realestate_Linear_regression.ipynb

{
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  "metadata": {
    "colab": {
      "name": "realestate Linear_regression.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rpaos-qFwccJ",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "b45837bd-19e6-44cb-aa6e-771c8de4d63d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-cc8b22ff-3c66-4694-ae2a-5f28e6406bdf\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-cc8b22ff-3c66-4694-ae2a-5f28e6406bdf\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving Realestate.csv to Realestate.csv\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd  \n",
        "import numpy as np\n",
        "from google.colab import files\n",
        "upload = files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "realestate = pd.read_csv('Realestate.csv')\n",
        "realestate"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "nFe0J76HwFWr",
        "outputId": "f9767946-77bf-4ff9-d35b-bccd2cd181cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      No  X1 transaction date  X2 house age  \\\n",
              "0      1             2012.917          32.0   \n",
              "1      2             2012.917          19.5   \n",
              "2      3             2013.583          13.3   \n",
              "3      4             2013.500          13.3   \n",
              "4      5             2012.833           5.0   \n",
              "..   ...                  ...           ...   \n",
              "409  410             2013.000          13.7   \n",
              "410  411             2012.667           5.6   \n",
              "411  412             2013.250          18.8   \n",
              "412  413             2013.000           8.1   \n",
              "413  414             2013.500           6.5   \n",
              "\n",
              "     X3 distance to the nearest MRT station  X4 number of convenience stores  \\\n",
              "0                                  84.87882                               10   \n",
              "1                                 306.59470                                9   \n",
              "2                                 561.98450                                5   \n",
              "3                                 561.98450                                5   \n",
              "4                                 390.56840                                5   \n",
              "..                                      ...                              ...   \n",
              "409                              4082.01500                                0   \n",
              "410                                90.45606                                9   \n",
              "411                               390.96960                                7   \n",
              "412                               104.81010                                5   \n",
              "413                                90.45606                                9   \n",
              "\n",
              "     X5 latitude  X6 longitude  Y house price of unit area  \n",
              "0       24.98298     121.54024                        37.9  \n",
              "1       24.98034     121.53951                        42.2  \n",
              "2       24.98746     121.54391                        47.3  \n",
              "3       24.98746     121.54391                        54.8  \n",
              "4       24.97937     121.54245                        43.1  \n",
              "..           ...           ...                         ...  \n",
              "409     24.94155     121.50381                        15.4  \n",
              "410     24.97433     121.54310                        50.0  \n",
              "411     24.97923     121.53986                        40.6  \n",
              "412     24.96674     121.54067                        52.5  \n",
              "413     24.97433     121.54310                        63.9  \n",
              "\n",
              "[414 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-38bfcef8-19a8-472a-a6b6-e3a71d75efa3\">\n",
              "    <div 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>No</th>\n",
              "      <th>X1 transaction date</th>\n",
              "      <th>X2 house age</th>\n",
              "      <th>X3 distance to the nearest MRT station</th>\n",
              "      <th>X4 number of convenience stores</th>\n",
              "      <th>X5 latitude</th>\n",
              "      <th>X6 longitude</th>\n",
              "      <th>Y house price of unit area</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2012.917</td>\n",
              "      <td>32.0</td>\n",
              "      <td>84.87882</td>\n",
              "      <td>10</td>\n",
              "      <td>24.98298</td>\n",
              "      <td>121.54024</td>\n",
              "      <td>37.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2012.917</td>\n",
              "      <td>19.5</td>\n",
              "      <td>306.59470</td>\n",
              "      <td>9</td>\n",
              "      <td>24.98034</td>\n",
              "      <td>121.53951</td>\n",
              "      <td>42.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2013.583</td>\n",
              "      <td>13.3</td>\n",
              "      <td>561.98450</td>\n",
              "      <td>5</td>\n",
              "      <td>24.98746</td>\n",
              "      <td>121.54391</td>\n",
              "      <td>47.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2013.500</td>\n",
              "      <td>13.3</td>\n",
              "      <td>561.98450</td>\n",
              "      <td>5</td>\n",
              "      <td>24.98746</td>\n",
              "      <td>121.54391</td>\n",
              "      <td>54.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2012.833</td>\n",
              "      <td>5.0</td>\n",
              "      <td>390.56840</td>\n",
              "      <td>5</td>\n",
              "      <td>24.97937</td>\n",
              "      <td>121.54245</td>\n",
              "      <td>43.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>409</th>\n",
              "      <td>410</td>\n",
              "      <td>2013.000</td>\n",
              "      <td>13.7</td>\n",
              "      <td>4082.01500</td>\n",
              "      <td>0</td>\n",
              "      <td>24.94155</td>\n",
              "      <td>121.50381</td>\n",
              "      <td>15.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>410</th>\n",
              "      <td>411</td>\n",
              "      <td>2012.667</td>\n",
              "      <td>5.6</td>\n",
              "      <td>90.45606</td>\n",
              "      <td>9</td>\n",
              "      <td>24.97433</td>\n",
              "      <td>121.54310</td>\n",
              "      <td>50.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>411</th>\n",
              "      <td>412</td>\n",
              "      <td>2013.250</td>\n",
              "      <td>18.8</td>\n",
              "      <td>390.96960</td>\n",
              "      <td>7</td>\n",
              "      <td>24.97923</td>\n",
              "      <td>121.53986</td>\n",
              "      <td>40.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>412</th>\n",
              "      <td>413</td>\n",
              "      <td>2013.000</td>\n",
              "      <td>8.1</td>\n",
              "      <td>104.81010</td>\n",
              "      <td>5</td>\n",
              "      <td>24.96674</td>\n",
              "      <td>121.54067</td>\n",
              "      <td>52.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>413</th>\n",
              "      <td>414</td>\n",
              "      <td>2013.500</td>\n",
              "      <td>6.5</td>\n",
              "      <td>90.45606</td>\n",
              "      <td>9</td>\n",
              "      <td>24.97433</td>\n",
              "      <td>121.54310</td>\n",
              "      <td>63.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>414 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-38bfcef8-19a8-472a-a6b6-e3a71d75efa3')\"\n",
