi0iIiIiIiLiA0q4RURERERERHxACbeIiIiIiIiIDyjhFhEREREREfGB/webw9Pg9gVK7QAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<p>We can see that the fitted values are reasonably close to the actual values since the two distributions overlap a bit. However, there is definitely some room for improvement.</p>\n"
      ],
      "metadata": {
        "id": "shpYYVOYQIQ8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**<h2>3. Polynomial Regression and Pipelines</h2>**"
      ],
      "metadata": {
        "id": "6qSFO5VXQJDu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<p><b>Polynomial regression</b> is a particular case of the general linear regression model or multiple linear regression models.</p>\n",
        "<p>We get non-linear relationships by squaring or setting higher-order terms of the predictor variables.</p>\n",
        "\n",
        "<p>There are different orders of polynomial regression:</p>\n"
      ],
      "metadata": {
        "id": "qW8GlxYMQODT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<center><b>Quadratic - 2nd Order</b></center>\n",
        "$$\n",
        "Yhat = a + b_1 X +b_2 X^2\n",
        "$$\n",
        "\n",
        "\n",
        "<center><b>Cubic - 3rd Order</b></center>\n",
        "$$\n",
        "Yhat = a + b_1 X +b_2 X^2 +b_3 X^3\\\\\\\\\n",
        "$$\n",
        "\n",
        "\n",
        "<center><b>Higher-Order</b>:</center>\n",
        "$$\n",
        "Y = a + b_1 X +b_2 X^2 +b_3 X^3 ....\\\\\\\\\n",
        "$$\n"
      ],
      "metadata": {
        "id": "RajyoJMEQQtA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We saw earlier that a linear model did not provide the best fit while using \"highway-mpg\" as the predictor variable. Let's see if we can try fitting a polynomial model to the data instead.\n",
        "\n",
        "We will use the following function to plot the data:"
      ],
      "metadata": {
        "id": "EnsKTPt1QTfY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def PlotPolly(model, independent_variable, dependent_variabble, Name):\n",
        "    x_new = np.linspace(15, 55, 100)\n",
        "    y_new = model(x_new)\n",
        "\n",
        "    plt.plot(independent_variable, dependent_variabble, '.', x_new, y_new, '-')\n",
        "    plt.title('Polynomial Fit with Matplotlib for Price ~ Length')\n",
        "    ax = plt.gca()\n",
        "    ax.set_facecolor((0.898, 0.898, 0.898))\n",
        "    fig = plt.gcf()\n",
        "    plt.xlabel(Name)\n",
        "    plt.ylabel('Price of Cars')\n",
        "\n",
        "    plt.show()\n",
        "    plt.close()"
      ],
      "metadata": {
        "id": "sITCb2COQb0B"
      },
      "execution_count": 153,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Variables\n",
        "\n",
        "x = df['highway-mpg']\n",
        "y = df['price']"
      ],
      "metadata": {
        "id": "7HbqspQ9QhLi"
      },
      "execution_count": 154,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's fit the polynomial using the function polyfit, then use the function poly1d to display the polynomial function."
      ],
      "metadata": {
        "id": "8kZIQDUNQqnV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# We use a polynomial of the 3rd order (Cubic)\n",
        "\n",
        "f = np.polyfit(x, y, 3)\n",
        "p = np.poly1d(f)\n",
        "print(p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PBBN2cHFQreP",
        "outputId": "c45ca0fa-6e13-46e9-99c4-d46b0a43a284"
      },
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        3         2\n",
            "-1.557 x + 204.8 x - 8965 x + 1.379e+05\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's Plot the Function\n",
        "\n",
        "PlotPolly(p, x, y, 'highway-mpg')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "BCNtaWFOQzU_",
        "outputId": "3c62fece-5d24-451e-d41d-389e68ad67f7"
      },
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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WxwAZjUYWLVrEW2+9xcmTJ826gCu7la0RERGBt7e3WVnbtm2BinFBvXr1qvKc6t6HUNEluGnTJgoLC6vst67qEmNNfv/9dyIiIvD19TUrr+yiufL3KDo62qb9t2rVivfee880jcm1115LSEhIlXrW7PfcuXPk5eXVOlfV0aNH2bdvX7Vdn9nZ2TU+f9asWRw/fpzXXnuN5s2bs3fvXr766ivGjBljGufVpk0bXnnllVpjrs2xY8dQFIWnn36ap59+utp4r7nmGtPP1v5tvPLvdGBgoKmuta48VuXxavv76GokaRLVuv766y1+i7tcXSdZvO+++3j55ZdZs2YNd911FytWrODWW281JSqXq+5uNeWKwanWqm5/dTlOTk4Offv2xc/Pj+eee442bdrg4eHBnj17mDlzZqMcbOru7k58fDw7d+7k2LFjZGVl0bt3b0JDQykrK+Onn37iu+++IzY21uZxNleyx7XT6XSMGDGCZcuWceLEiRonOHzxxRd5+umnuf/++3n++ecJDAxErVYzderURnktnNnlLazWsPaLgK37rYnRaOSWW26ptpW2MsmszpNPPmnW6jVs2DDmzJlDdnY2R48exd/fnw4dOthlstnK9+cTTzxBYmKixTpXJj/2/ttYk4Y8VmMmSZOok5YtW2I0Gjl69KjZYMGzZ8+Sk5NDy5Yta3x+x44due6661i+fDmRkZFkZGTwn//8p86xABw+fJjWrVubyvV6PSdPnqy3FhuouLPpwoULfPHFF2YDiSvvMGwotv7R7t27Ny+99BJbtmwhODiY2NhYVCoVHTp04LvvvuO7776zagbghpqZ/O677+aDDz5ArVbXeKPB559/Tv/+/fm///s/s/KcnByCg4NNP9cW95kzZ6q0Ch05cgSg2oHJl78Pr5Senk5wcLBpf/Z43eoSY01atmzJli1byM/PN2ttquxiru13uiE1b94cPz8/Dhw4UGO9Nm3aUFBQUOe/AdV1E4aEhFhsJbsalX+73N3d7fY3q/KaHTt2zKwF78KFC1VaiByxyoAzkjFNok6GDh0KwOuvv25W/uqrrwKQlJRU6z7uvfdevvnmG15//XWCgoIYMmRInWIZOHAgWq2WN954w+xbz//93/+Rm5trVSx1Vfnt6/Lj6vV63nrrrXo7piXe3t42zS7du3dvSktLef3117nppptMfzAr74Q7c+aMVeOZbD1uXfXv35/nn3+exYsXExYWVm09jUZT5ZvvqlWrqowFqUw0qou9vLycd955x/SzXq/nnXfeoXnz5nTv3t3ic8LDw+natSvLli0z2++BAwf45ptvTL8z1hzfGnWJsSZDhw7FYDCwePFis/LXXnsNlUpV59/P+lC59MvatWvZvXt3le2V74G///3vJCcns2nTpip1cnJyKC8vr/dYrRUSEkK/fv145513yMzMrLLd0jQrtRkwYABubm68/fbbZuVXXmOwz3uyKZCWJlEnXbp0YezYsbz77rumLqqff/6ZZcuWMXz4cPr371/rPu6++25mzJjB6tWrmTRpksXJ7azRvHlz0221gwcP5rbbbuPw4cO89dZb9OzZs8ZJIq/WDTfcQLNmzRg7diyPPvooKpWKjz76qMGbrLt3786WLVt49dVXiYiIIDo62mxQ+5USEhJwc3Pj8OHDPPTQQ6byPn36mP7AWpM02XrculKr1cyZM6fWerfeeivPPfcc48eP54YbbmD//v0sX77crAUSKlogAgICWLJkCb6+vnh7exMfH2/6Nh4REcFLL73Eb7/9Rtu2bU3jqd59990a36cvv/wyQ4YMISEhgQkTJlBcXMx//vMf/P39zboVK5Oaf/7zn4wePRp3d3eGDRtm03inusZYnWHDhtG/f3/++c9/8ttvv9GlSxe++eYbvvzyS6ZOnUqbNm1s3md9evHFF/nmm2/o27cvDz30EO3btyczM5NVq1bx/fffExAQwJNPPslXX33Frbfeyrhx4+jevTuFhYXs37+fzz//nN9++82sBbIhvPrqq1VWVFCr1Tz11FO8+eab3HTTTXTq1IkHH3yQ1q1bc/bsWZKTkzl9+rTZfGPWCA0N5bHHHuOVV17htttuY/Dgwezdu5cNGzYQHBxs1rrUtWtXNBoNL730Erm5ueh0Om6++Wa7t6g5PYfcsycatcrbWS3dynu5srIy5dlnn1Wio6MVd3d3JSoqSpk9e7bZ7bKKUnXKgcsNHTpUAZRdu3ZZHYelW7AVpWKKgdjYWMXd3V0JDQ1VJk2apFy6dKlKLFfeaq0oFbfUJiUlVSkHlMmTJ1eJ6fJbxX/44QelV69eiqenpxIREaHMmDHDNF3B5THaMuWApRgrWbr1Pz09XenTp4/i6elZ5Vbi6vTs2VMBlJ9++slUdvr0aQVQoqKiruq41d1eben1s+TyKQeqU92UA48//rgSHh6ueHp6KjfeeKOSnJxs8T345ZdfKnFxcYqbm5vZ1AGVr//u3buVhIQExcPDQ2nZsqWyePFii8e/ctqCLVu2KDfeeKPi6emp+Pn5KcOGDVMOHjxYJf7nn39eueaaaxS1Wm32mlg75YA1MVanuvdYfn6+Mm3aNCUiIkJxd3dXrr32WuXll182u61dUar+XtT1eFeqab9cMeWAoijK77//rtx3331K8+bNFZ1Op7Ru3VqZPHmyUlpaanZOs2fPVmJiYhStVqsEBwcrN9xwg/Lvf/+71qkZ7Knyd8LSQ6PRmOodP35cue+++5SwsDDF3d1dueaaa5Rbb71V+fzzz011bPnbWF5erjz99NNKWFiY4unpqdx8883KoUOHlKCgIGXixIlmz3/vvfeU1q1bKxqNxmw/1f19rOlvu6tSKUoTG8UlGpU77riD/fv3c+zYMUeHIgRQMSP4+fPnax0vI4SzysnJoVmzZrzwwgv885//dHQ4TkXGNAmHyczMZP369dx7772ODkUIIVxScXFxlbLKsaj9+vVr2GBcgIxpEg3u5MmT/PDDD7z//vu4u7tXu+SHEEKIq7Ny5UqWLl3K0KFD8fHx4fvvv+eTTz5h0KBB3HjjjY4Oz+lI0iQa3I4dOxg/fjwtWrRg2bJlNd4NJYQQou46d+6Mm5sbCxcuJC8vzzQ4/IUXXnB0aE5JxjQJIYQQQlhBxjQJIYQQQlhBkiYhhBBCCCvImCY7MRqNnDlzBl9fX5mOXgghhHASiqKQn59PRESEaVH66kjSZCdnzpwhKirK0WEIIYQQog5OnTpFZGRkjXUkabKTygUud+/ejY+Pj4OjEUIIIYQ1CgoK6NGjh9lC1dWRpMlOKrvkfHx8rHrhhRBCCNF4WDO0RgaCCyGEEEJYQZImIYQQQggrSNIkhBBCCGEFSZqEEEIIIawgSZMQQgghhBUkaRJCCCGEsIIkTUIIIYQQVpCkSQghhBDCCpI0CSGEEEJYQZImIYQQQggrSNIkhBBCCGEFSZpcTHa+npRT+WTn6x0dihBCCOFSZMFeF7I27QIvbcvAqIBaBTNvbsGwDkGODksIIYRwCY2mpWnBggWoVCqmTp1qKuvXrx8qlcrsMXHiRLPnZWRkkJSUhJeXFyEhITz55JOUl5eb1dm+fTvdunVDp9MRExPD0qVLqxz/zTffpFWrVnh4eBAfH8/PP/9cH6dZb7Lz9aaECcCowEvbMqTFSQghhLCTRpE0/fLLL7zzzjt07ty5yrYHH3yQzMxM02PhwoWmbQaDgaSkJPR6Pbt27WLZsmUsXbqUuXPnmuqcPHmSpKQk+vfvT2pqKlOnTuWBBx5g06ZNpjorV65k+vTpPPPMM+zZs4cuXbqQmJhIdnZ2/Z64HZ3KKTUlTJWMCpzOLXVMQEIIIYSLcXjSVFBQwJgxY3jvvfdo1qxZle1eXl6EhYWZHn5+fqZt33zzDQcPHuTjjz+ma9euDBkyhOeff54333wTvb6ihWXJkiVER0fzyiuv0L59e6ZMmcKoUaN47bXXTPt59dVXefDBBxk/fjxxcXEsWbIELy8vPvjgg/p/AewkKkCHWmVeplZBpL/OMQEJIYQQLsbhSdPkyZNJSkpi4MCBFrcvX76c4OBgOnbsyOzZsykqKjJtS05OplOnToSGhprKEhMTycvLIy0tzVTnyn0nJiaSnJwMgF6vJyUlxayOWq1m4MCBpjqWlJaWkpeXZ/ZwpBBfLTNvbmFKnCrHNIX4ah0alxBCCOEqHDoQ/NNPP2XPnj388ssvFrfffffdtGzZkoiICPbt28fMmTM5fPgwX3zxBQBZWVlmCRNg+jkrK6vGOnl5eRQXF3Pp0iUMBoPFOunp6dXGPn/+fJ599lnbTrieDesQRHwLX07nlhLpr5OESQghhLAjhyVNp06d4rHHHmPz5s14eHhYrPPQQw+Z/t+pUyfCw8MZMGAAx48fp02bNg0VqkWzZ89m+vTppp/z8vKIiopyYEQVQny1kiwJIYQQ9cBhSVNKSgrZ2dl069bNVGYwGNi5cyeLFy+mtLQUjUZj9pz4+HgAjh07Rps2bQgLC6tyl9vZs2cBCAsLM/1bWXZ5HT8/Pzw9PdFoNGg0Got1KvdhiU6nQ6eT8UJCCCFEU+GwMU0DBgxg//79pKammh49evRgzJgxpKamVkmYAFJTUwEIDw8HICEhgf3795vd5bZ582b8/PyIi4sz1dm6davZfjZv3kxCQgIAWq2W7t27m9UxGo1s3brVVEcIIYQQwmEtTb6+vnTs2NGszNvbm6CgIDp27Mjx48dZsWIFQ4cOJSgoiH379jFt2jT69Oljmppg0KBBxMXFce+997Jw4UKysrKYM2cOkydPNrUCTZw4kcWLFzNjxgzuv/9+tm3bxmeffcb69etNx50+fTpjx46lR48eXH/99bz++usUFhYyfvz4hntBhBBCCNGoNdoZwbVaLVu2bDElMFFRUYwcOZI5c+aY6mg0GtatW8ekSZNISEjA29ubsWPH8txzz5nqREdHs379eqZNm8aiRYuIjIzk/fffJzEx0VTnzjvv5Ny5c8ydO5esrCy6du3Kxo0bqwwOdwS384fwPrSSssC2FHW429HhCCGEEE2WSlEUpfZqojZ5eXn4+/uTnp6Or6+v3fbrdegzAr57hrLAtpwb9aXd9iuEEEIIyM/PJzY2ltzcXLO5IC1x+DxNombFrRNR1O64XzyC28Ujjg5HCCGEaLIkaWrkFJ0/JS36AOB5bJ2DoxFCCCGaLkmanEBxzK3An0mTYnRwNEIIIUTTJEmTEyhp0Q+juzduBZlos/Y4OhwhhBCiSZKkyRm4eVASPQiQLjohhBDCUSRpchLFMUkAeJ7YCAa9g6MRQgghmh5JmpxEaUQvDJ7BqEtz0Z3+wdHhCCGEEE2OJE3OQq2huM0QQLrohBBCCEeQpMmJFMcMA8Djt22o9IUOjkYIIYRoWiRpciJlzTtS7t8StaEEj9+31v4EIYQQQtiNJE3ORKWiuM1lczYJIYQQosFI0uRkiq6tSJp0p3ehLr7g4GiEEEKIpkOSJidj8G+FvnknVIoBz+NfOzocIYQQosmQpMkJFV9bMSDc8+hXDo5ECCGEaDokaXJCxW2Goqjc0J47gFvOCUeHI4QQQjQJkjQ5IaNnEKVRNwHgeURam4QQQoiGIEmTkyq69jYAPI99BYrRwdEIIYQQrk+SJidV0rI/Rncf3Aoy0WbudnQ4QgghhMuTpMlZuXlQ3DoRAM+jXzo4GCGEEML1SdLkxIrb3g6A54lvoLzEwdEIIYQQrk2SJiemD+tOuU8E6rICPH7f5uhwhBBCCJcmSZMzU6lNczZ5yZxNFmXn60k5lU92vt7RoQghhHBybo4OQFydomtvx/fXd9Cd+h518QWMnkGODqnRWJt2gZe2ZWBUQK2CmTe3YFgHeX2EEELUjbQ0OTlDQPRfy6ocW+/ocBqN7Hy9KWECMCrw0rYMaXESQghRZ5I0uYCia/8cEH70K+mO+tOpnFJTwlTJqMDp3FLHBCSEEMLpSdLkAkpihqKo3dGeT2PWso08svoYI5amsTbtgqNDc5ioAB1qlXmZWgWR/jrHBCSEEMLpSdLkAowezciN6A3AHeqdFWVNvDsqxFfLzJtbmBKnyjFNIb5axwYmhBDCaclAcBdxJGQo15/exgjN9ywsH40Bjak7qqkmCsM6BBHfwpfTuaVE+uua7OsghBDCPqSlyUVor+3HecWP5qpc+qj3AdIdBRUtTt0ifSVhEkIIcdUkaXIRIf7enLlmCACjNDukO0oIIYSwM0maXEhYwmgABrv/ypd3XSNzEgkhhBB21GiSpgULFqBSqZg6daqprKSkhMmTJxMUFISPjw8jR47k7NmzZs/LyMggKSkJLy8vQkJCePLJJykvLzers337drp164ZOpyMmJoalS5dWOf6bb75Jq1at8PDwID4+np9//rk+TrNelQfFUhbUHo2xjKizmx0djhBCCOFSGkXS9Msvv/DOO+/QuXNns/Jp06axdu1aVq1axY4dOzhz5gwjRowwbTcYDCQlJaHX69m1axfLli1j6dKlzJ0711Tn5MmTJCUl0b9/f1JTU5k6dSoPPPAAmzZtMtVZuXIl06dP55lnnmHPnj106dKFxMREsrOz6//k7ayo7XAAvI6scWgcQgghhKtRKYqi1F6t/hQUFNCtWzfeeustXnjhBbp27crrr79Obm4uzZs3Z8WKFYwaNQqA9PR02rdvT3JyMr169WLDhg3ceuutnDlzhtDQUACWLFnCzJkzOXfuHFqtlpkzZ7J+/XoOHDhgOubo0aPJyclh48aNAMTHx9OzZ08WL14MgNFoJCoqikceeYRZs2ZZdR55eXn4+/uTnp6Or6+vPV8im6iLLxL6cV9USjnZo76kPLCtw2IRQgghGrv8/HxiY2PJzc3Fz8+vxroOb2maPHkySUlJDBw40Kw8JSWFsrIys/LY2FhatGhBcnIyAMnJyXTq1MmUMAEkJiaSl5dHWlqaqc6V+05MTDTtQ6/Xk5KSYlZHrVYzcOBAUx1LSktLycvLM3s0BkbPQEpa9gXA68iXDo5GCCGEcB0OTZo+/fRT9uzZw/z586tsy8rKQqvVEhAQYFYeGhpKVlaWqc7lCVPl9sptNdXJy8ujuLiY8+fPYzAYLNap3Icl8+fPx9/f3/SIioqy7qQbQHHbO4CKZVUwltdSWwghhBDWcFjSdOrUKR577DGWL1+Oh4eHo8Kos9mzZ5Obm2t6nDp1ytEhmZS06IPBIxBN8Xl0p75zdDhCCCGES3BY0pSSkkJ2djbdunXDzc0NNzc3duzYwRtvvIGbmxuhoaHo9XpycnLMnnf27FnCwsIACAsLq3I3XeXPtdXx8/PD09OT4OBgNBqNxTqV+7BEp9Ph5+dn9mg01O4Ux9wKgNfh1Q4ORgghhHANDkuaBgwYwP79+0lNTTU9evTowZgxY0z/d3d3Z+vWrabnHD58mIyMDBISEgBISEhg//79Zne5bd68GT8/P+Li4kx1Lt9HZZ3KfWi1Wrp3725Wx2g0snXrVlMdZ1QUOxIAj9+/RV3cdBfuFUIIIezFYWvP+fr60rFjR7Myb29vgoKCTOUTJkxg+vTpBAYG4ufnxyOPPEJCQgK9evUCYNCgQcTFxXHvvfeycOFCsrKymDNnDpMnT0anq1g+ZOLEiSxevJgZM2Zw//33s23bNj777DPWr19vOu706dMZO3YsPXr04Prrr+f111+nsLCQ8ePHN9CrYX/lgW3RN++E9tx+PI9+RWFn5z0XIYQQojFo1Av2vvbaa6jVakaOHElpaSmJiYm89dZbpu0ajYZ169YxadIkEhIS8Pb2ZuzYsTz33HOmOtHR0axfv55p06axaNEiIiMjef/990lMTDTVufPOOzl37hxz584lKyuLrl27snHjxiqDw51NUbuRaM/txyv9fxR2GgcqlaNDEkIIIZyWw+dpchWNZZ6my6n0+YR+1Ae1oYRzt6+gLPQ6R4ckhBBCNCpONU+TqD+K1peS1hUtal6Hv3BwNEIIIYRzk6TJxVUOCPc8/jWqskIHRyOEEEI4L0maXJw+rAflfi1QlxXhcWJT7U8QQgghhEWSNLk6lYqidhWtTV7p/3NwMEIIIYTzkqSpCShqOxxFpUZ3dg9uOSccHY4QQgjhlCRpagKM3iGURvUBwFMGhAshhBB1IklTE1HUbgQAXke+BGOZg6MRQgghnI8kTU1ESct+GDyD0RSfx+P3bx0djhBCCOF0JGlqKtTuFLW7AwCvQ6scHIwQQgjhfCRpakKKYkcBoDv9A5r8PxwcjRBCCOFcJGlqQgx+LSi9JgEVCl7pnzs6HCGEEMKpSNLUxBS2/zsAXof/B8ZyB0cjhBBCOA9JmpqYkpY3Y/AIRFN0Do+MHY4ORwghhHAakjQ1NRotxW2HA+B16DPHxiKEEEI4EUmamqBC04Dw79EUnHFwNEIIIYRzkKSpCTIERFMaEY9KMeKVLjOE17fsfD0pp/LJztc7OhQhhBBXQZKmJqow9m9A4xgQ7spJxdq0C4xYmsYjq48xYmkaa9MuODokIYQQdeTm6ACEY5RE34JBF4CmMAvdqe8obdnfIXGsTbvAS9syMCqgVsHMm1swrEOQQ2Kxt+x8vencAIwKvLQtg/gWvoT4ah0bnBBCCJtJS1NTddmAcO9DKx0SQnVJhau0OJ3KKTWdWyWjAqdzSx0TkBBCiKsiSVMTVjlnky5jp0NmCHf1pCIqQIdaZV6mVkGkv84xAQkhhLgqkjQ1YYaAaEqv6VUxQ7gDph9w9aQixFfLzJtbmM6xsvtRuuaEEMI5yZimJq6w/V3o/vgRr8P/I7/7ZNA03Ad6ZVJx5ZgmV0oqhnUIIr6FL6dzS4n017nUuQkhRFMjSVMTV9KqPwav5hUzhJ/cTElMUoMevykkFSG+Wpc8LyGEaGqke64Jy87Xk/JHCedajwDA+9CnDokjxFdLt0i5o0wIIUTjJi1NTdTlt/pHqDryvU6DLnM3bhePUh54raPDE0IIIRodaWlqgq681f+MEsRmw3UAeDlo+gEhhBCisZOkqQmydKv/x4aBAHgd+RJVWaEDohJCCCEaN0mamiBLt/rvUjpS6hOFuqwAz+NfX/UxXHlpFCGEEE2TJE1NkKX5g2bc3IqSDqMB8Dr4KShKDXuomay3JoQQwhXJQPAmytKt/sUld+C3exHa8wdxz95LWWhXm/cr660JIYRwVQ5taXr77bfp3Lkzfn5++Pn5kZCQwIYNG0zb+/Xrh0qlMntMnDjRbB8ZGRkkJSXh5eVFSEgITz75JOXl5WZ1tm/fTrdu3dDpdMTExLB06dIqsbz55pu0atUKDw8P4uPj+fnnn+vlnBuTK2/1N3o0o7jNUAC801bUaZ+uvjSKEEKIpsuhSVNkZCQLFiwgJSWF3bt3c/PNN3P77beTlpZmqvPggw+SmZlpeixcuNC0zWAwkJSUhF6vZ9euXSxbtoylS5cyd+5cU52TJ0+SlJRE//79SU1NZerUqTzwwANs2rTJVGflypVMnz6dZ555hj179tClSxcSExPJzs5umBeiESnsMAYAzxMbURedN9tmzTglV18aRQghRNOlUpSrGLxSDwIDA3n55ZeZMGEC/fr1o2vXrrz++usW627YsIFbb72VM2fOEBoaCsCSJUuYOXMm586dQ6vVMnPmTNavX8+BAwdMzxs9ejQ5OTls3LgRgPj4eHr27MnixYsBMBqNREVF8cgjjzBr1iyr4s7Ly8Pf35/09HR8fX2v4hVwvOA1o9Fm7yWvx6MUdJsEmM/rVLncybAOQRafb0tdIYQQwpHy8/OJjY0lNzcXPz+/Gus2moHgBoOBTz/9lMLCQhISEkzly5cvJzg4mI4dOzJ79myKiopM25KTk+nUqZMpYQJITEwkLy/P1FqVnJzMwIEDzY6VmJhIcnIyAHq9npSUFLM6arWagQMHmuo0NZWtTd6HVoKxrNpxStW1OA3rEMQX4zqweEQMX4zrIAmTEEIIl+DwgeD79+8nISGBkpISfHx8WL16NXFxcQDcfffdtGzZkoiICPbt28fMmTM5fPgwX3zxBQBZWVlmCRNg+jkrK6vGOnl5eRQXF3Pp0iUMBoPFOunp6dXGXVpaSmnpX+N08vLy6vgKND7FrRPx+/ElNIVn8fhtK6fcb6x2nFJ1g7tlvTUhhBCuxuFJU7t27UhNTSU3N5fPP/+csWPHsmPHDuLi4njooYdM9Tp16kR4eDgDBgzg+PHjtGnTxoFRw/z583n22WcdGkO90Wgpiv0bvr8uwTttOVH9bkatwixxknFKQgghmhqHd89ptVpiYmLo3r078+fPp0uXLixatMhi3fj4eACOHTsGQFhYGGfPnjWrU/lzWFhYjXX8/Pzw9PQkODgYjUZjsU7lPiyZPXs2ubm5psepU6dsOOvGrzBuNIqqYj26CP3JKvM6zby5hbQkCSGEaFIcnjRdyWg0mnV7XS41NRWA8PBwABISEti/f7/ZXW6bN2/Gz8/P1MWXkJDA1q1bzfazefNm07gprVZL9+7dzeoYjUa2bt1qNrbqSjqdzjRVQuXDlRi9QymJvgWomH5AxikJIYRo6hyaNM2ePZudO3fy22+/sX//fmbPns327dsZM2YMx48f5/nnnyclJYXffvuNr776ivvuu48+ffrQuXNnAAYNGkRcXBz33nsve/fuZdOmTcyZM4fJkyej01V0HU2cOJETJ04wY8YM0tPTeeutt/jss8+YNm2aKY7p06fz3nvvsWzZMg4dOsSkSZMoLCxk/PjxDnldGovCDncD4HlsLarSXOCqJgoXQgghnJpDxzRlZ2dz3333kZmZib+/P507d2bTpk3ccsstnDp1ii1btvD6669TWFhIVFQUI0eOZM6cOabnazQa1q1bx6RJk0hISMDb25uxY8fy3HPPmepER0ezfv16pk2bxqJFi4iMjOT9998nMTHRVOfOO+/k3LlzzJ07l6ysLLp27crGjRurDA5vavRhPSgLbIv7xSOc3PEx9xy+QaYREEII0WQ1unmanJUrzdN0Oa9DnxHw3TNkKM3pV/oaxj8bJ9Uq+GJcBxnXZIXsfD2nckqJCtDJ6yWEEI2MU87TJBqn4muHoXf3o4XqHAPVKaZyWRrFOrJ4sRBCuA5JmkSNFDdPLl37NwDGa/5aekamHKidrZOCCiGEaNwkaRK1UrregxENCZqDxKl+kykHrCSLFwshhGtx+OSWovEz+oRR0iYRr+Nf807rXVzokyQJkxUqFy+WSUGFEMI1SEuTsEphx3sBiMzcRJhbvoOjcQ4hvlqZFFQIIVyItDQJq5SFdkUf0hlt9j68Dn5KQffJjg7JKQzrEER8C19O55YS6S93zwkhhDOTliZhtcKO9wHgffBTMMhgZmuF+GrpFukrCZMQQjg5SZqE1YpbD8LgFYKm+Dyexzc4OhwhhBCiQUnSJKyndqewwxgAvA/8V9ZUEUII0aRI0iRsUtj+bygaHdrzB9FmpdT+BCGEEMJFSNIkbKJ4NKPo2tsB8N631LHBCCGEEA1IkiZhs8LOYwHw+H0bmpyTDo6m8cvO15NyKl9mAhdCCCcnSZOwWXlAa0pa9EeFgs/+ZY4Op1GTteeEEMJ1SNIk6qSg8zgAvI6sQV1yybHBNFKy9pwQQrgWSZpEnejDe6IP7oDKUIrXwU+qbJcuKVl7TgghXI0kTaJuVCoKO48HwDttBZT/lQhIl1SFyrXnLidrzwkhhPOSpEnUWXHrQZR7h6MpvoDXsa8A6ZK6nKw9J4QQrkXWnhN1p3ansNO9+P+4EO99yyhqN7LGLqmmmCzI2nNCCOE6pKVJXJWi2L9hdPfBPec4ulPfSZeUBbL2nBBCuAZJmsRVUbQ+FLX/GwA+ez+QLikLZFC8EEK4BumeE1etoOO9eO//CF3mz7hn72dYh07SJfWntWkXTGO8KhPIYR2CHB2WEEKIOpCWJnHVjD7hFMckAeCz931AuqRABsULIYSrkaRJ2EVBlwkAeJzcLEur/EnmaRJCCNciSZOwi/LAaylp0a9iaZV9Hzo6nEZBBsULIYRrkaRJ2E1B1weAP5dWKcp2cDSOJ4PihRDCtchAcGE3+rDu6EOvQ3v2V7z3f0R+/OOODsnhZJ4mIYRwHdLSJOwqv0tFa5P3wU9R6fMdHE3juN1fBsULIYRrkJYmYVelLftR1qwN7peO433wUwq6PuiwWOR2fyGEEPYkLU3CvlRqCjpX3Ennvf+/Zgv5NiS53V8IIYS9SdIk7K44JgmDdxia4vN4Hf3SITHI7f5CCCHsTZImYX8aLQWdxwHgk/o+GMsbPAS53V8IIYS9OTRpevvtt+ncuTN+fn74+fmRkJDAhg0bTNtLSkqYPHkyQUFB+Pj4MHLkSM6ePWu2j4yMDJKSkvDy8iIkJIQnn3yS8nLzD+nt27fTrVs3dDodMTExLF26tEosb775Jq1atcLDw4P4+Hh+/vnnejnnpqIo9m8YPJrhln8Kz+NfN/jx5XZ/IYQQ9ubQpCkyMpIFCxaQkpLC7t27ufnmm7n99ttJS0sDYNq0aaxdu5ZVq1axY8cOzpw5w4gRI0zPNxgMJCUlodfr2bVrF8uWLWPp0qXMnTvXVOfkyZMkJSXRv39/UlNTmTp1Kg888ACbNm0y1Vm5ciXTp0/nmWeeYc+ePXTp0oXExESys2WuobpS3L0o7DQWAJ/Ud0ExNngMwzoE8cW4DiweEcMX4zrIIHAhhBBXRaUoilJ7tb/s2bMHd3d3OnXqBMCXX37Jhx9+SFxcHPPmzUOrvbpv8oGBgbz88suMGjWK5s2bs2LFCkaNGgVAeno67du3Jzk5mV69erFhwwZuvfVWzpw5Q2hoKABLlixh5syZnDt3Dq1Wy8yZM1m/fj0HDhwwHWP06NHk5OSwceNGAOLj4+nZsyeLFy8GwGg0EhUVxSOPPMKsWbOsijsvLw9/f3/S09Px9fW9qtfAVaj0+YSuGIBan8/FW96gJPoWR4ckhBBCmMnPzyc2Npbc3Fz8/PxqrGtzS9PDDz/MkSNHADhx4gSjR4/Gy8uLVatWMWPGjLpFTEWr0aeffkphYSEJCQmkpKRQVlbGwIEDTXViY2Np0aIFycnJACQnJ9OpUydTwgSQmJhIXl6eqbUqOTnZbB+VdSr3odfrSUlJMaujVqsZOHCgqY4lpaWl5OXlmT2EOUXrS2GHuwHw+fUdsC0/F0IIIRoVm5OmI0eO0LVrVwBWrVpFnz59WLFiBUuXLuV///ufzQHs378fHx8fdDodEydOZPXq1cTFxZGVlYVWqyUgIMCsfmhoKFlZWQBkZWWZJUyV2yu31VQnLy+P4uJizp8/j8FgsFinch+WzJ8/H39/f9MjKirK5nNvCgo73ofRzRPt+TR0p39wdDhCCCFEndmcNCmKgtFYMT5ly5YtDB06FICoqCjOnz9vcwDt2rUjNTWVn376iUmTJjF27FgOHjxo834a2uzZs8nNzTU9Tp065eiQGiWjZyBFsX8DwOfXJQ6ORgghhKg7m5OmHj168MILL/DRRx+xY8cOkpKSgIoB11e21lhDq9USExND9+7dmT9/Pl26dGHRokWEhYWh1+vJyckxq3/27FnCwsIACAsLq3I3XeXPtdXx8/PD09OT4OBgNBqNxTqV+7BEp9OZ7vqrfAjLCrrcj6J2R5eVgjZzt6PDEUIIIerE5qTp9ddfZ8+ePUyZMoV//vOfxMTEAPD5559zww03XHVARqOR0tJSunfvjru7O1u3bjVtO3z4MBkZGSQkJACQkJDA/v37ze5y27x5M35+fsTFxZnqXL6PyjqV+9BqtXTv3t2sjtFoZOvWraY64uoYvUMpansHIK1NQgghnJdNa88ZDAZycnLYuXMnzZo1M9v28ssvo9FobDr47NmzGTJkCC1atCA/P58VK1awfft2Nm3ahL+/PxMmTGD69OkEBgbi5+fHI488QkJCAr169QJg0KBBxMXFce+997Jw4UKysrKYM2cOkydPRqermMRw4sSJLF68mBkzZnD//fezbds2PvvsM9avX2+KY/r06YwdO5YePXpw/fXX8/rrr1NYWMj48eNtOh9RvYKuD+B1+H94nP4B9+y9lIV0cXRIQgghhE1sSpo0Gg2DBg3i0KFDVZImDw8Pmw+enZ3NfffdR2ZmJv7+/nTu3JlNmzZxyy0Vt6a/9tprqNVqRo4cSWlpKYmJibz11ltm8axbt45JkyaRkJCAt7c3Y8eO5bnnnjPViY6OZv369UybNo1FixYRGRnJ+++/T2JioqnOnXfeyblz55g7dy5ZWVl07dqVjRs31qm7UVhm8Iui+Nrb8TryBb4pb3JxyLuODkkIIYSwic3zNPXo0YOXXnqJAQMG1FdMTknmaaqdJi+DkJVDUSkGzg1fSVlIZ0eHJIQQoomr13maXnjhBZ544gnWrVtHZmamzFUkrGbwa0HxtcMA8N3zVi21hRBCiMbFpu45wDTFwG233YZK9deKqIqioFKpMBgM9otOuJz86x7G8+hXeGTswP3cAcqad3R0SEIIIYRVbE6avv322/qIQzQRBv9WFMfcitfRr/BNeYuLg6XFSQghhHOwOWnq27dvfcQhmpD86ybieWwdHhnf4n4+jbLgDo4OSQghhKiVzUlTpaKiIjIyMtDr9WblnTvL4F5RM0NANMVtkvA6thaflLe5lLjY0SEJIYQQtbI5aTp37hzjx49nw4YNFrfLmCZhjYJuE/E8vh7P37eSf/4g5cFxjg6p3mTn6zmVU0pUgI4QX62jwxFCCFFHNt89N3XqVHJycvjpp5/w9PRk48aNLFu2jGuvvZavvvqqPmIULqg8oDXFbSpuKvDb7botTWvTLnDHh2k8svoYd3yYxtq0C44OSQghRB3ZnDRt27aNV199lR49eqBWq2nZsiX33HMPCxcuZP78+fURo3BR+d3+gaJSV4xtyt7n6HDsLjtfz4KtGVROhKYAC7ZmkJ2vr+lpQgghGimbk6bCwkJCQkIAaNasGefOnQOgU6dO7Nmzx77RCZdmCIim+NrbAfDd/YaDo7G/fZmFXDlzrALszyx0RDhCCCGuks1JU7t27Th8+DAAXbp04Z133uGPP/5gyZIlhIeH2z1A4doqWpvc8Dj9A9rM3Y4Ox65UtVcRQgjhRGxOmh577DEyMzMBeOaZZ9iwYQMtWrTgjTfe4MUXX7R7gMK1GfwiKYodCfzZ2mTbqj6NWqdw7yqJk+rPciGEEM7H5rXnrlRUVER6ejotWrQgODjYXnE5HVl7ru7UBZmEfpqIyljG+aQP0F+T4OiQ7GZt2gVe2paBUQG1Cmbe3IJhHYIcHZYQQog/2bL2nE1TDuTl5eHj44Na/VcDlZeXF127dqWgoKBu0Yomz+gTTmH7O/FJ+xi/XxZxPqIXqFyjc2tYhyDiW/hyOreUSH+ZckAIIZyZ1d1zq1evpkePHpSUlFTZVlxcTM+ePVm7dq1dgxNNR8F1D2LUeKDN3ovu1E5Hh2NXIb5aukX6SsIkhBBOzuqk6e2332bGjBl4eXlV2ebt7c3MmTNZvNh159sR9cvoFUJRh7sB8P1lEShGB0ckhBBCmLM6aTpw4AD9+vWrdnufPn3Yv3+/PWISTVRBlwkY3b3QXjiEx8lvHB2OEEIIYcbqpOnSpUuUl5dXu72srIxLly7ZJSjRNBk9AynofD8Afr+8DsYyxwYkhBBCXMbqpKlVq1bs3l39PDq7d++mZcuWdglKNF2FncZh8GiGW+7veB1e7ehwhBBCCBOrk6YRI0bwz3/+k7Nnz1bZlpWVxZw5cxg5cqRdgxNNj6L1puC6iQD4prwJ5VVvPBBCCCEcwep5mvLz80lISCAjI4N77rmHdu3aAZCens7y5cuJiorixx9/bLJzFMk8TXZk0BOycjBuBZnkxj9BYZcJjo5ICCGEi6qXeZp8fX354YcfmD17NitXrjSNXwoICOCee+7hX//6lyQLwj40WvJ7PEqz7bPxTX2Poti/oehqfiMLIYQQ9a1OM4IrisL58+dRFIXmzZujcpGJCK+GtDTZmdFA8//djvul4+R3fZj866c6OiIhhBAuyJaWJpvXngNQqVQ0b96ckJAQSZhE/VBryO85DQDvA/9FXZTt4ICEEEI0dXVKmoRoCCUtb0Yf0gV1eTG+KW85OhwhhBBNnCRNovFSqciLfwIAr/TPccs54eCAhBBCNGWSNIlGTR/eg+KWA1ApBnx/esXR4QghhGjCrEqaAgMDOX/+PAD3338/+fn59RqUEJfLj5+OotLg+fs2tJm/ODocIYQQTZRVSZNerycvLw+AZcuWUVIiEw6KhlMe0Jqi2FEA+P34stMt5pudryflVD7Z+XpHhyKEEOIqWDVPU0JCAsOHD6d79+4oisKjjz6Kp6enxboffPCBXQMUAiC/+xQ8j61Fe24/Hsc3UBKT5OiQrLI27QIvbcvAqIBaBTNvbsGwDkGODksIIUQdWNXS9PHHHzN06FAKCgpQqVTk5uZy6dIliw8h6oPRK5iCP2cG9/vlNTA0/lab7Hy9KWECMCrw0rYMaXESQggnZVVLU2hoKAsWLAAgOjqajz76iKAg+bYsGlZhp3F4H/wUt/w/8E5bQWHncY4OqUanckpNCVMlowKnc0sJ8dU6JighhBB1ZvPdcydPnrRbwjR//nx69uyJr68vISEhDB8+nMOHD5vV6devHyqVyuwxceJEszoZGRkkJSXh5eVFSEgITz75JOXl5WZ1tm/fTrdu3dDpdMTExLB06dIq8bz55pu0atUKDw8P4uPj+fnnn+1ynsI+FHcv8ns8CoDvr0tQleQ4NqBaRAXoUF8x96taBZH+OscEJIQQ4qrUacqBHTt2MGzYMGJiYoiJieG2227ju+++q9N+Jk+ezI8//sjmzZspKytj0KBBFBYWmtV78MEHyczMND0WLlxo2mYwGEhKSkKv17Nr1y6WLVvG0qVLmTt3rqnOyZMnSUpKon///qSmpjJ16lQeeOABNm3aZKqzcuVKpk+fzjPPPMOePXvo0qULiYmJZGfLTNSNSVHbOygLbIu6NBffPY17wssQXy0zb25hSpwqxzRJK5MQQjgnm9ee+/jjjxk/fjwjRozgxhtvBOCHH35g9erVLF26lLvvvrvOwZw7d46QkBB27NhBnz59gIqWpq5du/L6669bfM6GDRu49dZbOXPmDKGhoQAsWbKEmTNncu7cObRaLTNnzmT9+vUcOHDA9LzRo0eTk5PDxo0bAYiPj6dnz54sXrwYAKPRSFRUFI888gizZs2qNXZZe67haP9IJnj9/SgqDedGfUl5szaODqlG2fl6TueWEumvk4RJCCEamXpde+5f//oXCxcuZOXKlTz66KM8+uijrFy5kgULFvD888/XOWiA3NxcoGJeqMstX76c4OBgOnbsyOzZsykqKjJtS05OplOnTqaECSAxMZG8vDzS0tJMdQYOHGi2z8TERJKTk4GKKRVSUlLM6qjVagYOHGiqc6XS0lLy8vLMHqJh6K9JoLjlzagUA34/Lqz9CQ4W4qulW6SvJExCCOHkbE6aTpw4wbBhw6qU33bbbZw8ebLOgRiNRqZOncqNN95Ix44dTeV33303H3/8Md9++y2zZ8/mo48+4p577jFtz8rKMkuYANPPWVlZNdbJy8ujuLiY8+fPYzAYLNap3MeV5s+fj7+/v+kRFRVV53MXtsvr9SSK2h2PUzvRnbK9a1gIIYSwlVV3z10uKiqKrVu3EhMTY1a+ZcuWq0ocJk+ezIEDB/j+++/Nyh966CHT/zt16kR4eDgDBgzg+PHjtGnjuG6Z2bNnM336dNPPeXl5kjg1IIN/Kwo7jMFn/1L8kl/i3DUJoLb57dwgsvP1nMopJSpAuueEEMKZ2fwp8/jjj/Poo4+SmprKDTfcAFSMaVq6dCmLFi2qUxBTpkxh3bp17Ny5k8jIyBrrxsfHA3Ds2DHatGlDWFhYlbvczp49C0BYWJjp38qyy+v4+fnh6emJRqNBo9FYrFO5jyvpdDp0OrkLypHyu03C88ga3HOO43VoJUUdxjg6pCpkckshhHAdNnfPTZo0iU8//ZT9+/czdepUpk6dyoEDB1i5ciUPP/ywTftSFIUpU6awevVqtm3bRnR0dK3PSU1NBSA8PByomK18//79Zne5bd68GT8/P+Li4kx1tm7darafzZs3k5CQAIBWq6V79+5mdYxGI1u3bjXVEY2PovMjv2fFFAR+u/+DqjTXwRGZk8kthRDCtdSpP+OOO+7gjjvuuOqDT548mRUrVvDll1/i6+trGj/k7++Pp6cnx48fZ8WKFQwdOpSgoCD27dvHtGnT6NOnD507dwZg0KBBxMXFce+997Jw4UKysrKYM2cOkydPNrUETZw4kcWLFzNjxgzuv/9+tm3bxmeffcb69etNsUyfPp2xY8fSo0cPrr/+el5//XUKCwsZP378VZ+nqD9FsX/DO20F7peO4ZvyJnk3POXokExkckshhHAtdZqnyV7efvttcnNz6devH+Hh4abHypUrgYoWoC1btjBo0CBiY2N5/PHHGTlyJGvXrjXtQ6PRsG7dOjQaDQkJCdxzzz3cd999PPfcc6Y60dHRrF+/ns2bN9OlSxdeeeUV3n//fRITE0117rzzTv79738zd+5cunbtSmpqKhs3bqwyOFw0Mmo3chNmA+CdtgK3i0ccHNBfZHJLIYRwLTbP0yQsk3ma6s4eA6WbffMonr9tpjT8ei7cuhRUqlqf0xBkTJMQQjRutszT1DhvNxJNhr2SiryEmRXTD2T+jMeJjZS0GVIP0dpuWIcg4lv4yuSWQgjhAhzaPSeaNnsOlDb4XkN+1wcB8P9xIaqyolqe0XBkckshhHANdU6a9Ho9hw8frrIwrhDWqmmgdF0UdJlAue81aAqz8Pn1XTtEKIQQQvzF5qSpqKiICRMm4OXlRYcOHcjIyADgkUceYcGCBXYPULguuw+UdvMgL6FinUCffR+gyf39KiMUQggh/mJz0jR79mz27t3L9u3b8fDwMJUPHDjQdNebENYI8dUy8+YWpsSpckzT1XRjlbQcQEnkTaiMZfgnz7dTpEIIIUQdBoKvWbOGlStX0qtXL1SX3aHUoUMHjh8/btfghOuz+0BplYrcG55C9/nteGTswOO3rZS0GmCfYIUQQjRpNrc0nTt3jpCQkCrlhYWFZkmUENay90BpQ0A0BZ3GAuC361+NalC4EEII52Vz0tSjRw+zmbQrE6X3339flhwRjUZBt0mU+0TgVpCJz563HB2OEEIIF2Bz99yLL77IkCFDOHjwIOXl5SxatIiDBw+ya9cuduzYUR8xCmEzxd2L3BvnELTpH/jsW0bxtbdRHtjW0WEJIYRwYja3NN10002kpqZSXl5Op06d+OabbwgJCSE5OZnu3bvXR4xC1Elpy/4UtxyASinH//vnQCa/F0IIcRXqNCN4mzZteO+99+wdixB2l3fjU+j+2IUuKwXPI2sobnf1C03byh7LxAghhHA8m1uavv76azZt2lSlfNOmTWzYsMEuQQlhLwafCAq6/QMAv59eRlVyqUGPvzbtAiOWpvHI6mOMWJrG2rQLdt1/dr6elFP5dZpFXQghhG1sTppmzZqFwWCoUq4oCrNmzbJLUELYU0HnsZQ1i0FTcgm/n19tsOPac5kYS+o7IRNCCGHO5qTp6NGjxMXFVSmPjY3l2LFjdglKCLtSu5Pbex4A3umfoz3zc4Mc1t7LxFyuvhMyIYQQVdmcNPn7+3PixIkq5ceOHcPb29suQQlhb/qw7hTG/h2AgO+egfKrT1xqY/dlYi5TnwmZEEIIy2xOmm6//XamTp1qNvv3sWPHePzxx7ntttvsGpwQ9pQX/zgGr+a45f6G769v1/vx6mOZmEr1mZAJIYSwzOakaeHChXh7exMbG0t0dDTR0dG0b9+eoKAg/v3vf9dHjELYhaLzI/fGOQD4pP4fbheP1Psxh3UI4otxHVg8IoYvxnVgWIcgu+w3xFfL4NhAs7LBsYFyd54QQtQjm6cc8Pf3Z9euXWzevJm9e/fi6elJ586d6dOnT33EJ4RdlUQPorjVADx/20rAzqc5f9sKUGvq9Zghvlq7JzPZ+Xo2pl80K9uYfpGHeoVL4iSEEPWkTvM0qVQqBg0axKBBg+wdjxD1LvfGp9H98RPa7H14H/yEwo73ODokm9U0pkmSJiGEqB9WJU1vvPEGDz30EB4eHrzxxhs11n300UftEpgQ9cXoHUpe/HQCvn8O319eo6TVzRh8IpxqEsrKMU2XJ04ypkkIIeqXSlFqX1siOjqa3bt3ExQURHR0dPU7U6ks3lnXFOTl5eHv7096ejq+vr6ODkfURjES9NW96M7uoSTyRpa1WMBL357CqPw1YNte44/qy9q0C6ZpB5wlZiGEaGzy8/OJjY0lNzcXPz+/GutalTSJ2knS5Hw0OScJ+d8dqAylzCp7kE8N/U3b1Cr4YlyHRt/ilJ2v53RuKZH+jb91TAghGiNbkiab7p4rKyujTZs2HDp06KoCFKIxMAREk9ejojv5KbePCeOvGbWdZc6jEF8t3SJ9JWESQogGYFPS5O7uTklJSX3FIkSDK+w0lsKgTvipipnv/j5Q0fDqiuODZJ06IYS4OjbP0zR58mReeuklysvL6yMeIRqWWkPhzfMpV2npr9nLSPV3dp2EsrFYm3aBO/5cp+4OWadOCCHqxOYxTXfccQdbt27Fx8eHTp06VVk65YsvvrBrgM5CxjQ5N5/U9/D7+VX0br4cSfqCwNBIR4dkN9n5eu74MI3Lf9FVwOrxjX/MlhBC1DdbxjTZPE9TQEAAI0eOrHNwQjRGBZ3H43HyG7TnDhDz67+4mPgWqFS1P9EJ7M8s5MpvRsqf5QMkaRJCCKvZnDR9+OGH9RGHEI6ldiOn779o/sUoPDK243X4fxTFjnJ0VHYht8cKIYR9WD2myWg08tJLL3HjjTfSs2dPZs2aRXFxcX3GJkSDKg9sS17PxwDwS56PJu+0gyOyj87h3lzZZqYCOoV7W6ouhBCiGlYnTf/617946qmn8PHx4ZprrmHRokVMnjy5PmMTosEVdhpHaVh31GVFBGyfBUaDo0O6aiG+WmYNaGFKnFTArAGuNdBdCCEagtVJ03//+1/eeustNm3axJo1a1i7di3Lly/HaDTW+eDz58+nZ8+e+Pr6EhISwvDhwzl8+LBZnZKSEiZPnkxQUBA+Pj6MHDmSs2fPmtXJyMggKSkJLy8vQkJCePLJJ6vc3bd9+3a6deuGTqcjJiaGpUuXVonnzTffpFWrVnh4eBAfH8/PP/9c53MTTkqtIafffIzuXuiyUvDev8zREdnFsA5BrB7fgcUjYlg9voPMHC6EEHVgddKUkZHB0KFDTT8