Big_Boss_DataSet.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Big Boss DataSet.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v9zG86hjlAsK"
      },
      "source": [
        "# **Big Boss India**  -  Statistical Methods for Decision Making\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "![download.jpg](data:image/jpeg;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zRXY62OYtaYf"
      },
      "source": [
        "### Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VyoaFSkm-ZQN"
      },
      "source": [
        "import pandas as pd\r\n",
        "import numpy as np\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import seaborn as sns\r\n",
        "import scipy.stats as stats\r\n",
        "from scipy.stats import ttest_ind\r\n",
        "from scipy.stats import t\r\n",
        "import pandas.util.testing as tm\r\n",
        "import statsmodels.api as sm\r\n",
        "from scipy.stats import chi2_contingency\r\n",
        "from scipy.stats import chi2\r\n",
        "\r\n",
        "import warnings\r\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "61S3b2bokgyo"
      },
      "source": [
        "### Import Big Boss Data Set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9CTeOC3y-vXC"
      },
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/Bigg_Boss_India.csv\",encoding=\"ISO-8859-1\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bVpJgbVXIfsc"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qmQRPdKq-gcL"
      },
      "source": [
        "bb=pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/Bigg_Boss_India.csv\",encoding=\"ISO-8859-1\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        },
        "id": "pN-Do517kwez",
        "outputId": "20c18d75-3d4a-4f79-9e29-eee5ea4e405c"
      },
      "source": [
        "bb.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-74c8e6791c7f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'bb' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "af1qVPLWC4_d"
      },
      "source": [
        "## Exploratory Ananlysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OtYKwi8gqRV-"
      },
      "source": [
        "### Shape and DataTypes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9vGoKovkBvmc",
        "outputId": "7a208441-15d9-4383-f1ba-3c5abfe21b46"
      },
      "source": [
        "## 24 attributes , 632 observations\r\n",
        "bb.shape\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(632, 24)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SHtO78CKFvJj",
        "outputId": "9d360b0c-7204-47c4-d5d7-7635ee7015d6"
      },
      "source": [
        "bb.dtypes.T"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Language                               object\n",
              "Season Number                           int64\n",
              "Name                                   object\n",
              "Profession                             object\n",
              "Gender                                 object\n",
              "Entry Date                             object\n",
              "Elimination Date                       object\n",
              "Elimination Week Number               float64\n",
              "Wild Card                              object\n",
              "Season Length                           int64\n",
              "Number of Housemates                    int64\n",
              "Season Start Date                      object\n",
              "Season End Date                        object\n",
              "Host Name                              object\n",
              "Guest Host Name                        object\n",
              "Prize Money (INR)                      object\n",
              "Broadcasted By                         object\n",
              "Average TRP                           float64\n",
              "Number of Evictions Faced             float64\n",
              "Number of re-entries                  float64\n",
              "Number of times elected as Captain    float64\n",
              "Social Media Popularity               float64\n",
              "Finalist                              float64\n",
              "Winner                                float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yebj10VKqVkj"
      },
      "source": [
        "### Data Cleaning (Null Values)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hRuj6gfprpoz",
        "outputId": "6f4c425f-13ff-4550-904b-f1b64e17318c"
      },
      "source": [
        "bb.isnull().values.any()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 227
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JBBO9xt4ct6X",
        "outputId": "d05c5b1b-2117-4e20-f53b-d8d03bb4020f"
      },
      "source": [
        "bb.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Language                                0\n",
              "Season Number                           0\n",
              "Name                                    0\n",
              "Profession                              0\n",
              "Gender                                  0\n",
              "Entry Date                             99\n",
              "Elimination Date                      500\n",
              "Elimination Week Number               108\n",
              "Wild Card                               0\n",
              "Season Length                           0\n",
              "Number of Housemates                    0\n",
              "Season Start Date                       0\n",
              "Season End Date                         0\n",
              "Host Name                               0\n",
              "Guest Host Name                       559\n",
              "Prize Money (INR)                       9\n",
              "Broadcasted By                          0\n",
              "Average TRP                           238\n",
              "Number of Evictions Faced              18\n",
              "Number of re-entries                   18\n",
              "Number of times elected as Captain     33\n",
              "Social Media Popularity               546\n",
              "Finalist                                9\n",
              "Winner                                  9\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YKDpEOBmtNQP",
        "outputId": "19b971bb-c3fe-47df-8fbe-f474ad7a02fd"
      },
      "source": [
        "bb.isnull().sum().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2146"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CGRJdnk9taM-"
      },
      "source": [
        "# Contestant might have faced at least one eviction, so filling NaN with 'Number of Evictions Faced' with 1\r\n",
        "bb['Number of Evictions Faced'] = bb['Number of Evictions Faced'].fillna(1)\r\n",
        "\r\n",
        "# Number of re-entries are very less, so filling NULLs in 'Number of re-entries' with 0\r\n",
        "bb['Number of re-entries'] = bb['Number of re-entries'].fillna(0)\r\n",
        "\r\n",
        "# Filling blank values in 'Average TRP' column with average\r\n",
        "bb['Average TRP'] = bb['Average TRP'].fillna(bb['Average TRP'].mean())\r\n",
        "\r\n",
        "# Filling blank Social Media Popularity values with average\r\n",
        "bb['Social Media Popularity'] = bb['Social Media Popularity'].fillna(round(bb['Social Media Popularity'].mean()))\r\n",
        "\r\n",
        "# Filling blank values for Winner with 0\r\n",
        "bb['Winner'] = bb['Winner'].fillna(0)\r\n",
        "\r\n",
        "# Filling blank values for Winner with 0\r\n",
        "bb['Finalist'] = bb['Finalist'].fillna(0)\r\n",
        "\r\n",
        "# Filling blank values for Number of times elected as Captain with 0\r\n",
        "bb['Number of times elected as Captain'] = bb['Number of times elected as Captain'].fillna(0)\r\n",
        "\r\n",
        "# Filling null values for Elimination Week Number \r\n",
        "bb['Elimination Week Number'] = bb['Elimination Week Number'].fillna(bb[\"Elimination Week Number\"].mean())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5AgpMfQUqEiN"
      },
      "source": [
        "### Changing the datatypes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kmvcnyo0PmbP"
      },
      "source": [
        "bb = bb.astype({'Elimination Week Number':'int64','Number of Evictions Faced':'int64','Number of re-entries':'int64',\r\n",
        "                'Number of times elected as Captain':'int64','Social Media Popularity':'object','Winner':'object',\r\n",
        "                'Finalist':'object','Entry Date':'Datetime64','Season Start Date':'Datetime64','Season End Date':'Datetime64'}) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9H7adfyTrEV9",
        "outputId": "6a37d32b-91b9-478a-c062-7f914d5ae79c"
      },
      "source": [
        "bb.dtypes.T"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Language                               object\n",
              "Season Number                           int64\n",
              "Name                                   object\n",
              "Profession                             object\n",
              "Gender                                 object\n",
              "Elimination Week Number                 int64\n",
              "Wild Card                              object\n",
              "Season Length                           int64\n",
              "Number of Housemates                    int64\n",
              "Host Name                              object\n",
              "Guest Host Name                        object\n",
              "Broadcasted By                         object\n",
              "Average TRP                           float64\n",
              "Number of Evictions Faced               int64\n",
              "Number of re-entries                    int64\n",
              "Number of times elected as Captain      int64\n",
              "Social Media Popularity                 int64\n",
              "Finalist                               object\n",
              "Winner                                 object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ivH8oaTzHe8"
      },
      "source": [
        "# Statistical Summmary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A5EpFQe91Tc9"
      },
      "source": [
        "# dropping few columns as it is not useful for analysis\r\n",
        "\r\n",
        "bb = bb.drop(['Season Start Date','Season End Date', 'Entry Date','Elimination Date','Prize Money (INR)'], axis = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "wC88n3y7sX91",
        "outputId": "ea625ddb-476b-455a-b12d-277fa980491a"
      },
      "source": [
        "bb.describe(include = 'object').T\r\n",
        "\r\n",
        "##The output gives:\r\n",
        "##count: number of values other than NaN\r\n",
        "##unique: number of unique values present in the variable\r\n",
        "##top: most repeated value\r\n",
        "##frequency: frequency of most repeated value"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>count</th>\n",
              "      <th>unique</th>\n",
              "      <th>top</th>\n",
              "      <th>freq</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Language</th>\n",
              "      <td>632</td>\n",
              "      <td>7</td>\n",
              "      <td>Hindi</td>\n",
              "      <td>258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Name</th>\n",
              "      <td>632</td>\n",
              "      <td>625</td>\n",
              "      <td>Vikas Gupta</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Profession</th>\n",
              "      <td>632</td>\n",
              "      <td>74</td>\n",
              "      <td>Actress</td>\n",
              "      <td>146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender</th>\n",
              "      <td>632</td>\n",
              "      <td>3</td>\n",
              "      <td>Female</td>\n",
              "      <td>331</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wild Card</th>\n",
              "      <td>632</td>\n",
              "      <td>2</td>\n",
              "      <td>No</td>\n",
              "      <td>522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Host Name</th>\n",
              "      <td>632</td>\n",
              "      <td>12</td>\n",
              "      <td>Salman Khan</td>\n",
              "      <td>213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Guest Host Name</th>\n",
              "      <td>73</td>\n",
              "      <td>4</td>\n",
              "      <td>Farah Khan</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Broadcasted By</th>\n",
              "      <td>632</td>\n",
              "      <td>11</td>\n",
              "      <td>Colors</td>\n",
              "      <td>243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Social Media Popularity</th>\n",
              "      <td>632</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Finalist</th>\n",
              "      <td>632</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Winner</th>\n",
              "      <td>632</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>599</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        count unique          top freq\n",
              "Language                  632      7        Hindi  258\n",
              "Name                      632    625  Vikas Gupta    3\n",
              "Profession                632     74      Actress  146\n",
              "Gender                    632      3       Female  331\n",
              "Wild Card                 632      2           No  522\n",
              "Host Name                 632     12  Salman Khan  213\n",
              "Guest Host Name            73      4   Farah Khan   19\n",
              "Broadcasted By            632     11       Colors  243\n",
              "Social Media Popularity   632      8            7  566\n",
              "Finalist                  632      2            0  484\n",
              "Winner                    632      2            0  599"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "uSWi21TAsQHi",
        "outputId": "794846c8-876d-4fda-ca51-de4ed1636dfc"
      },
      "source": [
        "bb.describe().T"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Season Number</th>\n",
              "      <td>632.0</td>\n",
              "      <td>4.908228</td>\n",
              "      <td>3.833433</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>7.0</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Elimination Week Number</th>\n",
              "      <td>632.0</td>\n",
              "      <td>7.574367</td>\n",
              "      <td>5.501837</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>13.0</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Season Length</th>\n",
              "      <td>632.0</td>\n",
              "      <td>102.208861</td>\n",
              "      <td>10.657648</td>\n",
              "      <td>70.00</td>\n",
              "      <td>98.0</td>\n",
              "      <td>105.000000</td>\n",
              "      <td>105.0</td>\n",
              "      <td>140.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Number of Housemates</th>\n",
              "      <td>632.0</td>\n",
              "      <td>18.303797</td>\n",