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              "  "
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "realestate.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "na5BKWakwXBg",
        "outputId": "55a79ea5-39ed-4187-fb89-b6743689ecdc"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(414, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "realestate['logprice']=np.log(realestate['Y house price of unit area'])\n",
        "realestate.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "Es-ffW2gwjOD",
        "outputId": "99cbb77b-1790-42da-ccf0-383cae94f2a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   No  X1 transaction date  X2 house age  \\\n",
              "0   1             2012.917          32.0   \n",
              "1   2             2012.917          19.5   \n",
              "2   3             2013.583          13.3   \n",
              "3   4             2013.500          13.3   \n",
              "4   5             2012.833           5.0   \n",
              "\n",
              "   X3 distance to the nearest MRT station  X4 number of convenience stores  \\\n",
              "0                                84.87882                               10   \n",
              "1                               306.59470                                9   \n",
              "2                               561.98450                                5   \n",
              "3                               561.98450                                5   \n",
              "4                               390.56840                                5   \n",
              "\n",
              "   X5 latitude  X6 longitude  Y house price of unit area  logprice  \n",
              "0     24.98298     121.54024                        37.9  3.634951  \n",
              "1     24.98034     121.53951                        42.2  3.742420  \n",
              "2     24.98746     121.54391                        47.3  3.856510  \n",
              "3     24.98746     121.54391                        54.8  4.003690  \n",
              "4     24.97937     121.54245                        43.1  3.763523  "
            ],
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              "      <th></th>\n",
              "      <th>No</th>\n",
              "      <th>X1 transaction date</th>\n",
              "      <th>X2 house age</th>\n",
              "      <th>X3 distance to the nearest MRT station</th>\n",
              "      <th>X4 number of convenience stores</th>\n",
              "      <th>X5 latitude</th>\n",
              "      <th>X6 longitude</th>\n",
              "      <th>Y house price of unit area</th>\n",
              "      <th>logprice</th>\n",
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              "      <td>37.9</td>\n",
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              "      <td>19.5</td>\n",
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              "      <td>9</td>\n",
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              "      <td>121.53951</td>\n",
              "      <td>42.2</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2013.583</td>\n",
              "      <td>13.3</td>\n",
              "      <td>561.98450</td>\n",
              "      <td>5</td>\n",
              "      <td>24.98746</td>\n",
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              "      <td>47.3</td>\n",
              "      <td>3.856510</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2013.500</td>\n",
              "      <td>13.3</td>\n",
              "      <td>561.98450</td>\n",
              "      <td>5</td>\n",
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              "      <td>121.54391</td>\n",
              "      <td>54.8</td>\n",
              "      <td>4.003690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2012.833</td>\n",
              "      <td>5.0</td>\n",
              "      <td>390.56840</td>\n",
              "      <td>5</td>\n",
              "      <td>24.97937</td>\n",
              "      <td>121.54245</td>\n",
              "      <td>43.1</td>\n",
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              "                                                     [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",
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            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "fig, axs = plt.subplots(2,2, figsize=(11,6))\n",
        "plt1 = sns.scatterplot(data = realestate, x = 'X2 house age', y = 'logprice', ax = axs[0,0])\n",
        "plt2 = sns.scatterplot(data = realestate, x = 'X1 transaction date', y = 'logprice', ax = axs[0,1])\n",
        "plt3 = sns.scatterplot(data = realestate, x = 'X3 distance to the nearest MRT station', y = 'logprice', ax = axs[1,0])\n",
        "plt4 = sns.scatterplot(data = realestate, x = 'X4 number of convenience stores', y = 'logprice', ax = axs[1,1])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "k6OPVm_5xCf-",
        "outputId": "2132f184-1c43-4086-ae96-af830f2e0545"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 792x432 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hexbin = sns.jointplot(x='X2 house age', y='Y house price of unit area', data= realestate, kind= \"reg\", color = 'blue')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "OthJwh_92Sso",
        "outputId": "4d1f6e4d-5690-4124-fe91-60cb0e591448"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hexbin = sns.jointplot(x='X5 latitude', y='Y house price of unit area', data= realestate, kind= \"reg\", color = 'blue')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "9QMsIkCt8PPb",
        "outputId": "ad2a9ae2-847d-4434-a0f3-f61eae7f20b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hexbin = sns.jointplot(x='X6 longitude', y='Y house price of unit area', data= realestate, kind= \"reg\", color = 'blue')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "FkIlaljC8V7b",
        "outputId": "65352bf5-0b57-4ae5-eb5c-0de02a9530c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hexbin = sns.jointplot(x='X3 distance to the nearest MRT station', y='Y house price of unit area', data= realestate, kind= \"reg\", color = 'blue')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "6Sw9g6cZ2vUO",
        "outputId": "725e23e3-ba88-42b5-acb0-9669ceb2e4e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hexbin = sns.jointplot(x='X4 number of convenience stores', y='Y house price of unit area', data= realestate, kind= \"reg\", color = 'blue')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "y5-ptABU2wEz",
        "outputId": "3656dfc8-78c3-4ada-d134-0669afdfcee9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ],
            "image/png": 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KiIhCj8GKiIhCj8GKiIhCj8GKiIhCj8GKiIhCj8GKiIhCj8GKiIhCj8GKiIgoRBisiIiKFHtWREQUegxWREQUegxWBSAit4vINhF5OenYaBF5REReTTyPShwXEfmhiLwmIi+KyAmu2kVEFBUMVoVxB4Az0o5dDeBPxpgZAP6UeA0ATQBmJB6XALjZYbuIiCKBwaoAjDF/AbAz7fA5AJYnPl4O4CNJx39u1FMARorIRFdtIyKKAgYrd8YbYzYnPt4CYHzi4zoAbyR9Xlvi2EFE5BIRaRGRlu3bt7trKRFRyCRf//R10C3yT2AJFsYYA8AM4utuNcY0GmMax44d66BlREThlHz9AxisXNpqh/cSz9sSxzcBmJL0eZMTx4iIiHwPVvcBuDDx8YUAfpd0/F8SWYEnA9idNFxIREQlrtzVNxaRXwKYC2CMiLQBuB7AYgC/EpGLAWwA8PHEpz8I4EwArwHYB+CzrtpFRETFx1mwMsZ8Mss/nZrhcw2Ay1y1hYiIihsrWBARFSkz4BS14sVgRUREocdgRUREocdgRUREocdgRUREocdgRURUpJhgQUREFCIMVkREFHoMVkREFHoMVkRERYpzVkRERCHCYEVERKHHYEVERKHHYEVERKHHYEVEVKSYYEFERBQiDFZERBR6DFZERBR6DFZEREWKc1ZEREQhwmBFREShx2BFREShx2BFREShx2BFRFSk4vGgW+AfBisioiLV2xt0C/zDYEVEVKR6eoJugX8YrIiIihSDFRERhR6HAYmIKPRKqWdVnusnisg4AEPsa2PMRictIiKinJRSsOq3ZyUiZ4vIqwBaATwOYD2AZsftIiKifjBYpVoE4GQAa40xDQBOBfCU01YREVG/GKxSdRtj3gIQE5GYMebPABodt4uIiPpRSsEqlzmrXSIyDMATAO4SkW0AOtw2i4iI+tPVFXQL/JNLz+ocAPsAXAHgIQCvA/iwy0YREVH/9u0LugX+6bdnZYzpEJFpAGYYY5aLSA2AMvdNIyKivnSU0BhXLtmAnwfwGwA/TRyqA/B/LhtFRET927s36Bb4J5dhwMsAnAKgHQCMMa8CGOeyUURE1D/2rFJ1GmPemcYTkXIAxl2TiIgoF+xZpXpcRK4BUC0ipwP4NYD73TaLiIj61sueVZqvA9gO4CUA/wrgQQD/4bJRRETUnzhuvPFWTJ5cH3RDfNFnNqCIlAFYaYw5AsB/+9MkIiLqXxxHH30JXn75X4NuiC/67FkZY3oBrBGRqT61h4iIctKL7u6g2+