PHDgQlUrFmTNn6nzwHTt2MHnyZH788Uc2b95MWVkZgwYNorCw0FRn2rRprF27llWrVrFjxw7OnDnDiBEjTNsNBgNJSUno9Xp27drFsmXLWLp0KXPnzjXVOXnyJElJSfTv35/U1FSmTp3KAw88wKZNm0x1Vq5cyfTp03nmmWfYs2cPXbp0ITExkezs7Dqfn3BOBr8o8hJmAeD3y+u4XTzi4IjsQybCFEKIq2P1lAMajYasrCyaN29uKvP19WXfvn01rkdni3PnzhESEsKOHTvo06cPubm5NG/enBUrVjBqVMWg3PT0dNq3b09ycjK9evViw4YN3HrrrZw5c4bQ0FAAlixZwsyZMzl37hxarZaZM2eyfv16Dhw4YDrW6NGjycnJYePGjQDEx8fTs2dPFi9eDFSM4YqKiuKRRx5h1qxZtcYuUw64GEUhcNM/8MjYTllQLOeGrwSNJBtCCOFq6mUZFUVRGDduHCNGjDA9SkpKmDhxolnZ1cjNzQUgMDAQgJSUFMrKyhg4cKCpTmxsLC1atCA5ORmA5ORkOnXqZEqYABITE8nLyyMtLc1U5/J9VNap3IderyclJcWsjlqtZuDAgaY6oolRqcjp8xwGXQDuF9Lx3f2GoyMSQgjhYFZPOTB27NgqZffcc4/dAjEajUydOpUbb7yRjh07ApCVlYVWqyUgIMCsbmhoKFlZWaY6lydMldsrt9VUJy8vj+LiYi5duoTBYLBYJz093WK8paWllJb+tTZZXl6ejWcsGpvsfD2nckqJCqhY/Nbo1ZzcPs8RuPlRfPZ+QGnkjeivSXB0mEIIIRzE6qSpvudnmjx5MgcOHOD777+v1+PYy/z583n22WcdHYawk7VpF3hpWwZGBdMyKsM6BFESfQuFsX/DO30Vzb6dRfaoNSgezRwdrhBCCAewee25+jBlyhTWrVvHt99+S2TkX8tXhIWFodfrycnJMat/9uxZwsLCTHWuvJuu8ufa6vj5+eHp6UlwcDAajcZincp9XGn27Nnk5uaaHqdOnbL9xEWjkJ2vNyVMAEYFXtqWYVrYNi9hFmX+0WiKsgnY+TTYtvKQEEIIF+HQpElRFKZMmcLq1avZtm1blQHl3bt3x93dna1bt5rKDh8+TEZGBgkJFd0kCQkJ7N+/3+wut82bN+Pn50dcXJypzuX7qKxTuQ+tVkv37t3N6hiNRrZu3WqqcyWdToefn5/ZQzinUzmlpoSpklGB07kV3a+KuxeXBvwbRe2O529b8Tr0mQOiFEII4WgOTZomT57Mxx9/zIoVK/D19SUrK4usrCzTTOP+/v5MmDCB6dOn8+2335KSksL48eNJSEigV69eAAwaNIi4uDjuvfde9u7dy6ZNm5gzZw6TJ09Gp9MBMHHiRE6cOMGMGTNIT0/nrbfe4rPPPmPatGmmWKZPn857773HsmXLOHToEJMmTaKwsJDx48c3/AsjGlRUgA71FVNmq1UQ6a8z/VweHEfe9RXvF7/kBbhdOt6QIQohhGgErJ5yoF4OXs2CqB9++CHjxo0DKia3fPzxx/nkk08oLS0lMTGRt956y6zb7Pfff2fSpEls374db29vxo4dy4IFC3Bz+2vI1vbt25k2bRoHDx4kMjKSp59+2nSMSosXL+bll18mKyuLrl278sYbbxAfH2/VuciUA86tujFNZhQjgV8/iMcfuyqmIbj9U3DTWd6hEEIIp2DLlAMOTZpciSRNzi87X8/p3FIi/XXVTgCpLsqm+efD0ZRcojBuNLk3PWPVfi+/K08IIUTjYUvSZPXdc0I4I1sSlhBfba11jF4h5PR/iaAND+F98FNKw6+npM2QauuvTbvAgm0ZKAqoVDDLUguWEEIIp9Ao7p4Toj6sTbvAiKVpPLL6GCOWprE27YJd9lsa1Zv8rg8CELDzaTR5GRbrZefrWbA1w3SznaLAgq1/3ZUnhBDCuUjSJFxSbdMIXK38Ho9SGtoNdVkhzbZMA0PV/e7PLOTKvm/lz/LaYk85lS/JlRBCNDKSNAmXVNs0AldN7calAf/GqPNHe/4gfj++XKVKXQYL1lfrmBBCiKsnSZNwSdZMI3C1jD7hXOq/AACftI/xOPGN2fbO4d5ceX+oCugU7m1xf/XdOiaEEOLqSNIkXFKIr5bBsYFmZYNjA+1+91ppi34UdL4fgIAdT6HJOWkWw5D25jEMaV99DHVpHZOuPFEdeW8IYX+SNAmXlJ2vZ2P6RbOyjekX7fYBcvkHUt710ygN74G6rJDAzY+gKiusUwy2to5JV56ojrw3hKgfkjQJl1SfY5qqfCAdyuXSgFcxeDXH/dJx/HfOBUWxOQZbWsekK09UR94bQtQfSZqES6qvMU3VfSBlGfy5NPA1FJUbXse/xjvtY5tjsKVlqt4Hujsp6ZKS94YQ9UmSJuGSQny1zLy5hSlpqVwa5WrHNNX0gaQP605erycB8EteSGThAZtisOXDriEGujsb6ZKqIO8NIeqPzAguXNawDkHEt/CtdWkUW1R+IF2e3Fz+gVTY8V60Z1PxPLGBZlumcvsdn9Pmb23Zd6aAzhE+xIVZvnPOmn1frjIpvHK9PEct0+LopWKqawGMb+Hb5JauaWzvDSFciSRNwqVZszSKrfur8QNJpSKn7/O4XTqK+6VjqL6cxJQLMyhRtNUvBGztvq9QH0lhXVi12HE9q6mVrikmC43lvSGEq5EFe+1EFuxtnOqrBaS2xX01eRkEffE33PR5rCrvw5PlDwMq1Cr4YlyHGmOxZuHgxiI7X8+IpWlVWsdqO0dXjUMI4XxsWbBXxjQJl1WfY1xCfLV0i6y+68fg14JfuvwLg6Lib247GafZBFg3ILe2fTcmjWXQcX2NYRNCiMtJ95xwSY1hjItnTB8WJN/NP92WM8ftY44okfyodHSpAbm2jMOqb9IlJYSob9LSJFxSY2gBCfHV8kfMPfzPcBNuKiNvur/BmDauNcamsbXwOFMrnRDC+UhLk3BJjaEFJDtfz4b0S2zlAdqoztBVfYJ7M+Zw/sJKgoMadqB0XVg7HkxaeIQQTYW0NAmX1BhaQPZlFqIApWh5WD+dLKUZbdV/ELxtOhjLGyyOSrZM/GjreLDG0sIjk1sKIeqTtDQJl1WfLSDWtMJcPr/gWQKZoH+CVdrniLj0M4W7/kXujXNBpbL4XHuzZVqAxjAerC4aw9QHQgjXJi1NwqXVRwvI2rQLDP+wohVm+IfVt8J0Cvc2S5zSlGimlv0DBRXeBz/F+8BHdoupJrauRdYYxoPZStZbE0I0BEmahLBBdr6e+VszzMrmb7X84Rziq2XWAPMuwu79R5IX/wQAfj++hC5ju8VjWNvFZE1dW5MgZ1yGwxkTPSGE85HuOSFs8N3JXIvl35/MZUTn5lXKLXURFirjccs9iXf65zTb+jjnh31EeXAcYFsXk7V1owJ0qIDLcwoV1SdBzrgMR2MY+C+EcH3S0iSEDU5dKrFYnlFNOVjoIlSpyL1pLqXX9EJdVkTQhofR5P9hUxdTQ3RHVa4V4AxrBjSGgf9CCNcnLU1C2CCqmYfF8hbVlFdL7c7FW94g+Kt7cb94mMCvH+TXHu9YvX6aLWutncop5cq8R6H6ddkqEzLlsrrOMBBcpj4QQtQ3aWkSwga9o/0tlt9UTXlNFK0vF4a8Q7l3OO65J7lp7ww8VeYtRdV1Mdky7sjWMUrOPD6osUx9IIRwTZI0CWGDEF8tQ9sHmpUNbR9Y5w9po3coF4e8g1Hrh8/5VL4O/wA3lRGouYvJlu6oEF8tg2PNYx4cW33MzjgQXAghGoJ0zwlhg+x8PRvTL5qVbUy/yEO9wuucOJUHXsvFQf8h6OsHiL64k+SO4fwUM53IAA+7zMRta8zOOBBcCCEagiRNQvzJmgkrbRlLZAt9xPVc6v8SzbY+TvOjK+nrG0h+1KO1Pi/EV1vrcesSs4wPEkKIqiRpEgLrb9/3dLfco+3hdvU93SVthpBbmkPA98/hu+dtjLoACjvdd9X7revt+NYkZEII0ZTImCbR5Nly+35mnuVb+qsrt1VR3F3k9ahoYfJPno/nkTVXvU9bxzQJIYSwTJIm0eTZcrdYbonlhXarKwfbF5EtuG4iBZ3GAhCwYw6637ZZ9byajm9pTJMsMSKEELZxaNK0c+dOhg0bRkREBCqVijVr1phtHzduHCqVyuwxePBgszoXL15kzJgx+Pn5ERAQwIQJEygoKDCrs2/fPnr37o2HhwdRUVEsXLiwSiyrVq0iNjYWDw8POnXqxNdff2338xWNky13i/l5WO7R9q+mfG3aBUYsrVinbsTS6tepM6NSkddrBkVth6NSDARunYb2j+Tan1cNZ55CQAghGhOHJk2FhYV06dKFN998s9o6gwcPJjMz0/T45JNPzLaPGTOGtLQ0Nm/ezLp169i5cycPPfSQaXteXh6DBg2iZcuWpKSk8PLLLzNv3jzeffddU51du3Zx1113MWHCBH799VeGDx/O8OHDOXDggP1PWjQ6tty+3/mKRXihYkmSTuHeVepe1azdKjU5fZ6nuNUAVAY9gRv/gTbzF9tO7E8yhYAQQtiHSlEaxyIJKpWK1atXM3z4cFPZuHHjyMnJqdICVenQoUPExcXxyy+/0KNHDwA2btzI0KFDOX36NBEREbz99tv885//JCsrC6224kNw1qxZrFmzhvT0dADuvPNOCgsLWbdunWnfvXr1omvXrixZssSq+PPy8vD39yc9PR1fX986vALC0bLz9VbdLbY27QILtlbMmK0CZg2wPGg85VQ+j6w+VqV88YgYukVa+R4x6An8Zgoep77D6O7FhaHvUxZ6nZVn9JcXNv/O14f+6qIb2j6QObe0tHk/QgjhavLz84mNjSU3Nxc/P78a6zb6MU3bt28nJCSEdu3aMWnSJC5c+Kt7Izk5mYCAAFPCBDBw4EDUajU//fSTqU6fPn1MCRNAYmIihw8f5tKlS6Y6AwcONDtuYmIiycnVd4mUlpaSl5dn9hDOzdrZpId1COK9v7fl0ZsieO/vbatdUNcuLTwaLRdveYPSaxIq1qn7+iHcz9nWAipjmoQQwj4addI0ePBg/vvf/7J161ZeeuklduzYwZAhQzAYDABkZWUREhJi9hw3NzcCAwPJysoy1QkNDTWrU/lzbXUqt1syf/58/P39TY+oqKirO1nhNNamXeChVUd44/szPLTqSLXjlOy2iKybBxcT36Q0vAfqsgKCvn4At/OHrH66jGkSQgj7aNTzNI0ePdr0/06dOtG5c2fatGnD9u3bGTBggAMjg9mzZzN9+nTTz3l5eZI4NQHVjVOqbjFbe00Sqbh5cjFxCUEbHkB7NpXg9eM5n/QB5cFxtT63rvM0CSGEMNeoW5qu1Lp1a4KDgzl2rGKcSFhYGNnZ2WZ1ysvLuXjxImFhYaY6Z8+eNatT+XNtdSq3W6LT6fDz8zN7CNdXl1Ybey0iq2i9uTDkXfQhnVGX5hK8brxVXXV2a/ESQogmzqmSptOnT3PhwgXCw8MBSEhIICcnh5SUFFOdbdu2YTQaiY+PN9XZuXMnZWVlpjqbN2+mXbt2NGvWzFRn69atZsfavHkzCQkJ9X1Kop7ZOkdSbaICdBbvnmuoVhtF68uFof+HPvQ61Po8gtbfj3v23lqfN6xDEF+M68DiETF8Ma5DteOwhBBCVM+hSVNBQQGpqamkpqYCcPLkSVJTU8nIyKCgoIAnn3ySH3/8kd9++42tW7dy++23ExMTQ2JiIgDt27dn8ODBPPjgg/z888/88MMPTJkyhdGjRxMREQHA3XffjVarZcKECaSlpbFy5UoWLVpk1rX22GOPsXHjRl555RXS09OZN28eu3fvZsqUKQ3+mgj7qdMcSU5A0fpwYch7lIZ1R63PJ2j9BLRZe2p9nr1avIQQoqlyaNK0e/durrvuOq67ruIW6unTp3Pdddcxd+5cNBoN+/bt47bbbqNt27ZMmDCB7t27891336HT/fWtfvny5cTGxjJgwACGDh3KTTfdZDYHk7+/P9988w0nT56ke/fuPP7448ydO9dsLqcbbriBFStW8O6779KlSxc+//xz1qxZQ8eOHRvuxRB2dVVzJNXgVE4pV87RodDwg6oVrTcXh7xLafj1qMsKCfz6QbRnfmrQGIQQoqlpNPM0OTuZp6lxscscSRZk5+sZsTStyqDqL8Z1cEgLjqq8mMBNk9H9kYyi0XJxwGuUtrq5wePIztdzKqeUqIC6D3YXQghHcKl5moSoi/qaBbsug6rtPa7qcoqbJxcS36a45Z8zh29+FM+jX9n9ODVx1W7Qxqg+30tCiNpJS5OdSEtT47M27YKpi64yubHXAGhbZg+vrxjMGMsJ2DEHr6NfApBzwxyKOo6x/3Gu0Nha3lxZg72XhGhibGlpatTzNAlxNWydI8mWLqYQX61V+7NlTqeronYjp9+LGLW++KR9TMCuF1Drcym4bhKorrzfz35qmoJBkib7adD3khCiWpI0CZdnTVtqfXyLb4iE4spEL++Gp1B0fvjueQu/3f9BU5hN7o1Pg1pjl+NdSSbObBiSnArROEjSJFyWtYlQfX2Lr5zT6fLPOnvO6VTd+eX3eASjRzP8dr2I96GVqIvOkzPgZc4Wa+w+WLtyjNeVccgHuX1JcipE4yBJk3BJtiRCdfkW7+i7xWo7v8KO92DwCqHZt0/i+ftW8lbdy4Tzj3BB8bP7eBh7LRUjqifJqRCNgyRNwiXZkgjZ+i3e2hasmuZ0utoPO2vOr6T1IC54BRGw8R+E5qfxmfuzjCubwSkl1O7jYawZ49UQHJ3M1idJToVwPJlyQLgkW6YcsGUaAVsmzayvaQ9s2bc+rDvfxb/HaSWYNupMvtQ+TQ9Veq3r5TmjpjD1gczqLoRjSdIkXJKt8ylZuzabLQv21udCubbsu1lkLCP1z7LX2JpAVQHLtS8yUrPTpcbD1NcM8EIIcTnpnhMuy9buDGu6mDzdLX/P8HCzXF6fXSrW7jvEV0tIWCR3Zj7Nq+5vM1TzM6+4LyH/kEJ+z0dB5fzfneTuMiFEQ5CkSbg0e4+1KS4zWiwvKbdcXh8x2Lrvg1mF7M8sBHRMLnuUx5VVTHH7Et/Ud3DLPUlOvxdR3L3rJb6GIneXCSEagvN/xRSiAdna0tQY7D1TYPq/gpp/l9/JdP1EDCo3PE9+Q/CXd6PJy3BghFevPrtChRCikrQ0CWGDzDzLY2Qy8/TEhTXO1pouET5Vyr4w9uGuG3vSLWUm7heP0Hz137l0878pjbrJARHah9xdJoSob43367EQjZAzLtQYF+bN0PaBZmVD2wdyTdwNnBvxOfqQzqhLcwnc+DA+qe9bN4X6VajPRWfl7jIhRH2SliYhbBDhZ/nDOLya8sZizi0t6dfGnx9/z6NXSz9uah0AgNE7lPPDPsL/++fwPvw//H5+Bfdz+8np+wKK1v4LT8uis0IIZyYtTULYoC4DwRuDtWkXmLX+JF/sr/jXbA4jjZbcPs+Tc9NcFLU7nie/ofkXo3A7f8iuMci0AEIIZydJkxA2qM8JK+uLVcmKSkVR3F2cv+0jyn3CccvLoPmXo/E69JnduutsmeNKCCEaI0mahPiTNWNtQny1DI41Hx80ODawUY+hsSVZKQvpwrkRX1DSoi8qg56A754h4NuZqPSFVx2HMyacQghxOUmahKCi++qODyuW4Ljjw+qX4MjO17Mx/aJZ2cb0i426iykqQMcVuQoqqk9WFI8ALia+Rd71j6OoNHgdW0vzL0bgnr3vquKQaQGEEM5OBoKLJi87X8+CrRmmO+MUYMFWywvaNpmZp1VqTrS5j0JVO3rufwZtXgbBX44hv8cUCro8AGqNWXVrF8qVaQGEEM5MkibR5O3LLKwylYAC7M8sZMAVH+rOOPP0qZxSi+dXU6L3111uPvirnuOLiE9oc2Ebfr+8ju70D1zq/xJGn/Ar6lp3R1x9zpAuhBD1SbrnRJN3ZddVTZyxi8nWsURXDhzPVby55cwEMuKfw+jmhS7zF0JW3YbnkTVk55XKHXFCiCZDWppEk9cp3BsV5hNXqv4st8TZupgqE70rW4Oqi9tyF6SKfUGD0Y3sSbNvZ6LN3kez7bMpDfmaIOUuzhFwWV37dVcezCpk75kCukT4NNoZ14UQTYckTaLJC/HVMmuA9UlF5XMae7J0OVsSvcqB41cmkZH+Ogy+rTh/23J89n2I7+7/EJb9Hd/ofuXpsvGsM/YCVDUOMrfFC5t/5+tDfw26H9o+kDm3tLzq/QohRF1J0iQEFUlFmyAP9p0poLOLtmrYkuhZGgNlonajoOuDlLToi8/WmTS7lM5i7X+4zbCLp8vGk00gV+tgVqFZwgTw9aGLjOgU7JLXRgjhHGRMkxBUDGZ+aNUR3vj+DA+tOlLtlANNwf5My3MyXVleHtiWbdd/wOvlI9ArGgZpUtise5Ixms2czim+qhj2nimwWL4v