              "      <td>2.131074</td>\n",
              "      <td>15.00</td>\n",
              "      <td>17.0</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>20.0</td>\n",
              "      <td>23.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Average TRP</th>\n",
              "      <td>632.0</td>\n",
              "      <td>6.230635</td>\n",
              "      <td>2.627777</td>\n",
              "      <td>2.72</td>\n",
              "      <td>4.4</td>\n",
              "      <td>6.230635</td>\n",
              "      <td>6.5</td>\n",
              "      <td>15.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Number of Evictions Faced</th>\n",
              "      <td>632.0</td>\n",
              "      <td>3.968354</td>\n",
              "      <td>2.872176</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>6.0</td>\n",
              "      <td>13.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Number of re-entries</th>\n",
              "      <td>632.0</td>\n",
              "      <td>0.099684</td>\n",
              "      <td>0.325172</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Number of times elected as Captain</th>\n",
              "      <td>632.0</td>\n",
              "      <td>0.509494</td>\n",
              "      <td>0.748949</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                    count        mean  ...    75%    max\n",
              "Season Number                       632.0    4.908228  ...    7.0   14.0\n",
              "Elimination Week Number             632.0    7.574367  ...   13.0   20.0\n",
              "Season Length                       632.0  102.208861  ...  105.0  140.0\n",
              "Number of Housemates                632.0   18.303797  ...   20.0   23.0\n",
              "Average TRP                         632.0    6.230635  ...    6.5   15.6\n",
              "Number of Evictions Faced           632.0    3.968354  ...    6.0   13.0\n",
              "Number of re-entries                632.0    0.099684  ...    0.0    3.0\n",
              "Number of times elected as Captain  632.0    0.509494  ...    1.0    5.0\n",
              "\n",
              "[8 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bab04BsqBky4",
        "outputId": "ce13d771-527e-40f2-8d2e-413135f3dbe1"
      },
      "source": [
        "bb.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Language</th>\n",
              "      <th>Season Number</th>\n",
              "      <th>Name</th>\n",
              "      <th>Profession</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Elimination Week Number</th>\n",
              "      <th>Wild Card</th>\n",
              "      <th>Season Length</th>\n",
              "      <th>Number of Housemates</th>\n",
              "      <th>Host Name</th>\n",
              "      <th>Guest Host Name</th>\n",
              "      <th>Broadcasted By</th>\n",
              "      <th>Average TRP</th>\n",
              "      <th>Number of Evictions Faced</th>\n",
              "      <th>Number of re-entries</th>\n",
              "      <th>Number of times elected as Captain</th>\n",
              "      <th>Social Media Popularity</th>\n",
              "      <th>Finalist</th>\n",
              "      <th>Winner</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Hindi</td>\n",
              "      <td>1</td>\n",
              "      <td>Amit Sadh</td>\n",
              "      <td>Soap Actor</td>\n",
              "      <td>Male</td>\n",
              "      <td>10</td>\n",
              "      <td>No</td>\n",
              "      <td>86</td>\n",
              "      <td>15</td>\n",
              "      <td>Arshad Warsi</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Sony TV</td>\n",
              "      <td>2.72</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Hindi</td>\n",
              "      <td>1</td>\n",
              "      <td>Anupama Verma</td>\n",
              "      <td>Model</td>\n",
              "      <td>Female</td>\n",
              "      <td>5</td>\n",
              "      <td>No</td>\n",
              "      <td>86</td>\n",
              "      <td>15</td>\n",
              "      <td>Arshad Warsi</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Sony TV</td>\n",
              "      <td>2.72</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Hindi</td>\n",
              "      <td>1</td>\n",
              "      <td>Aryan Vaid</td>\n",
              "      <td>Pageant Winner</td>\n",
              "      <td>Male</td>\n",
              "      <td>4</td>\n",
              "      <td>No</td>\n",
              "      <td>86</td>\n",
              "      <td>15</td>\n",
              "      <td>Arshad Warsi</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Sony TV</td>\n",
              "      <td>2.72</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Language  Season Number  ... Finalist Winner\n",
              "0    Hindi              1  ...        0      0\n",
              "1    Hindi              1  ...        0      0\n",
              "2    Hindi              1  ...        0      0\n",
              "\n",
              "[3 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gX9UwOBGHuX3",
        "outputId": "a2d64d55-5345-455a-f002-87e357df580d"
      },
      "source": [
        "bb.tail(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Language</th>\n",
              "      <th>Season Number</th>\n",
              "      <th>Name</th>\n",
              "      <th>Profession</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Elimination Week Number</th>\n",
              "      <th>Wild Card</th>\n",
              "      <th>Season Length</th>\n",
              "      <th>Number of Housemates</th>\n",
              "      <th>Host Name</th>\n",
              "      <th>Guest Host Name</th>\n",
              "      <th>Broadcasted By</th>\n",
              "      <th>Average TRP</th>\n",
              "      <th>Number of Evictions Faced</th>\n",
              "      <th>Number of re-entries</th>\n",
              "      <th>Number of times elected as Captain</th>\n",
              "      <th>Social Media Popularity</th>\n",
              "      <th>Finalist</th>\n",
              "      <th>Winner</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>629</th>\n",
              "      <td>Malayalam</td>\n",
              "      <td>2</td>\n",
              "      <td>Sooraj</td>\n",
              "      <td>Radio Jockey</td>\n",
              "      <td>Male</td>\n",
              "      <td>8</td>\n",
              "      <td>Yes</td>\n",
              "      <td>105</td>\n",
              "      <td>23</td>\n",
              "      <td>Mohanlal</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Asianet</td>\n",
              "      <td>11.03</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>630</th>\n",
              "      <td>Malayalam</td>\n",
              "      <td>2</td>\n",
              "      <td>Abhirami Suresh</td>\n",
              "      <td>Actress</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>105</td>\n",
              "      <td>23</td>\n",
              "      <td>Mohanlal</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Asianet</td>\n",
              "      <td>11.03</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>631</th>\n",
              "      <td>Malayalam</td>\n",
              "      <td>2</td>\n",
              "      <td>Amrutha Suresh</td>\n",
              "      <td>Singer</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>105</td>\n",
              "      <td>23</td>\n",
              "      <td>Mohanlal</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Asianet</td>\n",
              "      <td>11.03</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Language  Season Number  ... Finalist Winner\n",
              "629  Malayalam              2  ...        0      0\n",
              "630  Malayalam              2  ...        0      0\n",
              "631  Malayalam              2  ...        0      0\n",
              "\n",
              "[3 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KpSTg4xUz1X3"
      },
      "source": [
        "# Descriptive Statistics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZU5QF9_vHyXc",
        "outputId": "168796b2-6693-43de-d2bd-baea1e52b59c"
      },
      "source": [
        "bb.columns.values"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Language', 'Season Number', 'Name', 'Profession', 'Gender',\n",
              "       'Elimination Week Number', 'Wild Card', 'Season Length',\n",
              "       'Number of Housemates', 'Host Name', 'Guest Host Name',\n",
              "       'Broadcasted By', 'Average TRP', 'Number of Evictions Faced',\n",
              "       'Number of re-entries', 'Number of times elected as Captain',\n",
              "       'Social Media Popularity', 'Finalist', 'Winner'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0v83o-nWHRXv"
      },
      "source": [
        "**Measure of Central Tendency**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "AfvvDPNtz-ra",
        "outputId": "3b83d86e-27aa-4ed7-9981-d015fd30883a"
      },
      "source": [
        "# filter the categorical variables from the data\r\n",
        "bb_cat = bb.select_dtypes(include=object)\r\n",
        "\r\n",
        "# calculate the mode\r\n",
        "bb_cat.mode().T\r\n",
        "\r\n",
        "## Bimodal and Unimodal"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Language</th>\n",
              "      <td>Hindi</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Name</th>\n",
              "      <td>Vikas Gupta</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Profession</th>\n",
              "      <td>Films</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender</th>\n",
              "      <td>Female</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wild Card</th>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Host Name</th>\n",
              "      <td>Salman Khan</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Guest Host Name</th>\n",
              "      <td>Farah Khan</td>\n",
              "      <td>Samantha Akkineni</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Broadcasted By</th>\n",
              "      <td>Colors</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Social Media Popularity</th>\n",
              "      <td>7</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Finalist</th>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Winner</th>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                   0                  1\n",
              "Language                       Hindi                NaN\n",
              "Name                     Vikas Gupta                NaN\n",
              "Profession                     Films                NaN\n",
              "Gender                        Female                NaN\n",
              "Wild Card                         No                NaN\n",
              "Host Name                Salman Khan                NaN\n",
              "Guest Host Name           Farah Khan  Samantha Akkineni\n",
              "Broadcasted By                Colors                NaN\n",
              "Social Media Popularity            7                NaN\n",
              "Finalist                           0                NaN\n",
              "Winner                             0                NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "nKCA7Q_M2bZT",
        "outputId": "88f2d34f-41ee-4222-c000-86e9bbd6224a"
      },
      "source": [
        "bb.loc[bb.Name == 'Vikas Gupta'] ## checking why Vikas Gupta name has higher frequency ##"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Language</th>\n",
              "      <th>Season Number</th>\n",
              "      <th>Name</th>\n",
              "      <th>Profession</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Elimination Week Number</th>\n",
              "      <th>Wild Card</th>\n",
              "      <th>Season Length</th>\n",
              "      <th>Number of Housemates</th>\n",
              "      <th>Host Name</th>\n",
              "      <th>Guest Host Name</th>\n",
              "      <th>Broadcasted By</th>\n",
              "      <th>Average TRP</th>\n",
              "      <th>Number of Evictions Faced</th>\n",
              "      <th>Number of re-entries</th>\n",
              "      <th>Number of times elected as Captain</th>\n",
              "      <th>Social Media Popularity</th>\n",
              "      <th>Finalist</th>\n",
              "      <th>Winner</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>191</th>\n",
              "      <td>Hindi</td>\n",
              "      <td>11</td>\n",
              "      <td>Vikas Gupta</td>\n",
              "      <td>Producer</td>\n",
              "      <td>Male</td>\n",
              "      <td>15</td>\n",
              "      <td>No</td>\n",
              "      <td>106</td>\n",
              "      <td>19</td>\n",
              "      <td>Salman Khan</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Colors</td>\n",
              "      <td>7.640000</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>235</th>\n",
              "      <td>Hindi</td>\n",
              "      <td>13</td>\n",
              "      <td>Vikas Gupta</td>\n",
              "      <td>Producer</td>\n",
              "      <td>Male</td>\n",
              "      <td>13</td>\n",
              "      <td>Yes</td>\n",
              "      <td>140</td>\n",
              "      <td>22</td>\n",
              "      <td>Salman Khan</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Colors</td>\n",
              "      <td>9.060000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>252</th>\n",
              "      <td>Hindi</td>\n",
              "      <td>14</td>\n",
              "      <td>Vikas Gupta</td>\n",
              "      <td>Producer</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>105</td>\n",
              "      <td>22</td>\n",
              "      <td>Salman Khan</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Colors</td>\n",
              "      <td>6.230635</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Language  Season Number  ... Finalist Winner\n",
              "191    Hindi             11  ...        1      0\n",
              "235    Hindi             13  ...        0      0\n",
              "252    Hindi             14  ...        0      0\n",
              "\n",
              "[3 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 692
        },
        "id": "tGGOVPzpBW7J",
        "outputId": "76d48701-5a28-4344-dd02-47433c62605b"
      },
      "source": [
        "bb_cat1 = bb_cat[['Profession','Gender','Winner','Finalist','Wild Card', 'Guest Host Name']]\r\n",
        "\r\n",
        "fig, ax = plt.subplots(2, 3, figsize=(15, 10))\r\n",
        "for variable, subplot in zip(bb_cat1, ax.flatten()):\r\n",
        "    sns.countplot(bb_cat[variable], ax=subplot)\r\n",
        "    for label in subplot.get_xticklabels():\r\n",
        "        label.set_rotation(90)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x720 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1v2XI2RYHLWi"
      },
      "source": [