CfXCpYjAKwUkSeRlLlCmPM2YM9qYhcCeBz0ESNlwB8FsBEAHcDOATAMwA+nZzYQUREyeIlVcEil2B1bSFPKCJ1AP4NwCxjzH4R+RWA8wCcCWCpMeZuEbkFwMUAbi7kuYmIooPBKoUx5nFH560WkW4ANQA2A5gH4FOJf18O4JtgsCIiyqK3pIJVLhUsThaRFSKyV0S6RKRXRNoHe0JjzCYA3wOwERqkdkOH/XYZY2wN4TZopYxM7blERFpEpGX79u2DbQYRUdFJvv4BcXR2Bt0i/+SSun4TgE8CeBVANXSu6ceDPaGIjIIWx20AMAnAUABn5Pr1xphbjTGNxpjGsWPHDrYZRERFJ/n6x55VBsaY1wCUGWN6jTHLMIDgksFpAFqNMduNMd0Afgst5zQyUR0DACYD2JTHOYiIsHAhMGoUUF6uzwsXBt2iQuotqZ5VLgkW+0SkEsDzIvJd6NBdTkEui40ATk5Ub98P3Xm4BcCfAfwTNCPwQgC/y+McRFTiFi4EFi0CYjGgslK301i0SP/tuuuCbVthxGEMoANe0SfG9F3mL7E9yFYAlQCuBDACwE8Sva3BnVRkAYBPAOgB8Bx0aLEOGqhGJ45dYIzp876hsbHRtLS0DLYZRBRho0ZpgCpPuiXv6QFqaoC33w6uXTmQnD5JxhlgG4DxMGar4yb5KuP77zdYhRmDFRFlU16uPSpJuvQZA3R3I+yLaXMMVocY4C0AhyGPvkMYZXz/+QznERGF1vDhQG9v6rHeXmDYsGDaU3j2zdUG2gq/MFgRUSRdeSUQj+vQnzH6HI/r8WjQUbExY2YE3A5/5LLO6p9zOUZEFCbXXQdce63OUXV36/O110YluQLQKX9g2bJ7Am6HP3LpWX0jx2NERKFy3XWaTNHdrc/RCVSAHQZsH3SJhuKSNXVdRJqg9frqROSHSf9UCxvSiYgoIHEAwJ49ATfDJ32ts3oTuv7pbGg5JGsPNIWdiIgCoz2rkg9WxpgXALwgIncl1ewjIqJQYM8KACAivzLGfBzAcyJy0GIsY8yxTltGRET92IP29uFBN8IXfQ0Dfjnx/CE/GkJEQHMzsGQJ0NoKNDQA8+cDTU1Bt4rCaw/27CmNYJU1G9AYsznxvCHTw78mEpWG5mbg8suBzZuB0aP1+fLL9ThRZntKZhgwl3VWHxORV0Vkt4i0i8iefPazIqLMlizR8kBDh2qJoKFD9fWSJUG3jMKrnanrSb4L4MPGmNWuG0NUylpbtUeVrKYGWL8+kOZQUSidYJXLouCtDFRE7jU0aJXwZPv2AfX1gTSHisLbYa8gXzC5BKsWEblHRD6ZGBL8mIh8zHnLiErM/PlAVxfQ0aG17Do69PX8+UG3jMLrLbz1VtBt8EcuwaoWwD4AHwDw4cSDGYJEBdbUBNx0EzBxopYGmjhRXzMbkDITAG9j27Zu1NXVB90Y5/qdszLGfNaPhhCRp4i3mSPfGJx++tV45BHgzTd3Bt0Y5/oNViKyDLYWfRJjzEVOWkRUomzqemVlauo6e1eUTfU7O9ofEmQzfJFLNuDvkz4eAuCj0LqBRFRAS5boHNX27UBnJ1BVBYwYoccZrCiTmhr7EYMVjDH3Jr8WkV8C+KuzFhGVqFWrNFAB3maBHR2h34KdAsSeVd9mABhX6IYQlbo9e3QnWxF9bYw+SqVCAQ3c8HcqLU0Kshm+yGXOag9S56y2APi6sxYRlahsPSj2rCib2lr70dQgm+GLXIYBS6NKIlHAysq0JxWP67OIHisrC7plFFZlZcCwYcDevVOCbopzuayzIiIfzJypz5WVOhdRWZl6nCgT7V0xWBGRTxYvBsaMAWIxHfqLxfT14sVBt6xwmpuBefO0tNS8eawoXwijRgGaShBtWYOViDT42RCiUtfUBNx+O/CudwGTJ+vz7bdHJ22dW6C4MW4cABwa+UScvnpWvwEAEfmTT20hKnlNTcCjjwLr1ulzVAIVwC1QXBmXyM1etSrYdrjWV4JFTESuATBTRL6S/o/GmBvcNYuIooZboLhhg9VLL2lvPKr66lmdB6AXGtCGZ3gQEeWMW6AUmmDBAsGPfhQDsAdXXPHzoBvkVNaelTFmDYDviMiLxhiOKhNRXubP1zkqQHtU+/ZxC5T8GFx/vS6B/cUvgHXrZgfcHrdyyQb8m4jcICIticf3RWSE85YRUaRwCxR3pk4FgGOwa1fQLXEnl3JLtwN4GcDHE68/DWAZAG7ASEQD0tTE4OTCtGkAEMOf/wx89KNBt8aNXHpW040x1xtj1iUeCwAc6rphRESUmylTAGAXHngg6Ja4k0uw2i8i/2hfiMgpAPa7axIREQ2EluR6CL//vZbriqJcgtUXAPxYRNaLyHoANwH4V6etIiIqgNKqmHEftm4F/va3oNvhRr/ByhjzgjFmNoBjARxrjDneGPOi+6YREQ1e6VXMuA81NcBddwXdDjdyrg1ojGk3xrS7bAxRqVu4UGu9lZfr88KFQbeosPzs6ZRexYwOnHMO8Ktf6ZKAqGEhWyoqUR7WWbgQWLRI1x9VVurzokXRCVh+93RaW5O3fVdRr5jxL/8C7NwJ/Pa3Qbek8BisqGhEfVhn6VKttF5erj2B8nJ9vXRp0C0rDL97OqVYMeMDHwCmTwd+/OOgW1J4/QYrEakRkWtF5L8Tr2eIyIfcN40oVdSHdfbsOXijxbIyYO9ed+f0s6fqd09n/nxg1y5g9WrgxRf1edcu9xUzguz9x2LApZcCf/0r8MIL/p3XD7n0rJYB6ATw7sTrTQC+5axFVFSifLHz2/DhQG9v6rHeXt0J1gW/e6pB9HSMViOCSOprV8LQ+//sZ3Xzzqj1rsT0878nIi3GmEYRec4Yc3zimM0QDFRjY6NpaWkJuhkly/5hVlam1npzVUJn3jz94x861DvW0aFlex59tPDn85uds4rFtEfV26trZq69FrjuusKfz++fZyn8vhTonJLTJ0nMAMnX7zJo7XEA+Cm02FAD6uqGoK1tfc4nD4GM7z+XcktdIlKNxE9FRKZDe1pU4pKH5QDveckSNxefIAqhNjfr+2lt1Z7B/PnuygVddx2wdi1wzz36vsrLgU98wk2gAvzfssPWBlyyRM9RX+/25xnEliT+ntMrZJtu5079WZ988hY8+WROsS/0chkGvB7AQwCmiMhdAP4E4CqnraKi4PewnN+FUP0e0mluBp58EjjsMOCEE/T5ySejNSzn5+aSQby/sCR1jB4NHH00oANPY/w9uSO5LAp+BFq09jMAfgmg0RjzWD4nFZGRIvIbEXlFRFaLyLtFZLSIPCIiryaeR+VzDnIv6hc7vxM6/D7f/Pnag+vo0Lmcjo5obdkRxPsL08/0Pe8BursB4Er/T+5ALtmAHwXQY4x5wBjzewA9IvKRPM/7AwAPGWOOADAbwGoAVwP4kzFmBrT3dnWe5yDHwvSH6UJrq/6xr12ru7CuXauvXfUco95T9VsQ7y9MP9OxY4FZswDgcmzf7v/5Cy2nYUBjzG77whizCzo0OCiJvbDeC+C2xPfrSnzPcwAsT3zacgD5BkRyLEx/mC7U1gIbNmiAKi/X5w0bNGvPhaj3VIPkOgswWZh+pu9/PwDURGJheS7BKtPn5JKYkU0DgO0AlonIcyLyMxEZCmC8MWZz4nO2ABif6YtF5BK7EeT2KNwuFLkw/WG6YAzQ2alBo7PT7UUv6j1VvzU3AxddBPz978CmTfp80UXFvYg8+fqXy+ePGQMAt+KWW3RkoJjlEqxaEjsFT088bgDwTB7nLAdwAoCbE6nwHUgb8jOaT5/xsmCMudUY02iMaRw7dmwezSDq25Yt3seSlFC1daub80W9p+q3q68GduzQ9P+KCn3esUOPF6vk61/uX7UAQ4YU9/sGcushfQnAtQDuSbx+BMBleZyzDUCbMebvide/gQarrSIy0RizWUQmAtiWxzmI8tbVpeudqqq8Yz092sNyhTvpFs7atfr/1dNz8PHSsg1XXw38x38ATzyhiRfFKJdswA5jzNU2mhtjvmGM6RjsCY0xWwC8ISKHJw6dCmAVgPsAXJg4diGA3w32HESFUFWlw3F2M7t4XF8nBy8aGD8rnhw4MLDjUXbllcDUqVqKSTMEi0/WYCUiNyae7xeR+9IfeZ73SwDuEpEXARwH4D8BLAZwuoi8CuC0xGuiwMyaBYwfr0NIPT36PH68zbCKBj+DRxhKEZWqmhrgRz8CXn4ZuPHGoFszOFnLLYnIicaYZ0TkfZn+3RjzuNOW5YDllsglO0Hf3q53oxUVmiF4++3RGKqLevkj6aNwg5/ZgYOQY7klMdkqWCRbsEBgr/Mf+QjwyCNa1Hfq1Pwa6VDG95+1Z5UIVGUALjHGPJ7+cNZMohDZv1+Hjbq79Xn//qBbVDh+L0L2e91aeZYZ+WzHS8EPf6jP//ZvwbZjMHIpZPtXAPOMMaHbe5I9K3Jp9mxg5UpvrkpEi8wedVQ0tl9oaNDhuOQeiDGaibhuXeHPN3u23tHHYvqIx/Vx5JFufp4XXJB5i/fzzwfuvLPw5yugQRayzSa5wC0AfA3AEgBnA7gfAFBXNy1MxW4HXch2HYD/l5ineiexwhhzQ4EaRhRKq1dr5XMbpIzR16tXB92ywmhoOHhYzvUi5Hhcf4Y2+Pc1VJev888Hfve71P3Ahg3T49GQvZBtX3p7gVtvBTo778Oll2pvesGC8Be7zWWd1esAfp/43OFJj6IT5S3RgxLln6lNebYZgXYQIj0Vulj5vQg52/o0V+vWliwBJk8GTjzRe0yeHJ3NOgerrAw46yxg927g8SKa0MkldX2BMWYBgKUAbkh6XVSiuJo9aEFkd/kZHLPd9f5O28YAACAASURBVLvsDfipqQm48EJd/PzCC/p84YXukkc6O/VCOWSIbg44ZIi+drVuLeqbdeZj6lTg+OOBp54CthXJitZcCtk2ishLAF4E8JKIvCAiJ7pvWmFFcTV70PyeoPc7OKZvMd/f8WLT3AwsXw5MmKDzSRMm6GtXP8/KSn22w4B2V2RX69aCqLXY06PZo+3t7s5RKKedpj/7Bx4AcpwmC1Quw4C3A7jUGFNvjKmHVq9Y5rRVDqxdqxeZWOId291YS281e+H4fefqd3DMNtznchjQz57jkiU67Ldpk66/2bRJX7v6eR51FDBunP6f9fbq87hx7tat+THMaYwGwO3btcjxunW6eWZTU/iHxmtqgNNPBzZuBHQHqHDLJVj1GmOesC+MMX8FEJFRe8qH33eufqc+Z0uUdbVGx++e48qVOgRky0p1denrVavcnG/+fA1QdXW6MWBdnb52NUfmqtZiV5d+v02bgNdeA9ra9HVnp84BLVyoP8diWPh83HF2vdUS7NgRdGv6lkuwelxEfioic0XkfSLyEwCPicgJInKC6wYWysyZqZlIvb36eubMoFtWvPyeoPd7yw6/56z87ul0denfgV1D1t2tr13NIQVZqDefG4zeXmDPHp3TW7dOb462b/d+75Pddpu3yNqP3n++RDTZAqjF178edGv6lkuwmg1gJnQPq28COBLA8QC+D+B7zlpWYIsX651OWZn+8pWV6evFLOo0aEFcfET0AmEfLpMdpk0b2PF8+d3TAbybNyB1Hsk1PypIDLanaowu/t6xQ4fIXn9dv7a9vf8h4LY2nQfq6vJqEIY9qWPcOAD4Pm6/XQvdhlW/66yMMe/3oyGuNTUBy5bpHc769TpUNX9+NMrmBMnPKuHt7TpksW2b3v1XVekf2p49bs5XX5/5IuNqmLMrsezeJnCUlbmv8l5Wlhr4Y7ncvg5Scnmn5ODh6gbH9lS3b/d+X0aM0OPp5+vq0iHsjg4NVLZ4cS6M0SHqFSs0QG3ZokF/1Cjg0EPdJ3UUxiJMm3Y1vvhF4LnnNAktbEqq8Ai3XyhudhFr8tCtrS3nwl//OrDj+aqq0vcTj3sVHlxWea+s1GBlg5Tr8yUnyADec6bgUQirVgE7d+p7tMPGW7fqczzuBad9+wZWiTweB159FWhpAZ5+Wp8zzffY718cG2juw49+BJx9NrB0KXDVVUG352AlFayam/UPo7VVL3yue1Z+ny/q5s/XO3EgtfCqqwuB39mAs2bpRXD3bq8nMGYMMGOGm/MdddTB5xsxwt35Wlu1R5XM5RBZZ2dqb9EOGe/fr4kRuerpAV55RXtOTz8NPPOM/szSjRoFNDbqe1y9WkcCJk4snr/7D38YOOcc4JvfBP75n/WaFSYlE6z8HoLw+3ylwM6RRXUo1wbjujp/gvH8+bowPjnpqKfH3fn8Lu9UUaHvq6dHA5Ud2utviKurSxNcbM/p2We1h5Ru7FjgpJM0QJ10EjB9uhcQa2t13Vqx+dGP9KbpkkuAhx8O1wL4foOViNQA+CqAqcaYz4vIDACHG2N+77x1BeT3EITf5ysVUR7KDSIY20QHe1Fymfjgumccj2uvyQ7tHXqo/hz37tXzVFZqbcD04HjggFbwWLFCH88/n3mDxro6YM4cfTQ2aqJNsBdzKWBNvzLIO2/mi/jjH3+CWOwzAJaHpshtLj2rZQCeAfDuxOtNAH4NrRdYNPwegvD7fBQNK1boBPeePZphuWKFu2C1ZIkOXU2e7B3r6HB3Q+UiGB844AWnAwdSg+3FFwPXXJPac+zt1UK2TzyhPacVK4AXX8w8Z1Vf7wWnOXOASZMG3043BlfItt/vaoA77gC2bbsDl112B77//XB0r3IJVtONMZ8QkU8CgDFmn0iYOoe58XsIIoiK1gDnyYrZwoXAokU6x1JZqb8vixbpv113XeHPF8QNVb494+5u/bnYR3+p9snLHLq79QbgiisyZ/vNnOn1mubM0WG+UiSi81e33BKuxcy5BKsuEalGYuMUEZkOwGEyrRt+T87b+YCNG1N3mb3B4cYqnCcrbkuXaqCymwOWl+t8y9KlboJVUDdUAzGYrL233tJe07e/rYkQmRJiYjHdR8vON514ovYy81FRocV5hwxJ/ZkWozFjgPe+F/jznwHd9yp4uQSr6wE8BGCKiNwF4BQUQyGpNFGfDwA4T1bs9uzxir1aZWWp+zEV0vz5wGc/qzdUPT0aHIcPd3tD1R9jDh7a68/WrZoIYeecsm0caffPGjpUL8L5VD4pL/cCU1WVV0EeiM7oximnaPr/1q0/xfbtwfc0c1kU/IiIPAvgZGhp3i8bY0JeRSozPyfn/Z4PADhPVmgVFZnv5F0tmBw+XANW8iJdEXflpICDb6SCGODv7PSG9fpbkGuMVomwgWnFCuCNNw7+PFss1xivgLWIN2w4kJ9pLOYFJvsoz3LljNLoRlkZ8NGPArfcMgoXXQTcd1+wCSW5ZAOeAuB5Y8wDInIBgGtE5AfGmA3um1e8gggcxTCsU0xGj868MWD6/2uhnHVW6jbsNoBo7bbCW7IEGDlSs9ws1zdUgAYM23Pat6/vdWvGaE8peQHuli0Hf15NDXDCCd5807HHAnPn6jCgDfp20XN67zVdRYW331Z19cAWSUdtdGP8eAD4On7/+xtxyy3AF78YXFtyGQa8GcBsEZkN4CsAbgPwcwDvc9mwYhdE4PB7Xi7qsg2/uRqWe+mlgR3PV2urt01OcvmqQt9Q2Vp7du6pr/JR8bi2x/aaWlp0Dird8OFeYJozR9cGpfd2Djus/9R1kdTNIKur89uvzK+fqb9+iA9+8EZ85SvA+97nbkuX/uQSrHqMMUZEzgHwY2PMbSJyseuGueDnWHIQgSPqi2b9duCAV/jYcrmz7erV+pw81GKMd7zQamu1eK4dduzp0d+bo47K/3tnSyn/2teABx/0ikmfcQbwmc94aeTZqkOMHu1l6p10klbZ6C+oXHyxZliOH6+ByFaW/9KXNIDY+aZCDm3V1uqCYsD7mba26pYoxcvgjju0t3reecCTTwaTQJJLsNojIt8AcAGA94pIDEAIyxz2ze+x5KACR5QXzfptyBC94NphJLtWx9Ufqh0OS0/EcVXeqb09NRDb8w5ml1tbCNY+Ms07fe1rwP33e697e3WXWt2pNtW4cV51iDlzUqtD5OrUU7Wn9NOf6rxWQ4PWvHP599Henvre8/mZhsmECcAvfgGceaZmOd99t//zV7kEq08A+BSAi40xW0RkKoCQ7s6SXRBjyQwcxW3sWG/PouQA4iorylZZz3Tchba2gR1P1tubGpyypZTv3+9Vh0gOVOkmT05dgDtlysAuhrGYl5Vnh/PKy4E1a3QOyt5wuLZli57PJneI6P9fpnm2YvPBDwL/9V/A178OHH88cPXV/p4/l2zALQBuSHq9ETpnVVSYKRcNfg7l7t8/sOP5yrY9h6ttOwZSqNeWMrLBKdtQ6N69WkvPJkS8/HJua6P+9Kfc2y2SGpiGDMmcNBFUZl56z9iPvbv8Mn++Vli55hodFjzzTP/OnUs24B4kFgQDqIQOAe41xoxw2bBCa2jwt8I0FZ7fF59s+2S52j8r20UtiItdclKEDU6Z2rFrl84z2YSIVasyDwEefrj2cjLpr8dTWZkamHKdZwpiNGXiRL2RsozRn8eUKW7O5zcR3Q35lVeAT31Kb0j82m09l57VOysSEmWWzoGuuSoqc+dqPbBYzJsk37IF+Pzng25ZYUVlQWImfl98svUeXG6G6Ceb+JNuyBDdQiNTcNqxw8vSe/ppzXpLF4tpxpitDNHYqCnyJ56YOZMyeQ7QrmlKzs4bbM8yiNEUf284ClnIti/JRW6tqQBacPjh2zFx4sfx5psvO2/FgLYIMcYYAP8nItcD8HnEMj+PPaaThOk9q8cec1PKJghRWpCYid8Xn2x151xt/V5ZmXnIrL91QYP11a8C3/pW6oVURG/g7LHNm1ODU3Kvwaqo0Gy3k07S+abjj9cU8XRdXV6iSvL5urs1Y6+6urDvNYjlI5s2Dex4ftwUss3V+vXAz38+Fps3/+c7G4a6lMsw4MeSXsYANALIoQhKuLS2Hry4r6rK7V3WwoVa123PHl0XcuWVbgNj1BYkpov6omfXd+XpRWAvuECH8e64Q19XVwMf+5je1F19tQanTBfZqirguOO8ZIjZs/Vrc1FRkZowEo9rwBrhYFIhiOUjdr4v+cIdj7vL6AxSfb0mXTz00NlYsABYsMDt+XLpWX046eMeAOuhQ4FFxeWakkz8rqANRD+JJOqLnrPVwculPl4mvb2pi3HTe23GaMHSQw7Rv4fOTuDnGVKnbHUIm0p+zDH994Ay1c474gid04rF9BGPaxtdLTINYvlIebn+nNNvMLKVZyp2J50EPPTQ7Vi48CLMnq03O67kMmf1WXen908h15Tkwu8K2kD0ex5RX/ScrSZeX7XykiUHJxvI0//dVodoaQH+9rfMySI1NcDJJ3trnDJVh0iWXDvPzjVlSrdfvFjX6LS3ezsRjBmjx1256y6dq+7p0bVWkya5/X058ki9KbalnWzq+pFHujtnkHQq61K8610X4cIL9X26eq+5DANOBvAjaLV1AHgCWsw2h9UY4bFli/7BJf8SxWLu1j/4XUEbiH7PI5kfGXL27j/T8TDoL528u1urX9iaei0tmYNTWZne4NTU6Md1dcDNN2c/r83Os4Ep19p5TU26UPeeezR4GKOvXQWPCy5IrbXY0+O9vvNON+dcvFgr2e/Zk1rJ3mVADl4nfvMb7X2fey7w97+7Kb6c607B/wPgnxOvL0gcO73wzXGrrCw1gLiaKAeCqaAd9Z6H3wkkU6dmHkKdOrXw5wK83nem44C3EHf/fn2kB6euLt311qaRP/dc5my/CRO0x/TEEzoEmJwKbgzw5pve5ybv0WQD02CD9cKFGqhiMQ10vb36euZMN6MN99yjz+nzR/fc4y5YNTUBl16qIyidnXoDcOml0fkbzGbyZK1qcfrpWubqnnsKvwg7l2A11hizLOn1HSJyRWGb4d7Mmdo9t+PJNni4mrPyu4K2FeWqGX4nkGT7Y3NVCWHKlMzZdpMmadBMH9bbt8+rDrFiBfD88wd/DqAXEpupN2eOvhYB/uVf9Ptu2eIVeh0+XEsbTZqUf1HXdEuXevPF3d3e36CrofGBLHoulOZmYPlyvSE49FD9P1q+XH/uUf27tObN0w0vv/ENvc5deGFhv38uweqtxNYgv0y8/iSADHWQw+3cc3U1vR3WMUbvuM491835/K6gXQr8TiDZvDlzIdvNm92cb9iwg4cebS+kq0uHkO0C3JYW/V3KdOGdPt0r+NrYqBfOTN71Lv0+du1hV5euo/riFzOnnufL77p5/fVUXYh6Rm5/rrpKaz1++cvAaaelbj+Tr1z+2y6CzlkthVay+BuAoku6uPfeg+cf4nE97uKuLlulbFcVtEuB3wkkvb0HDxX39hZuziq5SsT+/cDOnfrHvXOnBo6KCg3GmzZpltXq1Qf/DotodQjba2ps1KG9TCorvXmmIUM02I0cqenr3d0asEaOjM7aw098Qkc30n9mn/iEu3NGPSO3P7EYsGyZlmL613/VepCFGonIJRtwA4CzC3O64PgdPIIYgog6v7dhdzEMeOBA6q64yYki48bpRoOdnd5QmZ1zWrlSn8vKNDvPBqcTT8y8Ril5n6ZsGXorV2qgsmud4nF9vWrV4N9fmNh5KZvQUV6ugcrVfBUQ/YzcXBx2mC42/+pXgT/8QbeBKYRcsgHHAvg8gPrkzzfGXFSYJviDwSMaurr0gm8XWg5kF9fBnGsgxzPp7EzN2Eu+y3/zzdTt2bPdfU+cCJxzjgan447LPESXnggxZEj/QXXv3sw9R1e1D0tBKWXk9uXyy4Ef/xj493/XhcOF6F3lMgz4O2i6+h8BOMyfixa7ODDTcRqcq6/2lgTYuZ09e/R4WOYDOjtTe07Jc6QbN3pp5CtW5F6Cp7ZWq59YhdrdttCLkPvjd6HeIFLXo56Rm6vKSp2/+sIXgL/+FXjPe/L/nmL6+U0RkeeNMcflf6rCa2xsNC0tLTl9bra6axUVA7tTztXs2akJHYBeYI8+WjO4aOCqq/XClnxh7u3Vi7eLbTv6uhu0fzZdXak9J9tTMUaLwSYHp+3bD/4+Q4fq+pQ5c4Af/CDzcorycmDDhtQAVYg71VzeXyH5fb6KCg1Q6anr2W4kQySn/12RmPE2xAhSGTL3Y6oBbAPwCwCX9vkd6uqmoa1tvX2Z8f3ncp//exE50xjzYA6fG1p1dZmHWQqZrZLMZh+m/4G6yj4MSpSrvPflzTc1SNng0turW2DYIb2WFuDttw/+Olt93CZDHHmk19teujTzueJxTSUvtPSissnHXfB7kXX0h/6DLWSbi1/9CnjzzS/iiiu+2Ofn5VI9PmuwStrHSgBcIyKdALoTr40xpnYgjQ5abW3mtOBaR+/iscf0AsMq74Uzc6ZmsKVX7T7mmMKfqz9vv60JCjYwPfNM5rmeQw5J3QF3xozUi7Oda7K9xkxcDZP5fb4xY4Bt2zIfdyGI1HVKVVenSWz79+de7DibrP9tyftYuSAiZQBaAGwyxnxIRBoA3A3gEADPAPi0MaZgA3Rbt3ollpIXBW/dWqgzpGpt1eyu8eO9Y8ZEK4XV7zUlo0dn3oU1PVU4HzZbr79hxTlzMn/OxImpmXqHHur1VPrL0MvW04mKCRMyB6ts68DyFUTqOqWymap79zoMVj74MoDVAGzf5jsAlhpj7haRWwBcDKCPCmUD09mpF4bkuypbadqFoHYm9nNYrrVVf6Zr13rvcdw4dwH5L38Z2PFcZEuIyFSmKJkNVNOmaVCyFSImT/Y+J31dU3+Zi2HaKdiFDRsGdjxfd96pSSyPP+7doL7vfW5T1ymVvRkrxNBrIMEqURz3LADfBvCVxA7E8wB8KvEpywF8EwUMVnabDruo016UXKU+B7Ezsd/DcrW12sVProCwcaO7qsv5ViUHUlPJk+ec2tuBZ5/15pzsuqZsvv99DU625yySmp2XrfJ4X/yeQ/JbtpR4V6nyzc36+3j44V4a+caNerwU5lXDwN701dTk/736mrN6EMClxpj1+Z/mIDcCuAqAHWo8BMAuY4yNv20AMqY+iMglAC4BgKkDqCh61FFa5HPXLr1A2dX6rvbSCWJn4iBKvSQPqYZxGMv2nGzxVxucdu7UeSabrbd69cDafs453nBedXVqMdjBqq/X3mry9zEmOgtKC3GzMRBRLH2UfP0rBlu2eDUn89VXz2oZgIdFZDmA7xpjCpLsKSIfArDNGPOMiMwd6NcbY24FcCugqeu5ft3cuRoo7AWpt1cvWHMH3ILcBDFn5Xepl/Z2HQbbts0LyHV17u6UR4zQ4J/pOKA/3wMHvMB04IAXnLZu9erqrVihQ7Tpysp0aYHdx+kLX8jelunT838/6X78Y+D88/U92m3CR47U4zRwfg9T+yH5+iciIbs1TGWMVmSZMqUwGZ99JVj8WkSaAVwLoEVEfgEgnvTvgy1ycwqAs0XkTABDoHNWPwAwUkTKE72ryQByXDKZm2XLMk/OL1vmpqcTRNkVv89pzzdzpneso0OTDFyYNk17x+nq6oC2ttTyRZs2pVaHyDQvUlGhNczmzNE5p+OO8352LitjZNPUBFxxhaaw792rlSquuKJ4ewHp/E5dr63V0lHl5d7aqg0b3I2mUKpNm7RDcMop/X9uLvqbs+oC0AGgCjpkl3eH3RjzDQDfAIBEz+prxpjzReTXAP4JmhF4IbRyRsFs3Diw4/kKouzK/Pm6E+vGjd5OrLW17mrn+f0es90Rb9igc0x28e2KFal7MlnV1cDxx3sVyY891gtKVVX6Hqqr9TmIDRaTt5ewP0+X20vY4JG+aNbVe/c7dR3whqaT95VzrVTXHqZ76in9uyrUNkx9zVmdAeAGAPcBOMEY009+VN6+DuBuEfkWgOcA3FbIb+73eHlTE/Dudx9cRNP1L60d/orHdQisosLduVyXlunpSZ1vyja82NGRuVjmsGGpC3CPOkrHz0X0j8gGpurqzBfo9O1Bko+74Pcci91c0haytb1SV5tLdnQM7Hi+2tv1vSQPU48b57b2od9JTmFlbyD/8R8LN0rRV8/q3wH8szGmn7yowTPGPAbgscTH6wCc5Opcfg9B+L0rKuDVzrN3kMa4r51XyM0eOzu9OacDB7wyWL29wCuv9J8AMXKkN980Zw5wxBEaWGymXvL6plz+34cP14ScTMdd8HuO5Sc/AT71KW+fKbtI/ic/cXM+v4OV38PUQDSTOgaqp0e3Bhk5Enjvewv3ffuasypA6cHwqK7O/EeR70K1bJYu1T9+u67LrqZ3tSsqkHm/I2Pc7qF1wQWD24Kht9cLSvZh297V5VWHWLFCU8r37u37+91/v25NEIvpY8gQr9c02Fp62TYFdLVZYG1taj3Jri79nT36aDfna2oC/ud//Cu66ndqfhBD8aW+nxWghWvfekuThQo5slMyhUcqKjKXW3I1TGargycrK+v/opsPv2uhDaSqtS34ah/JhUQ7Ow/enn2ghWlnz9aLwtChhUkjB/wfOt66NfMGoa6qrACF7RmHTVOTbq2+dKn+PQ4frtXrXb5ff5OcJKeaev46HMALAO7FXXedn/NX1dVN6/dz+q26HmYDqbo+frxmpvT2esNkZWVau23LlsK3bdQo/SVNr5hRU5O5wGkh2FJS6ezGeoWWrap1WZleHGyPKbkaOaC9heef99Y4vfBC5irY06al1tWbNy97W6JQtdvv/z+/DRuWeXRj6FA3N3HJ80fJPSuX80cFOmeOVdfFBFnIdsECQXL8MAZ4//v17/mVV1KX7QzQoKuuR8KECcCOHfqxveuOx/P6gfbpyiuBRYv0Ymcn6uPx1H2JCs3vPbRsjy39AtvbC7zxhve6vd1b49TSokN8mXp7M2Z4mXqNjTpfA3g19bIVJnXVO/a7tlzUyy2NHZs5WI0d6+Z8QcwflfJ+VsuWaWmr//5vN9fVkglWgDeHZIcDXW4VYOelktfMXHml24rrfmyDkrzwNlu2XCwGPPywN6yXKTkiFtOyTDYh4sQTvbF+G5zsnFN1tR6bPNnfbV7OPx/43e9S7/qHDdPjNDgTJui+XraKzNix7uasgpo/ivLQajbbtgFf+5pusniRoz3kSyZY+V1tAdDA5Od2IHYbFCB1TUk+26D09KRWhejs9ALPmWdqYkN6IIrHgS99KfVYebmmjtsFuCec4GXVJVcjtwEqDPXwlizRdtkyTWVl+trVnfmQIZl36R0ypPDnCoKdz0m+uXCZnRfEwvxS9ZWv6E3dT3/qLsO6ZIJVEGmsfi8ObG/X8+SzriQ5fTw9EQLQwNTWpsN5b72V/ftUVmrSgx3WO+44r5hleip5rsFpy5bMSTIu5hwB4LnnNHXd1j2Mx3Uo+bnn3JxvxAid30h/f7aclAt+/o76nZ0XRDZgKXr0UR0uv/Zad0WsgRIKVn7/4gaxONBuS5KsszP7tiTx+MG19DKlvre2anCyCRGbN2f+fvaiXlcHPPCAtxgwOTjV1Aw+ldzO+6W/h0xDkYVgh//Se46uMjpnzdLNJd9+2+vJjRrlrjyQ37+jfs/nlPL8kV96erQkWH09cM01bs9VMsHK71/cICZ3+9uWpKcnNX08015e8bgGvOTt2W1iSrLhw/WinXwhtx9v3aoX2ZoafRQqlTzb8EJUtkW3/39lZfq709urPTtXxZaDSkDwM1iU4vyRn267TW+wfv1r98PVJROsAH9/cYOo+Jy8LcmBA3rO2lrgD3/QNVGZLrI9Pbpo2NbVe+aZzFUbRo5MTSM//PDMNb+M0aHD5E0IC8XvdU9+Vz3xe1uZKFYlJz9V4frrtUrFuee6P1tJBSs/1dZqinY87l3A9+0rXFHHdL29wOuva49m1CjvImuMFra1gaqrS6skJFeHyJZObHe/nTNHt2dPvkj3lS7uKjmirEzfj/2Zini9SBeyvQ+X2Wt+bivj9+aZFDWfwtatWgXFj4QoBitH9uzRAGLncQB9XYjsQ2O8HW/twtvubk0W2b49tYTUvn06ZHfTTTrn9MILmTPO6uq8gq8nnaQFQJN/AcvLvWG96moNVn5vtjhz5sE7+BqTmjRTSLFY9tR8F4LIXgv75pn5YgV0N/T35Aoce6wuBPYDg5UjmzdnzlzLlpzQF1tHLzk4ZbqoXHwx8M1v6gWup0fnlGxgSq8PWF+fGpwmTUr997Ky1OCUXjoK0DtzG5STv85Voddzz9Xxcfve7YXW1RBEZaXeBKTv3JvpZ1EIficBBbGcw0+sgO7Opk0AcCz+7d/8W2bCYOWIzVxL/o/MJXPN9ppsUMqUPp5s9+7UHXC3bMk8zzJzphec5sw5uGqArQ6fnBTRn7POSq0NCOj7O+us/r92MO6917sBSB4GvPdeN3M6w4Z5NwbJ5xs2rPDnAvxPAor6cg5WQHdn7VoA6MFHP+pfCCmZ2oB+y6UOmjF655xcebyrq++hmB07NBnCJkSsWZP98ysqgNNP1wv5qFGp/5ZcJWKw6eQNDZnnU+rr9WJUaNXV+l6T56jsUOtAC9/mYt68zKnkxxyja0uKnd+18/w+X0OD9qjSe8Zvv63brYdYjrUBYwYI6vr9LIAOGPOPLr55adcG9Fu2BITycp1XSq8Gkc3WrTrXZHtOmf7IKip0G4lYTHtZVne3XiCmT9eLREWFV5m8ELvhZpv4d5lN1tur7yu5p+Oq9uHcuVrrzJ4rHtdiyK5Syf0W9eUc0a9gYRBEIdsDB4DvfAfQLQ+dBKuMGKwcMSbznFU8nr3quq0OYQPTihWpBWGtqqqDq0NUV+uxTN/zZz8Dvv99t7sG+2HiRO2x2TtlY3RubsoUN+e7997UbdFtEoKr2uuPCgAAGNxJREFUYUfA/4QAv5dz+FmrjxUs3Ni+3X70gq/nZbAqELtfk51nylZtIX0R7bp1qdUhMpUOqqnRWnp2zunYYzNP8mfK8gO0PcUeqABN3CgrS52zcpnQsXat9trShx11vL7wgkgI8DM4+t3TYQULN7xg5WwT+YxKKlgV6g8zuUyRfU4PTNkSKbq7gV/8wqsOkam+3vDhqduzz5qVfajLzjslXwCiqr1dLzj51D4MM7+HyfwOjkH0dFjBovDeftuOOGzw9bwlE6yam4FPfUovePG4Ln587jld0NbfL3N398GVx/uTLYOvsxP41rdSj40e7QWnk07SWn7ZFrqWl3tzTjU1qZ/nd8UFv8830NqH+Zo5E1i1ypsbs9mcrmr1+T1M5ndwZE8nGg4csLsR+DtfVjLB6tJLU8sIxeP6+tJLUzPXkhfc2p5TrrXguro0e6ylpe+vGTfO22DwpJO0OkRf1RKqq70A1VdKeXV15gzE5EXChTRmjPZyMh13ob/ah4W2eLHuzdPerjcfFRX63hYvdnM+v4fJgtjviT2d4tfZ6W2d46eSCVYbsvRY16/3