03K5EEI0BEmaRJMny3uYs2X+78hAb94wjOJW/YvsMcbgpyrmBfcP6ffLRNwuHatzDF0ifCyWdw63XF6pPhcDFnUj10S4EumeE01ek5l7yUqdbRgY/9cgcxiln8e9ms38U/cZPudT8f78Dgo73Ud+93+guNvWpRbs7W5TOchiwI2RXBPhaqSlSTR5sryHucqB8ZUviQqYNaD6gfHDOgTxxbgOvDGiLXfcN51Lo9dR3PJmVEo5Pvs+IOSzJDyOb7BpDbtTOZbXo6tunTppLWx85JoIVyQtTaLJs/WW/KbA1mkVLh9kbiCCS4lvUvT7t/jvehG3/NMEbp1Oafpn5CbM5ox7q1pnD/d0t/x9zsPNcrm0FjY+ck2EK5KkSQicb+6lhnC10yqUtuxP9jUJ+Oz9P3xT30X3x48Ef34HWwz9eaVsFJdU/tV21xSXGS3us6TccrkzztTu6uSaCFfk0O65nTt3MmzYMCIiIlCpVKxZs8Zsu6IozJ07l/DwcDw9PRk4cCBHjx41q3Px4kXGjBmDn58fAQEBTJgwgYIC8zts9u3bR+/evfHw8CAqKoqFCxdWiWXVqlXExsbi4eFBp06d+Prrr+1+vqJxC/HV0i2y6npz4iq4eVDQfTLZf1tHTuQA1Bi5W7OVb3XTeVC9lte2HbPYXWPrIsMhvloGx5pPdTA4NlCupQM54+z5QtTGoUlTYWEhXbp04c0337S4feHChbzxxhssWbKEn376CW9vbxITEykpKTHVGTNmDGlpaWzevJl169axc+dOHnroIdP2vLw8Bg0aRMuWLUlJSeHll19m3rx5vPvuu6Y6u3bt4q677mLChAn8+uuvDB8+nOHDh3PgwIH6O3khGjlb7nqqra7BL4rvOs3n76VPs9/YCj9VMbPdP2GL9glI+x8YDVcd68Z083mdNqZflPEzDlY53m3xiBi+GNdBBoELp6dSFBtGZ9YjlUrF6tWrGT58OFDRyhQREcHjjz/OE088AUBubi6hoaEsXbqU0aNHc+jQIeLi4vjll1/o0aMHABs3bmTo0KGcPn2aiIgI3n77bf75z3+SlZWFVlvxDWfWrFmsWbOG9PR0AO68804KCwtZt26dKZ5evXrRtWtXlixZYlX8eXl5+Pv7k56ejq+vr71eFiEcwpa7ntamXWDBtgwUBVQqmFVN3YNZhTzw2RFUGLlD/T0z3FcSproEQFmzNuT3nEpJywGgUpFyKp9HVledsmDxiBi6RVb9/bK1vhBCVMrPzyc2Npbc3Fz8/PxqrNto7547efIkWVlZDBw40FTm7+9PfHw8ycnJACQnJxMQEGBKmAAGDhyIWq3mp59+MtXp06ePKWECSExM5PDhw1y6dMlU5/LjVNapPI4lpaWl5OXlmT2EcAW23PWUna9nwdYM041xigILtlquWzlOSUHNF8Y+9C19jRfL7kLv7of7peMEfvMIwV+ORnf6B4r15RZjK9JbbpGSOyCFEA2h0SZNWVlZAISGhpqVh4aGmrZlZWUREhJitt3NzY3AwECzOpb2cfkxqqtTud2S+fPn4+/vb3pERUXZeopCNEo13fV0pf2ZhRaXXLE0q/iVd8SVouVdwzB2DfyS/K4PY3TzRJu9j6CvHyBh1wT6qvdy5VSb6WeLLMYs42eEEA1B7p6ro9mzZzN9+nTTz3l5eZI4CZdgy11PtvTtV3dHXIHKm/zrp1LYcQw+qe/jfWglUcWHWKY9RKqxDW+U38E243WAisAaJre09Q7I7Hx9rVMfiKsnr7NwJY02aQoLCwPg7NmzhIeHm8rPnj1L165dTXWys7PNnldeXs7FixdNzw8LC+Ps2bNmdSp/rq1O5XZLdDodOp00/QvXU3kn2uUL5lZ3J1qEn+UPwXAL5bUlY0av5uTdMJuCrg+g/uU9fNNX0lV9nA+0/ybdGMW75Un0bvFArbFb88EsM1U3DHmdhatptN1z0dHRhIWFsXXrVlNZXl4eP/30EwkJCQAkJCSQk5NDSkqKqc62bdswGo3Ex8eb6uzcuZOysjJTnc2bN9OuXTuaNWtmqnP5cSrrVB5HiKbEljvRbJlPydouNKNXc8r7PsWq6z9nSfmt5CuexKpP8ap2CR3X34r3vqWo9HVfuFdmqm4Y8joLV+TQpKmgoIDU1FRSU1OBisHfqampZGRkoFKpmDp1Ki+88AJfffUV+/fv57777iMiIsJ0h1379u0ZPHgwDz74ID///DM//PADU6ZMYfTo0URERABw9913o9VqmTBhAmlpaaxcuZJFixaZda099thjbNy4kVdeeYX09HTmzZvH7t27mTJlSkO/JMIFOduCpbaMaYoKsNzaWtMA7MsHjddk4HXX0uvef/HDoHWc6fIYBs9gNIVZ+P/4EqHL++L3w7/Q5JyseScW2HJ+ou7kdRauyKHdc7t376Z///6mnysTmbFjx7J06VJmzJhBYWEhDz30EDk5Odx0001s3LgRDw8P03OWL1/OlClTGDBgAGq1mpEjR/LGG2+Ytvv7+/PNN98wefJkunfvTnBwMHPnzjWby+mGG25gxYoVzJkzh6eeeoprr72WNWvW0LFjxwZ4FYQrc8buCVuWMDlfWGahZkX5lS1IlS0PlZ+jChUtD/Etqp9QtKK7LQKiJ3K2x/14Hf0K731Lcc85jk/ax/ikfUxJVG8KO9xDadRNZBeU232JFlE3MiO4cEWNZp4mZyfzNIkrZefrGbE0rcqHxhfjOjTqAbG2zHn0yZ6z/Of7M1XqPto7gtHXmd+Rare5lBQF7R/J+Bz4GF3GdlR/pmH5ujDeLezNyvJ+nFc1qzZBlTmdGo4zfmkQTY8t8zQ12oHgQjg7Z12w1JYWgi4RPhb30Tm8anldWh4OZhWy90wBXSJ8iAvzrihUqdBH3sDFyBvQ5GXgfWA5HkfW4FuaxeNuq3hM8z+2Ga/js+39iI+8ixB/7ypxWCItIPYnazoKVyPt0ULUE2edcLG+5jyydb8vbP6dBz47wn++P8MDnx3hhc2/V6lj8GtB3g2z2dRvHVP1/+BnYzvcVEYGaVJ43/0V2q++Bb9dL+J2/qBpEFVNXYrC/mRNR+FKpKVJiHpSmSRc2T3hDB8e1rYQ7D1j+S62fZkFf7UM1WG/B7MKzaY8APj60EVGdAq2uF+dhxdrjDexRn8TMarT3KnZznDNDzTXX0J34CN8DnxEWbNrKW4zlN9LemLp+2J1MQshRCVJmoSoR87cPWHNnEe2dM/Zsl9bk7HLpz44pkTyr/J7WFB+F5/cdJ5OFzfh8fs23C8dxX33IiYAPbXRrDP04mtjPKeVkFpjFkIIkKRJiHpn7YSLzii4mhm6qyu3lq3JmKXxUopKg/bam7nkOxhVaR4ev23B8/jX6P74kc7qk3RWn+QpPuGgsSWpXgl0dvs75UpsxarDQghhgYxpEkLU2T4La8yB5bXn6lNt46UUnR/F7UZwcej7HBi+hX+W3c8uQxzlipo49e/cXfIpIV+MIOSTgfh//yy6379FVWZ5nTt7c7Z5vIRoyqSlSQhRZ/XVJmNr9xxY3xWaeknHcsNAlhsGEkA+A9S/MkizmwHu+3ErOIPbwU/xPvgpitodfXgPSiJvovSaBMqD2oHKvt8z5ZZ8IZyLJE1CCIusWWi1U7g3KswX7lX9WX416jotgDVdoadySkz/z8GX/xn78D9jHyZfF8D94b/hceo7dKd24pb/B7o/ktH9kQyAwaMZ+oh4SiN6oQ/vQXlA66vqyqtumZGaJvsUDUMWGRbVkaRJCFGFtS0gIb5aOoZ7m3XHdQz3vuoPGk93jcVyL63l8krWfNjlFJdbLD9X6kZpy/6UtuwPioJb7kl0GTvR/ZGMNms3mpJLeJ7YiOeJjQAYdAHow7qjD++OPvQ6yoLag5vO6g9cZ53Hy9VJ65+oiSRNQggztrSAHMwqrDJ+aX9mIQezCq/q9v2oAJ3FFqyaWprWpl1gwdaKZVpUwKwBlj/sAjwt/9lrdnm5SkV5QGvKA1pT2HkcGMvQZu9He+ZHdGd+xv3sXjSlOXj+vhXP3ysW+1bU7pzzimFvbhS/GmI4SCtG9Yvn1o4hFo8ny7k0PnVp/bM4AatwWZI0CSHM2NICUpexR/XR9ZGdrzclTFCRbC3YavnDLirAo8rzASKrKQdA7Y4+rBv6sG4UdPsHGPS4nz+INisFXWYK7tl70ZRcJKTgEGM1hxir+QaA4h+0GA/HQmgcZUHtKWt2LeWBMShaX7NpEi5XUm65HOQDur7Z2vr3wubfzeYTG9o+kDm3tKzvMIUDSdIkhDBjy3Into49srbr41ROKVd8dqFQ/YfXvsxCi/X3ZxYy4Ir6dhmHpdFSFtqVstCuFHaZAIrCwSOHWbN5G13Vx+iiPk6c6ne8VaVwYV/F4zLl3uEkeEczx82fE0oEJ5RwjhvDOUdAtS1N8gFd/2xp/bN1AlbhGiRpEkKYsWUmc1vGHtnS9WFr95wtw7FDfLXMGmDnmdpVKoIjWvO1Usy68gQA1BiJVmfxf/3KCS44gtvFI7hfOoqm8CxuhZkEF2bywBV/gfMVT4zfRqINaonBL4py30gMvtdwpCSA7w4VAF6muvIBbX+2tP7VpZVVOD9JmoQQVVh7+74trVL1OfDZ1tajYR2CaOapIfm3PBJa+XFT64CrOj5UTTZRqfl7/15oOwSRd3lcpbm4XzrGud/S2LUnldaqM7RWZRKpOoevqhjyj1Y8LpMA7PeAPMWTTCWIs0ozzhGA389ReLduhcEzGKNnEEbPwIp/df52nx6hKaivxaqF65CkSQhhkTW379vSKmVrgmVL95ytrUeXd3WtPnDBbl1d1iSbis4ffVh3due15vnyTqZyLWVEqbJ5qoeK7r45uOWfQpN3CreCTIx5Z9CV5eKnKsZPdZp2nK54UtafjyuPoVKjaH0xegRg1PlXPLR+KFofjFqfim1aHxR3bxQ3LxT3iofRzRPFzQPcdCgaDxQ3HYpGBypNk5gp3Zb3c1yYN0PbB1bpMnVUK5NMk9AwJGkSQlwVa1ul6ivBsjWO+h6LYu2yOTkl5lMf6HHnuHINh7wjad+xudm2lFP5zFh9gAjVBcJVFwnhEqGqHP5+rUKYKgd1yQXUxRfRFF9AXZqLSjGiKs1FXZp71ecDoKACjRZF7Y6icQe1G4raDVR//qtW/5lYaVBU6j9buVQVzX1//l+5ohNVhQIooFT+C2Cs+FkxVmxXjH8+KsuMYDRW1DP++fNl2y0+DwVV5c+mE7oyJa8MSsVDKhUPeqkwokKlUqP6WQ27NSgqTcW5VJ672o1FajdeClFTYNDg4eGJZ5knbNRWJJtuHigaHcqfiaji7oXR3asiUf3zX6PWryJ51flh1PqCpm7JjkyT0HAkaRJCXDVb1ter/Lyq7nOrcn+DY82/xQ+ODbSq5au2Oj+ctJxI7DqZ26CtBAUllueLyrdQHhWgo1TlwXHlGo4r1wAVH46JCR3QXXm+xjLUJTmo/0yaVCW5qEtzUJfmodYXoNLnoyorqPh/WRGqsiLU5cWoygpRlRejKi+t+NdYZtqlCgUMpagMpVCG6/rzPanC+jXG3AFfADusHGR088SoC0DxCMDo4Y9R1wzD5d2unkEYvJpj8A7F6BkEajeZJLWBSdIkhLDI3s39lX/cL58WoLo/7tn5ejamm7cGbUy/yEO9wq86lsBqFhOurry+6A2Ws8YyC+U2JZFqd4xezTF6Na+6zRZGQ0WSZNCjMupRGcr+/H8ZGMtRGQ2glKMylle04hgNf7boGP5q1VGUv1p+LFGpMLVCqf588GdLlerPcrWGilarinJFpfqzVeuyOpXdh5UtXWCqz2X7/qu1q/JYV7i8ZQpM56JSjPDneaqMBjCWVZy3sbzi9TCUVbxG5aV//Vtegqq8BAwlqMuKKxLRssKKJLWsEJW+AJW+ALU+H3VZxaBydXkx6vJiKMys9fIoKjVGzyC83IJ4y82XP5TgPx9BnFZCyDofRohvuHXXWlhNkiYhRBX10dxvy0Dw+hw03jvan1e2n65SflO0/1Xt11beWsttGV4WyrPz9Wy4oktxwyH7JJHVUmtQ1F7g7lVlfJmwM6OhovWvNLeilbAkp6J1sOQS6uKLqIsvVHS9Fl9AU3QOddE5VIoBTdE5AjjHYEs3sW6uWPrH4BdFuV9LygNamSZsLfdrCW41L0kkLJOkSQhhpr6a+22d/8nWMU3WCvHVMntAC+ZvzTCVzR5wlVMO1MEfuXqL5WfyqpbbMg+VM2uyg5nVGhSdPwadPwa/FrXXNxpQl1xEU5SNuuAsh44d4+CRI0SoLnCN6jyxuvN4luWgKbmEpuQS2mzzecIUVBj8oigLvJbywLaUBbalPLBtRTKlrnmpoqZOkiYhhJn6auWxZSC4LXXrqnKKAkfdE9Y+1IvVBy5UKY8N8apS9uupvCplAHtO5TGgbTO7x+YIMpjZBmrNX12wwR1o3epmfOL1nM4tReWv45Kvlhx9QcXdl3kZFY+ck7jlnMAt5wRqfb6pnN+2mnZrdPOiLCiWsuA4ypp3oCw4jvKANvWWSDljkixJkxDCTH228lh7h5utdW1hy9iq+hTuZ/n1jLDwOh+/WGKx7slqyp2NDGa+elfeBKFofSgPbk95cHvzioqCuvg8bpeO437xcMWkqxeP4nbxKOryInRn96A7u8dU3ejuTVlIJ/QhXdCHdkUf0hXFI+Cq43XWJFmSJiGEmfpu5bHlTjtb6lqrPsdL2cKW5LR1oAf7MouqlLcKqmG9PCfSWK5Jk6BSYfRqjt6rOfprev1VbjTglnsS9/MH/3yk4X7uIOqyQnR//Ijujx9NVcsC26IP60FpeA/04T1svunAmZNkSZqEEFXUVytPY1CfLWm2sCU5bdPcC7hYpTwmuGpXnjOyZc03UU/UGsqbxVDeLIbia2+rKDMacLt0DO3ZVLTZe3E/m4p77kncLx7B/eIRvA+uAKCsWQyl19xAaWQC+rCeKNqap+5w5iRZkiYhhEX10crTGDTEeClrWZucZuaWWi7Ps1zubGxZ8000ILWG8qB2lAe1oyjuzoqiovNos/agzdqNLnM3bhfScb90DPdLx/A58F8UlRv6sOsoadGX0hZ9K8ZEXTG9Q2P54lIXkjQJIZqcxtSSZk1yWt2ddn/kWC53Ns78IdrUGL2CKWk9iJLWgwBQlVxCd+ZndKd3ofsjGbf8U+gyf0GX+Qv89G/Kfa+hNKoPJa0GUBpxPajdG9UXF1tJ0iSEaJKcqSUtNsSTHSeqzmTeLsTTAdHYnzN/iDZ1ikczSlonUtI6EQBN3il0GTvxOLUD3ZmfcMv/A7eDn+B98BOMOn9KWvSjJHogw9rdRHyLDo3ii4stVIpS02IGwlp5eXn4+/uTnp6Or6+vo8MRQriQ7Hw9wz9Mq1K+ZnwHh3zYHMwqZO+ZArpE+Nh16ZnsfL3TfYiK6qnKitCe+QmP37fh8ds2NCV/jcszuntR0nIAxTFDKY28EdQNOyP/5fLz84mNjSU3Nxc/P78a60rSZCeSNAkh6sLauWrWpl2oMiGnI27RfmHz72bLuQxtH8icW1o2eBzCyRgNaM/uwePkFjx+24xbwV9LxRh1/hRHJ1Lc9nb0oddZXuKmHknS5ACSNAkhbGXrXDW2tMTUx8SBB7MKeeCzI1XK3/9722pbnJxxAkNRzxQF9+xUPI9vwPP4BjTF502byv1aUNT2Dorb3obBJ6JBwrElaZIxTUKIBiUfohXqMleNteOw6mviwL1nCiyW78sssJg0OesEhqKeqVSUhV5HWeh15PWaiTbzZ7yOrsXjxCbc8jLw270I391vUHpNAkXt76SkVX+Hdt9drlFPgjFv3jxUKpXZIzY21rS9pKSEyZMnExQUhI+PDyNHjuTs2bNm+8jIyCApKQkvLy9CQkJ48sknKS8vN6uzfft2unXrhk6nIyYmhqVLlzbE6QnR5KxNu8CIpWk8svoYI5amsTat6jIizi47X0/KqXyy82u+s62muWqu9viWkrHa4rFGVIDlu9ks3eVWn3GIxsva97+JWoP+mgRy+r3I2Xt3cqnffErDr0eFgscfuwjc8hihy2/G95dFaArO1G/wVmj0LU0dOnRgy5Ytpp/d3P4Kedq0aaxfv55Vq1bh7+/PlClTGDFiBD/88AMABoOBpKQkwsLC2LVrF5mZmdx33324u7vz4osvAnDy5EmSkpKYOHEiy5cvZ+vWrTzwwAOEh4eTmJjYsCcrhAtz5lmArWVLy0p93WZfnxMHerpbXoPMS1u13JknMBR1c7Uti4q7N8Vth1PcdjiavFN4pX+O1+H/oSk+j++vS/BJfZfimGHk9F9Qj2dRs0bd0gQVSVJYWJjpERwcDEBubi7/93//x6uvvsrNN99M9+7d+fDDD9m1axc//lgx3fs333zDwYMH+fjjj+natStDhgzh+eef580330Svr8iClyxZQnR0NK