        "**Measure of Disperion**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Khga0Hn4vsg"
      },
      "source": [
        "# filter the numerical variables from the data\r\n",
        "bb_num = bb.select_dtypes(include=np.number)\r\n",
        "\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FUYBEtUj4-YW",
        "outputId": "eaefd64f-517a-4c1c-d97c-b42e5bfc0dfd"
      },
      "source": [
        "## Mean ##\r\n",
        "bb_num.mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Season Number                           4.908228\n",
              "Elimination Week Number                 7.574367\n",
              "Season Length                         102.208861\n",
              "Number of Housemates                   18.303797\n",
              "Average TRP                             6.230635\n",
              "Number of Evictions Faced               3.968354\n",
              "Number of re-entries                    0.099684\n",
              "Number of times elected as Captain      0.509494\n",
              "Social Media Popularity                 6.944620\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rPKpUhwnEWqG",
        "outputId": "4b3991a8-f582-48d7-cdca-9a4a8a8c5ecd"
      },
      "source": [
        "## Standard Deviation ##\r\n",
        "bb_num.std()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Season Number                          3.833433\n",
              "Elimination Week Number                5.501837\n",
              "Season Length                         10.657648\n",
              "Number of Housemates                   2.131074\n",
              "Average TRP                            2.627777\n",
              "Number of Evictions Faced              2.872176\n",
              "Number of re-entries                   0.325172\n",
              "Number of times elected as Captain     0.748949\n",
              "Social Media Popularity                0.610346\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Rb0PfLEEiRm",
        "outputId": "a30f59e6-7cf2-4199-f02e-28b816a9f56b"
      },
      "source": [
        "## Median ##\r\n",
        "bb_num.median()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Season Number                           4.000000\n",
              "Elimination Week Number                 8.000000\n",
              "Season Length                         105.000000\n",
              "Number of Housemates                   18.000000\n",
              "Average TRP                             6.230635\n",
              "Number of Evictions Faced               3.000000\n",
              "Number of re-entries                    0.000000\n",
              "Number of times elected as Captain      0.000000\n",
              "Social Media Popularity                 7.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UF6o5wJmJEiT"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QcDeV9ejJJid"
      },
      "source": [
        "**Distribution of Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "_5srJ24OHnZI",
        "outputId": "ffc49cbb-d1e7-423e-8cf6-b99798cf5809"
      },
      "source": [
        "bb_num.plot(kind='density',subplots=True,sharex=False,layout=(4,3),figsize=(15,10))\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x720 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "11a0EEBpI4w7"
      },
      "source": [
        "**Skewness & Kurtosis**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kH4Y8tQ4InG6",
        "outputId": "c74e6889-5ec3-4a22-c3d0-0a1d3683aa74"
      },
      "source": [
        "bb_num.skew()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Season Number                         0.957377\n",
              "Elimination Week Number               0.041765\n",
              "Season Length                         0.678514\n",
              "Number of Housemates                  0.202793\n",
              "Average TRP                           1.334841\n",
              "Number of Evictions Faced             0.835795\n",
              "Number of re-entries                  3.626119\n",
              "Number of times elected as Captain    1.738601\n",
              "Social Media Popularity              -2.278360\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V0hfzpQ6IyUe",
        "outputId": "c136de67-ce65-429d-ca39-e3ce22722040"
      },
      "source": [
        "bb_num.kurt()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Season Number                         -0.211486\n",
              "Elimination Week Number               -1.297932\n",
              "Season Length                          5.149473\n",
              "Number of Housemates                  -0.478670\n",
              "Average TRP                            2.758878\n",
              "Number of Evictions Faced             -0.043675\n",
              "Number of re-entries                  16.117140\n",
              "Number of times elected as Captain     4.104004\n",
              "Social Media Popularity               21.110185\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zwrHF5ukzBVk"
      },
      "source": [
        "**Data Preperation**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cgPsYwr1za1X"
      },
      "source": [
        "Narrowing of Profession column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "snLUKgvvu96y"
      },
      "source": [
        "## before narrowing down\r\n",
        "bb.groupby(bb.Profession)['Profession'].count()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DbllNXe7Roob"
      },
      "source": [
        "# Common people have many professions, so clubbing them into one category\r\n",
        "bb.loc[bb['Profession'].str.contains('Commoner'),'Profession']='Commoner'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lAHj87ai25Ai"
      },
      "source": [
        "bb['Profession'] = bb['Profession'].replace(['Wrestler','Sports Person'],'Sports')\r\n",
        "bb['Profession'] = bb['Profession'].replace(['Peagent Winner'],'Model')\r\n",
        "bb['Profession'] = bb['Profession'].replace(['Soap Actor','Soap Actress','Comedian','TV Anchor','Video Jockey','Reality Show Alumni'],'Television')\r\n",
        "bb['Profession'] = bb['Profession'].replace(['International Star','International star'],'International')\r\n",
        "bb['Profession'] = bb['Profession'].replace(['Actress','Actor','Film director','Film Director','Film Critic','Lyricist','Writer'],'Films')\r\n",
        "bb['Profession'] = bb['Profession'].replace(['Dancer','Choreographer','Musician','Singer'],'Arts')\r\n",
        "bb['Profession'] = bb['Profession'].replace(['News Presenter','Journalist','Social Media Star','Radio Jockey'],'Media')\r\n",
        "bb['Profession'] = bb['Profession'].replace(['Politician','Political Analyst'],'Politics')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "txKMvI36-K2V"
      },
      "source": [
        "counts = bb['Profession'].value_counts()\r\n",
        "idx = counts[counts.lt(6)].index\r\n",
        "bb.loc[bb['Profession'].isin(idx), 'Profession'] = 'Others'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZHcI3t8ntRj3",
        "outputId": "caf23457-b43f-48a1-bd84-b4978efc3ac7"
      },
      "source": [
        "## after narrowing down ##\r\n",
        "bb.groupby(bb.Profession)['Profession'].count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Profession\n",
              "Arts              49\n",
              "Commoner          39\n",
              "Films            262\n",
              "International      7\n",
              "Media             38\n",
              "Model             49\n",
              "Others            42\n",
              "Politics           6\n",
              "Sports            10\n",
              "Television       130\n",
              "Name: Profession, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fwdvFv9dlx0B"
      },
      "source": [
        "# Simulation \r\n",
        "\r\n",
        "# Central Limit Theorem"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-E8bS4_ohb64"
      },
      "source": [
        "def central_limit_theorem(df,column_names,n_samples = 1000, sample_size = 500):\r\n",
        "    min_value = 0 \r\n",
        "    max_value = df.count()  \r\n",
        "    b = {}\r\n",
        "    for i in range(n_samples):\r\n",
        "        x = np.unique(np.random.randint(min_value, max_value, size = sample_size)) \r\n",
        "        b[i] = df[x].mean()\r\n",
        "    c = pd.DataFrame()\r\n",
        "    c['sample'] = b.keys()   \r\n",
        "    c['Mean'] = b.values()  \r\n",
        "    plt.figure(figsize= (15,5))\r\n",
        "\r\n",
        "    plt.subplot(1,2,2)\r\n",
        "    sns.distplot(c.Mean,color='green')\r\n",
        "    plt.title(f\"Sampling Distribution. \\n \\u03bc = {round(c.Mean.mean(), 3)} & SE = {round(c.Mean.std(),3)}\")\r\n",
        "    plt.xlabel(column_names)\r\n",
        "    plt.ylabel('freq')\r\n",
        "\r\n",
        "    plt.subplot(1,2,1)\r\n",
        "    sns.distplot(df,color='red')\r\n",
        "    plt.title(f\"Population Distribution. \\n \\u03bc = {round(df.mean(), 3)} & \\u03C3 = {round(df.std(),3)}\")\r\n",
        "    plt.xlabel(column_names)\r\n",
        "    plt.ylabel('freq')\r\n",
        "\r\n",
        "    plt.show()\r\n",
        "series1 = bb[\"Number of Evictions Faced\"]\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H_S68_DOmgdB"
      },
      "source": [
        "Simulation with sample size = 20 and no of samples = 10"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "id": "GQTA--THh2iq",
        "outputId": "7bf20b04-6829-4561-a3a4-8afbb78a708f"
      },
      "source": [
        "central_limit_theorem(series1,'Numbe of Evictions Faced',n_samples = 10, sample_size = 20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-E7Ovp2cmWMR"
      },
      "source": [
        "Simulation with sample size = 100 and no of samples = 100"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "id": "Ttn_XEBJi_a0",
        "outputId": "bc00943d-d086-45a9-9645-210872c1e849"
      },
      "source": [
        "central_limit_theorem(series1,'Numbe of Evictions Faced',n_samples = 100, sample_size = 100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sRmqT8w-pUMw"
      },
      "source": [
        "# Hypothesis Testing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kPjXlhBEXulc"
      },
      "source": [
        "## Shapiro Test"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mtHFnC60u_pW"
      },
      "source": [
        "### Null and Alternate Hypothesis\r\n",
        "\r\n",
        "H0: Number of Evictions Faced follow a Normal distribution\r\n",
        "\r\n",
        "H1: Number of Evictions Faced do not follow a Normal distribution\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TP_9dTe9v-vQ"
      },
      "source": [
        "n=100\r\n",
        "k=100\r\n",
        "means=[np.mean(bb[\"Number of Evictions Faced\"].sample(n)) for i in range(k)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7dTx_1Q1p4Xj"
      },
      "source": [
        "Perform Shapiro Test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MyFBQICau5KL",
        "outputId": "cc076961-042e-46c0-c8f4-b263cd1c2809"
      },
      "source": [
        "## Shapiro Test ##\r\n",
        "from scipy.stats import shapiro\r\n",
        "stat, p = shapiro(means)\r\n",
        "print('Statistics={}, p={}'.format(stat, p))\r\n",
        "alpha = 0.05\r\n",
        "if p > alpha:\r\n",
        "    print('Sample looks Normal (do not reject H0)')\r\n",
        "else:\r\n",
        "    print('Sample does not look Normal (reject H0)')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Statistics=0.9751766920089722, p=0.05570291727781296\n",
            "Sample looks Normal (do not reject H0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d-XHOArti_6A"
      },
      "source": [
        "## Chi - Square One Factor"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nYfq8SGyi6vZ"
      },
      "source": [
        "### The Null and Alternative Hypothesis is:\r\n",
        "\r\n",
        "H0: 50% of Females ,40% of Males and 10% Transgender form the participants proportion.\r\n",
        "\r\n",
        "H1: The ratio of participants are different from claimed."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5AHeT_dGhE_t"
      },
      "source": [
        "obs = bb['Gender'].value_counts().values\r\n",
        "n=sum(obs)\r\n",
        "exp=np.array([n*0.5,n*0.4,n*0.1])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1SfiCDLUiaHe",
        "outputId": "779517fa-f3c3-4897-d1ab-0dae0de4b9bf"
      },
      "source": [
        "chi_square_stat, p_value = stats.chisquare(obs, f_exp=exp)\r\n",
        "print(\"Chi-square stat:\",chi_square_stat)\r\n",
        "print(\"P-value:\",p_value)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Chi-square stat: 63.89319620253165\n",
            "P-value: 1.335883940757945e-14\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kt4Qk4G7isiX"
      },
      "source": [
        "Since p-value is less than 0.05 thus we can reject the null hypothesis and conclude that the proportion of gender of the participant doesnt follow the trend projected"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fYf2gdopX-Hn"
      },
      "source": [
        "## Chi - Square Two Factor "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bkF7OWbyTkAo"
      },
      "source": [
        "### The Null and Alternative Hypothesis are:\r\n",
        "\r\n",
        "H0: Finalist is dependent on Gender\r\n",
        "\r\n",
        "H1: FInalist is not dependent on Gender"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "JJkxtsvBUJoG",
        "outputId": "82456c9a-75cb-42b9-f235-39facdc8c7d4"
      },
      "source": [
        "pd.crosstab(index= bb['Finalist'],columns=bb['Gender'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Gender</th>\n",
              "      <th>Female</th>\n",
              "      <th>Male</th>\n",
              "      <th>Transgender</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Finalist</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0.0</th>\n",
              "      <td>269</td>\n",
              "      <td>211</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1.0</th>\n",
              "      <td>62</td>\n",
              "      <td>86</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Gender    Female  Male  Transgender\n",
              "Finalist                           \n",
              "0.0          269   211            4\n",
              "1.0           62    86            0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B4hH5JdAVJSe",
        "outputId": "dd801d43-1302-4da8-a98d-ebf427cc043a"
      },
      "source": [
        "quality_array = pd.crosstab(index= bb['Finalist'],columns=bb['Gender'])\r\n",
        "chi_sq_Stat, p_value, deg_freedom, exp_freq = stats.chi2_contingency(quality_array)\r\n",
        "stats.chi2_contingency(quality_array)\r\n",
        "\r\n",
        "print('Chi-statistic: {} \\nP-value: {} \\nDegrees of freedom: {}' .format(chi_sq_Stat, p_value,deg_freedom))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Chi-statistic: 10.357088551390824 \n",
            "P-value: 0.005636205206270326 \n",
            "Degrees of freedom: 2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ELIy8KiYUhw"
      },
      "source": [
        "### The Null and Alternative Hypothesis are:\r\n",