aukdOJBbr8nav1+3Z7dzTi+8kNvX/+UvfaeJV1V5PaeamtxTyrMNFbrKlmtvH9jxfD32mNYofPttvTGwqeSuauc1NQG33+5fT8DvYbLoZ8uRC7Zn5beSCVZ9pYhv3Zrb99i7V2vp2eD08suZh/saGvpeZ5QefGxKeaahvYGwCQfp2WuuVphnS+jIdjxfq1bpjUVFhQb0eFxfr1rl5nyAvz0Bv4fJmC1Hg8Fg5dhg6p7t2pW6AHfVquzVIeywXmOjVoc4/PDs39eWMrJDe4Xq+VRV6TBgWVnqHEsu1SiKQWenN38EeO9xIL3hsItycKRoOHDA3VB/X0omWE2blnksPnmL7e3bvcC0YkXmFOVYTCfYbabeiSfq0NRATJ8+sM/P1axZwIsvapC1FRdGjnSXEOC3ykrN/EvuzcZi0QnGQeAcEg2U3X7IbyUTrD77WS3ymt67Ouoo3Y55xYrMQ3cVFVpexwamE0/MrTZcdXXmCUiXKeZz5+p8mO392SSSqFRcmDDBS/W3w5xA6pYahcaq3UQeY/S6xmFAhx57TCuLv/WWN2xkDPDww6mfV1WlFSFsz2n27Nyy6dILwX7sYwcXeQWAj3wk77eS1b336rO9iNtnlxUX/Cai/0d2CNDVrr0Aq3a7wOBf3Lq69G/OVTHnvpRMIduGBs0ce+ml1AucCHDKKd6c0zHH5LYFhA1ONkClb90+cqRO/qcbMSLzTryF4Heh174SN1z8WjU06HtLXxQcj7spTDpv3sHZcrYqeRQK2frN70K2RSzEhWwPA/AqgAsA3A2gF3V109DWtr6QJyntQrY2TXfECP0DsYkNdXXAbbfl9j2Sq0X0V6U8U6Dq63ihxOOphV7LytztpGsvOJmOu+D3lhZBrEOKMm7ZUWj+F7J9/XXgzjuBCy+8E/X1dwIAFizwZ0MrR7UGwmf+fK1a8dZbOkm/dat+/LnPZf+asjLdYHDCBE2KmDpVs2Cqq/3bcGwgJkzQQGVr59nANWGCm/ONGzew4/maP19vNDo69P11dLhfh5QejLkOafBaWw++kWHwLy52mY+rv/G+lEywuuuugy88Bw4A99/vvbbVIsaM0cA0fbpe6Gtr3fVOCqm2VocnYzEvxTsW0+Mu6G6huR/Pl021njhRFwZPnOh2CMnv4BiE5mYd7mxo0OfmZnfnYvAvflu3au1SV6MnfSmZYHXPPZmPP/igzi9NmqTBacoUHfoJItslX+3tesdj15SJuC30mq3+YV87G+erqUnni9at02fX22f4GRz9ZueQNm9OTSBxFbBKIfhHmTHaC05e7uOnkglW2bLGenv1gj5smLsCrH6prdWhzfJy7SGWl+trV1tolJIizkPKKnkOSUSfKyv1uAtRD/5Rt3Wr3hC7KhzdnyK/PFO69DJLLi+y2fbIcrl31sKFmtVZXq7PCxe6O5ffPQ+/BTGH5GfPmAprzRp9Tk5w8hODVYS0t2upp+5unQvo7tbXroYBsw0HuBomWLgQWLRI31tlpT4vWuQuYPnd8/Ab55AoV/G4bqk0bVowa6yAEgpWflckzzak6HKosbZWu+q2d2WMNyHqQqbtQfo6nq+lS/XnV16u76+8XF8vXermfFHPXuMcEuVqzRpddvOudwXXBgYrR8EqW6FHlwUg7caSyanr8bi7LTsyrbHq63i+9uw5OCuzrEyr4bvQ0KCBd+1aXUy+dq2+jkrPg3NIlAtjgKee0jWqfRXodq1kFgVnS7BwVa7HLh5O3gxx6FC3tQH9TiX32/DhGgiTbzB6e90NS/i92WMQWMiW+rN2ra5RbWoKNgmtZHpWfger2lpvR82aGn3u7HSbmWffi11fZX+xXNbP89OVV3r1AI3R53hcj7vw2GO6zq6qyttqZcIEPU5UCnp7gUceAQ45RIt4B6lkelZBsPNGyZshulRe7pVaSj8eBdddp3d599yjcyvl5cAnPuGuSG9rqy5rSK7qbteaEJWCp5/W5S/nnRd8YYSIXMbCp71dq2CkF111lZkHAEceqbsXJ28QGYvpcRds5fNMx11obgaefBI47DCvLuGTT+pxF0NZ3Padwk0c1+WbDuBFAI/i7rs/nPWz6uqmOWyDh8HKkYYG4NVXU491drpdUHfuubr5YrJ4XI+7kClQ9XU8X34XQuW27xRuhS9ku2CBwBiDeFznbF98EVi58kOoqwt+VXzJzFn5be5cnYzv7EydnHe5EeK99+q5kvezKivz9rkqdn6nkjNbjkrV0qWaXLR0aXDlldL5HqxEZIqI/FlEVonIShH5cuL4aBF5REReTTyP8rtthRTE5PzatV6ppZoar+TS2rXuzumnIBaxsuIClZoVK4Crr9aNYj/zmaBb4wmiZ9UD4KvGmFkATgZwmYjMAnA1gD8ZY2YA+FPiddGyk/MzZ+qGjjNn6mtOzg8eF7ESuTYc552nowi33RaurZB8D1bGmM3GmGcTH+8BsBpAHYBzACxPfNpyAA43gHcviF7AzJnai7NzRvG4vnZVy+vYYwd2PF8cliNyR7OIf4oNG4Bf/vLgjUeDFmiChYjUAzgewN8BjDfGbE780xYA47N8zSUALgGAqVOnum/kIM2fD1x0kS6m6+7W4q61tcANN7g75+LFes72du+cY8bocb/OV1vr7nwAF7FSaUu+/hXa888DwCexYAFwyikuzpCfwBIsRGQYgHsBXGGMSSkIZIwxADKmnxhjbjXGNBpjGseOHetDSwfPrneyXWnX20w0NQG33671uyZP1ufbb3d3cff7fESlLvn6V8jvu3273U3gUVwd0gkYMQFs1CMiFQB+D+APxpgbEsfWAJhrjNksIhMBPGaM6bMSVWNjo2lpacnxnNn/zcWPYN68g9fodHTo0NWjjxb+fEQUGTnNFImIKUTqek8P8LOf6RrQffsmwZg38/6eecr4/oPIBhQAtwFYbQNVwn0ALkx8fCGA3/ndtkKKesVuy89t0Ymo8B5+WHdnOOccANjc36cHJog5q1MAfBrASyLyfOLYNQAWA/iViFwMYAOAjwfQtoJpaNAFdbt2aZJDWRkwcqS75IMg2M0JKytTNydk0gNRcXjlFU1Vf9e7gttUMVe+BytjzF+RvZt7qp9tcWnSJODPf/Ze9/Zqja1Jk4JrU6H5XVGCiAqnvR247z5d/3naaUG3pn8lU8HC780QH3hAe1OxmM6X2W0mHnjAzfmCUCpDnURRYwzwf/+n81X/9E/FUey6CJpYGO99b+bqEe99r5vz7dmjvY7kxA5j3G0UGAQWeiUKUj6FbL8A4GYAl+Cmm/77naN+FaUdjJIJVjt3Dux4vvzeKDAILPRKFKTBFbJdsKAeQ4fejH/4B+APf7gVIrcWvmkOlMww4OrVAzueL783CgwCK0oQFRddpnMbYjFNVw9TOaX+lEzPqrt7YMfz5fdGgUFhRQmi4vHsswBwKr73Pd1vr5iUTM/Kb8kbBZ5wgj7bjQKJiPzW0QH88Y8A8Cg+//mgWzNwDFaOJKd1i+hzZaUeJyLy2x//qKM8wGVFNfxnMVg5wrRuIgqLjRu1UO273w0ArwTdnEFhsHIkiC1CiIjSxePAgw/qjgiulur4oWSCVbZur6vu8Pz5Wmpp9Wotu7R6tb5mWjcR+enpp7X23xln6FREsSqZYJWtsrrLovN+bxFCRJRs714t+3bYYcARRwTdmvyUTOq635YsAUaN0n2erI4O1s0jIv889piu8TzjjOJaU5UJg5Ujra1aC3DtWqCzE6iqAsaNY4IFEflj2zZdVzVnDnDIIUG3Jn8lMwzot9pazcDp6tKg1dWlr4cPD7plRFQKHn5Yb5Lf976gW1IYJdOzisU0KybTcVd6e/VhTPF3wYkobPoqZPtBAA8BuBJLltyY8i9hLlbbl5LpWY0ZM7Dj+dqyRQOhMd4jFtOsHCKi/GkhW1vM1hgDYwy6uw2OOuohHHYY0Nm59J3j9tHWtj7YZg9SyfSsamp0p95du7xjI0embm9RSF1dGpySU0V7enT+iojIlZ//HFi5Erj33uJOVU9XMsHK7r00fbp3rKNDK4W7UFWl3z8e94YgjdHjREQudHcD3/oW0NgIfPSjQbemsEpmGHD+fO3tdHRo0OjocLv30qxZwPjxQEWF9qgqKvT1rFluzkdEtHy5ZiJ/85vRmycvmWDl995L8+drF7yuDjj6aH2urGQFCyJyo6tLe1UnnQSceWbQrSm8khkGBPzde8kGxyVLdG1Vfb0GKi4IJiIXfv5zYMMG4Oabo9erAkosWPmNGxMSkV9uvBE47jitVhFFDFZEREVvHlauBJYti2avCiihOSsioui6DGPHAuedF3Q73GGwIgqR5mZg3jxdajFvnr4m6ovum/chfPrTwJAhQbfGHQYropBobgYuv1zXA44erc+XX86ARX1buRIAKvHpTwfdErcYrIhCYskSXd4wdKjOOwwdqq+XLAm6ZRRmL70EAC9h9uygW+IWEyyIQqK1VXtUyWpquK0MZVZRUZkoZFuPMWOOh8hvg26SU+xZEYVEQ4Odf/Ds26dr9IjSHXvsMYnitK3Yvj3agQpgsCIKDb9LghEVEwYropDwuyQYUTHhnBVRiLDqCVFm7FkREVHoMVgREVHoMVgREVHoMVgREVHoMVgREVHoMVgREVHoMVgREVHoMVgREVHoMVgREVHoiTEm6DYMmohsB7BhEF86BsCOAjcnbKL+Hvn+ilvU3x8w+Pe4wxhzRn+fJCIP5fJ5UVHUwWqwRKTFGNMYdDtcivp75PsrblF/f0BpvEc/cRiQiIhCj8GKiIhCr1SD1a1BN8AHUX+PfH/FLervDyiN9+ibkpyzIiKi4lKqPSsiIioiDFZERBR6JResROQMEVkjIq+JyNVBt6eQRGSKiPxZRFaJyEoR+XLQbXJBRMpE5DkR+X3QbXFBREaKyG9E5BURWS0i7w66TYUkIlcmfj9fFpFfisiQoNuUDxG5XUS2icjLScdGi8gjIvJq4nlUkG2MgpIKViJSBuDHAJoAzALwSRGZFWyrCqoHwFeNMbMAnAzgsoi9P+vLAFYH3QiHfgDgIWPMEQBmI0LvVUTqAPwbgEZjzNEAygCcF2yr8nYHgPTFuVcD+JMxZgaAPyVeUx5KKlgBOAnAa8aYdcaYLgB3Azgn4DYVjDFmszHm2cTHe6AXubpgW1VYIjIZwFkAfhZ0W1wQkREA3gvgNgAwxnQZY3YF26qCKwdQLSLlAGoAvBlwe/JijPkLgJ1ph88BsDzx8XIAH/G1URFUasGqDsAbSa/bELGLuSUi9QCOB/D3YFtScDcCuApAPOiGONIAYDuAZYmhzp+JyNCgG1UoxphNAL4HYCOAzQB2G2MeDrZVTow3xmxOfLwFwPggGxMFpRasSoKIDANwL4ArjDHtQbenUETkQwC2GWOeCbotDpUDOAHAzcaY4wF0IEJDSIm5m3OgQXkSgKEickGwrXLL6PogrhHKU6kFq00ApiS9npw4FhkiUgENVHcZY34bdHsK7BQAZ4vIeugQ7jwRuTPYJhVcG4A2Y4ztEf8GGryi4jQArcaY7caYbgC/BfAPAbfJha0iMhEAEs/bAm5P0Su1YLUCwAwRaRCRSujE7n0Bt6lgRESgcx2rjTE3BN2eQjPGfMMYM9kYUw/9v3vUGBOpu3JjzBYAb4jI4YlDpwJYFWCTCm0jgJNFpCbx+3oqIpRAkuQ+ABcmPr4QwO8CbEsklAfdAD8ZY3pE5HIAf4BmId1ujFkZcLMK6RQAnwbwkog8nzh2jTHmwQDbRAP3JQB3JW6o1gH4bMDtKRhjzN9F5DcAnoVmrz6HIi9LJCK/BDAXwBgRaQNwPYDFAH4lIhdDtzH6eHAtjAaWWyIiotArtWFAIiIqQgxWREQUegxWREQUegxWREQUegxWREQUegxWEZWowN4qIqMTr0clXtcnfU6tiLSJyE0+t+2bIvI1H87znkR17+dFpNr1+fKRKKsUiqLDIjJXRKK4UJeKGINVRBlj3gBwM3S9BxLPtxpj1id92iIAf/G5aXkRlevv7fkA/ssYc5wxZr/LduXLGPM5Y0xYFv/OxQCrSiSK0hI5w2AVbUuh1QKuAPCP0AKiAAARORFaXDNrEVERWS8iC0TkWRF5SUSOSBxP6Rkl9iWqTzxeEZE7RGStiNwlIqeJyP9L7OtzUtK3ny0iTyaOfz7pe80XkRUi8qKILEgcq0/sQfZzAC8jtWQWROTURNHX/9/euYVKWUVx/PdX8IJKeSEo0Q6YdpWkBLUSNdIIiZCUCEvLbmZZBEIPhQQWUb6ERClIeUkzDoGRlGbqUfNWqZ1zPA/5kCbiS0VGQRrq6mGtwa9pzswcCJpO6web2XvPXvuyvmHW7P19s1Z7xBbqLelR/I+YSyStq7C2OTFGq6S1hXG2R/02ScOjfpWkZZL2SvpO0syo3yBpeqHPVZJmyuNtLS2s44l4f7KkFl2MVbUuvDgQ9WMjPy10c0hSc/h6rHY9+kt6N+raJN1brZ8yPTwjj3/WFutpAuYDz8WOdGINvSyXdAB4XdIISZslHZS0uzC/WfEZaZX0n/pxlDQQZpapGyfgTtyJ5tRCXQ+gBfeN+BDwZieyx4GFkV8ArIz8S8CiQrsjQFOkc8DoGOMg8A4g3HnpxoJ8K9AXGIJ7wr8CmIZ7M1DIb8LDZTThXtbHV5hjn5AfFeU1uANf8DhDMyvIXA8cBYZEeVC8fgzMjfy8wnxXAc0xp+vwMDMAM4DVke8V8+gLPA68GPW9ga9xx62TgV9C7z2AfcBt0a4FGBv62AX0i/rngcU1rsdrwBuF9Q2s1k+ZLk4BvSN/aSfXt5peNgE9o7wNGBn5cbg7LIB2YGhxjEyZuppyZ9X9uQsPxXBDoW4B8ImZnaxDvuQM9yBuNGpxzMzazewC0IEHoDP8C6so/5GZ/W5mPwI78Fhj0yIdxt3xXAOMjPbfm9n+CuNdHWMejfJq3MBV43agOcbGzEqxiCYA6yO/Ft+NlthoZhfMj+pK4R4+BaZI6o3reZf5ceM0YI7c5dUBYHBhHV+a2cnQzzf8XafjcYO4J+TnAlcW3q90Pe7Ag4oS6/m5jn5KtOGunR7Af2hUoppems3sfOzabgGaY7wVwOXRZg+wKnbQPTsZI0mqkufM3RhJY4Cp+BfXF5I2mMfYmQBMlLQA6A/0kvSbmVUKRXE2Xs9z8fNyjr8eIfep0B58N3S2kC9+3sr9fBm+o3rVzFaUraMJD5Xxb1JclwDM7IykFnz3eh/uCb70/kIz21LsQNLksn6KOi32vdXM7q8xj0qyXemnxHTcuN8NvCBpdI325ZSuSw/gtJmNKW9gZvMljYuxDkq62cx+6uI4yf+c3Fl1U+JeyNv4kdgJYClxz8rMZpvZcHPv5YuANZ0Yqs44ToStkHQTfsTVVe6R1EfSYPx47CvcwfC8wj2aoZIuq9HPt0CTpKui/CCws4bMdmBWjI3iiUlgLxdDrM8Gdtexjg9wR7MTgc1RtwV4Uh6uBUmjVH8Axf3AraX1SOonaVQNma3AU6WCPGZUzX7kD6oMM7Md+DHhJfiPl1+BAYWmNfViHjftmKRZ0bck3Rj5EWZ2wMwW44Elh5XLJ0kt0lh1Xx4DTpjZ1ii/BVwradI/0PeHwCBJHcDT+P2frtKGH//tB5aY2SnziLHrgX2S2vFYTgOq9IGZncGNRXPIXACW15DpAF4BdkpqBUrhVBYCD0tqw43es3Ws4zNgEvC5mf0RdSvxsB6HJB3Bj8TqOsUwsx/w+4jvxzz24ceh1XgZGFh6iAGYUmc/PYH3Qm+HgWVmdhq/RzWj9IAF9etlNvBIzKEDv08JsDQe/jiCG77WenSRJEXS63qSJEnS8OTOKkmSJGl40lglSZIkDU8aqyRJkqThSWOVJEmSNDxprJIkSZKGJ41VkiRJ0vCksUqSJEkanj8BXYY8USr5xEUAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hexbin = sns.jointplot(x='X1 transaction date', y='Y house price of unit area', data= realestate, kind= \"reg\", color = 'blue')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "35Q8os_k2wb4",