+88grt27dnypQpjBo1itdee81h5yyEK6qvlpXGwtaWlcrb7NV/jnm11232lcnY5ew155Et+67POETjY++WRYNfFPnXT+Ps3du4OOBVSiN6oVKMGO2w7t3VaPRJ09GjR4mIiKB169aMGTOGjIyKu0dSUlIoKytj4MCBprqxsbG0aNGC5ORkAJKTk+nUqROhoaGmOomJieTl5ZGWlmaqc/k+KutU7qM6paWl5OXlmT2EENVz9Q/RuiSFwzoE8cW4DiweEcMX4zrYZbxPfSVjtu67PuMQjU+9fSnSaClpM4QLt37I2b9/TUHn8Ve3v6vUqLvn4uPjWbp0Ke3atSMzM5Nnn32W3r17c+DAAbKystBqtQQEBJg9JzQ0lKysLACysrLMEqbK7ZXbaqqTl5dHcXExnp6WJ4+bP38+zz77rD1OU4gmwdUnMKxrd1t9TLJZnzOe27LvxjTzuqhfDTGruyEg2m77qqtGnTQNGTLE9P/OnTsTHx9Py5Yt+eyzz6pNZhrK7NmzmT59uunnvLw8oqKiHBiREI2fK3+INraksD5nPLdl384087qou8b2/q8vjTppulJAQABt27bl2LFj3HLLLej1enJycsxam86ePUtYWBgAYWFh/Pzzz2b7qLy77vI6V95xd/bsWfz8/GpMzHQ6HTqda3QrCNGQXPlD1JWTQiFq0xTe/41+TNPlCgoKOH78OOHh4XTv3h13d3e2bt1q2n748GEyMjJISEgAICEhgf3795OdnW2qs3nzZvz8/IiLizPVuXwflXUq9yGEELYI8dXSLdJ17ggUwhau/v5v1EnTE088wY4dO/jtt9/YtWsXd9xxBxqNhrvuugt/f38mTJjA9OnT+fbbb0lJSWH8+PEkJCTQq1cvAAYNGkRcXBz33nsve/fuZdOmTcyZM4fJkyebWokmTpzIiRMnmDFjBunp6bz11lt89tlnTJs2zZGnLoQQQohGplF3z50+fZq77rqLCxcu0Lx5c2666SZ+/PFHmjdvDsBrr72GWq1m5MiRlJaWkpiYyFtvvWV6vkajYd26dUyaNImEhAS8vb0ZO3Yszz33nKlOdHQ069evZ9q0aSxatIjIyEjef/99maNJCCGEEGZk7Tk7kbXnhBBCCOdjy9pzjbp7TgghhBCisZCkSQghhBDCCpI0CSGEEEJYQZImIYQQQggrSNIkhBBCCGEFSZqEEEIIIawgSZMQQgghhBUkaRJCCCGEsEKjnhHcmVTOEVpQUODgSIQQQghhrcrPbWvm+pakyU7y8/MB6NGjh4MjEUIIIYSt8vPz8ff3r7GOLKNiJ0ajkTNnzuDr64tKpbLrvvPy8oiKiuLUqVO1TvHujOT8nJ+rn6Ornx+4/jnK+Tm/+jpHRVHIz88nIiICtbrmUUvS0mQnarWayMjIej2Gn5+fy/4ygJyfK3D1c3T18wPXP0c5P+dXH+dYWwtTJRkILoQQQghhBUmahBBCCCGsIEmTE9DpdDzzzDPodDpHh1Iv5Pycn6ufo6ufH7j+Ocr5Ob/GcI4yEFwIIYQQwgrS0iSEEEIIYQVJmoQQQgghrCBJkxBCCCGEFSRpEkIIIYSwgiRNjcTOnTsZNmwYERERqFQq1qxZY7Z93LhxqFQqs8fgwYMdE2wdzJ8/n549e+Lr60tISAjDhw/n8OHDZnVKSkqYPHkyQUFB+Pj4MHLkSM6ePeugiG1nzTn269evynWcOHGigyK2zdtvv03nzp1NE8slJCSwYcMG03Znv361nZ8zXztLFixYgEqlYurUqaYyZ7+GV7J0js5+HefNm1cl/tjYWNN2Z7+GtZ2fo6+fJE2NRGFhIV26dOHNN9+sts7gwYPJzMw0PT755JMGjPDq7Nixg8mTJ/Pjjz+yefNmysrKGDRoEIWFhaY606ZNY+3ataxatYodO3Zw5swZRowY4cCobWPNOQI8+OCDZtdx4cKFDorYNpGRkSxYsICUlBR2797NzTffzO23305aWhrg/NevtvMD5712V/rll19455136Ny5s1m5s1/Dy1V3juD817FDhw5m8X///femba5wDWs6P3Dw9VNEowMoq1evNisbO3ascvvttzsknvqQnZ2tAMqOHTsURVGUnJwcxd3dXVm1apWpzqFDhxRASU5OdlSYV+XKc1QURenbt6/y2GOPOS4oO2vWrJny/vvvu+T1U5S/zk9RXOfa5efnK9dee62yefNms3NypWtY3TkqivNfx2eeeUbp0qWLxW2ucA1rOj9Fcfz1k5YmJ7J9+3ZCQkJo164dkyZN4sKFC44Oqc5yc3MBCAwMBCAlJYWysjIGDhxoqhMbG0uLFi1ITk52SIxX68pzrLR8+XKCg4Pp2LEjs2fPpqioyBHhXRWDwcCnn35KYWEhCQkJLnf9rjy/Sq5w7SZPnkxSUpLZtQLX+h2s7hwrOft1PHr0KBEREbRu3ZoxY8aQkZEBuM41rO78Kjny+smCvU5i8ODBjBgxgujoaI4fP85TTz3FkCFDSE5ORqPRODo8mxiNRqZOncqNN95Ix44dAcjKykKr1RIQEGBWNzQ0lKysLAdEeXUsnSPA3XffTcuWLYmIiGDfvn3MnDmTw4cP88UXXzgwWuvt37+fhIQESkpK8PHxYfXq1cTFxZGamuoS16+68wPnv3YAn376KXv27OGXX36pss1VfgdrOkdw/usYHx/P0qVLadeuHZmZmTz77LP07t2bAwcOuMQ1rOn8fH19HX79JGlyEqNHjzb9v1OnTnTu3Jk2bdqwfft2BgwY4MDIbDd58mQOHDhQpZ/alVR3jg899JDp/506dSI8PJwBAwZw/Phx2rRp09Bh2qxdu3akpqaSm5vL559/ztixY9mxY4ejw7Kb6s4vLi7O6a/dqVOneOyxx9i8eTMeHh6ODqdeWHOOzn4dhwwZYvp/586diY+Pp2XLlnz22Wd4eno6MDL7qOn8JkyY4PDrJ91zTqp169YEBwdz7NgxR4dikylTprBu3Tq+/fZbIiMjTeVhYWHo9XpycnLM6p89e5awsLAGjvLqVHeOlsTHxwM4zXXUarXExMTQvXt35s+fT5cuXVi0aJHLXL/qzs8SZ7t2KSkpZGdn061bN9zc3HBzc2PHjh288cYbuLm5ERoa6vTXsLZzNBgMVZ7jbNfxSgEBAbRt25Zjx465zO/h5S4/P0sa+vpJ0uSkTp8+zYULFwgPD3d0KFZRFIUpU6awevVqtm3bRnR0tNn27t274+7uztatW01lhw8fJiMjw2xMSWNW2zlakpqaCuA01/FKRqOR0tJSl7h+llSenyXOdu0GDBjA/v37SU1NNT169OjBmDFjTP939mtY2zlaGsrgbNfxSgUFBRw/fpzw8HCX/D28/PwsafDr57Ah6MJMfn6+8uuvvyq//vqrAiivvvqq8uuvvyq///67kp+frzzxxBNKcnKycvLkSWXLli1Kt27dlGuvvVYpKSlxdOhWmTRpkuLv769s375dyczMND2KiopMdSZOnKi0aNFC2bZtm7J7924lISFBSUhIcGDUtqntHI8dO6Y899xzyu7du5WTJ08qX375pdK6dWulT58+Do7cOrNmzVJ27NihnDx5Utm3b58ya9YsRaVSKd98842iKM5//Wo6P2e/dtW58k4kZ7+Gllx+jq5wHR9//HFl+/btysmTJ5UffvhBGThwoBIcHKxkZ2criuL817Cm82sM10+Spkbi22+/VYAqj7FjxypFRUXKoEGDlObNmyvu7u5Ky5YtlQcffFDJyspydNhWs3RugPLhhx+a6hQXFyv/+Mc/lGbNmileXl7KHXfcoWRmZjouaBvVdo4ZGRlKnz59lMDAQEWn0ykxMTHKk08+qeTm5jo2cCvdf//9SsuWLRWtVqs0b95cGTBggClhUhTnv341nZ+zX7vqXJk0Ofs1tOTyc3SF63jnnXcq4eHhilarVa655hrlzjvvVI4dO2ba7uzXsKbzawzXT6UoitIwbVpCCCGEEM5LxjQJIYQQQlhBkiYhhBBCCCtI0iSEEEIIYQVJmoQQQgghrCBJkxBCCCGEFSRpEkIIIYSwgiRNQgghhBBWkKRJCNGg+vXrx9SpU6vdrlKpWLNmjdX72759OyqVqsp6W0IIYW9ujg5ACCEul5mZSbNmzRwdhhBCVCFJkxCiUXHW1diFEK5PuueEEA3OaDQyY8YMAgMDCQsLY968eaZtV3bP7dq1i65du+Lh4UGPHj1Ys2YNKpXKtLp5pZSUFHr06IHX/7d3byFRbXEYwL/xUqZWOgamWV5wMknHgTTUSkfoqTKG6EIFZRdSyJcS2cSkgTcsu1iYRj6okakVCWZEJOWIWZI6mpDahZksRIISS6h0xnUeon3OHLMzdTDlnO8HA7O2a/1n7fUgH2svHVdXxMbGoq+vDwAwPDwMR0dHtLW1yZ+tVCoRHR0tj718+TIWL14styVJwtKlS+Hq6oqgoCBkZGRgbGwMAGA2m+Hg4CDX+6awsBD+/v4YHx//7j0nJSVBp9MhLy8P3t7e8PDwQFZWFiwWC9LT06FUKuHn54eysjJ5jNlshkKhQHV1NWJjY+Hi4oKwsDAYDAab2nV1dVCpVHBxcUFCQgIqKir4yJJoCjA0EdFvV1FRATc3N7S2tuLEiRPIysrC3bt3J/T78OEDEhMTER4ejo6ODmRnZ0OSpO/W1Ov1OHXqFNra2uDk5IS9e/cCAObPnw+NRoPGxkYAQHd3NxQKBYxGI0ZGRgAABoMB8fHxcq25c+eivLwcT58+xdmzZ1FaWoozZ84AAAICArB27VqbcAMAZWVlSEpKgoPD5L9W7927h4GBATQ1NeH06dM4duwYNmzYAE9PT7S2tiIlJQXJycl48+aNzbj09HSkpaXBaDQiJiYGiYmJePfuHQDAZDJh8+bN0Ol06OrqQnJyMvR6/Y+Wn4h+1W/7amAiIvH1W+dXr15tcy0qKkpIkiSEEAKAqK2tFUIIUVJSIry8vMSnT5/kvqWlpQKAMBqNQggh7t+/LwCIhoYGuc+tW7cEAHnc4cOHxfr164UQQhQWFopt27aJiIgIcfv2bSGEEMHBweLixYuTzrmgoECsWLFCbtfU1AhPT0/x+fNnIYQQ7e3tQqFQCJPJNGmN3bt3C39/f2G1WuVrISEhYs2aNXLbYrEINzc3UVVVJYQQwmQyCQAiPz9f7jM2Nib8/PzE8ePHhRBCSJIkwsLCbD5Lr9cLAGJoaGjS+RDRz+NOExH9dmq12qbt4+ODt2/fTujX19cHtVoNFxcX+drKlSv/saaPjw8AyDXj4+PR3NwMq9UKg8EArVYLrVaLxsZGDAwM4MWLF9BqtfL4mpoarFq1CgsXLoS7uzuOHj2K/v5++ec6nQ6Ojo6ora0FAJSXlyMhIQEBAQHo7++Hu7u7/MrLy5PHLV++3GYnytvbG+Hh4XLb0dERXl5eE9YiJiZGfu/k5ITIyEj09PTIaxQVFWXTf7I1IqJ/h6GJiH47Z2dnm7ZCoZj0LNCv1FQoFAAg14yLi8PHjx/R0dGBpqYmm9BkMBjg6+sLlUoFAHj48CF27tyJdevWob6+HkajEXq9HqOjo3L9WbNmYdeuXSgrK8Po6CiuXLkiPw709fVFZ2en/EpJSfnhfU/FWhDR1GBoIqIZKyQkBN3d3fjy5Yt87fHjxz9dx8PDA2q1GkVFRXB2dsayZcsQFxcHo9GI+vp6m/NMLS0t8Pf3h16vR2RkJFQqFV69ejWh5v79+9HQ0IDi4mJYLBZs2rQJwNedoODgYPmlVCp/4c5tPXr0SH5vsVjQ3t6O0NBQAF/X6O+H0n9ljYjonzE0EdGMtWPHDoyPj+PAgQPo6enBnTt3cPLkSQB/7ibZS6vVorKyUg5ISqUSoaGhqKmpsQlNKpUK/f39qK6uxsuXL3Hu3Dn5MdxfhYaGIjo6GpIkYfv27ZgzZ86/uNMfO3/+PGpra9Hb24uDBw9iaGhI3tlKTk5Gb28vJEnCs2fPcPXqVZSXlwP4+TUioh9jaCKiGWvevHm4efMmOjs7odFooNfrkZmZCQA255zsER8fD6vVanN2SavVTri2ceNGHDp0CKmpqdBoNGhpaUFGRsZ3a+7btw+jo6NygJkq+fn5yM/PR0REBJqbm1FXV4cFCxYAAAIDA3H9+nXcuHEDarUaJSUl8l/PzZ49e0rnRfR/oxBCiOmeBBGRvSorK7Fnzx4MDw9P6e6OPbKzs3Ht2jU8efJkSuqbzWYEBgbCaDRCo9HYPS43NxcXLlzA69evp2ReRP9X/I/gRDSjXbp0CUFBQVi0aBG6urogSRK2bt06rYFpZGQEZrMZRUVFyMnJmbZ5fFNcXIyoqCh4eXnhwYMHKCgoQGpq6nRPi+g/h6GJiGa0wcFBZGZmYnBwED4+PtiyZQtyc3OndU6pqamoqqqCTqeb8kdz9nj+/DlycnLw/v17LFmyBGlpaThy5Mh0T4voP4eP54iIiIjswIPgRERERHZgaCIiIiKyA0MTERERkR0YmoiIiIjswNBEREREZAeGJiIiIiI7MDQRERER2YGhiYiIiMgODE1EREREdvgDxAjNknxkcyQAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.polyfit(x, y, 3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xFl3tKlwQ8OZ",
        "outputId": "7527a11e-55b6-47f1-fb8a-7b4a4d31d9b5"
      },
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-1.55663829e+00,  2.04754306e+02, -8.96543312e+03,  1.37923594e+05])"
            ]
          },
          "metadata": {},
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can already see from plotting that this polynomial model performs better than the linear model. This is because the generated polynomial function \"hits\" more of the data points."
      ],
      "metadata": {
        "id": "gAgvTsU8RBle"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create 11 order polynomial model with the variables x and y from above.\n",
        "\n",
        "# Polynomial of the 11rd order (cubic)\n",
        "\n",
        "f1 = np.polyfit(x, y, 11)\n",
        "p1 = np.poly1d(f1)\n",
        "print(p1)\n",
        "PlotPolly(p1,x,y, 'Highway MPG')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "id": "i2MbgAQTRCOD",
        "outputId": "c2acfb17-2474-499f-f321-1d7c65353640"
      },
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            11             10             9           8         7\n",
            "-1.243e-08 x  + 4.722e-06 x  - 0.0008028 x + 0.08056 x - 5.297 x\n",
            "          6        5             4             3             2\n",
            " + 239.5 x - 7588 x + 1.684e+05 x - 2.565e+06 x + 2.551e+07 x - 1.491e+08 x + 3.879e+08\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<p>The analytical expression for Multivariate Polynomial function gets complicated. For example, the expression for a second-order (degree=2) polynomial with two variables is given by:</p>\n",
        "\n",
        "\n",
        "$$\n",
        "Yhat = a + b_1 X_1 +b_2 X_2 +b_3 X_1 X_2+b_4 X_1^2+b_5 X_2^2\n",