        "H0: Finalist is dependent on Profession\r\n",
        "\r\n",
        "H1: FInalist is not dependent on Profession"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Xf6e-tRVn2A"
      },
      "source": [
        "Since the p-value(0.0057) is less than 0.05, we reject the null hypothesis. Therefore we can say with 95% confidence that Finalist is not dependent on Gender"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "NHwFfejEV0Vu",
        "outputId": "bae6b503-bda5-4b49-eed4-59e9720e5d8b"
      },
      "source": [
        "pd.crosstab(index= bb['Finalist'],columns=bb['Profession'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Profession</th>\n",
              "      <th>Arts</th>\n",
              "      <th>Commoner</th>\n",
              "      <th>Films</th>\n",
              "      <th>International</th>\n",
              "      <th>Media</th>\n",
              "      <th>Model</th>\n",
              "      <th>Others</th>\n",
              "      <th>Politics</th>\n",
              "      <th>Sports</th>\n",
              "      <th>Television</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Finalist</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0.0</th>\n",
              "      <td>36</td>\n",
              "      <td>33</td>\n",
              "      <td>188</td>\n",
              "      <td>6</td>\n",
              "      <td>34</td>\n",
              "      <td>40</td>\n",
              "      <td>38</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1.0</th>\n",
              "      <td>13</td>\n",
              "      <td>6</td>\n",
              "      <td>74</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Profession  Arts  Commoner  Films  ...  Politics  Sports  Television\n",
              "Finalist                           ...                              \n",
              "0.0           36        33    188  ...         6       7          96\n",
              "1.0           13         6     74  ...         0       3          34\n",
              "\n",
              "[2 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 197
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "agyVMa5tV-Ro",
        "outputId": "a1fcb416-7437-496c-95bf-21944292ae25"
      },
      "source": [
        "quality_array = pd.crosstab(index= bb['Finalist'],columns=bb['Profession'])\r\n",
        "chi_sq_Stat, p_value, deg_freedom, exp_freq = stats.chi2_contingency(quality_array)\r\n",
        "stats.chi2_contingency(quality_array)\r\n",
        "\r\n",
        "print('Chi-statistic: {} \\nP-value: {} \\nDegrees of freedom: {}' .format(chi_sq_Stat, p_value,deg_freedom))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Chi-statistic: 16.75506004308788 \n",
            "P-value: 0.052692760613474145 \n",
            "Degrees of freedom: 9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-8HqwO8TZ8o5"
      },
      "source": [
        "Since the p-value is greater than 0.05, we fail to reject the null hypothesis.\r\n",
        "\r\n",
        "Therefore we can say with 95% confidence that Finalist is dependent on Profession."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GrVAr5DAinv6"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P7S4voIGarSx"
      },
      "source": [
        "## Kruskal Wallis H Test"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "twahZWKxa0vU"
      },
      "source": [
        "\r\n",
        "### The Null and Alternative Hypothesis is:\r\n",
        "H0: Average of TRP is same accross the Language\r\n",
        "\r\n",
        "H1: Average of TRP is different for at least one Language"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W19unUIubKq4",
        "outputId": "53fc0da2-5982-4723-d20f-0d4d33ffe1fc"
      },
      "source": [
        "alpha=0.05\r\n",
        "test_stat, p_value = stats.kruskal(bb[bb['Language'] == 'Tamil'][\"Average TRP\"],\r\n",
        "              bb[bb['Language'] == 'Telugu'][\"Average TRP\"],\r\n",
        "              bb[bb['Language'] == 'Kannada'][\"Average TRP\"],\r\n",
        "              bb[bb['Language'] == 'Bengali'][\"Average TRP\"],\r\n",
        "              bb[bb['Language'] == 'Malayalam'][\"Average TRP\"],\r\n",
        "              bb[bb['Language'] == 'Hindi'][\"Average TRP\"])\r\n",
        "\r\n",
        "print('P_value for Kruskal : ', p_value)\r\n",
        "print('Significance level: ',alpha)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "P_value for Kruskal :  8.286927886883681e-68\n",
            "Significance level:  0.05\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HHmwrHepc7lO"
      },
      "source": [
        "\r\n",
        "Here P value is less than alpha thus we reject null hypothesis. \r\n",
        "We can conclude that average of TRP rating is different for at least one Language"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9K1xs6O3-R3x"
      },
      "source": [
        "#Part 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "atyaOiMK-_yc",
        "outputId": "ade21acc-16df-4c60-80d7-6ceba2d77f24"
      },
      "source": [
        "np.mean(bb[\"Elimination Week Number\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.13549618320609"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "riXR47f93xZZ",
        "outputId": "2398f6ee-3db5-431e-9662-3759d2d2b8a6"
      },
      "source": [
        "sns.kdeplot(df[\"Elimination Week Number\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7e24eb2a10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEGCAYAAAB7DNKzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deXxc1ZXg8d/RVtolW4slS7YlS94XDJaNweyELSSYBOgAWYDQIZnE6elkkh7S3cNk6HQPSToh6Q7pCQlJOwthTYMJBrMYDDbYWF7wLluWNwlbu2VJttY680c9m0ohyZKtp1dVOt/PRx+/uu++qvNUVp169953r6gqxhhjTH9ivA7AGGNMeLNEYYwxZkCWKIwxxgzIEoUxxpgBWaIwxhgzoDivAxgu2dnZWlRU5HUYxhgTUTZu3NigqjkD1YmaRFFUVER5ebnXYRhjTEQRkYNnqmNNT8YYYwZkicIYY8yALFEYY4wZkCUKY4wxA7JEYYwxZkCWKIwxxgzIEoUxxpgBWaIwZpRSVbp6/F6HYSJA1NxwZ4wZnEONJ/inF3fy+q5a/AqXTc3hH2+cwdRxaV6HZsKUXVEYM4psPNjMdT95i7WVDdx9cTFfvnwyWw4188l/X8OWw8e8Ds+EKbuiMGaU2Fvbyhf/cwPj0n08/qVFjM9MAuDeS4r59M/f4Uu/LWf50sXkZyR5HKkJN3ZFYcwo0NnTy9ce30R8bAy/u/fC00kCIDctkcfuWsCJzh4eeH6Hh1GacOVqohCR60WkQkQqReT+Pvb7RORJZ/96ESlyyuNFZJmIbBORXSLyHTfjNCba/fyNfeypbeMHt85hwtjkj+yflpfGf7uihFd31lJ+oMmDCE04cy1RiEgs8AhwAzATuENEZoZUuxdoVtVS4GHg+075bYBPVecA84Evn0oixpih2d/Qzs/frOTmeeO5avq4fut98ZJictJ8PPTSblR1BCM04c7NK4qFQKWqVqlqF/AEsCSkzhJgmbP9DHC1iAigQIqIxAFJQBdw3MVYjYlaP3qlgvjYGP7hxtDvaX8pOSGOr19VSvnBZjYdso5t8yE3E0UBcDjocbVT1mcdVe0BWoAsAkmjHTgCHAL+VVXtetiYIdrxQQt/3nqELy4OXC2cyacvKCQlIZY/vndoBKIzkSJcO7MXAr3AeKAY+B8iMjm0kojcJyLlIlJeX18/0jEaE/Z+8tpe0hPj+NJlH/nz6VOqL46b5hXw560fcLyj2+XoTKRwM1HUABOCHhc6ZX3WcZqZMoBG4E7gZVXtVtU6YC1QFvoCqvqoqpapallOzoAr+Rkz6uyrb+PVnbXcvbiYjKT4QR9358KJdHT7eX5z6J+rGa3cTBQbgCkiUiwiCcDtwPKQOsuBu5ztW4FVGuhFOwRcBSAiKcAiYLeLsRoTdX719n58cTF84aJJQzpuTmEG0/PSeOH9Iy5FZiKNa4nC6XNYCqwEdgFPqeoOEXlQRG5yqj0GZIlIJfBN4NQQ2keAVBHZQSDh/EZVt7oVqzHRpqGtk2c3VXPL/EKyU8/cNxHqull5bDjYRENbpwvRmUjj6p3ZqroCWBFS9kDQdgeBobChx7X1VW6MGZwnNxymq8fPFxcXn9Xx183K46ev7+W1nbXcvnDiMEdnIk24dmYbY85Sr195fP0hFpdmUZqbelbPMSM/jQljk1i54+gwR2cikSUKY6LMG7vrqDl2ks9dOLS+iWAiwrUz81hb2UhbZ88wRmcikSUKY6LMH9YfZFy6j4/N7P8u7MG4ekYuXb1+1u1rHKbITKSyRGFMFDna0sHqPfXcNn8C8bHn9ud9wcQxJMbHsHZfwzBFZyKVJQpjosifNlfjV7hlfuE5P1difCwLisayttISxWhnicKYKKGqPFNezYKiMRRnpwzLcy4uzWZPbRt1xzuG5flMZLJEYUyU2HSomaqGdm6bP+HMlQfpktJsAN6xfopRzRKFMVHimY3VJMXH8vG5+cP2nDPz08lMjmeNNT+NapYojIkCJ7t6eeH9I3x8Tj6pvuG7jzYmRiibNJaNB5uH7TlN5LFEYUwUeHnHEdo6e7it7Nw7sUOVFY1hf0M7jTadx6hlicKYKPB0eTUTxyZzYfHYYX/uskljAOyqYhSzRGFMhDvcdIJ39jVy6/xCAgtEDq/ZBRkkxMaw8ZAlitHKEoUxEe7ZTdWIwKcvCF1AcngkxscyuyCdjQcsUYxWliiMiWB+v/LMxmouLsmicEyya68zf9IYtta00NnT69prmPBlicKYCLZ+fxPVzSeH9d6JvsyfNIauHj87Pzju6uuY8GSJwpgI9vTGw6T54rhuVp6rr3PehEwAtla3uPo6Jjy5mihE5HoRqRCRShG5v4/9PhF50tm/XkSKnPLPisiWoB+/iMxzM1ZjIk1bZw8vbTvKJ84bT1JCrKuvlZeeSHaqzxLFKOVaohCRWAJLmt4AzATuEJGZIdXuBZpVtRR4GPg+gKr+QVXnqeo84PPAflXd4lasxkSiF7d+wMnuXlfunQglIswtzGBbzTHXX8uEHzevKBYClapapapdwBPAkpA6S4BlzvYzwNXy0fF9dzjHGmOCPF1eTUlOCuc7zUJum1uYQWVdG+22kNGo42aiKAAOBz2udsr6rKOqPUALkBVS5zPAH12K0ZiItLe2lfKDzfxV2QRX7p3oy9zCDPwKO6xDe9QJ685sEbkQOKGq2/vZf5+IlItIeX19/QhHZ4x3nthwmPhY4dZhWHdisOYUnOrQtuan0cbNRFEDBI/ZK3TK+qwjInFABhA8n/HtDHA1oaqPqmqZqpbl5OQMS9DGhLuO7l6e3VTNtbPyyEr1jdjr5qT5GJ+RaB3ao5CbiWIDMEVEikUkgcCH/vKQOsuBu5ztW4FVqqoAIhID/BXWP2HMX1i54yjHTnRzx4KJI/7aM8dnsPOINT2NNq4lCqfPYSmwEtgFPKWqO0TkQRG5yan2GJAlIpXAN4HgIbSXAYdVtcqtGI2JRH987xATxyZzcUlod577ZuSnUVXfRke33aE9mgzfxPV9UNUVwIqQsgeCtjuA2/o59k1gkZvxGRNpqurbWFfVxLevm0ZMzMh0YgebkZ+OX6Gyro3ZBRkj/vrGG2HdmW2M+UtPbDhMXIyMyL0TfZmelwbALmt+GlUsURgTITq6e3lmYzVXz8glNy3RkxgmZaWQGB/D7qOtnry+8YYlCmMixJ821dDU3sXdFxd7FkNsjDBtXBq7j9oVxWhiicKYCOD3K79aU8XsgnQWTR7+VeyGYnpeOruOtOIMUDSjgCUKYyLAGxV1VNW386VLJ4/Yndj9mZ6fRlN7F/Wttob2aGGJwpgI8Ku395OfkcjH5+R7HQrT89IB2GX9FKOGJQpjwtz2mhberWrknsVFxMd6/yc7Iz8w8mm3jXwaNbz/X2eMGdCv3q4iJSGW2xeO/J3YfclMTiA/I9FGPo0iliiMCWMHG9v589Yj3L5wIumJ8V6Hc9r0vDS7l2IUsURhTBj76et7iY0RvnzZZK9D+QvT89PZV99GV4/f61DMCLBEYUyY2lffxnOba/jCRZPITffmBrv+TM9Lo7tX2Vff5nUoZgRYojAmTP3w5QoS42P58uUlXofyETPyAyOf7Ma70cEShTFhaF1VIy/vOMp/u7yE7BFcc2KwirNTSIiNYfcR69AeDSxRGBNmev3K917cyfiMRL4UZn0Tp8THxlCam2prU4wSliiMCTPL3jnA9prjfOfjM0iMj/U6nH5Ny0ujss76KEYDSxTGhJHDTSf411cquHJaDp+Y6/1d2AMpzU3lSEsHrR3dXodiXOZqohCR60WkQkQqReT+Pvb7RORJZ/96ESkK2jdXRN4VkR0isk1EwmvYhzHDrNevfOvp9wH43qfmeD6n05mU5qYCsK++3eNIjNtcSxQiEgs8AtwAzATuEJGZIdXuBZpVtRR4GPi+c2wc8HvgK6o6C7gCsK8tJqr9bFUl6/c38eCS2RRkJnkdzhlNcRLF3lrr0I52bl5RLAQqVbVKVbuAJ4AlIXWWAMuc7WeAqyXwNepaYKuqvg+gqo2qaov0mqj1xu46fvr6Hj51fgG3XFDgdTiDMnFsMgmxMVTavRRRz81EUQAcDnpc7ZT1WUdVe4AWIAuYCqiIrBSRTSLydy7GaYyndnzQwtLHNzFzfDrfu3l22Dc5nRIXG0NxdgqVtZYool2c1wH0Iw64BFgAnABeF5GNqvp6cCURuQ+4D2DixPCYMM2Yodj5wXE+/9h7pCfF89hdC0jxheufZN9Kx6WyrbrF6zCMy9y8oqgBJgQ9LnTK+qzj9EtkAI0Erj7eUtUGVT0BrAAuCH0BVX1UVctUtSwnJ8eFUzDGPe/sa+COX64jMS6GP35pEePCbJqOwZiSm8rh5hN0dFvLcDRz8+vLBmCKiBQTSAi3A3eG1FkO3AW8C9wKrFJVFZGVwN+JSDLQBVxOoLPbmHPS0d3L+4ePUVHbyp7aVvY3tHPsRDctJ7tp6+zBFxdDSkIcKb44xmcmUpSdwuTsFGaNz2B6Xhpxw7AeRHevn0ffquLHr+6hODuF39y9gAljk4fh7EZeaW4qqoF5qWaNz/A6HOMS1xKFqvaIyFJgJRAL/FpVd4jIg0C5qi4HHgN+JyKVQBOBZIKqNovIjwkkGwVWqOqLbsVqopeqcqDxBKsr6nhzTz3rqhrp6A7MeJqWGEdJTip56YlMG5dGii+Orh4/J7p7OX6ym8q6NlbtrqO7N7A2dHJCLHMLMzh/4hgumDiGeRMyyUkb/PQafr/y2q5afvzqHnYfbeXGOfl8/9a5pEZYc1OwKbmBRYwq6yxRRDNX/4eq6goCzUbBZQ8EbXcAt/Vz7O8JDJE1ZkhOdPXw7r5GVu+p582Keg41nQAC8xPdvmAil07JZub4dPLSE8/YcdzrV6qbT7Dl8DE2HzrG5kPN/PKtKnr8geRROCaJeRMyOX/iGEpzU8nPSCQvI5GUhDi6e/00tHWyp7aV9fubeGnbUQ41nWDC2CR+8fn5XDcrz/XfhduKspOJjRG7QzvKRe5XGWMcqsreujZWV9Szek897+1voqvXT1J8LBeXZPGlS4u5bGoOk7JShvzcsTHCpKwUJmWlsGReYNBeR3cv22pa2HLo2OkE8uetR874PBeXZPE/rp3KjXPyh6UJKxz44mKZNDaZvTbyKapZojBD1tXjZ+eR42yrPsaRlg7qWztpbO+ix6+oBr5ppyXGkZGUQGZyPJlJ8YxJTiAnzUdOmo/cdB9ZKT5iY85uGGhbZw+7jxxne00LGw40s66qkcb2LgCmjkvlrosnccW0XMqKxuCLG/65khLjY1lQNJYFRWNPl9W1dnC46QRHWjo4cqyDk929xMYI2akJFGWlMLsgI+JGNA1WaW6q3UsR5aLzf64ZdnWtHbzw/hFW7jjK+4eP0emsbBYXI2Sn+shKTSDe+ZaswJGWDqeTuOt0G3+wGIGsVB85qYHEkZPqIzvNR1J8LL64GOJiY+jq8dPZ08vJrl5qj3dwpKWDD1pOcrjp5OnnGZ+RyOXTclhUnMXiKdme3dGcm5ZIblrkjVoaDlPGpbJqdx1dPX4S4qLjSsn8JUsUZkAHG9v591WVPL+lhu5eZUZ+Op9bNIn5kwKduXnpicQMcGWgqpzo6qWpvYu61k7qWwNXIIHtD//ddeQ4jW1dp9v+gyXExpCb7iM/I5F5E8bwmbIJzMhPZ0Z+OvkZZ+5nMO4qzU2lx68cbGxnyrg0r8MxLrBEYfrU0d3LT1/fy2Nv7ycmBu5cOJHPXzSJ0tyhfRCICCm+wHDTwQwB7en109njp7s38O3UFxd71k1UZmQEj3yyRBGdLFGYj6isa+Vrf9hMRW0rt84v5NvXTRuxm8HiYmOipqN3tCjJSUUE9ta1cYPXwRhXWKIwf2H1nnqW/mETvvgYfnPPAq6clut1SCbMJSXEUpCZZENko5glCnPay9uPsvTxTZTmpvLY3QsiYqprEx6m5Kay1xJF1LJrfAPAqt21LH18E3MKM3jqKxdZkjBDUpqbSlV9G719DEYwkc8ShXGmud7M9Pw0ln1xIemJ8V6HZCJMSU4qnT1+Pjh28syVTcSxRDHKNbd38dfLyslwprm2JGHOxqllUa2fIjpZohjFVJVvP/M+jW1d/PILZRE5zbUJDyU5liiimSWKUez36w7y2q467r9hOrMLbOZPc/bGpCSQlZLAPpvKIypZohilPjh2kode2s2lU7K5Z3GR1+GYKFCSm2pXFFHKEsUo9cDzO+hV5Z9vnmNTYJhhUZITmBzw1MSQJnpYohiFVu+p57Vdtfztx6YyMSsyV1Yz4ac0N5VjJ7ppcmbyNdHD1UQhIteLSIWIVIrI/X3s94nIk87+9SJS5JQXichJEdni/Pw/N+McTXr9yr+8uIuJY5OtyckMKxv5FL1cSxQiEgs8AtwAzATuEJGZIdXuBZpVtZTAmtjfD9q3T1XnOT9fcSvO0ebp8sNU1LZy/w3TXVmrwYxeJTmBhaH21bd7HIkZbm5eUSwEKlW1SlW7gCeAJSF1lgDLnO1ngKvFGsxd09Xj599XVTJvQiY3zI78ZThNeBmfkURSfKxdUUQhNxNFAXA46HG1U9ZnHVXtAVqALGdfsYhsFpHVInKpi3GOGs9uqqbm2En+9mNTrAPbDLuYGGFyToqtdheFBpUoRORPInKjiIxU5/cRYKKqng98E3hcRNL7iOs+ESkXkfL6+voRCi0ydff6eeSNSs4rzODyqTleh2OiVGluKvvsiiLqDPaD/+fAncBeEXlIRKYN4pgaYELQ40KnrM86IhIHZACNqtqpqo0AqroR2AdMDX0BVX1UVctUtSwnxz78BrJi2xGqm0+y9Cq7mjDuKc1JpebYSU509XgdihlGg0oUqvqaqn4WuAA4ALwmIu+IyD0i0t/kQBuAKSJSLCIJwO3A8pA6y4G7nO1bgVWqqiKS43SGIyKTgSlA1VBOzHxIVXlszX4mZ6dw9XRbX8K4p8QZ+VRlHdpRZdBNSSKSBdwN/DWwGfgpgcTxal/1nT6HpcBKYBfwlKruEJEHReQmp9pjQJaIVBJoYjo1hPYyYKuIbCHQyf0VVW0a4rkZR/nBZrZWt3DPJcUDrm9tzLk6NUTWpvKILoNauEhE/guYBvwO+KSqHnF2PSki5f0dp6orgBUhZQ8EbXcAt/Vx3LPAs4OJzZzZb9buJyMpnlsuCB1LYMzwmpSVTIzYvRTRZrAr3P3S+dA/TUR8Tl9CmQtxmWFS19rBKztquWdxEckJtqChcZcvLpZJWSl2RRFlBtv09L0+yt4dzkCMO54ur6bHr9yxcKLXoZhRoiTHJgeMNgN+xRSRPAL3OiSJyPnAqQbudMAmCQpzfr/yx/cOcdHkLCY76wUY47aS3BTe2lNPT6+fuFibTi4anKkt4joCHdiFwI+DyluBv3cpJjNM1lU1Ut18kr+7frrXoZhRpDQnla5eP4ebT1KcneJ1OGYYDJgoVHUZsExEbnE6mE0EeW5LDSkJsVw7c5zXoZhRpCRockBLFNHhTE1Pn1PV3wNFIvLN0P2q+uM+DjNhoKO7l5e2H+W62Xkkxtvkf2bkBA+RvQb7khINztT0dOrrgDVwR5g3K+po7ejh5nk2JNaMrPTEeHLTfNahHUXO1PT0C+ff/zMy4Zjh8tzmD8hO9XFxSdaZKxszzGzkU3QZ7KSAPxCRdBGJF5HXRaReRD7ndnDm7LSc7GbV7jo+MTffRp0YT5TmprLPlkWNGoP9FLlWVY8DnyAw11Mp8G23gjLn5uXtR+jq9XPz+dbsZLxRkpNCa0cP9a2dXodihsFgE8WpJqobgadVtcWleMwweH7LBxRlJXNeYYbXoZhRqjQ3DcDWpogSg00UfxaR3cB84HURyQE63AvLnK3a4x28W9XITfMKbDpx45nTI5+snyIqDHaa8fuBi4EyVe0G2vnosqYmDLyy4yiq8Mm5+V6HYkaxcek+Un1x1qEdJYYyS9x0AvdTBB/z22GOx5yjV3bWUpydcvobnTFeEBFKclLYZ+tSRIXBTjP+O6AE2AL0OsWKJYqwcryjm3VVjdyzuNianYznSnJSeWdfo9dhmGEw2CuKMmCm2li3sPZmRT3dvco1NmWHCQMluan8aXMNbZ09pPpsivtINtjO7O1AnpuBmHP36s5aslISuGDiGK9DMcY6tKPIYBNFNrBTRFaKyPJTP2c6SESuF5EKEakUkfv72O8TkSed/etFpChk/0QRaRORbw0yzlGrq8fPm7vruHpGLrG23KkJAyU5H04OaCLbYK8HvzvUJxaRWOAR4BqgGtggIstVdWdQtXuBZlUtFZHbge8Dnwna/2PgpaG+9mi0fn8jrZ09XDPTLvxMeJiUlUxcjNhqd1FgsMNjVxO4Izve2d4AbDrDYQuBSlWtUtUu4Ak+OqR2CbDM2X4GuFqcXlgRuRnYD+wYTIyj3Ss7akmMj+GS0myvQzEGgPjYGCZlJdsVRRQY7FxPXyLwQf4Lp6gAeO4MhxUAh4MeVztlfdZR1R6gBcgSkVTgfwIDTkYoIveJSLmIlNfX1w/mVKKSqvLarloum5JDUoJNKW7CR2luqt2dHQUG20fxNWAxcBxAVfcCuW4FRaCp62FVHfB/mKo+qqplqlqWk5PjYjjhbXvNcY60dNhoJxN2SnNTOdR4gu5ev9ehmHMw2D6KTlXtOjU237np7kxDZWuACUGPC52yvupUO8+ZATQCFwK3isgPgEzALyIdqvqzQcY7qrxZUQfAldPdzN3GDF1JTio9fuVgY/vp+Z9M5BnsFcVqEfl7IElErgGeBl44wzEbgCkiUiwiCcDtQOhIqeXAXc72rcAqDbhUVYtUtQj4CfAvliT6t3pPPXMKMshO9XkdijF/ofT0sqh2h3YkG2yiuB+oB7YBXwZWAP840AFOn8NSYCWwC3hKVXeIyIMicpNT7TECfRKVwDed1zFD0HKim02Hmrli2uhtejPha3LOh8uimsg1qKYnVfWLyHPAc6o66F5jVV1BIKkElz0QtN0B3HaG5/juYF9vNFpT2YBf4fKplihM+En1xZGfkWgjnyLcgFcUEvBdEWkAKoAKZ3W7BwY6zoyc1XvqSEuMY96ETK9DMaZPp1a7M5HrTE1P3yAw2mmBqo5V1bEEOpoXi8g3XI/ODEhVWb2nnkunZNuSpyZsleSksq/OlkWNZGf6dPk8cIeq7j9VoKpVwOeAL7gZmDmzitpWao93WrOTCWsluam0d/Vy9LitdRapzpQo4lW1IbTQ6aeIdyckM1irKwLdRZdZojBhrCQnBbA5nyLZmRJF11nuMyNg9Z56puelkZ+R5HUoxvTrwyGyligi1ZlGPZ0nIsf7KBcg0YV4zCC1dfaw4UATX1xc7HUoxgwoJ9VHZnI8e2pbvQ7FnKUBE4Wq2sRBYerdfY1096o1O5mwJyJMG5fG7qOWKCKVDZWJUGsrG0iMj6GsyBYpMuFvel4ae4624vfbyKdIZIkiQq2pbGBhcRa+OLvoM+FvWl467V291Bw76XUo5ixYoohAR1s6qKxr45LSLK9DMWZQpucHJgS05qfIZIkiAq2tDIxYXmyLFJkIMXVcIFFUHO1rbIwJd5YoItDaygbGpiQwIy/d61CMGZRUXxwTxiaxy64oIpIligijqqypbODikixiYsTrcIwZtGnj0qmwRBGRLFFEmL11bdS1dnLpFGt2MpFlel4a+xva6ezp9ToUM0SWKCLMmr3WP2Ei07S8NHr9andoRyBLFBFmbWUDRVnJFI5J9joUY4ZkRv6pDm1rfoo0riYKEbleRCpEpFJEPrJ6nYj4RORJZ/96ESlyyheKyBbn530R+ZSbcUaK7l4/66oa7WrCRKSirBQS4mIsUUQg1xKFiMQCjwA3ADOBO0RkZki1e4FmVS0FHga+75RvB8pUdR5wPfALERnUanzR7P3Dx2jv6uUSSxQmAsXFxlCak2r3UkQgN68oFgKVqlqlql3AE8CSkDpLgGXO9jPA1SIiqnrCWXMbApMP2n3/BO7GFoGLSuxGOxOZpuelsdvupYg4biaKAuBw0ONqp6zPOk5iaAGyAETkQhHZAWwDvhKUOE4TkftEpFxEyuvrB72Ud8RaW9nA3IIMMpMTvA7FmLMyLS+N2uOdHDthqxREkrDtzFbV9ao6C1gAfEdEPjKtuao+qqplqlqWkxPds6i2dfaw+dAx658wEW1ank3lEYncTBQ1wISgx4VOWZ91nD6IDKAxuIKq7gLagNmuRRoB1lc10uNX658wEW1GfmA2AevQjixuJooNwBQRKRaRBOB2YHlIneXAXc72rcAqVVXnmDgAEZkETAcOuBhr2FtT2YAvLoYLJtm04iZy5ab5GJuSwM4PrJ8ikrg2kkhVe0RkKbASiAV+rao7RORBoFxVlwOPAb8TkUqgiUAyAbgEuF9EugE/8NW+1u4eTdZWNrCweCyJ8TatuIlcIsKs8elsq2nxOhQzBK4OOVXVFcCKkLIHgrY7gNv6OO53wO/cjC2S1B7vYE9tG5++oNDrUIw5Z3MKMnj0rSo6unvti0+ECNvObPOhU9N2WP+EiQZzCzPo8at1aEcQSxQRYE1lA1kpCczMt2nFTeSbXZABYM1PEcQSRZg7Pa14abZNK26iQkFmEmOS49lebYkiUliiCHMVta3Ut3ZyqTU7mSghIswpzGSrXVFEDEsUYe50/4StP2GiyJyCdPbWttLRbWtTRAJLFGFuTWUDk3NSGJ+Z5HUoxgybOQXWoR1JLFGEsc6eXtZXNVmzk4k6cwozAdhWfczjSMxgWKIIY5sOHuNkd6/N72SizviMRMamJNjIpwhhiSKMramsJzZGWGTTipsoIyLMLshgW41N5REJLFGEsTV7G5g3IZP0xHivQzFm2M0tyGCPdWhHBEsUYerYiS621rTY3dgmas0uyKDXr+w6YlcV4c4SRZh6Z18jqnCpDYs1UWpOYeAO7e3WTxH2LFGEqbf3NpDqi+O8CZleh2KMK8ZnJJKVksCWw5Yowp0lijC1prKeRZOziI+1t8hEJxHh/Ilj2Hyo2etQzBnYp1AYOtjYzuGmk9bsZKLe/EljqGpop6nd1tAOZ5YowtDbNnrBRCoAABSpSURBVG2HGSXmOys2bjpoVxXhzNVEISLXi0iFiFSKyP197PeJyJPO/vUiUuSUXyMiG0Vkm/PvVW7GGW5W76mnIDOJydkpXodijKvmFmYQFyNstOansOZaohCRWOAR4AZgJnCHiMwMqXYv0KyqpcDDwPed8gbgk6o6h8Ca2qNmtbvOnl7WVjZw5fQcRGxacRPdEuNjmVWQwUa7oghrbl5RLAQqVbVKVbuAJ4AlIXWWAMuc7WeAq0VEVHWzqn7glO8AkkTE52KsYeO9/U2c6Orlqum5XodizIiYP3EM7x8+RleP3+tQTD/cTBQFwOGgx9VOWZ91VLUHaAFC56u4Bdikqp2hLyAi94lIuYiU19fXD1vgXlq1u46EuBgummz9E2Z0KCsaQ2eP3+Z9CmNh3ZktIrMINEd9ua/9qvqoqpapallOTs7IBueSNyvquWhyFkkJtui8GR0WFo8FYP3+Ro8jMf1xM1HUABOCHhc6ZX3WEZE4IANodB4XAv8FfEFV97kYZ9jY39DO/oZ2a3Yyo0p2qo+p41JZV9XkdSimH24mig3AFBEpFpEE4HZgeUid5QQ6qwFuBVapqopIJvAicL+qrnUxxrDyxu46AK6cZonCjC6LJmdRfqCJ7l7rpwhHriUKp89hKbAS2AU8pao7RORBEbnJqfYYkCUilcA3gVNDaJcCpcADIrLF+Yn6T883KuooyUlhYlay16EYM6IWTc7iRFev9VOEqTg3n1xVVwArQsoeCNruAG7r47jvAd9zM7Zw097Zw/qqJr5w0SSvQzFmxJ3qp1hX1cgFE8d4HI0JFdad2aPJ2soGunr91j9hRqXsVB/TxqWxtrLB61BMHyxRhInXd9WR6oujrGis16EY44lLp2SzYX8zJ7p6vA7FhLBEEQZ6ev28svMoV07PJSHO3hIzOl0+LYeuXj/rbfRT2LFPpTDw3v4mmk90c8PsPK9DMcYzC4rGkhgfw+o90XHzbDSxRBEGXtp+lMT4GK6YFh03DRpzNhLjY7mwOIu39lqiCDeWKDzm9ysrdxzl8qk5JCe4OgjNmLB3+dQcqurbOdx0wutQTBBLFB7bdKiZutZOPj4n3+tQjPHcqVF/r+6s9TgSE8wShcde2n6UhNgYGxZrDFCUncLUcam8svOo16GYIJYoPKSqvLz9KJdMySYtMd7rcIwJC9fNyuO9/U22PGoYsUThoW01LdQcO8n1NtrJmNOunZmHX+H1Xdb8FC4sUXjope1HiY0RrpkxzutQjAkbswvSyc9I5OXt1vwULixReMTvV5Zv+YDFpdmMSUnwOhxjwoaIcOOcfN7aW0+zNT+FBUsUHtlwoImaYyf59Pmhi/4ZY24+v4DuXuXFbUe8DsVgicIz/7W5huSEWK6dZc1OxoSaNT6d0txUnt8SutaZ8YIlCg90dPfy4rYjXD8rz26yM6YPIsKnzi9gw4Fmu/kuDFii8MDKHUdp7ejh0xcUeh2KMWHr5vMLiBF4csNhr0MZ9VxNFCJyvYhUiEiliNzfx36fiDzp7F8vIkVOeZaIvCEibSLyMzdj9MIT7x1mwtgkLi7J8joUY8JWQWYSV03P5YkNh+jqsSVSveRaohCRWOAR4AZgJnCHiMwMqXYv0KyqpcDDwPed8g7gfwHfcis+rxxoaOfdqkY+UzaBmBjxOhxjwtrnFk2ioa2Ll3fYUFkvuXlFsRCoVNUqVe0CngCWhNRZAixztp8BrhYRUdV2VV1DIGFElSc2HCY2RritbILXoRgT9i6bksPEsckse+eA16GMam4migIguHGx2inrs46q9gAtwKDbY0TkPhEpF5Hy+vrwn5q4o7uXJzcc4urpuYxLT/Q6HGPCXkyMcO8lxWw82Mz6qkavwxm1IrozW1UfVdUyVS3LyQn/tRye31JD84lu7llc7HUoxkSMzyyYQHaqj5+9Uel1KKOWm4miBghuXyl0yvqsIyJxQAYQlV8bVJXfrD3A9Lw0Fk22dbGNGazE+Fj++tJi3t7bwKZDzV6HMyq5mSg2AFNEpFhEEoDbgeUhdZYDdznbtwKrVFVdjMkz7+xrZPfRVu5ZXISIdWIbMxSfWzSJ7FQf3/vzTqL0IyKsuZYonD6HpcBKYBfwlKruEJEHReQmp9pjQJaIVALfBE4PoRWRA8CPgbtFpLqPEVMR5WerKhmX7mPJPJuyw5ihSvXF8XfXTWPToWO8sNWm9Rhprt4WrKorgBUhZQ8EbXcAt/VzbJGbsY2kjQebeLeqkX+8cQaJ8bFeh2NMRLplfiHL3j3AP7+4k8un5JCRbGu4jJSI7syOFP/2eiVjUxK488KJXodiTMSKjREe+vRcGtu6+N/Lt3sdzqhiicJl7+xrYPWeer582WSb18mYczSnMIOvXzWF57Z8wJ82VXsdzqhhicJFqspDL+1mfEYid11c5HU4xkSFr15ZwqLJY7n/2W1sPGijoEaCJQoXPb/lA7ZWt/CNa6Za34QxwyQ+Nob/+Ox8xmcmcu+yDWyrbvE6pKhnicIlLSe7+d6LOzlvQia32CyxxgyrMSkJ/PaLF5KSEMedv1zHmr0NXocU1SxRuOSHK3fT1N7FP9882yb/M8YFE7OSeforF5GXkcjnf72eH67cTUd3r9dhRSVLFC5Yvaee3687xD2Li5ldkOF1OMZErfGZSTy/dDG3XlDII2/s4+ofreb36w7S3tnjdWhRRaLlLseysjItLy/3Ogya2ru4/idvkZEUzwtfv8T6JowZIe/ua+T/vrSLrdUtJCfEctmUHMqKxjA9L51peWlkpybYrAh9EJGNqlo2UB0brzmMunv9LH18E8dOdvObexZYkjBmBF1UksXzX1vMpkPNPLuphlW76v5iHYuEuBgyk+LJTI4nMymBjOR4xiTHMyY5gczkBMYkxzMuI5HirBQKxyQRF2sNLqdYohgmqso//Xkn7+xr5Ee3nces8dbkZMxIExHmTxrL/Elj4VNQ39pJxdFWKmpbqWvtoOVEN8dOdHPsZBeHm06wrbqbphNdH1lBLy5GmJyTwrwJmcybMIYFRWMozU0dtVckliiGyU9e28tv3z3Ily4t5pb5NsrJmHCQk+YjJ83HJVOy+62jqpzs7qWpvYsjLR0caGjnQGM7u4608urOWp4qD9zYNz4jkcun5XDltFwum5ozqloMLFGcI1Xl4Vf38G+rKrltfiHfuWGG1yEZY4ZAREhOiCM5IY7CMcksKPpwGQBV5UDjCdZVNfJmRR0vvH+EP753mFRfHNfMHMeNc/K5dGo2vrjoThrWmX0OOrp7+cfntvPMxmpum1/IQ7fMJdaGwhoTtbp7/by7r5EXtx7h5R1HaTnZTZovjmtn5fHJ8/JZXJpNfIT1bQymM9sSxVmqONrKf39iM7uPtvI3V0/hGx+bMmrbL40Zjbp6/Lyzr4E/bz3Cyu1Hae3sYWxKAjfMzuOm88azoGhsRNxDZYnCBc3tXfzH6n38es1+0pPi+dFt53Hl9FzXX9cYE746untZvaeeF97/gNd21dLR7ScvPZHrZo3jyum5LJqcFbZ9GpYohtG++jb+uP4QT2w4THtXz+n+iDEpCa69pjEm8rR39vDarlpeeP8Iayrr6ej2kxgfw+KSbK6YnsuFxWMpzUkNm6sNz++jEJHrgZ8CscCvVPWhkP0+4LfAfAJrZX9GVQ84+74D3Av0An+jqivdjDVUy8ludtS08Oaeel7fVcu++nbiYoQb5uTz9atKmToubSTDMcZEiBRfHEvmFbBkXgEd3b28W9XIm7vrWFVRx+u76wDISIrn/ImZTM9LZ+q4VKaOS6M0NzV8rzrcuqIQkVhgD3ANUE1gDe07VHVnUJ2vAnNV9SsicjvwKVX9jLPs6R+BhcB44DVgqqr2O5HL2V5RNLZ18tbeeo60dHC0pYOa5pPsPtpKzbGTAMTHCosmZ3HltFw+MTef3PTEIb+GMcacGkFVfqCJjQeb2XzoGFUNbXT3Bj6DRWB8RhLj0n3kZSSSm5ZIbrqPzKQEUnyxpPriSPXFkeL8mxgfS3ysEB8Xgy8u5qxHXnl9RbEQqFTVKieYJ4AlwM6gOkuA7zrbzwA/k0CP8BLgCVXtBPY7a2ovBN4d7iBrjp3kG0++D0B6Yhz5GUmcPzGTzy6ayIz8dBYUjSXVZ6OIjTHnRkQozk6hODuF28omAIFRVAca2tlT28ae2lYONZ2g9ngHFUdbeXtPA62DnLPqxrn5PHLnBa7F7uYnYAFwOOhxNXBhf3VUtUdEWoAsp3xdyLEFoS8gIvcB9zkP20Sk4lyD3ga8cq5Pcm6ygWieM9nOL7LZ+YWhnwM//+ygq4ee46QzHRDRX5VV9VHgUa/jGE4iUn6my8BIZucX2ez8It/ZnKObd4bUABOCHhc6ZX3WEZE4IINAp/ZgjjXGGDMC3EwUG4ApIlIsIgnA7cDykDrLgbuc7VuBVRroXV8O3C4iPhEpBqYA77kYqzHGmH641vTk9DksBVYSGB77a1XdISIPAuWquhx4DPid01ndRCCZ4NR7ikDHdw/wtYFGPEWZqGpK64OdX2Sz84t8Qz7HqLnhzhhjjDsia/YqY4wxI84ShTHGmAFZoggTInK9iFSISKWI3O91PG4QkQMisk1EtoiI9wucnyMR+bWI1InI9qCysSLyqojsdf4d42WM56Kf8/uuiNQ47+EWEfm4lzGeCxGZICJviMhOEdkhIv/dKY+K93CA8xvye2h9FGFgMNOdRAMROQCUqWrE3dDUFxG5DGgDfquqs52yHwBNqvqQk/DHqOr/9DLOs9XP+X0XaFPVf/UytuEgIvlAvqpuEpE0YCNwM3A3UfAeDnB+f8UQ30O7oggPp6c7UdUu4NR0JyaMqepbBEbrBVsCLHO2lxH4w4xI/Zxf1FDVI6q6ydluBXYRmAEiKt7DAc5vyCxRhIe+pjs5qzc0zCnwiohsdKZfiUbjVPWIs30UGOdlMC5ZKiJbnaapiGyWCSUiRcD5wHqi8D0MOT8Y4ntoicKMpEtU9QLgBuBrTtNG1HJuHo22tt3/AEqAecAR4EfehnPuRCQVeBb4W1U9HrwvGt7DPs5vyO+hJYrwMCqmLFHVGuffOuC/CDS5RZtap234VBtxncfxDCtVrVXVXlX1A78kwt9DEYkn8CH6B1X9k1McNe9hX+d3Nu+hJYrwMJjpTiKaiKQ4HWqISApwLbB94KMiUvC0NHcBz3sYy7A79QHq+BQR/B46Sxo8BuxS1R8H7YqK97C/8zub99BGPYUJZ4jaT/hwupN/9jikYSUikwlcRUBg6pjHI/0cReSPwBUEpm2uBf438BzwFDAROAj8lapGZIdwP+d3BYEmCwUOAF8Oas+PKCJyCfA2gdUF/E7x3xNox4/493CA87uDIb6HliiMMcYMyJqejDHGDMgShTHGmAFZojDGGDMgSxTGGGMGZInCGGPMgCxRmBEhIr1Bs1VuOTVDroi8KSJlzvYKEckcwnPedLYz7YpIpoh8NejxeBF55myeK+R5l4jIc0GPv+Os4Hjq8SdFZMj3yIjI3SLys0HU8YvI3KCy7c70DedMRNqG43lM5HFtKVRjQpxU1XkDVVDVIU1Z7Syne7Y3JmYCXwV+7jzXBwTWbT9X7wC/CHp8EXBcRHKdO9Ivduq4pRr4B+AzLr7GkIlInKr2eB2HOTt2RWHChrNeRbaIFInIbhH5TxHZIyJ/EJGPichaZ42AhU7909+ynbr/JiLviEiViNzqlKeKyOsiskkCa2GcmpX3IaDEubr5ofOa251jEkXkN079zSJyZdDr/UlEXnbi+EHoOahqPYHEUOoUFRCYQuFi5/HFwFoRyRGRZ0Vkg/Oz2HmNFGeitvec1/7ILMIicqOIvCsi2X38Gv8MzBKRaX0c1xa0fauI/GfQ7+4/RGSd87u7wolh16k6Qcc9LIG1DV4XkRynrMT5nWwUkbdFZHrQ8/4/EVkPfOR3ZSKHJQozUpJCmp7O9I23lMBkZdOdnzuBS4BvEbi7tC/5Tp1PEEgEAB3Ap5zJCK8EfuRMbXA/sE9V56nqt0Oe52sE5oObQ+Au1mUikujsm0fg2/oc4DMiMoGPWgtc7HxY7wXWOY/jgPMITNnyU+BhVV0A3AL8yjn2H4BVqrrQifeHEpjyBAAR+ZQT+8f7WdfDT+BDub/fUX/GELj6+QaBq7SHgVnAHBE5dSWYApSr6ixgNYE7tQEeBb6uqvMJvD8/D3reQuBiVf3mEOMxYcSansxIOWPTU4j9qroNQER2AK+rqorINqCon2OecyY62ykip6aGFuBfJDBTrZ/AN/wzTRt9CfDvAKq6W0QOAlOdfa+raosT105gEn85RTwEmpYuJjAdy7vAe8ADBKZ53q2qHSLyMWBmIGcBkC6BWT6vBW4SkW855YkEppIAuAooA64NneU0xOPAP4hI8RnOM9gLQb/f2pDffRGwhcDv70mn/u+BPzkxXww8HXQuvqDnfVpVe4cQhwlDlihMuOoM2vYHPfbT///b4GNOfWp9FsgB5qtqtwRW2UsMPfAs4+rtJ5a1wNcJJIpfqmqrc0VyBR/2T8QAi1S1I/hA52rnFlWtCCm/ENgHTCaQtPpdSlZVe0TkR0DoqmzB8/WE/g6Cf7+hv/v+ft/qnMexAb4EtPcXp4kc1vRkol0GUOckiSsJXAEAtAJp/RzzNoEEg4hMJfCNvqKfun3ZBYwncGWy2SnbAnyFQBIBeIVAMsF5nVMftCuBrzsJAxE5P+h5DxJopvqtiMw6Qwz/CXyMQJI8pVZEZohIDIFZQ4cqhg87/O8E1jhXNvtF5DYnXhGR887iuU0Ys0RhRkpoH8VDZz5kWPwBKHOaVL4A7AZQ1UYCncrbReSHIcf8HIhxjnkSuFtVOxkkZ7Gb9UCjqnY7xe8SuBo4dUXxN05cW50mrK845f8ExANbnWaffwp57t0EktjTIlIyQAxdwL8BuUHF9xPo7H6HwII1Q9UOLHQ6/a8CHnTKPwvcKyLvAzuwZXyjjs0ea4wxZkB2RWGMMWZAliiMMcYMyBKFMcaYAVmiMMYYMyBLFMYYYwZkicIYY8yALFEYY4wZ0P8HrKQDAIFt8CUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yh8ES01Q5TW3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "3a4eacd9-4650-41ba-8d2f-8c6f34865245"
      },
      "source": [
        "sns.kdeplot(df[\"Number of Evictions Faced\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f66b5bc3dd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "psMxFGH697vW",
        "outputId": "b639eb07-cbe1-4f41-92f6-474097b778bc"
      },
      "source": [
        "stats.shapiro(bb[\"Number of Evictions Faced\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.9046778082847595, 1.999248882384241e-19)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KVGE64Fn9wI5",
        "outputId": "f268b6a2-086e-4941-d4d0-c6b3215ba9d1"
      },
      "source": [
        "# when we see a double peak , is it that these are from 2 different distributions or sample distribution/process\r\n",
        "# We can run the t test to see if this is statistical or are they from 2 different distributions\r\n",
        "# Null Hypotheses: They are from the same distribution\r\n",
        "# Alternate: They are from different distributions\r\n",
        "\r\n",
        "#df_=df[\"Elimination Week Number\"].fillna(np.mean(df[\"Elimination Week Number\"]))\r\n",
        "x2 = df[\"Elimination Week Number\"].sample(20)\r\n",
        "x1 = df[\"Elimination Week Number\"].sample(20)\r\n",
        "t_statistic , P = stats.mannwhitneyu(x1,x2)\r\n",
        "print(t_statistic,P)\r\n",
        "print(\"Since the pvalue is greater than 0.05 we conclude that they are from the same distribution and the peaks are just statistical\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "158.0 0.130180355407705\n",
            "Since the pvalue is greater than 0.05 we conclude that they are from the same distribution and the peaks are just statistical\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BAqlAtMl8XvM"
      },
      "source": [
        "df_hindi1 = df[df[\"Language\"]== \"Hindi\"]\r\n",
        "df_Kannada = df[df[\"Language\"]== \"Kannada\"]\r\n",
        "df_Telugu = df[df[\"Language\"]== \"Telugu\"]\r\n",
        "df_Tamil = df[df[\"Language\"]== \"Tamil\"]\r\n",
        "df_Marathi = df[df[\"Language\"]== \"Marathi\"]\r\n",
        "df_Bengali = df[df[\"Language\"]== \"Bengali\"]\r\n",
        "df_Malayalam = df[df[\"Language\"]== \"Malayalam\"]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R8kVr9Dh8ZKK"
      },
      "source": [
        "df_hindi1[\"Elimination Week Number\"].fillna(np.mean(df_hindi1[\"Elimination Week Number\"]),inplace=True)\r\n",
        "df_Tamil[\"Elimination Week Number\"].fillna(np.mean(df_Tamil[\"Elimination Week Number\"]),inplace=True)\r\n",
        "df_Kannada[\"Elimination Week Number\"].fillna(np.mean(df_Kannada[\"Elimination Week Number\"]),inplace=True)\r\n",
        "df_Bengali[\"Elimination Week Number\"].fillna(np.mean(df_Bengali[\"Elimination Week Number\"]),inplace=True)\r\n",
        "df_Malayalam[\"Elimination Week Number\"].fillna(np.mean(df_Malayalam[\"Elimination Week Number\"]),inplace=True)\r\n",
        "df_Telugu[\"Elimination Week Number\"].fillna(np.mean(df_Telugu[\"Elimination Week Number\"]),inplace=True)\r\n",
        "df_Marathi[\"Elimination Week Number\"].fillna(np.mean(df_Marathi[\"Elimination Week Number\"]),inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nBc_Z0Ww6i-1"
      },
      "source": [
        "##Hypothesis -1 Are the variations/mean in Elimination week different for different regions or languages\r\n",
        "\r\n",
        "Null Hypotheses: They are the same\r\n",
        "\r\n",
        "\r\n",
        "Alternate: They are Different , atleast one is different"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "DyK119NQ7HsW",
        "outputId": "b3717ef1-617f-4b63-e90c-39e62fb02639"
      },
      "source": [
        "sns.kdeplot(bb[\"Elimination Week Number\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7e24dfbb90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mU_GmTFk7M9n",
        "outputId": "748afe8d-380a-49fd-d28e-647a7469f7d4"
      },
      "source": [
        "stats.shapiro(bb[\"Elimination Week Number\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.96217280626297, 1.0998514599069864e-11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "61XfWN037TTj"
      },
      "source": [
        "We concluded after the shapiro test that the Elimination week number is not normally distributed"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tW_pJkUb6Cx6"
      },
      "source": [
        "#Since it is not normally distributed we use the Kruskal H Test for checking the distributions\r\n",
        "\r\n",
        "test_stat, p_value = stats.kruskal(bb[bb['Language'] == 'Tamil'][\"Elimination Week Number\"],\r\n",
        "              bb[bb['Language'] == 'Telugu'][\"Elimination Week Number\"],\r\n",
        "              bb[bb['Language'] == 'Kannada'][\"Elimination Week Number\"],\r\n",
        "              bb[bb['Language'] == 'Bengali'][\"Elimination Week Number\"],\r\n",
        "              bb[bb['Language'] == 'Malayalam'][\"Elimination Week Number\"],\r\n",
        "              bb[bb['Language'] == 'Hindi'][\"Elimination Week Number\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fKYKkOG_6ORl",
        "outputId": "6e0d4f44-2aad-4cdf-debe-d586d3dddc28"
      },
      "source": [
        "print('H-statistic: {} \\nP-value: {}' .format(test_stat, p_value))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "H-statistic: 2.177640414947859 \n",
            "P-value: 0.8240590329537104\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pzGc3Z5S6qqX"
      },
      "source": [
        "Since the p value is greater than the significance value 0.05 ,\r\n",
        "we fail to reject the null hypothesis, hence, the elimination is the same across diffrent regions "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nXM1t5RI3E1A"
      },
      "source": [
        "## Hypotheses 2 Is there a gender bias with respect to elimination week, for Big Boss Hindi?\r\n",
        "\r\n",
        "Null Hypothises- There is a significant link or bias between gender and elimination\r\n",
        "\r\n",
        "\r\n",
        "Alternate Hypothesis: There is no significance or bias between gender and eleimination week number "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tqf_H8luBSJ_"
      },
      "source": [
        "df_hindi1_Male= df_hindi1[df_hindi1[\"Gender\"]==\"Male\"]\r\n",
        "df_hindi1_Female=df_hindi1[df_hindi1[\"Gender\"]==\"Female\"]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d5QboEJfiTPK",
        "outputId": "6c32bd40-c6dc-4474-e197-cad4132c9c8b"
      },
      "source": [
        "df_hindi1_Female.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(134, 24)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fJJ8viJXBUFc"
      },
      "source": [
        "pvalue_gender_elimination = stats.mannwhitneyu(df_hindi1_Male[\"Elimination Week Number\"].sample(30),df_hindi1_Female[\"Elimination Week Number\"].sample(30))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t9glbimsBXjb",
        "outputId": "139ff53a-fa23-42c2-c00a-0a8aaa27eab2"
      },
      "source": [
        "pvalue_gender_elimination  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MannwhitneyuResult(statistic=390.0, pvalue=0.18911674880929885)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9wonuPTd9RDt"