        "outputId": "a45c7658-0e40-4ca9-aee4-d64cd8cf8122"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check the correlation among all variables\n",
        "\n",
        "sns.heatmap(realestate.corr(),annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "id": "CMRc_5hOxXg5",
        "outputId": "a0cf1b78-5b72-4490-af67-0f18f6f77edf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcc45fd5150>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Ssingle variable linear regression\n",
        "\n",
        "X = realestate['X2 house age']\n",
        "#dependent variable to y\n",
        "y = realestate['Y house price of unit area']"
      ],
      "metadata": {
        "id": "MhLrHoziylbl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VaR4dSFy0cNE",
        "outputId": "15db5855-5414-4149-a4d6-2679f47fa8dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      32.0\n",
              "1      19.5\n",
              "2      13.3\n",
              "3      13.3\n",
              "4       5.0\n",
              "       ... \n",
              "409    13.7\n",
              "410     5.6\n",
              "411    18.8\n",
              "412     8.1\n",
              "413     6.5\n",
              "Name: X2 house age, Length: 414, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.api as sm\n",
        "#method sm.add_contant() to add a constant\n",
        "X = sm.add_constant(X)\n",
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "id": "ZEm_1wlRz66x",
        "outputId": "6871cb32-3277-4d05-806a-5bf780b49869"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/tsatools.py:117: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
            "  x = pd.concat(x[::order], 1)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     const  X2 house age\n",
              "0      1.0          32.0\n",
              "1      1.0          19.5\n",
              "2      1.0          13.3\n",
              "3      1.0          13.3\n",
              "4      1.0           5.0\n",
              "..     ...           ...\n",
              "409    1.0          13.7\n",
              "410    1.0           5.6\n",
              "411    1.0          18.8\n",
              "412    1.0           8.1\n",
              "413    1.0           6.5\n",
              "\n",
              "[414 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-17cb2782-593f-4504-9f1c-15f1d186783f\">\n",
              "    <div 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>const</th>\n",
              "      <th>X2 house age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>32.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>19.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>13.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>13.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>409</th>\n",
              "      <td>1.0</td>\n",
              "      <td>13.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>410</th>\n",
              "      <td>1.0</td>\n",
              "      <td>5.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>411</th>\n",
              "      <td>1.0</td>\n",
              "      <td>18.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>412</th>\n",
              "      <td>1.0</td>\n",
              "      <td>8.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>413</th>\n",
              "      <td>1.0</td>\n",
              "      <td>6.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>414 rows × 2 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create a fitted model \n",
        "lm_1 = sm.OLS(y,X).fit()\n",
        "print(lm_1.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OK5TsB8I0f2m",
        "outputId": "7939c2b6-4164-432e-c5c2-e65a0f704008"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                OLS Regression Results                                \n",
            "======================================================================================\n",
            "Dep. Variable:     Y house price of unit area   R-squared:                       0.044\n",
            "Model:                                    OLS   Adj. R-squared:                  0.042\n",
            "Method:                         Least Squares   F-statistic:                     19.11\n",
            "Date:                        Sat, 18 Jun 2022   Prob (F-statistic):           1.56e-05\n",
            "Time:                                16:00:40   Log-Likelihood:                -1658.3\n",
            "No. Observations:                         414   AIC:                             3321.\n",
            "Df Residuals:                             412   BIC:                             3329.\n",
            "Df Model:                                   1                                         \n",
            "Covariance Type:                    nonrobust                                         \n",
            "================================================================================\n",
            "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
            "--------------------------------------------------------------------------------\n",
            "const           42.4347      1.211     35.042      0.000      40.054      44.815\n",
            "X2 house age    -0.2515      0.058     -4.372      0.000      -0.365      -0.138\n",
            "==============================================================================\n",
            "Omnibus:                       48.404   Durbin-Watson:                   1.957\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              119.054\n",
            "Skew:                           0.589   Prob(JB):                     1.40e-26\n",
            "Kurtosis:                       5.348   Cond. No.                         39.0\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#model 2\n",
        "X= realestate[['X2 house age','X3 distance to the nearest MRT station']]\n",
        "y=realestate['Y house price of unit area']\n",
        "\n",
        "X = sm.add_constant(X)\n",
        "lm_2 = sm.OLS(y,X).fit()\n",
        "print(lm_2.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZItZV7fJ0zO3",
        "outputId": "34ac3b86-c6c3-4ff6-abc0-eb01e40db1d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                OLS Regression Results                                \n",
            "======================================================================================\n",
            "Dep. Variable:     Y house price of unit area   R-squared:                       0.491\n",
            "Model:                                    OLS   Adj. R-squared:                  0.489\n",
            "Method:                         Least Squares   F-statistic:                     198.3\n",
            "Date:                        Sat, 18 Jun 2022   Prob (F-statistic):           5.07e-61\n",
            "Time:                                16:01:04   Log-Likelihood:                -1527.9\n",
            "No. Observations:                         414   AIC:                             3062.\n",
            "Df Residuals:                             411   BIC:                             3074.\n",
            "Df Model:                                   2                                         \n",
            "Covariance Type:                    nonrobust                                         \n",
            "==========================================================================================================\n",
            "                                             coef    std err          t      P>|t|      [0.025      0.975]\n",
            "----------------------------------------------------------------------------------------------------------\n",
            "const                                     49.8856      0.968     51.547      0.000      47.983      51.788\n",
            "X2 house age                              -0.2310      0.042     -5.496      0.000      -0.314      -0.148\n",
            "X3 distance to the nearest MRT station    -0.0072      0.000    -18.997      0.000      -0.008      -0.006\n",