        "$$\n"
      ],
      "metadata": {
        "id": "SCxE_mCMRQMK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Perform a Polynomial Transform on Multiple Features.\n",
        "\n",
        "from sklearn.preprocessing import PolynomialFeatures"
      ],
      "metadata": {
        "id": "J1xvOeZ1RT4J"
      },
      "execution_count": 159,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Polynomial Features Object of Degree 2\n",
        "\n",
        "pr=PolynomialFeatures(degree=2)\n",
        "pr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "hGCyOqwZRg-Q",
        "outputId": "2cf91654-5253-4a30-8d0c-28385d862137"
      },
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PolynomialFeatures()"
            ],
            "text/html": [
              "<style>#sk-container-id-25 {color: black;background-color: white;}#sk-container-id-25 pre{padding: 0;}#sk-container-id-25 div.sk-toggleable {background-color: white;}#sk-container-id-25 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-25 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-25 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-25 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-25 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-25 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-25 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-25 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-25 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-25 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-25 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-25 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-25 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-25 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-25 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-25 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-25 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-25 div.sk-item {position: relative;z-index: 1;}#sk-container-id-25 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-25 div.sk-item::before, #sk-container-id-25 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-25 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-25 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-25 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-25 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-25 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-25 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-25 div.sk-label-container {text-align: center;}#sk-container-id-25 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-25 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-25\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PolynomialFeatures()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-34\" type=\"checkbox\" checked><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PolynomialFeatures</label><div class=\"sk-toggleable__content\"><pre>PolynomialFeatures()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Z_pr=pr.fit_transform(Z)"
      ],
      "metadata": {
        "id": "IbLh3z3lRtlA"
      },
      "execution_count": 161,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Z.shape # In Original Data, 201 Samples & 4 Features."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h0sdW9ZiRxzU",
        "outputId": "e96682b5-d2a2-419f-8294-7fb25e8884a9"
      },
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(201, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Z_pr.shape  # After Transformation, 201 Samples & 15 Features."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O_H-j7alSBiu",
        "outputId": "59735b3f-0590-4a0f-abd2-75efd523a1e2"
      },
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(201, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**<h2>Pipeline</h2>**\n",
        "\n",
        "<p>Data Pipelines simplify the steps of processing the data. We use the module <b>Pipeline</b> to create a pipeline. We also use <b>StandardScaler</b> as a step in our pipeline.</p>"
      ],
      "metadata": {
        "id": "NskY6d5ISNrX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "EI6EbDfFSX_C"
      },
      "execution_count": 164,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We create the pipeline by creating a list of tuples including the name of the model or estimator and its corresponding constructor.\n"
      ],
      "metadata": {
        "id": "lAALok2nSg52"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Input=[('scale',StandardScaler()), ('polynomial', PolynomialFeatures(include_bias=False)), ('model',LinearRegression())]"
      ],
      "metadata": {
        "id": "arQnYGUCSgWs"
      },
      "execution_count": 165,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Input the list as an Argument to the Pipeline Constructor\n",
        "\n",
        "pipe=Pipeline(Input)\n",
        "pipe"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 159
        },
        "id": "g3esiaaTSrZw",
        "outputId": "d43db0e3-f13a-42c4-a1c5-8001694665a9"
      },
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('scale', StandardScaler()),\n",
              "                ('polynomial', PolynomialFeatures(include_bias=False)),\n",
              "                ('model', LinearRegression())])"
            ],
            "text/html": [
              "<style>#sk-container-id-26 {color: black;background-color: white;}#sk-container-id-26 pre{padding: 0;}#sk-container-id-26 div.sk-toggleable {background-color: white;}#sk-container-id-26 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-26 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-26 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-26 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-26 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-26 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-26 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-26 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-26 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-26 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-26 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-26 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-26 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-26 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-26 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-26 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-26 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-26 div.sk-item {position: relative;z-index: 1;}#sk-container-id-26 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-26 div.sk-item::before, #sk-container-id-26 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-26 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-26 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-26 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-26 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-26 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-26 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-26 div.sk-label-container {text-align: center;}#sk-container-id-26 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-26 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-26\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scale&#x27;, StandardScaler()),\n",
              "                (&#x27;polynomial&#x27;, PolynomialFeatures(include_bias=False)),\n",
              "                (&#x27;model&#x27;, LinearRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scale&#x27;, StandardScaler()),\n",
              "                (&#x27;polynomial&#x27;, PolynomialFeatures(include_bias=False)),\n",
              "                (&#x27;model&#x27;, LinearRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-37\" type=\"checkbox\" ><label for=\"sk-estimator-id-37\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PolynomialFeatures</label><div class=\"sk-toggleable__content\"><pre>PolynomialFeatures(include_bias=False)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-38\" type=\"checkbox\" ><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, we convert the data type Z to type float to avoid conversion warnings that may appear as a result of StandardScaler taking float inputs.\n",
        "\n",
        "Then, we can normalize the data, perform a transform and fit the model simultaneously."