      },
      "source": [
        "\r\n",
        "Since the P value is greater than the significance  aplha =0.05 , we fail to reject the null hypotheses, hence, there is a bias or dependency between gender and elimination week number"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rncvw1NcAyKJ"
      },
      "source": [
        "## Hypotheses 3:Is there a link/dependency between gender and the region\r\n",
        "Null Hypotheses: Gender and language are dependent\r\n",
        "\r\n",
        "Alternate-There is no statistical dependency between gender and Language\r\n",
        "\r\n",
        "We will use chisquare dependency test for this hypothesis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BU4oRjXhBdkI",
        "outputId": "94bd0466-a03f-4f4f-d756-76f8def682f7"
      },
      "source": [
        "gender_language = [[ df_hindi1[df_hindi1[\"Gender\"]==\"Male\"].Language.count(),df_Tamil[df_Tamil[\"Gender\"]==\"Male\"].Language.count(),\r\n",
        "                    df_Kannada[df_Kannada[\"Gender\"]==\"Male\"].Language.count(),df_Bengali[df_Bengali[\"Gender\"]==\"Male\"].Language.count(),\r\n",
        "                   df_Telugu[df_Telugu[\"Gender\"]==\"Male\"].Language.count(),df_Malayalam[df_Malayalam[\"Gender\"]==\"Male\"].Language.count(),\r\n",
        "                   df_Marathi[df_Marathi[\"Gender\"]==\"Male\"].Language.count()],\r\n",
        "                   [df_hindi1[df_hindi1[\"Gender\"]==\"Female\"].Language.count(),df_Tamil[df_Tamil[\"Gender\"]==\"Female\"].Language.count(),\r\n",
        "                    df_Kannada[df_Kannada[\"Gender\"]==\"Female\"].Language.count(),df_Bengali[df_Bengali[\"Gender\"]==\"Female\"].Language.count(),\r\n",
        "                   df_Telugu[df_Telugu[\"Gender\"]==\"Female\"].Language.count(),df_Malayalam[df_Malayalam[\"Gender\"]==\"Female\"].Language.count(),\r\n",
        "                   df_Marathi[df_Marathi[\"Gender\"]==\"Female\"].Language.count()]]\r\n",
        "                    \r\n",
        "chi_sq_Stat, p_value, deg_freedom, exp_freq = stats.chi2_contingency(gender_language)\r\n",
        "print('Chi-square statistic %3.5f P value %1.6f Degrees of freedom %d' %(chi_sq_Stat, p_value,deg_freedom))\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Chi-square statistic 0.47162 P value 0.998167 Degrees of freedom 6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "njhsj7xqjuFn"
      },
      "source": [
        "Since the p value is greater than the critical value 0.05 , we do not reject the null hypotheses .\r\n",
        "\r\n",
        "Hence, Gender and Language are dependent features statistically "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AUY27LFV-K8N"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r6dSPEvt-Mle"
      },
      "source": [
        "## Hypotheses 4 - \r\n",
        "Are the No of evictions dependent on the gender \r\n",
        "\r\n",
        "Null- They are dependent\r\n",
        "\r\n",
        "Alternate Hypothses : They are not dependent on each other"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TRsTnvLT-Xdu"
      },
      "source": [
        "pvalue_gender_evictions = stats.mannwhitneyu(df_hindi1_Male[\"Number of Evictions Faced\"].sample(30),df_hindi1_Female[\"Number of Evictions Faced\"].sample(30))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GB9MbZBK-ktf",
        "outputId": "c8a01c82-80b6-4e9b-d003-b9ea5ea6bcba"
      },
      "source": [
        "pvalue_gender_evictions"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MannwhitneyuResult(statistic=370.5, pvalue=0.11836019382817542)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r6JK24eZ-8Rh"
      },
      "source": [
        "Since the p value is greater than 0.05 we cannot reject the null hypotheses,\r\n",
        "Hence , The Evictions are dependent on the gender"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5UuMBqZLCWGm"
      },
      "source": [
        "##Hypotheses 5: Are the winners vs gender proportions significantly different\r\n",
        "\r\n",
        "Null Hypotheses: They are the same\r\n",
        "\r\n",
        "Alternate Hypotheses: They are different"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XwizhJumBWIe",
        "outputId": "83b5f830-0994-4535-bd37-ffa3c97a91a3"
      },
      "source": [
        "df[\"Gender\"].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Female         331\n",
              "Male           297\n",
              "Transgender      4\n",
              "Name: Gender, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J_tIM8ZUBFTr"
      },
      "source": [
        "female_winners = df[df['Gender'] == 'Female'].Winner.value_counts()[1]  # number of female winners\r\n",
        "male_winners = df[df['Gender'] == 'Male'].Winner.value_counts()[1] # number of male winners\r\n",
        "n_females = df.Gender.value_counts()[0] # number of females in the data\r\n",
        "n_males = df.Gender.value_counts()[1] "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2l0lJs9wBKg-",
        "outputId": "e1903a25-0502-46f5-9d18-4c1ca2b758ca"
      },
      "source": [
        "print([female_winners, male_winners] , [n_females, n_males])\r\n",
        "print(f' Proportion of winners in females, males = {round(9/331,2)}%, {round(24/297,2)}% respectively')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[9, 24] [331, 297]\n",
            " Proportion of winners in females, males = 0.03%, 0.08% respectively\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1hImnuVyBusU"
      },
      "source": [
        "stat, pval = proportions_ztest([female_winners, male_winners] , [n_females, n_males])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c9YHwxpLB-kz",
        "outputId": "1b404e7a-c0e1-48c7-a262-de3b171a766b"
      },
      "source": [
        "if pval < 0.05:\r\n",
        "    print(f'With a p-value of {round(pval,4)} the difference is significant. We reject the null hypotheses')\r\n",
        "else:\r\n",
        "    print(f'With a p-value of {round(pval,4)} the difference is not significant. We fail to reject the null')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "With a p-value of 0.0026 the difference is significant. We reject the null hypotheses\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "itHEW2h1COpO"
      },
      "source": [
        "Since the p value is less than 0.05 we reject the null hypotheses,\r\n",
        "\r\n",
        "Hence, there is the proportions of gender vary significantly among the winners"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E_kBjjtcJeyF"
      },
      "source": [
        "#Hypothes 6 : \r\n",
        "## H0 \"Elimination Week Number\" influence \"Number of Evictions Faced\"\r\n",
        "## H1 \"Elimination Week Number\" does not influence \"Number of Evictions Faced\"\r\n",
        "##Using - ONE WAY ANOVA\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "01WrYoYPIJpX"
      },
      "source": [
        "# Cleaning of data records\r\n",
        "pf1=df\r\n",
        "# Average TRP Mean values imputation\r\n",
        "pf1['Average TRP'] = pf1['Average TRP'].fillna(pf1['Average TRP'].mean())\r\n",
        "\r\n",
        "# Finalist fill null values imputation\r\n",
        "pf1['Finalist'] = pf1['Finalist'].fillna(0)\r\n",
        "\r\n",
        "# Number of Evictions Faced Mean values imputation\r\n",
        "pf1['Number of Evictions Faced'].fillna(pf1['Number of Evictions Faced'].mean())\r\n",
        "\r\n",
        "# Number of times elected as Captain null values imputation\r\n",
        "pf1['Number of times elected as Captain'] = pf1['Number of times elected as Captain'].fillna(0)\r\n",
        "\r\n",
        "# Number of of Housemates as null values imputation\r\n",
        "pf1[\"Number of Housemates\"] = pf1[\"Number of Housemates\"].fillna(0)\r\n",
        "\r\n",
        "# Social Media Popularity Mean values imputation\r\n",
        "pf1[\"Social Media Popularity\"] = pf1[\"Social Media Popularity\"].fillna(round(pf1[\"Social Media Popularity\"].mean()))\r\n",
        "\r\n",
        "# Elimination Week Number Mean values imputation\r\n",
        "pf1[\"Elimination Week Number\"] = pf1[\"Elimination Week Number\"].fillna(pf1[\"Elimination Week Number\"].mean())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "mW4nKQ5bJ-nl",
        "outputId": "387522cc-5516-4cc5-a7d8-00253d6b1580"
      },
      "source": [
        "## Numerical columns for Annova hypothesis\r\n",
        "\r\n",
        "pf1_data = pf1[[\"Elimination Week Number\", \"Number of Evictions Faced\"]]\r\n",
        "pf1_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Elimination Week Number</th>\n",
              "      <th>Number of Evictions Faced</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10.000000</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12.000000</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>627</th>\n",
              "      <td>8.000000</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>628</th>\n",
              "      <td>9.135496</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>629</th>\n",
              "      <td>8.000000</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>630</th>\n",
              "      <td>9.135496</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>631</th>\n",
              "      <td>9.135496</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>632 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Elimination Week Number  Number of Evictions Faced\n",
              "0                  10.000000                        3.0\n",
              "1                   5.000000                        1.0\n",
              "2                   4.000000                        1.0\n",
              "3                   1.000000                        1.0\n",
              "4                  12.000000                        6.0\n",
              "..                       ...                        ...\n",
              "627                 8.000000                        4.0\n",
              "628                 9.135496                        0.0\n",
              "629                 8.000000                        2.0\n",
              "630                 9.135496                        3.0\n",
              "631                 9.135496                        3.0\n",
              "\n",
              "[632 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "sU6fn_VOKJD6",
        "outputId": "8a8f49b4-368a-4cf5-82ce-469aaf86a6b8"
      },
      "source": [
        "# data description used in analysis\r\n",
        "\r\n",
        "pf1_data.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Elimination Week Number</th>\n",
              "      <th>Number of Evictions Faced</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>632.000000</td>\n",
              "      <td>614.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>9.135496</td>\n",
              "      <td>4.055375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.292658</td>\n",
              "      <td>2.867981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>9.135496</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>13.000000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>20.000000</td>\n",
              "      <td>13.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Elimination Week Number  Number of Evictions Faced\n",
              "count               632.000000                 614.000000\n",
              "mean                  9.135496                   4.055375\n",
              "std                   4.292658                   2.867981\n",
              "min                   1.000000                   0.000000\n",
              "25%                   6.000000                   2.000000\n",
              "50%                   9.135496                   3.000000\n",
              "75%                  13.000000                   6.000000\n",
              "max                  20.000000                  13.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "def3d-adKLC1",
        "outputId": "57e8e70a-d7d2-44f3-b0a4-c0aa1be0fd4b"
      },
      "source": [
        "# Measures of Central Tendency\r\n",
        "\r\n",
        "# Mean\r\n",
        "pf1_data.mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Elimination Week Number      9.135496\n",
              "Number of Evictions Faced    4.055375\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9l6N9ULiKQnQ",
        "outputId": "a7f10ade-2c72-4f4b-8404-d5d5992afc6f"
      },
      "source": [
        "# Median\r\n",
        "pf1_data.median()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Elimination Week Number      9.135496\n",
              "Number of Evictions Faced    3.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 78
        },
        "id": "1cnnIbePKSor",
        "outputId": "b26f193b-0a10-466c-df6f-ea31348e84e3"
      },
      "source": [
        "# Mode\r\n",
        "pf1_data.mode()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Elimination Week Number</th>\n",
              "      <th>Number of Evictions Faced</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9.135496</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Elimination Week Number  Number of Evictions Faced\n",
              "0                 9.135496                        1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jZckmJ9TKci1",
        "outputId": "70b537c5-b41a-46e6-814a-fcc9b08babf6"
      },
      "source": [
        "#  Measures of dispersion\r\n",
        "\r\n",
        "# Variance\r\n",
        "pf1_data.var()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Elimination Week Number      18.426909\n",
              "Number of Evictions Faced     8.225314\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6VIlNTuCKj-w",
        "outputId": "3f842e3a-70ec-4598-afc2-a834f17cd440"
      },
      "source": [
        "# Standard Deviation\r\n",
        "pf1_data.std()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Elimination Week Number      4.292658\n",
              "Number of Evictions Faced    2.867981\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "id": "Ayxp-okWKoVB",
        "outputId": "3aa5e22d-3d02-4b83-bc0a-a990e97f355e"
      },
      "source": [
        "# Presence of Outliers \r\n",
        "mean=pf1_data.mean()\r\n",
        "from scipy.stats import trim_mean\r\n",
        "trimmed_mean=list(trim_mean(pf1_data,proportiontocut=0.01))\r\n",
        "pd.DataFrame({'mean': mean,'trimmed_mean': trimmed_mean})"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>mean</th>\n",
              "      <th>trimmed_mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Elimination Week Number</th>\n",
              "      <td>9.135496</td>\n",
              "      <td>9.109086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Number of Evictions Faced</th>\n",
              "      <td>4.055375</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               mean  trimmed_mean\n",
              "Elimination Week Number    9.135496      9.109086\n",
              "Number of Evictions Faced  4.055375           NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4zHKv-gxKr4x",
        "outputId": "e2785848-be28-41e3-d1f6-4d812ee00b4a"
      },