            "==============================================================================\n",
            "Omnibus:                      161.397   Durbin-Watson:                   2.130\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1297.792\n",
            "Skew:                           1.443   Prob(JB):                    1.54e-282\n",
            "Kurtosis:                      11.180   Cond. No.                     3.37e+03\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 3.37e+03. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/tsatools.py:117: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
            "  x = pd.concat(x[::order], 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#model3\n",
        " X = realestate[['X2 house age','X3 distance to the nearest MRT station','X4 number of convenience stores']]\n",
        "y = realestate['Y house price of unit area']\n",
        "\n",
        "X = sm.add_constant(X)\n",
        "lm_3 = sm.OLS(y,X).fit()\n",
        "print(lm_3.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hquA5yKz24o5",
        "outputId": "b2c063db-e5b0-4ac4-ba14-3d27b7bc8cd0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                OLS Regression Results                                \n",
            "======================================================================================\n",
            "Dep. Variable:     Y house price of unit area   R-squared:                       0.541\n",
            "Model:                                    OLS   Adj. R-squared:                  0.538\n",
            "Method:                         Least Squares   F-statistic:                     161.1\n",
            "Date:                        Sat, 18 Jun 2022   Prob (F-statistic):           5.44e-69\n",
            "Time:                                16:04:48   Log-Likelihood:                -1506.5\n",
            "No. Observations:                         414   AIC:                             3021.\n",
            "Df Residuals:                             410   BIC:                             3037.\n",
            "Df Model:                                   3                                         \n",
            "Covariance Type:                    nonrobust                                         \n",
            "==========================================================================================================\n",
            "                                             coef    std err          t      P>|t|      [0.025      0.975]\n",
            "----------------------------------------------------------------------------------------------------------\n",
            "const                                     42.9773      1.385     31.041      0.000      40.256      45.699\n",
            "X2 house age                              -0.2529      0.040     -6.305      0.000      -0.332      -0.174\n",
            "X3 distance to the nearest MRT station    -0.0054      0.000    -11.874      0.000      -0.006      -0.004\n",
            "X4 number of convenience stores            1.2974      0.194      6.678      0.000       0.916       1.679\n",
            "==============================================================================\n",
            "Omnibus:                      222.427   Durbin-Watson:                   2.115\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3065.900\n",
            "Skew:                           1.958   Prob(JB):                         0.00\n",
            "Kurtosis:                      15.744   Cond. No.                     5.09e+03\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 5.09e+03. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/tsatools.py:117: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
            "  x = pd.concat(x[::order], 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "upload = files.upload()"
      ],
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
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                [
                  "content-type",
                  "application/javascript"
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              "status": 200,
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            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 79
        },
        "id": "OOj9Idubgtqb",
        "outputId": "55781ff2-d46d-4dca-92bb-e65134c29c17"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-dcbae6cf-f5fe-4cc2-ba96-3efa91522062\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-dcbae6cf-f5fe-4cc2-ba96-3efa91522062\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving wine_test.csv to wine_test.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#model3FINAL\n",
        "X = realestate[['X2 house age','X3 distance to the nearest MRT station','X4 number of convenience stores','X1 transaction date','X5 latitude','X6 longitude' ]]\n",
        "y = realestate['Y house price of unit area']\n",
        "\n",
        "X = sm.add_constant(X)\n",
        "lm_3 = sm.OLS(y,X).fit()\n",
        "print(lm_3.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5fLzjIqM83VD",
        "outputId": "bc5c6b35-35e0-4c99-a27e-59c34cd44178"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                OLS Regression Results                                \n",
            "======================================================================================\n",
            "Dep. Variable:     Y house price of unit area   R-squared:                       0.582\n",
            "Model:                                    OLS   Adj. R-squared:                  0.576\n",
            "Method:                         Least Squares   F-statistic:                     94.59\n",
            "Date:                        Sat, 18 Jun 2022   Prob (F-statistic):           4.86e-74\n",
            "Time:                                17:55:19   Log-Likelihood:                -1487.0\n",
            "No. Observations:                         414   AIC:                             2988.\n",
            "Df Residuals:                             407   BIC:                             3016.\n",
            "Df Model:                                   6                                         \n",
            "Covariance Type:                    nonrobust                                         \n",
            "==========================================================================================================\n",
            "                                             coef    std err          t      P>|t|      [0.025      0.975]\n",
            "----------------------------------------------------------------------------------------------------------\n",
            "const                                  -1.444e+04   6775.671     -2.131      0.034   -2.78e+04   -1117.421\n",
            "X2 house age                              -0.2697      0.039     -7.000      0.000      -0.345      -0.194\n",
            "X3 distance to the nearest MRT station    -0.0045      0.001     -6.250      0.000      -0.006      -0.003\n",
            "X4 number of convenience stores            1.1333      0.188      6.023      0.000       0.763       1.503\n",
            "X1 transaction date                        5.1462      1.557      3.305      0.001       2.085       8.207\n",
            "X5 latitude                              225.4730     44.567      5.059      0.000     137.863     313.083\n",
            "X6 longitude                             -12.4236     48.582     -0.256      0.798    -107.927      83.079\n",
            "==============================================================================\n",
            "Omnibus:                      231.634   Durbin-Watson:                   2.153\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3599.138\n",
            "Skew:                           2.026   Prob(JB):                         0.00\n",
            "Kurtosis:                      16.865   Cond. No.                     3.72e+07\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 3.72e+07. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/tsatools.py:117: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