      ],
      "metadata": {
        "id": "D1IfSn54ST9S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pipe = Pipeline([\n",
        "    ('scale', StandardScaler()),\n",
        "    ('polynomial', PolynomialFeatures(include_bias=False)),\n",
        "    ('model', LinearRegression())\n",
        "])"
      ],
      "metadata": {
        "id": "bmh2jdlTS79N"
      },
      "execution_count": 167,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pipe.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 159
        },
        "id": "H7LbmF8_VB4Q",
        "outputId": "2be281f5-8f06-4613-c927-c080ad7b6a6f"
      },
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('scale', StandardScaler()),\n",
              "                ('polynomial', PolynomialFeatures(include_bias=False)),\n",
              "                ('model', LinearRegression())])"
            ],
            "text/html": [
              "<style>#sk-container-id-27 {color: black;background-color: white;}#sk-container-id-27 pre{padding: 0;}#sk-container-id-27 div.sk-toggleable {background-color: white;}#sk-container-id-27 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-27 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-27 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-27 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-27 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-27 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-27 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-27 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-27 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-27 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-27 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-27 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-27 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-27 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-27 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-27 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-27 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-27 div.sk-item {position: relative;z-index: 1;}#sk-container-id-27 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-27 div.sk-item::before, #sk-container-id-27 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-27 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-27 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-27 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-27 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-27 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-27 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-27 div.sk-label-container {text-align: center;}#sk-container-id-27 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-27 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-27\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scale&#x27;, StandardScaler()),\n",
              "                (&#x27;polynomial&#x27;, PolynomialFeatures(include_bias=False)),\n",
              "                (&#x27;model&#x27;, LinearRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-39\" type=\"checkbox\" ><label for=\"sk-estimator-id-39\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scale&#x27;, StandardScaler()),\n",
              "                (&#x27;polynomial&#x27;, PolynomialFeatures(include_bias=False)),\n",
              "                (&#x27;model&#x27;, LinearRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-40\" type=\"checkbox\" ><label for=\"sk-estimator-id-40\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-41\" type=\"checkbox\" ><label for=\"sk-estimator-id-41\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PolynomialFeatures</label><div class=\"sk-toggleable__content\"><pre>PolynomialFeatures(include_bias=False)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-42\" type=\"checkbox\" ><label for=\"sk-estimator-id-42\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a pipeline that standardizes the data, then produce a prediction using a linear regression model using the features Z and target y.\n",
        "\n",
        "Input=[('scale',StandardScaler()),('model',LinearRegression())]\n",
        "\n",
        "pipe=Pipeline(Input)\n",
        "\n",
        "pipe.fit(Z,y)\n",
        "\n",
        "ypipe=pipe.predict(Z)\n",
        "ypipe[0:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LvAXVITzVqsD",
        "outputId": "6c053f82-e46c-4cab-afc6-4de4b4fc2a02"
      },
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([13699.11161184, 13699.11161184, 19051.65470233, 10620.36193015,\n",
              "       15521.31420211, 13869.66673213, 15456.16196732, 15974.00907672,\n",
              "       17612.35917161, 10722.32509097])"
            ]
          },
          "metadata": {},
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**<h2>4. Measures for In-Sample Evaluation</h2>**\n",
        "\n",
        "\n",
        "<p>When evaluating our models, not only do we want to visualize the results, but we also want a quantitative measure to determine how accurate the model is.</p>\n",
        "\n",
        "<p>Two very important measures that are often used in Statistics to determine the accuracy of a model are:</p>\n",
        "<ul>\n",
        "    <li><b>R^2 / R-squared</b></li>\n",
        "    <li><b>Mean Squared Error (MSE)</b></li>\n",
        "</ul>\n",
        "    \n",
        "<b>R-squared</b>\n",
        "\n",
        "<p>R squared, also known as the coefficient of determination, is a measure to indicate how close the data is to the fitted regression line.</p>\n",
        "    \n",
        "<p>The value of the R-squared is the percentage of variation of the response variable (y) that is explained by a linear model.</p>\n",
        "\n",
        "\n",
        "\n",
        "<b>Mean Squared Error (MSE)</b>\n",
        "\n",
        "<p>The Mean Squared Error measures the average of the squares of errors. That is, the difference between actual value (y) and the estimated value (ŷ).</p>"
      ],
      "metadata": {
        "id": "LTQf_S8sV1i3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### <h3>Model 1: Simple Linear Regression</h3>\n"
      ],
      "metadata": {
        "id": "2jon6scmWGIx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the R^2\n",
        "\n",
        "#highway_mpg_fit\n",
        "\n",
        "lm.fit(X, Y)\n",
        "# Find the R^2\n",
        "\n",
        "print('The R-square is: ', lm.score(X, Y))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zRLF4p2sWMYy",
        "outputId": "c896aabb-a54d-4de6-9e72-59facccedb6d"
      },
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The R-square is:  0.4965911884339176\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can say that ~49.659% of the variation of the price is explained by this simple linear model \"horsepower_fit\".\n",
        "\n",
        "Let's calculate the MSE:-\n",
        "\n",
        "We can predict the output i.e., \"yhat\" using the predict method, where X is the input variable"
      ],
      "metadata": {
        "id": "Jge9rg9UWY5W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Yhat=lm.predict(X)\n",
        "print('The output of the first four predicted value is: ', Yhat[0:4])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t6OjCpO8WW6n",
        "outputId": "d0ec439a-3a85-42fb-dea9-103c07cf8746"
      },
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The output of the first four predicted value is:  [16236.50464347 16236.50464347 17058.23802179 13771.3045085 ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import the Function mean_squared_error from the Module Metrics:\n",
        "\n",
        "from sklearn.metrics import mean_squared_error"
      ],
      "metadata": {
        "id": "ttTInqtIWUa1"
      },
      "execution_count": 172,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Comparing the Predicted Results with the Actual Results\n",
        "\n",
        "mse = mean_squared_error(df['price'], Yhat)\n",
        "print('The mean square error of price and predicted value is: ', mse)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zaUgClEZWpWQ",
        "outputId": "0eaee2c5-8b9d-40e6-d2ee-d12e88bbaed7"
      },
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The mean square error of price and predicted value is:  31635042.944639888\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h3>Model 2: Multiple Linear Regression</h3>\n"
      ],
      "metadata": {
        "id": "EHYRq0zUWo1J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating the R^2\n",
        "\n",
        "# Fit the Model\n",
        "\n",
        "lm.fit(Z, df['price'])\n",
        "# Find the R^2\n",
        "\n",
        "print('The R-square is: ', lm.score(Z, df['price']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IJVNRZlTW0xs",
        "outputId": "a0daa48b-3369-445b-8f9a-c35da69530f0"
      },
      "execution_count": 174,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The R-square is:  0.8093562806577457\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can say that ~80.896 % of the variation of price is explained by this multiple linear regression \"multi_fit\".\n",
        "\n",
        "Let's calculate the MSE."
      ],
      "metadata": {
        "id": "xqj5YblpXCfa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Producing Prediction\n",
        "\n",
        "Y_predict_multifit = lm.predict(Z)"
      ],
      "metadata": {
        "id": "PpJue8OyXE9q"
      },
      "execution_count": 175,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Comparing the Predicted Results with the Actual Results\n",
        "\n",
        "print('The mean square error of price and predicted value using multifit is: ', \\\n",
        "      mean_squared_error(df['price'], Y_predict_multifit))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r7MEVt-HXKdH",
        "outputId": "a25a5bae-a7fc-4045-b2a7-129bafbb82f4"
      },
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The mean square error of price and predicted value using multifit is:  11980366.87072649\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h3>Model 3: Polynomial Fit</h3>"
      ],
      "metadata": {
        "id": "2atGnJijXZdx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating the R^2.\n",
        "\n",
        "# Importing the function r2_score from the module metrics as we are using a different function.\n",
        "\n",
        "from sklearn.metrics import r2_score"
      ],
      "metadata": {
        "id": "0mFL_DpZXaip"
      },
      "execution_count": 177,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Applying the function to get the value of R^2:\n",
        "\n",
        "r_squared = r2_score(y, p(x))\n",
        "print('The R-square value is: ', r_squared)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IPZn_RwSXnrc",
        "outputId": "2d836dd3-8d5c-443c-a96c-b0b0327cc3da"
      },
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The R-square value is:  0.674194666390652\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can say that ~67.419 % of the variation of price is explained by this polynomial fit."
      ],
      "metadata": {
        "id": "k5S5B6q1Xutq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h3>MSE</h3>"
      ],
      "metadata": {
        "id": "5Blf_g7BX0uv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating the MSE\n",
        "\n",
        "mean_squared_error(df['price'], p(x))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pXUMgFweXwJJ",
        "outputId": "874e33a3-e80f-490a-9064-447d4993ceba"
      },
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20474146.426361218"
            ]
          },
          "metadata": {},
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**<h2>5. Prediction and Decision Making</h2>**\n",
        "<h3>Prediction</h3>\n",
        "\n",
        "<p>In the previous section, we trained the model using the method <b>fit</b>. Now we will use the method <b>predict</b> to produce a prediction. Lets import <b>pyplot</b> for plotting; we will also be using some functions from numpy.</p>"
      ],
      "metadata": {
        "id": "-3OWDiSDX-UA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "9cTmeVovX_x9"
      },
      "execution_count": 180,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating New Input\n",
        "\n",
        "new_input=np.arange(1, 100, 1).reshape(-1, 1)"
      ],
      "metadata": {
        "id": "vCCmoodyYJFl"
      },
      "execution_count": 181,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitting the Model\n",
        "\n",
        "lm.fit(X, Y)\n",
        "lm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "ro6xyGJJYPue",
        "outputId": "d9bfc1c8-5276-4ab2-8dec-43f54a50987b"
      },
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-28 {color: black;background-color: white;}#sk-container-id-28 pre{padding: 0;}#sk-container-id-28 div.sk-toggleable {background-color: white;}#sk-container-id-28 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-28 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-28 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-28 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-28 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-28 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-28 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-28 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-28 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-28 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-28 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-28 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-28 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-28 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-28 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-28 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-28 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-28 div.sk-item {position: relative;z-index: 1;}#sk-container-id-28 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-28 div.sk-item::before, #sk-container-id-28 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-28 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-28 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-28 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-28 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-28 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-28 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-28 div.sk-label-container {text-align: center;}#sk-container-id-28 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-28 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-28\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-43\" type=\"checkbox\" checked><label for=\"sk-estimator-id-43\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Produce a Prediction\n",
        "\n",
        "yhat=lm.predict(new_input)\n",
        "yhat[0:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ct3tKZm2YWn7",
        "outputId": "75d07cb2-1178-481b-9301-fd0f13185850"
      },
      "execution_count": 183,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([37601.57247984, 36779.83910151, 35958.10572319, 35136.37234487,\n",
              "       34314.63896655])"
            ]
          },
          "metadata": {},
          "execution_count": 183
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the Data\n",
        "\n",
        "plt.plot(new_input, yhat)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "0eU7QpLIYcHP",
        "outputId": "e425cc8e-c5be-4c11-92e9-5ddcfe603e9d"
      },
      "execution_count": 184,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**<h3>Decision Making: Determining a Good Model Fit</h3>**"
      ],
      "metadata": {
        "id": "-TF7xkz5YmBN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<p>Now that we have visualized the different models, and generated the R-squared and MSE values for the fits, how do we determine a good model fit?\n",
        "<ul>\n",
        "    <li><i>What is a good R-squared value?</i></li>\n",
        "</ul>\n",
        "</p>\n",
        "\n",
        "<p>When comparing models, <b>the model with the higher R-squared value is a better fit</b> for the data.\n",
        "<ul>\n",
        "    <li><i>What is a good MSE?</i></li>\n",
        "</ul>\n",
        "</p>\n",
        "\n",
        "<p>When comparing models, <b>the model with the smallest MSE value is a better fit</b> for the data.</p>\n",
        "\n",
        "\n",
        "<h4>Let's take a look at the values for the different models.</h4>\n",
        "<p>Simple Linear Regression: Using Highway-mpg as a Predictor Variable of Price.\n",
        "<ul>\n",
        "    <li>R-squared: 0.49659118843391759</li>\n",
        "    <li>MSE: 3.16 x10^7</li>\n",
        "</ul>\n",
        "</p>\n",
        "    \n",
        "<p>Multiple Linear Regression: Using Horsepower, Curb-weight, Engine-size, and Highway-mpg as Predictor Variables of Price.\n",
        "<ul>\n",
        "    <li>R-squared: 0.80896354913783497</li>\n",
        "    <li>MSE: 1.2 x10^7</li>\n",
        "</ul>\n",
        "</p>\n",
        "    \n",
        "<p>Polynomial Fit: Using Highway-mpg as a Predictor Variable of Price.\n",
        "<ul>\n",
        "    <li>R-squared: 0.6741946663906514</li>\n",
        "    <li>MSE: 2.05 x 10^7</li>\n",
        "</ul>\n",
        "</p>\n"
      ],
      "metadata": {
        "id": "T9pn1A_GYrBe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**<h3>Simple Linear Regression Model (SLR) vs Multiple Linear Regression Model (MLR)</h3>**\n"
      ],
      "metadata": {
        "id": "0WQLLNLqYvl_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<p>Usually, the more variables you have, the better your model is at predicting, but this is not always true. Sometimes you may not have enough data, you may run into numerical problems, or many of the variables may not be useful and even act as noise. As a result, you should always check the MSE and R^2.</p>\n",
        "\n",
        "<p>In order to compare the results of the MLR vs SLR models, we look at a combination of both the R-squared and MSE to make the best conclusion about the fit of the model.\n",
        "<ul>\n",
        "    <li><b>MSE</b>: The MSE of SLR is  3.16x10^7  while MLR has an MSE of 1.2 x10^7.  The MSE of MLR is much smaller.</li>\n",
        "    <li><b>R-squared</b>: In this case, we can also see that there is a big difference between the R-squared of the SLR and the R-squared of the MLR. The R-squared for the SLR (~0.497) is very small compared to the R-squared for the MLR (~0.809).</li>\n",
        "</ul>\n",
        "</p>\n",
        "\n",
        "This R-squared in combination with the MSE show that MLR seems like the better model fit in this case compared to SLR.\n"
      ],
      "metadata": {
        "id": "R3j13R0fYvEu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**<h3>Simple Linear Model (SLR) vs. Polynomial Fit</h3>**"
      ],
      "metadata": {
        "id": "MlPcCp-uY54B"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<ul>\n",
        "    <li><b>MSE</b>: We can see that Polynomial Fit brought down the MSE, since this MSE is smaller than the one from the SLR.</li>\n",
        "    <li><b>R-squared</b>: The R-squared for the Polynomial Fit is larger than the R-squared for the SLR, so the Polynomial Fit also brought up the R-squared quite a bit.</li>\n",
        "</ul>\n",
        "<p>Since the Polynomial Fit resulted in a lower MSE and a higher R-squared, we can conclude that this was a better fit model than the simple linear regression for predicting \"price\" with \"highway-mpg\" as a predictor variable.</p>"
      ],
      "metadata": {
        "id": "WN4HOdmKY5hX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**<h3>Multiple Linear Regression (MLR) vs. Polynomial Fit</h3>**"
      ],
      "metadata": {
        "id": "ARKMWvd6ZDJD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<ul>\n",
        "    <li><b>MSE</b>: The MSE for the MLR is smaller than the MSE for the Polynomial Fit.</li>\n",
        "    <li><b>R-squared</b>: The R-squared for the MLR is also much larger than for the Polynomial Fit.</li>\n",
        "</ul>"
      ],
      "metadata": {
        "id": "1UxT7MK7ZFgL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**<h2>Conclusion</h2>**\n",
        "\n",
        "<p>Comparing these three models, we conclude that <b>the MLR model is the best model</b> to be able to predict price from our dataset. This result makes sense since we have 27 variables in total and we know that more than one of those variables are potential predictors of the final car price.</p>\n"
      ],
      "metadata": {
        "id": "T-BC7oAAZI6_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr>\n",
        "\n",
        "## <h3 align=\"center\"> **© YASH ASHOK SHIRSATH.** <h3/>"
      ],
      "metadata": {
        "id": "mVk0QBNdZZ01"
      }
    }
  ]
}