            "  x = pd.concat(x[::order], 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "realestatetest = pd.read_csv('Realestate.csv')\n",
        "realestatetest"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "JCXTAoGQguH1",
        "outputId": "56c710f3-387a-4b45-9789-a2959e73750f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      No  X1 transaction date  X2 house age  \\\n",
              "0      1             2012.917          32.0   \n",
              "1      2             2012.917          19.5   \n",
              "2      3             2013.583          13.3   \n",
              "3      4             2013.500          13.3   \n",
              "4      5             2012.833           5.0   \n",
              "..   ...                  ...           ...   \n",
              "409  410             2013.000          13.7   \n",
              "410  411             2012.667           5.6   \n",
              "411  412             2013.250          18.8   \n",
              "412  413             2013.000           8.1   \n",
              "413  414             2013.500           6.5   \n",
              "\n",
              "     X3 distance to the nearest MRT station  X4 number of convenience stores  \\\n",
              "0                                  84.87882                               10   \n",
              "1                                 306.59470                                9   \n",
              "2                                 561.98450                                5   \n",
              "3                                 561.98450                                5   \n",
              "4                                 390.56840                                5   \n",
              "..                                      ...                              ...   \n",
              "409                              4082.01500                                0   \n",
              "410                                90.45606                                9   \n",
              "411                               390.96960                                7   \n",
              "412                               104.81010                                5   \n",
              "413                                90.45606                                9   \n",
              "\n",
              "     X5 latitude  X6 longitude  Y house price of unit area  \n",
              "0       24.98298     121.54024                        37.9  \n",
              "1       24.98034     121.53951                        42.2  \n",
              "2       24.98746     121.54391                        47.3  \n",
              "3       24.98746     121.54391                        54.8  \n",
              "4       24.97937     121.54245                        43.1  \n",
              "..           ...           ...                         ...  \n",
              "409     24.94155     121.50381                        15.4  \n",
              "410     24.97433     121.54310                        50.0  \n",
              "411     24.97923     121.53986                        40.6  \n",
              "412     24.96674     121.54067                        52.5  \n",
              "413     24.97433     121.54310                        63.9  \n",
              "\n",
              "[414 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b0b0cfff-45ff-4f92-a332-c0fc380d8f3d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>No</th>\n",
              "      <th>X1 transaction date</th>\n",
              "      <th>X2 house age</th>\n",
              "      <th>X3 distance to the nearest MRT station</th>\n",
              "      <th>X4 number of convenience stores</th>\n",
              "      <th>X5 latitude</th>\n",
              "      <th>X6 longitude</th>\n",
              "      <th>Y house price of unit area</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2012.917</td>\n",
              "      <td>32.0</td>\n",
              "      <td>84.87882</td>\n",
              "      <td>10</td>\n",
              "      <td>24.98298</td>\n",
              "      <td>121.54024</td>\n",
              "      <td>37.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2012.917</td>\n",
              "      <td>19.5</td>\n",
              "      <td>306.59470</td>\n",
              "      <td>9</td>\n",
              "      <td>24.98034</td>\n",
              "      <td>121.53951</td>\n",
              "      <td>42.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2013.583</td>\n",
              "      <td>13.3</td>\n",
              "      <td>561.98450</td>\n",
              "      <td>5</td>\n",
              "      <td>24.98746</td>\n",
              "      <td>121.54391</td>\n",
              "      <td>47.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2013.500</td>\n",
              "      <td>13.3</td>\n",
              "      <td>561.98450</td>\n",
              "      <td>5</td>\n",
              "      <td>24.98746</td>\n",
              "      <td>121.54391</td>\n",
              "      <td>54.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2012.833</td>\n",
              "      <td>5.0</td>\n",
              "      <td>390.56840</td>\n",
              "      <td>5</td>\n",
              "      <td>24.97937</td>\n",
              "      <td>121.54245</td>\n",
              "      <td>43.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>409</th>\n",
              "      <td>410</td>\n",
              "      <td>2013.000</td>\n",
              "      <td>13.7</td>\n",
              "      <td>4082.01500</td>\n",
              "      <td>0</td>\n",
              "      <td>24.94155</td>\n",
              "      <td>121.50381</td>\n",
              "      <td>15.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>410</th>\n",
              "      <td>411</td>\n",
              "      <td>2012.667</td>\n",
              "      <td>5.6</td>\n",
              "      <td>90.45606</td>\n",
              "      <td>9</td>\n",
              "      <td>24.97433</td>\n",
              "      <td>121.54310</td>\n",
              "      <td>50.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>411</th>\n",
              "      <td>412</td>\n",
              "      <td>2013.250</td>\n",
              "      <td>18.8</td>\n",
              "      <td>390.96960</td>\n",
              "      <td>7</td>\n",
              "      <td>24.97923</td>\n",
              "      <td>121.53986</td>\n",
              "      <td>40.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>412</th>\n",
              "      <td>413</td>\n",
              "      <td>2013.000</td>\n",
              "      <td>8.1</td>\n",
              "      <td>104.81010</td>\n",
              "      <td>5</td>\n",
              "      <td>24.96674</td>\n",
              "      <td>121.54067</td>\n",
              "      <td>52.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>413</th>\n",
              "      <td>414</td>\n",
              "      <td>2013.500</td>\n",
              "      <td>6.5</td>\n",
              "      <td>90.45606</td>\n",
              "      <td>9</td>\n",
              "      <td>24.97433</td>\n",
              "      <td>121.54310</td>\n",
              "      <td>63.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>414 rows × 8 columns</p>\n",
              "</div>\n",
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              "      \n",
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              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "          document.querySelector('#df-b0b0cfff-45ff-4f92-a332-c0fc380d8f3d 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-b0b0cfff-45ff-4f92-a332-c0fc380d8f3d');\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",
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              "          dataTable['output_type'] = 'display_data';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    }
  ]
}