      "source": [
        "# Interquartile Range\r\n",
        "\r\n",
        "qrt1 =pf1_data['Number of Evictions Faced'].quantile(0.25)\r\n",
        "qrt3=pf1['Number of Evictions Faced'].quantile(0.75)\r\n",
        "IQR = qrt3 - qrt1\r\n",
        "outliers=[]\r\n",
        "\r\n",
        "lower_bound=qrt1-(1.5*IQR)\r\n",
        "higher_bound=qrt3+(1.5*IQR)\r\n",
        "\r\n",
        "for i in pf1_data['Number of Evictions Faced']:\r\n",
        "    if i < lower_bound:\r\n",
        "        outliers.append(i)    \r\n",
        "for i in pf1_data['Number of Evictions Faced']:\r\n",
        "    if i > higher_bound:\r\n",
        "        outliers.append(i) \r\n",
        "print(outliers)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[13.0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "eL1bWY4NLBQl",
        "outputId": "dea56a39-80ea-46e2-a56f-e1c68cc29dc7"
      },
      "source": [
        "# kde distribution of data\r\n",
        "\r\n",
        "pf1_data.plot(kind='density',subplots=True,sharex=False,layout=(1,2),figsize=(12,5))\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAAEvCAYAAACOp+20AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeXzcZbX48c+Z7EmTNE23tGmb7mtoadOWLiwFhaJABVkFoYiCF9HrclHUKype7gX1d1GRe0HlQlEUKAJWqbIIyFKWFloKpXubtkmX7PueOb8/vjMxSbNMkpl8MzPn/XrlNTPf9Uwoz5w8c57nEVXFGGOMMcYYM3AetwMwxhhjjDEmUlhybYwxxhhjTJBYcm2MMcYYY0yQWHJtjDHGGGNMkFhybYwxxhhjTJBYcm2MMcYYY0yQxLodQLCMHDlSc3Jy3A7DGGP65d133y1R1VFuxzGYrN02xoSrntrsiEmuc3Jy2LJli9thGGNMv4jIIbdjGGzWbhtjwlVPbbaVhRhjjDHGGBMkllwbY4wxxhgTJJZcG2OMMcYYEyQRU3NtTFeam5spKCigoaHB7VCMASAxMZHs7Gzi4uLcDsWYIcHaaTOU9afNtuTaRLSCggJSU1PJyclBRNwOx0Q5VaW0tJSCggImT57sdjjGDAnWTpuhqr9ttpWFmIjW0NBAZmamNdhmSBARMjMzrYfOmHasnTZDVX/bbEuuTcSzBtsMJfbv0ZiT2f8XZqjqz79NS66NCbGYmBgWLFjQ9nPXXXcBcNZZZ7XN8fuJT3yCioqKgK+5YcOGtuv0VUVFBf/zP//T9vro0aNceuml/bpWe3/605/41Kc+1fb6v/7rv5g2bVrb6z//+c9cdNFFfb7uww8/zC233NLrMR6Ph+3bt7dtmzdvHvn5+X2+X1eGDRsWlOsYY4YmEeEb3/hG2+uf/vSn/OAHPwjKtdeuXcuTTz4ZlGv1ZP369cyePZtVq1Z12J6fn09SUlKHz6FHHnmkx2stX768x/0PP/wwR48ebXv9+c9/no8++qj/wfegq/ibmppCcq9gtfVWc21MiCUlJbFt27Yej9m4cWOfrnnRRRf1K1GFfybXN998MwDjxo0LSsO/fPlybrrpprbXb775JmlpaRQVFTF69Gg2bdrUa4M9ENnZ2dx55508/vjjIbtHf7S0tBAba02tMUNZQkICTz31FN/+9rcZOXKk2+G06Uv78eCDD/LrX/+alStXnrRv6tSpvX4Otbdp06Ye9z/88MPMmzePcePGAfCb3/wm4Gv3R1/jd5v1XJuo1dTSSkVdE16vuh0KOTk5lJSUkJ+fz6xZs1i7di0zZszg6quv5sUXX2TFihVMnz6dd955B+jYm7t27Vq+8pWvsHz5cqZMmdKWKNfU1HDOOeewcOFCcnNz+dOf/gTAbbfdxv79+1mwYAG33nor+fn5zJs3D3BqH6+//npyc3M59dRTefnll9vud8kll7B69WqmT5/ON7/5zZPew6hRo0hLS2Pfvn0AFBYW8ulPf7qtkd60aRMrVqyguLiYT3/60yxevJjFixfzxhtvAFBbW8vnPvc5lixZwqmnntoWb3vPPvssy5Yto6Sk5KR9F1xwATt27GD37t0n7WvfG/Hkk0+ydu3att/dv/zLv3DaaacxZcoUXnnlFT73uc8xe/bstmP8vva1rzF37lzOOecciouLAdi/fz+rV69m0aJFnH766ezatavtul/84hdZunRpl78rY4aMmmL48I9QfcLtSFwVGxvLjTfeyD333HPSvs49z/725JVXXuHMM89kzZo1TJkyhdtuu41HH32UJUuWkJuby/79+9vOefHFF8nLy2PGjBn85S9/AaC1tZVbb72VxYsXc8opp/DAAw+0Xff000/noosuYs6cOSfF84c//IHc3FzmzZvHt771LQDuuOMOXn/9dW644QZuvfXWgN7z/fff3+HY9p8r7dvMu+++m9zcXObPn89tt93Gk08+yZYtW7j66qtZsGAB9fX1Hb6J7So+/zW/+93vMn/+fE477TROnHD+za1fv5558+Yxf/58zjjjjIBiz8/P5/TTT2fhwoUsXLiwwx8DneOF7tvqgwcPsmzZMnJzc/n3f//3gO4dEFWNiJ9FixapMZ199NFHXW5vamnVHYWV+v6Rcj1YXKNerzdkMXg8Hp0/f37bz2OPPaaqqmeeeaZu3rxZVVUnTZqkxcXFevDgQY2JidHt27dra2urLly4UK+//nr1er36zDPP6Jo1a1RV9aGHHtIvfelLqqp63XXX6aWXXqqtra26Y8cOnTp1qqqqNjc3a2VlpaqqFhcX69SpU9Xr9erBgwd17ty5bfG1f/3Tn/5Ur7/+elVV3blzp06YMEHr6+v1oYce0smTJ2tFRYXW19frxIkT9fDhwye917Vr1+q6det0165desUVV+iLL76ot956qzY3N2t6errW19frVVddpa+99pqqqh46dEhnzZqlqqrf/va39be//a2qqpaXl+v06dO1pqam7b0+9dRTunLlSi0rKzvpvv5j1q1bp9dee62qqs6dO1cPHjyoqqopKSltx65fv16vu+66tt/dFVdc0fb7TU1N7fC737p1q6qqAvq73/1OVVV/+MMftv3uzz77bN2zZ4+qqr711lu6atWqtut+8pOf1JaWli7/TXT17xLYokOgLR3MH2u3XVZZqPqTGarfT1P9r4mqRbtcCaO7dnowpaSkaGVlpU6aNEkrKir0Jz/5iX7/+99XVef/5/Xr13c4VlX15Zdf1vT0dD169Kg2NDTouHHj9Pbbb1dV1Z/97Gf6r//6r23nn3feedra2qp79uzR8ePHa319vT7wwAP6ox/9SFVVGxoadNGiRXrgwAF9+eWXNTk5WQ8cOHBSnIWFhTphwgQtKirS5uZmXbVqlT799NOq2vEzpb2DBw9qYmJih8+hV199VYuKito+L1RVV69e3dY2+9/jxo0bddmyZVpbW6uqqqWlpV3ey/+6p/gA3bBhg6qq3nrrrW3vfd68eVpQUKCqTtvfW/w333yz1tbWan19vaqq7tmzR/1tSXfxdtdWX3jhhbpu3TpVVf3lL3/Z4bOivb622fZdpYkaP/zzDj46WgVAU4uX5lYvsTEeWlq9JMbFEOPp+6CFOePS+P6Fc3s8JpCykPYmT55Mbm4uQFtPqYiQm5vbbQ3xpz71KTweD3PmzGnrDVBVvvOd7/Dqq6/i8XgoLCxs29ed119/nS9/+csAzJo1i0mTJrFnzx4AzjnnHNLT0533PWcOhw4dYsKECR3OX758OZs2baK1tZVly5axZMkS7rjjDrZu3cqsWbNITEzkxRdf7FCbV1VVRU1NDc8//zwbNmzgpz/9KeD0oh8+fBiAl156iS1btvD888+TlpbWbfyf+cxnuPPOOzl48GCP77O9Cy+8sO33O2bMmA6/+/z8fBYsWIDH4+GKK64A4JprruGSSy6hpqaGTZs2cdlll7Vdq7Gxse35ZZddRkxMTMBxDAUishr4ORAD/EZV7+q0/wzgZ8ApwJWq+mS7fROB3wATAAU+oar5gxS66Y8Xvg8NFXDJr+Fv34ZnboYbXgCPi19q//U2OP5BcK85NhfO732MSlpaGtdeey2/+MUvSEpKCujSixcvJisrC3BKF84991wAcnNz2775A7j88svxeDxMnz6dKVOmsGvXLp5//nm2b9/e1iteWVnJ3r17iY+PZ8mSJV1O/bZ582bOOussRo0aBcDVV1/Nq6++2mG8S1e6K6uYMmUKb731FtOnT2fXrl2sWLGiw/4XX3yR66+/nuTkZABGjBjR4316ii8+Pp4LLrgAgEWLFvHCCy8AsGLFCtauXcvll1/OJZdcElD8lZWV3HLLLWzbto2YmJi2z6mu4u2prX7jjTf44x//CMBnP/vZDj3tA2HJtYlKLV4vsTFCQqyHVq+XFq/2K7kOhYSEhLbnHo+n7bXH46GlpaXXc5w/qOHRRx+luLiYd999l7i4OHJycgY0BVz7e8TExHQZy4oVK7j33ntpbW3lC1/4AqmpqTQ0NPDKK6+01Vt7vV7eeustEhMTO5yrqvzxj39k5syZHba//fbbTJ06lQMHDrBnzx7y8vK6jTE2NpZvfOMb3H333R22tx/t3fl30P732/l3393vW0Twer0MHz682z+cUlJSuo1zKBKRGOA+4ONAAbBZRDaoavtRSoeBtcC/dXGJR4A7VfUFERkGeEMcshmI6uOw4ylYchOccjk018OfvwIHXoZp57gdnWu++tWvsnDhQq6//vq2bbGxsXi9zj9nr9fbYTBdoO115xknRARV5d577+W8887rsO+VV14ZtPbjyiuv5IknnmDWrFlcfPHFIZ21JS4uru367T9D7r//ft5++22effZZFi1axLvvvktmZmaP17rnnnsYM2YM77//Pl6v96TPk/Z6a6tD8Z4tuTZRw9/DXNvYwv7iGiZlJpOeFE9BWR2V9c3MHpeGJ4Kmg6qsrGT06NHExcXx8ssvc+jQIQBSU1Oprq7u8pzTTz+dRx99lLPPPps9e/Zw+PBhZs6cyXvvvRfQPWfPns3Ro0d5/fXX22YkWbBgAffffz8//vGPATj33HO5995722r9tm3bxoIFCzjvvPO49957uffeexERtm7dyqmnngrApEmT+MlPfsIll1zC+vXrmTu3+28L1q5dy49//OMO73HMmDHs3LmTmTNn8vTTT5OamhrQ+/Hzer08+eSTXHnllfz+979n5cqVpKWlMXnyZNavX89ll12GqrJ9+3bmz5/fp2sPIUuAfap6AEBEHgPWAG3Jtb8nWkQ6JM4iMgeIVdUXfMfVDFLMpr92/hm8LbBorfN6/pXw0o/gnV+5m1wH0MMcSiNGjODyyy/nwQcf5HOf+xzgjIl59913ufzyy9mwYQPNzc19vu769eu57rrrOHjwIAcOHGDmzJmcd955/O///i9nn302cXFx7Nmzh/Hjx/d4nSVLlvCVr3yFkpISMjIy+MMf/tD2bWN/XHzxxdx5551s3br1pE4JgI9//OPccccdXH311SQnJ1NWVsaIESO6/RzpT3z79+9n6dKlLF26lL/+9a8cOXKk1+S6srKS7OxsPB4P69ato7W1tcd4u2urV6xYwWOPPcY111zDo48+2offXM9sQKOJOtUNLQiQkuD8bZmaFEerKvVNrSG5X319fYcphPwDLELt6quvZsuWLeTm5vLII48wa9YsADIzM1mxYgXz5s07aeDLzTffjNfrJTc3lyuuuIKHH364Q89Mb0SEpUuXkpmZ2bZU7LJlyzhw4EBbz/UvfvELtmzZwimnnMKcOXO4//77Afje975Hc3Mzp5xyCnPnzuV73/teh2vPmjWLRx99lMsuu6zDQKHO4uPj+cpXvkJRUVHbtrvuuosLLriA5cuXt32F2xcpKSm88847zJs3j5deeonbb78dcL4dePDBB5k/fz5z587tchBmGBkPHGn3usC3LRAzgAoReUpEtorIT3w94Wao2vs8jJgCo2Y4r2MTYOG1zvaaYndjc9k3vvGNDoOmv/CFL/CPf/yD+fPn8+abb/arV3nixIksWbKE888/n/vvv5/ExEQ+//nPM2fOHBYuXMi8efO46aabuv22zC8rK4u77rqLVatWMX/+fBYtWsSaNWt6vb9/ELv/5xe/+AUAGRkZzJ49m0OHDrFkyZKTzlu9ejUXXXQReXl5LFiwoK1szz9o2z+gcSDx3XrrrW0DIJcvXx5QB8XNN9/MunXrmD9/Prt27Wr7b9JdvN211T//+c+57777yM3NpbCwsNf7Bkr8XyGHu7y8PPWPVDXGb+fOncyePbvDtn1FTqfatNHOaOjmVi87j1WRlZ7IqNTuv1oyJli6+ncpIu+qavc1LyEmIpcCq1X1877XnwWWqupJk4yLyMPAX/w1175zHwROxSkdeRzYqKoPdnHujcCNABMnTlzk/0bFDKLmerg7x+m1Pr9db+XxD+H+FXDBzyDv+u7ODrqu/n8wZijpa5ttPdcmqnhVqW9uJSX+n51qcTEeEmI91DaGpufamDBRiDMY0S/bty0QBcA2VT2gqi3AM8DCrg5U1V+pap6q5vkHPZlBdux9aGmAyWd23D5mrtObvXODO3EZEyEsuTZRpbHZi6qSFN/xG+vk+FjqmlqJlG9yjOmHzcB0EZksIvHAlUCgWdZmYLiI+LPls2lXq22GmMJ3ncfxizpuF4HZF8GBf0B94CvGGmM6suTaRJX6Zqd3OjGuY3KdFBdDi2/WEGOika/H+RbgOWAn8ISq7hCRO0TkIgARWSwiBcBlwAMissN3bivODCJ/F5EPAAF+7cb7MAEofBfSJ0DqmJP3TT8XtBXyXx/8uIyJEDZbiIl4qto21U59cysecabgay8xznnd0NxKXIz9zWlCZyh/O6KqG4GNnbbd3u75Zpxyka7OfQFn/msz1BW+C+O7rNqB7MUQlwIHXoHZFwxaSO3baWOGkv602ZZFmIiWmJhIaWlp2/8cDc2tJMbFnNSI+3uyG5ptal4TOqpKaWlpj3OyGhNSjTVQng9jcrveHxsPOSuc5HqQdG6njRkq+ttmh7TneoCrfV0H+Bd6/w9VXRfKWE1kys7OpqCggOJiZ2qpY5UNJMZ6aC6NP+nYksoGao57KEk5eZ8xwZKYmEh2dpedv8aEXslu53H0rO6PmXKWMyVfZQGkh/7faud22pihpD9tdsiS64Gs9iUiI4DvA3k4y+i+6zu3PFTxmsgUFxfXtoRsVUMz5//geb61ehb/kjf1pGP/88G3qapv5k+3rBzsMI0xZnAU+5LrUT1MfTflLOfxwCtw6jUhDqhjO21MJAhlWUjbal+q2gT4V/tqo6r5qrqdk5fJPQ94QVXLfAn1C8DqEMZqosCB4loApo7qegGAnMwUDpbU2leTxpjIVbwLYuIhI6f7Y0bPgaQRcOjNQQvLmEgSyuR6IKt9DeRcY7p0oNhZPGbKqGFd7p+UmUxVQwvldX1f2tYYY8JC8W7InA4xPXxxLQITT4Mjbw1eXMZEkLAe0CgiN4rIFhHZYrVapjcHimuJ8QgTRyR3uX/ySKdH+2BJ7WCGZYwxg6d0P2SeXBZ3kglLoXRf1C+Fbkx/hDK5HshqXwGdayt9mb7YX1zDpBHJxMd2/c8+x5dcHyq15NoYE4G8Xqg41HNJiN/EZc6j9V4b02ehTK4HstrXc8C5IpIhIhnAub5txvRbfmldW+90VyZkJOMRyLeea2NMJKo+Bq1NgSXX4xZATAIctuTamL4KWXI9wNW+yoAf4STom4E7fNuM6beC8jomdFMSAhAf6yE7I5mDpXWDGJUxxgyS8nznMZDkOjbBWR7dkmtj+iyk81wPcLWv/wP+L5TxmehRWd9MdUML2RlJPR6XMzLFeq6NMZGp4pDzGEhyDTBxKWy6F5rqIL77jgljTEdhPaDRmEAVlDu90b0l1xNHJHGk3HqujTERqDwfEEif0NuRjonLwNviLJdujAmYJdcmKhSU1wOQndFz78v44clU1DVT29gyGGEZY8zgKc93VlyMDXAV2glLnEcb1GhMn1hybaLCP5Prnnuuxw1PBKCwoj7kMRljzKAqzw+8JAQgKcNZydEWkzGmTyy5NlGhoLyOYQmxpCfF9XicP/m25NoYE3EqDsPwiX07Z+JpULAZvK2hicmYCGTJtYkKBeX1ZGckISI9Hjd+uFM2UlhuybUxJoK0tkDNCUgb17fzJp4GjVVQtDM0cRkTgSy5NlHBn1z3ZnRqAnExYj3XxpjIUnMC1AupWX07b+JpzuNhKw0xJlCWXJuoUFBe1+tgRgCPR8hKT7Kea2NMZKk+5jz2ted6+CQYNhaOvB38mIyJUJZcm4gX6BzXfuOHJ1nPtTEmslQddR772nMt4vRe22IyxgTMkmsT8fy90OOGB5hcZ1jPtTEmwvS35xqc5LryCFQWBDcmYyKUJdcm4p2oagBgbHpiQMePH57EieoGmlq8oQzLGGMGT9VR8MRB8si+n9tWd22918YEwpJrE/GO+5PrtMCTa1U4XtkQyrCMMWbwVB+D1LHg6cfH/phciEuxumtjAmTJtYl4xysbEIFRqQkBHT/e5ro2UUpEVovIbhHZJyK3dbH/DBF5T0RaROTSLvaniUiBiPxycCI2Aas62r+SEICYWMjOsxlDjAmQJdcm4p2oaiAzJYG4mMD+ufvLR/zlJMZEAxGJAe4DzgfmAFeJyJxOhx0G1gK/7+YyPwJeDVWMZgCqj/V9MGN7E0+DEzugoSp4MRkToSy5NhHvRFUDY9MD67WGf5aPHLOyEBNdlgD7VPWAqjYBjwFr2h+gqvmquh04aUCCiCwCxgDPD0awpg9UB9ZzDU5yrV5ntUZjTI8suTYR73hVY8D11gApCbGkJsZaz7WJNuOBI+1eF/i29UpEPMD/A/4tBHGZgWqohOa6gfVcZy8G8VjdtTEBsOTaRLwTVQ2M6UNyDU7vtQ1oNCZgNwMbVbXXudpE5EYR2SIiW4qLiwchNDOgafj8ElJhzDyruzYmAJZcm4jW2NJKWW1T35Pr9ESOWc+1iS6FwIR2r7N92wKxDLhFRPKBnwLXishdXR2oqr9S1TxVzRs1atRA4jWB8ifXqWMHdp2Jp0HBFmhtHnhMxkQwS65NRCuqagQCn4bPb2xaIies59pEl83AdBGZLCLxwJXAhkBOVNWrVXWiqubglIY8oqonzTZiXFJb4jymjB7YdSae5pSXHN8+8JiMiWCWXJuI5q+bHhPgAjJ+Y9MTKa5ppKXVFpIx0UFVW4BbgOeAncATqrpDRO4QkYsARGSxiBQAlwEPiMgO9yI2Aav1ld8MG+A3BZNWOo8HbUIYY3oS63YAxoRSXxeQ8RuTlkirVympaQp4ZUdjwp2qbgQ2dtp2e7vnm3HKRXq6xsPAwyEIz/RXTZGzOmPi8IFdJ3UMjJ4DB16BlV8LSmjGRCLruTYRzT8ocUxa4FPxAWT5EurjVndtjAl3tSWQMgpEBn6tKWfBoTeh2RbZMqY7llybiHaiqoGEWA/pSXF9Os8/APJ4pX2AGGPCXG0xpIwMzrWmrILWRpuSz5geWHJtItqJqkbGpicifeyx8ZeC2HR8xpiwV1sEwwY4mNFv0nLwxDqlIcaYLllybSLa8X7McQ0wIjme+BgPx32zjRhjTNjyl4UEQ8IwyF5iybUxPbDk2kS0/iwgA+DxCKPTEqwsxBgT3lSDWxYCTt310W1QVxa8axoTQSy5NhFLVTle2cDYPg5m9BublmgDGo0x4a2xGloaBj7HdXvTzgEU9v09eNc0JoJYcm0iVmV9M40t3n71XINTd33CykKMMeHMP8d1sMpCAMYthGFjYPezwbumMRHEkmsTsfyJcX/nqR6blsixynpUNZhhGWPM4AnWAjLteTwwYzXsfRFarAPCmM4suTYRq78LyPiNTU+kodlLVX1LMMMyxpjBE4qea4BZn4Smash/LbjXNSYCWHJtItaJtgVk+p9cAxyrskGNxpgw1ZZcB7HmGmDyGRCXDLs29n6sMVHGkmsTsfw916MHMKARbK5rY0wYq/En10GcLQQgLskZ2LjrWfC2BvfaxoQ5S65NxDpe1cCIlHgSYmP6db6/x/uEzRhijAlXtcWQlAExfVulNiDzLoWa41YaYkwnllybiFXUzzmu/fznHrOea2NMuKotDn69td+M1ZCQBtufCM31jQlTllybiOWszti/khCA+FgPI4fFW8+1MSZ8hTK5jkuEOWvgow3QVBeaexgThiy5NhHreGVjv2cK8Rubnmg918aY8BXK5BrglCucWUN228BGY/wsuTYRqbnVS2lt44DKQgDGpiXZgEZjTPgK9tLnnU1aAcMnwrsPh+4exoQZS65NRCqqbkS1/wvI+I1NT7Al0I0x4cnbCvUVkJwZunt4PJD3OWdQY9HO0N3HmDBiybWJSP466YHUXANkpSdRUddMQ7NNNWWMCTMNlYA6s4WE0qnXQkwCvPPr0N7HmDAR0uRaRFaLyG4R2Scit3WxP0FEHvftf1tEcnzb40RknYh8ICI7ReTboYzTRJ6BLiDjN8bmujbGhKv6cucxaURo75OSCbmXwvuPOT3lxkS5kCXXIhID3AecD8wBrhKROZ0OuwEoV9VpwD3A3b7tlwEJqpoLLAJu8ifexgRioEuf+2Wl23R8xpgwVVfmPCaHOLkGWHoTNNfCZuu9NiaUPddLgH2qekBVm4DHgDWdjlkDrPM9fxI4R0QEUCBFRGKBJKAJqAphrCbCHK9qID7Gw4iU+AFdxxaSMcaErbae6xCXhQBkzXfmvX7zPmisDv39jBnCQplcjweOtHtd4NvW5TGq2gJUApk4iXYtcAw4DPxUVctCGKuJMEVVjYxOS8D5W63/xlrPtTEmXNX7PjYHI7kGOOObTkK/+TeDcz9jhqihOqBxCdAKjAMmA98QkSmdDxKRG0Vki4hsKS4uHuwYzRB2vHJgqzP6DUuIJTUxluOV9UGIypihLYBxMmeIyHsi0iIil7bbvkBE3hSRHSKyXUSuGNzITZcGsywEIHsRTD0HNv0SmmoH557GDEGhTK4LgQntXmf7tnV5jK8EJB0oBT4D/E1Vm1W1CHgDyOt8A1X9larmqWreqFEhnCTfhJ0TVQ0Drrf2G5uWaNPxmYgX4DiZw8Ba4PedttcB16rqXGA18DMRGR7aiE2v6stBPJCQPnj3POs2qCuBtx8YvHsaM8SEMrneDEwXkckiEg9cCWzodMwG4Drf80uBl1RVcRrwswFEJAU4DdgVwlhNBFFV39LnQUqu0xNtthATDXodJ6Oq+aq6HfB22r5HVff6nh8FigDr8XBbfRkkDnfmoh4sE5bA9HPhjZ/bzCEmaoXs/zhfDfUtwHPATuAJVd0hIneIyEW+wx4EMkVkH/B1wP815H3AMBHZgZOkP+Rr0I3pVXVjC3VNrYxNH9gc137Wc22iRCDjZHolIkuAeGB/N/utnG+w1JUNXklIe2f/OzRUOIMbjYlCsaG8uKpuBDZ22nZ7u+cNONPudT6vpqvtxgQiWHNc+2WlJ1JU3Uhzq5e4mKE6TMEY94lIFvBb4DpV9XZ1jKr+CvgVQBzPQxsAACAASURBVF5eng5ieNGnvnzwBjO2lzUf5qyBt/7HmaIvlMuvGzMEWaZgIk6w5rj2G5OeiCoUVzcG5XrGDFGBjJPploikAc8C31XVt4Icm+mP+rLQLyDTnVXfheY6eP0ed+5vjIssuTYRxz9tXlZ6UlCu519IxkpDTIQLZJxMl3zHPw08oqpPhjBG0xd15e6UhQCMmgmnXOFMy1d1zJ0YjHGJJdcm4vjLQkanBavm2knSbVCjiWSBjJMRkcUiUoBTtveAb1wMwOXAGcBaEdnm+1ngwtsw7blVFuJ35rfA2wKv/sS9GIxxQUhrro1xw/GqBjKS40iMiwnK9fwLyVhybSJdAONkNuOUi3Q+73fA70IeoAlcSxM0VbtXFgIwYjIsvBbeWwcrvgIZOe7FYswgsp5rE3GOVzYwNkglIQAZyXHEx3qsLMQYEz78S58nu9hzDXDGreCJhVfudjcOYwaRJdcm4hyvamBskEpCAETEmY7Peq6NMeFisJc+707aOFj8edj+GBTvdjcWYwaJJdcm4jg918GZKcTPFpIxxoQVf8+1m2Uhfiu+CrFJ8Mp/uR2JMYPCkmsTURpbWimtbWobhBgsWemJHKuqD+o1jTEmZOp8PdduzRbS3rBRcNq/wI6n4ZitB2cinyXXJqIUVTlzUQdrdUa/sWmJnKhsRNXWvDDGhIGhUhbit/zLkJgOL9/pdiTGhJwl1yaitC0gE8QBjc71Emlq9VJW2xTU6xpjTEgMpbIQgKThToK9529wYkfvxxsTxiy5NhHFXxcdrNUZ/fzXO2Z118aYcFBXBjHxEJ/idiT/lHcDxCbCO792OxJjQsqSaxNRTgR56XO/ccOdnvCjFaGpuy6sqOfGR7Zw4b2v87cPbTUzY8wA1Zc5JSEibkfyT8kjIPdS2P441Fe4HY0xIWPJtYkoxyobSIqLIS0puOsjZWc4yXVBefCT67qmFq75zdts2l9KbVMLNz/6Hm/uLw36fYwxUaS+fOiUhLS3+AvQXAfv/8HtSIwJGUuuTUQ5XuVMwydB7q0ZkRJPUlxMSJLrX/x9HwdLavn1tXn85csrmTAimR/+eYcNnjTG9F9d+dCYKaSzcQtgwlKnNMTrdTsaY0LCkmsTUU5UNjAmiAvI+IkI2RlJFJTXBfW6FXVNPLzpIBefOp5lUzNJjo/lllXT2HW8mjcPWO+1Maaf/GUhQ1HeDVC2Hw5vcjsSY0LCkmsTUY5VNpAV5JlC/JzkOrg9149vPkJDs5cbz5jStu3C+eMYkRLPo28fDuq9jDFRpL586CbXsy+EhDTY+qjbkRgTEpZcm4jh9SpF1Q2MCfJgRr/sjGQKgzig0etVfvvWIU6bMoLZWWlt2xPjYjhv7hhe2VVEY0tr0O5njIkSqs5sIUOxLAQgPhnmXgwfPQON1W5HY0zQWXJtIkZxTSPNrcr44aFKrpOorG+mqqE5KNfbVlBBQXk9l+dNOGnfuXPGUtvUagMbjTF911wHrY1Dt+ca4NRrnDh3PON2JMYEnSXXJmL4SzbGZ4SqLCQZgMIglYZs3H6M+BgPH5sz5qR9y6Zmkhjn4R97ioNyL2NMFBlqC8h0JXsxjJwBW3/ndiTGBJ0l1yZi+Es2xg9PDsn1gzkdn6ry1w+Pc/r0kaQlxp20PzEuhoUTM3j7QNmA72WMiTJ1vnZjqJaFgDP/9oKr4chbULLP7WiMCSpLrk3EKAx5z7U/uR74jCHbCyoprKjn/Nysbo9ZMnkEO49XUVkfnDIUY0yUqPcl10O55xpg/pUgHtj+mNuRGBNUllybiFFYUUd6UhzDEoK7gIxfMOe6fnl3ESJwzqzR3R6zdHImqvDeofIB388YE0XaykKGcM01QOpYmLIK3n/c5rw2EcWSaxMxjlY0tC1THgoiwvggzXX9+t4SThmfTkZKfLfHnJKdjojTy22MMQELh7IQvwWfgcrDNue1iSiWXJuIUVhez/gQJtcQnLmuqxua2XqkgpXTR/Z4XEpCLFNGpvDhUUuujTF90FYWMsR7rgFmfgLiU2GbLYduIocl1yYiqCqFFfVtddGhMnFEModL6wa0NPlbB8po9Sorp43q9dh549P5sNCSa2NMH9SVQ1wKxAZ/tdqgi0+GuWucOa+bgrsCrjFuseTaRISq+hZqGltC3nM9eWQK1Y0tlNQ09fsar+0tJikuhoWThvd6bO74dI5VNlBS09jv+xkTKBFZLSK7RWSfiNzWxf4zROQ9EWkRkUs77btORPb6fq4bvKjNSerLw6MkxG/+VdBUA7uedTsSY4LCkmsTEQoqnB6PUM0U4jd5ZAoAB0tq+32N1/eWsHTKCBJiY3o9dt74dADrvTYhJyIxwH3A+cAc4CoRmdPpsMPAWuD3nc4dAXwfWAosAb4vImFQkxCh6svCoyTEb+JyGD4R3v9978caEwYsuTYRoW0avhD3XE8ZOQyAgyU1/Tq/sKKeAyW1rJzWc72135xxzrLoHx2r6tf9jOmDJcA+VT2gqk3AY8Ca9geoar6qbgc6T+1wHvCCqpapajnwArB6MII2XagLs+Ta44FTroQDr0DVUbejMWbALLk2EaFtAZkQ91yPz0giPsbDgX72XL++11lx8fTpvddbA6QlxpGVnsi+ov4l88b0wXjgSLvXBb5toT7XBFu4lYWAM+e1euGD9W5HYsyAWXJtIkJheT2JcR4ye5jaLhhiPMKkzGQOFPcvuX5tbwmjUxOYMWZYwOdMGz3MkmsTMUTkRhHZIiJbiouL3Q4nMtWXDf0FZDrLnArZS5xZQwYwYNyYocCSaxMRCsrrGTc8CREJ+b0mj0zpV82116ts2l/Kymkj+xTn9NGp7Cuqweu1DxwTUoXAhHavs33bgnquqv5KVfNUNW/UqMC+wTF94PU6PdfhVBbit+AqKN4Jx953OxJjBsSSaxMRDpXVkZOZMij3mjwqhUOltbT2Mdn96FgVZbVNnD4jsHprv+ljhlHX1NpW+mJMiGwGpovIZBGJB64ENgR47nPAuSKS4RvIeK5vmxlsjVVOeUW4lYUAzL0YYuLhfVsO3YQ3S65N2FNVDpXWMikzeVDuN3XkMJpbtW0QZaBe21sCwIoABzP6TRvtlJBYaYgJJVVtAW7BSYp3Ak+o6g4RuUNELgIQkcUiUgBcBjwgIjt855YBP8JJ0DcDd/i2mcHWtoBMGCbXSRkw83yn7rq12e1ojOk3S65N2CuuaaSuqXXQeq6njHLus6+4uk/nvb6vmFljUxmdmtin86aNcpLrPSf6dj9j+kpVN6rqDFWdqqp3+rbdrqobfM83q2q2qqaoaqaqzm137v+p6jTfz0NuvYeoV1fuPIZjWQg4c17XlcC+F92OxJh+s+TahL1Dpc4c14PVcz1jbCoAO48FnuzWNbWw+WA5p/ey5HlXMlLiyUyJJ7+0/3NrG2OiRL0vuQ7HshCAaR+D5JHwvi2HbsKXJdcm7OX7BhcOVs91WmIc2RlJfZp7+u2DZTS1ejljRv8GcE3KTCa/xJYGNsb0IpzLQgBi4iD3Mtj913/+oWBMmLHk2oS9Q6V1xHgk5HNctzc7K42dfUiuX9tTQkKsh8U5/fvAy8l0BlEaY0yP6vzJdZiWhYAz53VrE3z4lNuRGNMvIU2uRWS1iOwWkX0iclsX+xNE5HHf/rdFJKfdvlNE5E0R2SEiH4hI3wpVTdTIL60lOyOJuJjB+1txTlYa+SW11De1BnT8q3uLWTJ5BIlxvS953pVJmSkcrWygoTmw+xljolR9OSCQNNztSPovaz6Mmm2zhpiwFbJsRERigPuA84E5wFUiMqfTYTcA5ao6DbgHuNt3bizwO+CLvgEzZwE2dNh0Kb+0lkmDVBLiNzsrDa/C7gAGGR6tqGdfUQ1n9rMkBCBnpFNPfqTMSkOMMT2oL4PEdPD07w/5IUHEmfO64B0o3e92NMb0WSi7+pYA+1T1gKo2AY8BazodswZY53v+JHCOOKtrnAtsV9X3AVS1VFWty86cRFU5VFJHziANZvSbOy4NgA8LK3s99rU+LnneFf8fD/mlllwbY3pQVxbeJSF+uZeDeGDb792OxJg+Cyi5FpGnROSTItKXZHw8cKTd6wLfti6P8c2xWglkAjMAFZHnROQ9EflmH+5rokhJTRPVjS2DNpjRLzsjiVGpCbx7qPcBNy/vKmZsWmKfljzvzP/Hg9Vdm0D0s802kaC+LHxnCmkvLcuZOeS9ddDc4HY0xvRJoA3v/wCfAfaKyF0iMjOEMQHEAiuBq32PF4vIOZ0PEpEbRWSLiGwpLi4OcUhmKNrrK8uYMSZ1UO8rIuRNymBzfs/rZNQ3tfLKniLOnTtmQEuzD0+OJz0prl/LrpuoNNhtthkq6svDd6aQzk67GWqL4cMn3Y7EmD4JKLlW1RdV9WpgIZAPvCgim0TkehGJ6+a0QmBCu9fZvm1dHuOrs04HSnF6uV9V1RJVrQM2+u7dOa5fqWqequaNGtX/r9xN+NrrW7Vw+gB6hfsrL2cEBeX1HKvsfqXGf+wpoqHZy+q5Ywd8v5zM5LY5vY3pST/bbBMJIqUsBGDKWTBmHrx5H6i6HY0xAQv4K0MRyQTWAp8HtgI/x2m4X+jmlM3AdBGZLCLxwJXAhk7HbACu8z2/FHhJVRVn+d1cEUn2Jd1nAh8FGquJHnuLqklNjGV0asKg33txjvMBtiW/+9KQv314nOHJcSyZPPCepJyRKbaQjAlYP9psEwnqyyOjLAScgY3LvgRFH8H+l9yOxpiABVpz/TTwGpAMXKiqF6nq46r6ZaDLLkNfDfUtOInyTuAJVd0hIneIyEW+wx4EMkVkH/B14DbfueXAf+Mk6NuA91T12f6+SRO59pyoYcaY1AGVXPTXnKw0UhNieX1vSZf7G5pb+fvOIj4+ewyxQZgmcEJGMscqG2hp9Q74Wiay9afNNhGgtRkaqyKnLARg3qWQmgWv/T/rvTZhIzbA436tqhvbbxCRBFVtVNW87k7ynbOx07bb2z1vAC7r5tzf4UzHZ0y39hXVcO6cMa7cOzbGw5kzR/H3XSfwehWPp2OCv/GDY1Q3tnDxws7jePsnOyOJVq9yvKqB7IzBnR3FhJ1+tdkmzNVXOI+RUhYCEBsPK78Of70VDrwCU1e5HZExvQq0O+0/utj2ZjADMaavSmoaKattYvogD2Zs7+NzxlBS08TWIyeXhjz2zhFyMpNZNiUzKPfyJ9QF5d3XeBvjY212NPIvfR4pZSF+i66D9Anw0n9Y77UJCz0m1yIyVkQWAUkicqqILPT9nIXzdaMxrvEvPz5rrHvJ9apZo0mKi+GJzQUdtn9YWMk7+WVcsXhi0EpW/Mu7W3JtumNtdpSr9/2RH0k91wCxCXDmN6FwC+zsPHTLmKGnt7KQ83AGxGTj1ED7VQPfCVFMxgTkw0InuZ43Lt21GNIS4/jUqeN4emsh31w9k8xhzsDK/35hD2mJsVx92sSg3Wvc8EQACsptxhDTLWuzo1mdr+c60pJrgPmfgbcfgOe+C9M+DvH2t6IZunrsuVbVdaq6Clirqqva/Vykqk8NUozGdOnDo5VMGJFEerK7M4vdsHIyLa3KnRt3oqo89V4BL+0q4kurppGWGLzYEmJjGJOWQKH1XJtuWJsd5SK1LAQgJhY+8ROoPOIMbjRmCOux51pErvENLMwRka933q+q/93FacYMih2Fla72WvtNG53Kv5w1lXtf2sf+4lo+LKxkSc4IPn/6lKDfKzsj2cpCTLeszY5ybWUhEZhcA0xaDvOvgjd+DnMugqz5bkdkTJd6G9DoX1N6GJDaxY8xrqhqaCa/tI55491PrgG+9rEZfOPjM2hu8XJ53gQeXJtHjCf40wOOH55EQYWVhZhuWZsdzerKwBMLCRH8n/q8/4TkTHjqRlsW3QxZPfZcq+oDvscfDk44xgTmw8JKAOaOS3M5EofHI3z5nOl8+ZzpIb1PdkYSGz84RkurNyhzZ5vIYm12lKv3rc7owrz/gyZ5BHzqPvjdp+H578InrUTEDD2BLiLzYxFJE5E4Efm7iBSLyDWhDs6Y7mzJL0cETp0QgQN3epCdkUyLVzlR3eh2KGYIszY7StWXR25JSHvTPgbLvwybfwPv/dbtaIw5SaBdX+eqahVwAZAPTANuDVVQxvRmc34ZM8ekuj6YcbBl+6bjs0GNphfWZkejurLInCmkK+f8AKacBc9+HQq2uByMMR0Fmlz7y0c+CaxX1coQxWNMr1pavbx3qJzFOVHQQ9PJP+e6trpr06N+tdkislpEdovIPhG5rYv9CSLyuG//2yKS49seJyLrROQDEdkpIt8O1hsxfVBfHpkzhXQlJhYufchZGv2xq6HqqNsRGdMm0OT6LyKyC1gE/F1ERgE2ksC4YuexamqbWsnLiZIemnbGD7eFZExA+txmi0gMcB9wPjAHuEpE5nQ67AagXFWnAfcAd/u2XwYkqGqu7543+RNvM4jqyqKjLMQveQRc9QdoqoHHPgPN1i6aoSGg5FpVbwOWA3mq2gzUAmtCGZgx3XljfwkASycHZ1nxcJIYF8PIYTbXtelZP9vsJcA+VT2gqk3AY12cswZY53v+JHCOOEuQKpAiIrFAEtAEVAXlzZjA1ZdBcpR1OoyZC5f8Co5ugz/dYsujmyGhtxUa25uFM3dq+3MeCXI8xvTqpV1FzBqbytj0RLdDccW44YkcrbTk2vSqr232eOBIu9cFwNLujlHVFhGpBDJxEu01wDGcZda/pqplAwvf9ElTHbQ0RFfPtd+sT8I534O/3wGjZ8MZ/+Z2RCbKBZRci8hvganANqDVt1mx5NoMssq6Zt49VM4Xzwz+Ai3hIis9kf3FtW6HYYYwF9rsJb77jAMygNdE5EVVPdBFbDcCNwJMnDgxROFEoUhenTEQK78ORTvhpR/BqJkw+0K3IzJRLNCe6zxgjqp932Lc9Y+9xbR6lbNnjXY7FNdkpSfx+t4SVBWJ5PlszUD0p80uBCa0e53t29bVMQW+HvF0oBT4DPA3XwlKkYi84YvhpORaVX8F/AogLy/PPlOCpc6XXEdjzzU4c3tfdC+UHYCnboIbZzhJtjEuCHRA44fA2FAGYkwgNmw7yqjUBBZE2fzW7Y0bnkhtUytVDS1uh2KGrv602ZuB6SIyWUTigSuBDZ2O2QBc53t+KfCSL4E/DJwNICIpwGnArn7Gbvoj2nuuAeKS4IpHITYBnv4itFobadwRaHI9EvhIRJ4TkQ3+n1AGZkxnxdWNvLy7iEsWjg/J0uLhIivdmTHkmNVdm+71uc1W1RbgFuA5YCfwhKruEJE7ROQi32EPApkisg/4OuCfru8+YJiI7MBJ0h9S1e0heF+mO9Hec+2XluWs2nj0PXjjZ25HY6JUoGUhPwhlEMYE4pmthbR6lUsXZrsdiqvGDXcGch6raGDW2KGx/LsZcn7Qn5NUdSOwsdO229s9b8CZdq/zeTVdbTeDqK7UeUyOvlmUTjLvEvjoT/CPH0PupZCR43ZEJsoEOhXfP3BW+YrzPd8MvBfCuIzpoKnFy0NvHCRvUgbTx6S6HY6r/D3XNmOI6Y612VGovtx5jJYVGntz3n+CJwZeuL33Y40JsoCSaxH5As5USw/4No0HnglVUMZ09uS7BRytbOBLq6a5HYrrRqcm4BGn59qYrlibHYXqyiA+FWLj3Y5kaEgfDyu+6vRgH37L7WhMlAm05vpLwAp8iwKo6l4geqdrMIOqvLaJnz6/m8U5GZw1c5Tb4bguNsbDmDSb69r0yNrsaBONC8j0ZvmXIWUU/OPu3o81JogCTa4bfSt2AeCbgsmmUDIh1+pVvrH+faobmrljzTybes4nKz3Req5NT6zNjjbRtvR5IOKTYdktsP8lKNjidjQmigSaXP9DRL4DJInIx4H1wJ9DF5YxUNfUwlcf38ZLu4q4/YI5zM6ywXt+WcOTbLYQ0xNrs6NNfVl0T8PXncWfd/7oeO2/3Y7ERJFAZwu5DbgB+AC4CWc0+W9CFZQJf16vsvN4Fe8cLONQaR3HKutpaVW8qqQlxTEiJZ6RwxIYNSyBUWkJjE5NYHSqMwtGSU0jm/aX8vCmgxSU1/Ot1bP47LIcd9/QEDMuPZEXPjphC8mY7libHW3qyiBjsttRDD0JwyDvenj9Hqg4AsMn9H6OMQMUUHKtql4ReQZ4RlWLQxyTCWO1jS08vCmfR986xNFKp2xhWEIsWemJxMd68IhwoKSWspomqht7nuB/wYTh/PjT81k21aaW6iwrPYmmFi+ltU2MHJbgdjhmiLE2OwpZz3X3FvmS6y3/Bx/7vtvRmCjQY3ItTpfY93EWFvD4trUC96rqHaEPz4STl3cX8Z2nPuBYZQMrp43k6+fOZOW0kYxJS+iyd7WhuZXi6kaKqhsprm6gqLoRESE9KY7c8elMHpniwrsID+OG+xaSqWiw5Nq0sTY7SrW2QEOlzXHdneETYMb58N4jcNZtzgqOxoRQbz3XX8MZcb5YVQ8CiMgU4H9F5Guqek+oAzTh4TevHeDOjTuZOSaVX37mVBZN6r0HJTEuhgkjkpkwInkQIows/oVkjlbWk5ud7nI0ZgixNjsatc1xbT3X3Vp8A+x+Fnb+2VlYxpgQ6m1A42eBq/yNNICqHgCuAa4NZWAmfDz0xkH+49mdrJ47lqdvXhFQYm0Gpm0J9Aob1Gg6sDY7GtX7lj63spDuTVkFadmw/Qm3IzFRoLfkOk5VSzpv9NXwxYUmJBNO/r7zBHf85SPOnTOGX35mIUnxMW6HFBUyU+KJj/FwrNKm4zMdWJsdjep8ybWtztg9jwdOuQz2vQg1NgzBhFZvyXVTP/eZKFBc3citT25n9tg0fnHVqcR4bNaKweLxCGPTE9sGjRrjY212NLKe68CccgVoK+x4yu1ITITrreZ6vohUdbFdgMQQxGPCyH88+xE1jS38/MoFJMZZj/VgcxaSsbIQ04G12dGorefakusejZ4NY3Ph/cdg6U1uR2MiWI8916oao6ppXfykqqp9xRjFth2p4E/bjnLj6VOYPibV7XCi0rjhSVYWYjqwNjtKWc914HIvh6PvQXm+25GYCBboCo3GdPCfz+5k5LAEvnjWVLdDiVpZ6Ykcr2qg1WurWhsT1epKwRMH8cPcjmTom32h87jzL+7GYSKaJdemz949VMY7+WXcsmoqwxICXeTTBFvW8CRavUpxdaPboRhj3FRX5sxxbau19m7EZBiTC7ssuTahY8m16bNfv3qQ9KQ4Ll9sy8i6aVz6P+e6NsZEsfpyKwnpi9kXwuG3oPqE25GYCGXJtemTw6V1PPfRca5eOpHkeOu1dtM/57q2umtjolpdmQ1m7IvZFwLqLCpjTAhYcm365IktRxDg2mU5bocS9fyrNB6znmtjolt9GSTbHNcBGz0bRkx1Vms0JgQsuTYB83qVp7cWcvr0UYxNt1m93JaeFEdyfAyFNh2fMdHNeq77RgRmXwAHX4WGrmauNGZgQppci8hqEdktIvtE5LYu9ieIyOO+/W+LSE6n/RNFpEZE/i2UcZrAvH2wjMKKei5ZON7tUAwgIr65rq0sxJioperrubbkuk+mnwfeFjjwstuRmAgUsuRaRGKA+4DzgTnAVSIyp9NhNwDlqjoNuAe4u9P+/wb+GqoYTd88vbWAYQmxnDtnrNuhGB9nrmvruTYmajVWOUmi9Vz3zYSlkJgOe55zOxITgULZc70E2KeqB1S1CXgMWNPpmDXAOt/zJ4FzRJy5hETkU8BBYEcIYzQBamn18vxHJ/jY7NEkxdtqjEPFuPQkCq3n2gTJQL5tFJFTRORNEdkhIh+IiNWODQb/6ozJme7GEW5iYmHax2Dv8+D1uh2NiTChTK7HA0favS7wbevyGFVtASqBTBEZBnwL+GEI4zN9sOVQORV1zZw713qth5Jxw5MoqWmksaXV7VBMmBvIt40iEgv8Dviiqs4FzgKaByn06FZb4jymjHQ3jnA0/TyoLYZjW92OxESYoTqg8QfAPapa09NBInKjiGwRkS3FxcWDE1mUen7HCeJjPZwxY5TboZh2snwzhhy3ZdDNwA3k28Zzge2q+j6Aqpaqqv3FNxjqLLnut2kfA/FYaYgJulAm14VA+1VGsn3bujzG1/ORDpQCS4Efi0g+8FXgOyJyS+cbqOqvVDVPVfNGjbKkL1RUlRd2HmfltJG2IuMQM8431/VRKw0xA9fvbxuBGYCKyHMi8p6IfLO7m1inSJD5e66TLbnus5RMyF5sybUJulAm15uB6SIyWUTigSuBDZ2O2QBc53t+KfCSOk5X1RxVzQF+Bvynqv4yhLGaHuwvruFIWT3nzB7tdiimE/9c10dtOj7jrlhgJXC17/FiETmnqwOtUyTIan1/oFjPdf9MPxeObYPq425HYiJIyJJrX6/GLcBzwE7gCVXdISJ3iMhFvsMexKmx3gd8HThpAI1x3+t7nZ6RM6bbB+FQ07ZKo80YYgZuIN82FgCvqmqJqtYBG4GFIY/YQF0pxCVDfIrbkYSnGec5j3tfcDcOE1FC+h2/qm7EaWTbb7u93fMG4LJervGDkARnAvb6vlImZSYzYUSy26GYTpLiYxiREs9Rq7k2A9f2bSNOEn0l8JlOx/i/bXyTdt82ishzwDdFJBloAs7EGfBoQq222HqtB2LMPEgbD3v+Bgs/63Y0JkIM1QGNZohoafXy1oFSVkyzxnuoykpPtLIQM2AD+bZRVctx1iXYDGwD3lPVZwf7PUSl2hKrtx4IEac05MAr0NLodjQmQtjoNNOj9wsqqWlsYaUl10PWuOFJHC6tczsMEwEG8m2jqv4OZzo+M5hqiyE1y+0owtuM8+Ddh+DQGzD1bLejMRHAeq5Nj17fW4IILJtiCxQMVeOs59qY6FVXamUhAzX5TIhNhN1/czsSEyEsuTY92rS/hLnj0shIiXc7FNONrOFJVDe2UNVga3YYE1VUnbIQS64HtL/UnQAAIABJREFUJj4ZppwFe/7q/E6NGSBLrk23mlq8bDtSwZIc67UeysYN980YYnNdGxNdGquhtdFqroNhxmqoOAxFO92OxEQAS65Nt3YcraSxxUteTobboZgejEv3zXVt0/EZE13aVme0aVIHbMZq53HPX92Nw0QES65Nt949VA5A3iRLrocyf8+11V0bE2VqbenzoEnLgqwFVndtgsKSa9OtLfnlTBiRxOi0RLdDMT0YnZqAR6wsxJioY8l1cM38BBRshppityMxYc6Sa9MlVWXLoXLyJo1wOxTTi9gYD2PTEq0sxJho41/63Gqug2PmakBh73NuR2LCnCXXpkuHy+ooqWlkkZWEhIWs4UlWFmJMtKmznuugGnuKs1rjbqu7NgNjybXpUlu9tQ1mDAtZ6YkcsyXQjYkutaUQPwziktyOJDKIOAvK7H8Zmq09Nf1nybXp0pZD5aQmxjJjdKrboZgAjB+exLGKBrxem6PVmKhRWwzJNlVqUM04H5pr4eCrbkdiwpgl16ZL7x+pYMGE4Xg84nYoJgBZ6Yk0tXoprW1yOxRjzGCpK7Fp+IJtypmQkAYfPeN2JCaMWXJtTtLQ3MqeE9Xkjk93OxQTIJuOz5goVFNsyXWwxSbArE/Czr9Ai3VWmP6x5NqcZPfxappb1ZLrMNK2SqPNGGJM9Kg5Aalj3I4i8sy9GBor4cDLbkdiwpQl1+YkHxRWAjDPkuuw4U+uC22ua2OiQ2uLU3M9zJLroJuyChLT4cOn3I7EhClLrs1JPiysZHhyHNkZNgI9XGQkx5EQ6+GYlYUYEx3qSgCFYaPdjiTyxMbDrAth90abNcT0iyXX5iQfFFaSOz4dERvMGC5EhPHDk2whGWOiRfVx53HYWHfjiFRzL4bGKltQxvSLJdemg4bmVnYft8GM4Wh8RhJHyiy5NiYq1BQ5j1YWEhpTzoLULNj6qNuRmDBkybXpYPfxalq8NpgxHE0YkcyR8jq3wzDGDIaaE86jDWgMjZhYmH8V7HsBqo66HY0JM5Zcmw5sMGP4mpCRTEVdM9UNzW6HYowJtRpfWUiK1VyHzKnXgHrh/T+4HYkJM5Zcmw4+LKwkwwYzhqUJI5z/ZlYaYkwUqClyZrSIS3Q7ksiVORUmrYCt/7+9O4+vqr7zP/76ZF9YkkAACUHCpoLgBmhtcS3j1kqtqNhF21q1rUy3aaeOM+PYfexmf63+HG3VWjektCrTolaL1boUWVxS9rAH2QIhELZs3/njHDDGQFjuvd9zz30/H49Lzj333OR9DzfnfHLud3kYnGa/lUOn4lre4+3aBk5UZ8a0VFlaBKCmISKZYMcGdWZMhVM+DVtXwMoXfSeRNKLiWvbTzIzprbIsLK63qriWI2NmF5rZEjOrMbObO3k838weDx+fbWaDOjw+0MwazewbqcqcsRo3aRi+VBh5GRT1hr/f7TuJpBEV17KfOjOmt9KiXIrzsqmtV7MQOXxmlg3cBVwEjACuNrMRHTa7Dqh3zg0F7gBu7/D4z4Cnk51VCDo0aqSQ5MstgLGfh6XPQN0y32kkTai4lv32dWYcNUDFdToys2DEEF25liMzDqhxzq1wzjUBU4GJHbaZCDwYLk8HzrewDZmZfQxYCSxIUd7M5Vw49bmahaTE2OsgO09Xr+WQqbiW/aprg86MFSXqzJiuNByfHIUKYG27+7Xhuk63cc61AA1ALzPrBnwL+HYKckpTIzTvUrOQVOnWB0ZfBW8+AtvX+04jaUDFtexXvU6dGdNdZWkRa7fuxqlnu6TWbcAdzrnGrjY0sxvMbK6Zzd28eXPyk8XRjnCMazULSZ3x/wKtzfDyz3wnkTSg4loAdWaMi8qyQnY3t1LX2OQ7iqSfdUBlu/sDwnWdbmNmOUBPYAtwOvAjM1sFfBW4xcymdPZDnHP3OufGOOfGlJeXJ/YVZIrt4X9Lj/5+c2SSsio45ZMw7zfQUOs7jUScimsB3u3MOFrtrdOahuOTozAHGGZmVWaWB0wGZnTYZgZwbbg8CZjlAuOdc4Occ4OAnwM/cM7dmargGWffjIE9OrbakaQ665tBe/eXfuw7iUScimsB4G3NzBgLGo5PjlTYhnoK8CywCJjmnFtgZt8xs0vDze4jaGNdA3wdeN9wfZICunLtR8lAOO0zMP8h2LjQdxqJsBzfASQa/qHOjLGwb2ZNDccnR8I5NxOY2WHdre2W9wBXdPE9bktKOHnX9nVQWAa5Ol6n3Lm3QPXv4JlvwTUzQH2UpBO6ci2AOjPGRXF+Dr275bFmi65ci8TW9nfUJMSXojI4999h5Uuw+I++00hEqbiW/Z0Z1d46Hgb1KmbVlp2+Y4hIsjSsg54qrr0Z8zkoPwGevQWadCFD3k/FtbBYMzPGSlXvYlbWqbgWia3t69Te2qfsHLj4x7BtDfz1h77TSASpuJb9MzOqM2M8DOpdzKYde2nc2+I7iogkWvNu2L1VxbVvVePh1GvgtTvhnTd8p5GIUXEt6swYM4N7FwOwSlevReJn/zB8A/zmEJjwXSjuA0/9czDBjEgoqcW1mV1oZkvMrMbM3jdkk5nlm9nj4eOzzWxQuH6Cmc0zs+rw63nJzJnpqtc1MGpAiTozxkRVeVBcr1BxLRI/GoYvOgpL4JKfwMZqePWXvtNIhCStuDazbOAu4CJgBHC1mY3osNl1QL1zbihwB3B7uL4O+KhzbhTBhAUPJStnpnt3ZsYevqNIggzqpSvXIrGlCWSi5YSPBre//jdsXuI7jUREMq9cjwNqnHMrnHNNwFRgYodtJgIPhsvTgfPNzJxzbzjnwiMIC4BCM8tPYtaMtWj99rAzY4nvKJIgBbnZVJQUqlOjSBztm3pbV66j45KfQV4xPHGjmocIkNziugJY2+5+bbiu023C2cEagF4dtrkcmO+c25uknBltX2dGDcMXL1W9i9UsRCSOGmqDCWTyinwnkX269YGP3BF0bPzbT32nkQiIdIdGMxtJ0FTkxgM8foOZzTWzuZs3b05tuJh4u7aB3t3yOaZnge8okkBVvYtZubkR55zvKCKSSPWroHSQ7xTS0ciPwagr4KUfa/QQSWpxvQ6obHd/QLiu023MLAfoCWwJ7w8AngCucc4t7+wHOOfudc6Ncc6NKS8vT3D8zFBd28DoAZqZMW4G9S5m+54Wtu5s8h1FRBKpfhWUHus7hXTm4h9DcTk88QVo3uM7jXiUzOJ6DjDMzKrMLA+YDMzosM0Mgg6LAJOAWc45Z2YlwJ+Am51zryQxY0bb1dTCsk07NHlMDO0bjk/trkVipK0VGtbqynVUFZbCxDth82KY9V3facSjpBXXYRvqKcCzwCJgmnNugZl9x8wuDTe7D+hlZjXA14F9w/VNAYYCt5rZm+GtT7KyZqoF72ynzam9dRwN7dMNgGWbGj0nEZGE2b4O2lqgRFeuI2voh4Pp0V+7C1a97DuNeJKTzG/unJsJzOyw7tZ2y3uAKzp53veA7yUzmwTtrQFduY6hipJCivOyWbJhh+8oIpIo9auCr7pyHW0Tvgsr/gq/vx6+8DIUdxynQeIu0h0aJbmqa7fRr0cBfXqoM2PcZGUZQ/t2Z+lGFdcisVG/Oviq4jra8rvBpAdgVx08+UVoa/OdSFJMxXUGe3tdA6PUJCS2juvbTcW1SJzUrwLLgp6a+jzy+p8M//R9WPYs/P0u32kkxVRcZ6gde5pZsXkno9UkJLaG9+1OXWMTdY0aIl4kFupXBYV1dq7vJHIoxl0fzN74/G2w+lXfaSSFVFxnqH2Tx+jKdXwd1687gK5ei8TFttVqEpJOzODSO4P/s8c/9W6beYk9FdcZSp0Z4++4vmFxrU6NIvFQv0ojhaSbwhK4+vFglJdHJ8Oe7b4TSQqouM5Q81fXM6hXEb265fuOIklS3j2fkqJclujKtUj6210POzdDr6G+k8jh6j0Urvwt1C2Fx66Gpl2+E0mSqbjOQM455q+p59RjS31HkSQyM07o14MF7+hKiUjaq6sJvpYf5zeHHJnB58Bl98DqV+DxT2oGx5hTcZ2B1mzdRV1jE6epuI69UQN6snj9DppaNBSUdM3MLjSzJWZWY2Y3d/J4vpk9Hj4+28wGhesnmNk8M6sOv56X6uyxV7c0+Np7uN8ccuRGXxHM4Lh8FjwyCXZv851IkkTFdQaat7oeQMV1BhhV0ZOm1jZ1apQumVk2cBdwETACuNrMRnTY7Dqg3jk3FLgDuD1cXwd81Dk3CrgWeCg1qTNI3VLIylWb63R3yqfg47+CNX+HBy6CbWt8J5IkUHGdgeatrqd7fg7D+nT3HUWSbF+H1X2jw4gcxDigxjm3wjnXBEwFJnbYZiLwYLg8HTjfzMw594Zz7p1w/QKg0MzUoSOR6pZBryGQndSJlSUVRl8Jn5oODbXwP+Nh6bO+E0mCqbjOQPNW13PywBKys8x3FEmyY3sV0b0gR8W1HIoKYG27+7Xhuk63cc61AA1Ax7mdLwfmO+c0wHoi1S2F3sN8p5BEGXwO3PgilAyER6+EZ25RR8cYUXGdYXbsaWbJxh1qEpIhzIxRFT2prlVxLclnZiMJmorceJBtbjCzuWY2d/PmzakLl85amqB+pdpbx03ZYLjuORh7fTCL4/98CFa/5juVJICK6wzz1toGnFN760wyakBPFm/Yzt6WVt9RJNrWAZXt7g8I13W6jZnlAD2BLeH9AcATwDXOueUH+iHOuXudc2Occ2PKy8sTGD/G6pYG4yT36dgEXtJebgFc8hO49n+hrTloh/30zbqKneZUXGeY2Su3kJ1lnFxZ4juKpMhpA0tpbnX7Jw4SOYA5wDAzqzKzPGAyMKPDNjMIOiwCTAJmOeecmZUAfwJuds69krLEmWJDdfC132i/OSR5qs6CL74GYz8Ps++Ge8ZD7TzfqeQIqbjOMK8u38LoAT3pXpDrO4qkyNhBZQC8vnKr5yQSZWEb6inAs8AiYJpzboGZfcfMLg03uw/oZWY1wNeBfcP1TQGGArea2ZvhrU+KX0J8baiGnMKgQ6PEV3634Cr2NTOCcbDvmwCzvhc0C5K0ouI6gzTubeGttds4c0jH/kcSZ6XFeRzXtzuzVVxLF5xzM51zw51zQ5xz3w/X3eqcmxEu73HOXeGcG+qcG+ecWxGu/55zrtg5d3K72yafryVWNlZD3xGQle07iaTC4LPhS6/C6KvgpR/Dgx+FHRt8p5LDoOI6g8xZuZWWNseZQ3r7jiIpNq6qjHmrttLSqslkRNKKc8GV636jfCeRVCroCZfdDZffBxvehnvOhjWzfaeSQ6TiOoO8uryOvJwsdWbMQOOqytjZ1MrC9ZoKXSStNNTC7nroe6LvJOLDqEnw+echtxAe/Ags7NgNQqJIxXUGeaVmC6cNLKUgVx8tZprTq4J2168u3+I5iYgclnVzg68Vp/rNIf70HQnXz4L+p8DvroW5D/hOJF1QcZ0htjTuZdGG7WpvnaH69Cjg+H7deWGxmsGKpJW1r0NOAfRVs5CMVlQGn34Shk6AP34VXvmF70RyECquM8SsxZtwDs49Xh34M9V5x/dh7up6tu9p9h1FRA7V2teh/6mQk+c7ifiWVwSTH4GRl8Fz/wmz7/WdSA5AxXWGmLV4E/16FDCyfw/fUcSTc4/vQ2ub429L63xHEZFD0bwH1r8FlWN9J5GoyM6Fj/8Kjv8IPP1NmP9b34mkEyquM8DellZeWrqZ807og5n5jiOenFJZQmlRLs8s0JBOImlh/ZvBrH0DxvlOIlGSnQuT7oehH4YZX4YFT/pOJB2ouM4As1dsZWdTK+erSUhGy8nO4uJRx/D8wo3s3NviO46IdGXFXwGDY8/0nUSiJicfrnoYKsfBH26ANX/3nUjaUXGdAWZWr6c4L5sPDtX41pnuY6dUsLu5lecXbfQdRUS6snxWMEpIUZnvJBJFuYVw9VQoqYTHJkPdMt+JJKTiOub2trQys3o9F4zspyH4hNMGllJRUsi0uWt9RxGRg9m9DWrnwuBzfSeRKCsqg09OB8uGhy+HRo0IFQUqrmPuxSWb2b6nhUtP7u87ikRAVpbxyTMG8krNFpZs2OE7jogcyMoXwbXCEBXX0oWyKvjEtKCwfvQqaNrpO1HGU3Edc0+9+Q69ivP4kJqESOjqsQMpyM3i/pdX+o4iIgeycAYUlkHl6b6TSDoYcBpc8UDQCfZ3n4VW9avxScV1jG3asYc/L9zApSf3Jydb/9USKC3O46oxlUyfX8vyzY2+44hIR827YekzcMJHg5EhRA7FcRfBJT+FZc/Cn74GzvlOlLFUccXYY7PX0tzq+PQZx/qOIhHzz+cPoyAnix/OXITTAVgkWpY9B02NwWQhIodjzOdg/DeC8a9f/JHvNBlLxXVMNbe28cjs1Zw9vJzB5d18x5GI6d0tny+fP4znF21i+rxa33FEpL35v4Vu/WDQeN9JJB2d9x9w0ifgrz/QJDOeqLiOqT/Mr2XTjr185sxBvqNIRH1+/GBOryrjP5/6B3NWbfUdR0QAtiyHmufhtM9Ado7vNJKOzODSX8CQ8+B/vxq035eUUnEdQ3tbWvnFX2o4qbKEc44r9x1HIio7y7jzE6fSv6SQz9z/Ok+8UdtlExHnHFsa97Lwne2sqtvJriZ1mhFJqNn3QFZ2UFyLHKnsXLjyt1BxGkz/LCx8yneijKI/i2PogVdWsW7bbn748VGa7lwOqrx7Po9dfwY3PTKfrz3+Fve+tJIJI/oyoLQQHGzZ2cQ723ZTW7+L2vrd1NbvZndz6/7nm8GQ8m58cEgvLh51DOOqyvSeEzlS29bAvAfgpMnQ4xjfaSTd5XeHT/0eHpkUjCBy+a/hxI/7TpURVFzHzNqtu/j580uZMKIvZw3XVWvpWt8eBUy94Qz+MH8dD89ezS9nLXtPJ/OehbkMKC1kcHkxZw8vZ0BpIX16FLC7qZW19bt4a+02ps5Zy4Ovrea4vt25bnwVE0/uT36OJi0SOSx/+S5gcPbNvpNIXBT0CArshyfB9M8Ff8B98CvBlRFJGhXXMdLU0saXp75BTlYWt1060nccSSM52VlcObaSK8dWsquphS2NTQD06pZHUV7Xh4mde1uYWb2e+15eyb9Of5s7nlvKF88ZwpVjKjUzqMihWDwTqqfBWf8aTGctkij53eGaJ+HJL8Lz/wV1S+Hin0Beke9ksaU21zHR1ub4jyereWPNNm6/fDQVJYW+I0maKsrLobKsiMqyokMqrAGK83O4YkwlT39lPA9+bhwVJYXc+tQCzvrRC9z38kp2N7V2/U1EMlVdDTz1Jeh7Ipz1Td9pJI5yC+Hy++Hsb8Gbj8A946F2nu9UsaXiOgaaW9u45Ylqps2t5cvnDeWS0WqrJ36YGWcPL+d3X/gAj15/OoPLi/nuHxcy/kezuOfF5ezcqw6QIu9RVwMPXwaWBVc9BDl5vhNJXGVlwbm3wDUzoHkP/Pp8ePIm2L7ed7LYSWpxbWYXmtkSM6sxs/c1IjOzfDN7PHx8tpkNavfYv4Xrl5jZBcnMmc4Wb9jOlfe8xtQ5a5ly7lC+NmG470gimBlnDunN1Bs+wLQbP8AJx/Tgh08v5kO3z+KuF2rYurPJd0TphI7ZKeQcvPko/Po8aNoVtIstG+w7lWSCwWfDl16FM6fA24/D/xsNT3wR1s7RrI4JYsmanc3MsoGlwASgFpgDXO2cW9humy8Bo51zXzCzycBlzrmrzGwE8BgwDugPPA8Md84d8LPlMWPGuLlz5ybltUTNrqYWXqnZwu/n1fLnhRvoWZjLtyeeyKUn9fcdTeSA5q+p55d/WcYLSzaTZXB6VS/OPb6cUweWcmJFz4xvm21m85xzYzz+/JQesyGzjtv7NayDpU/DnPth0wKoPAMuu1uFtfixdSW8dlfwh17zTuhRAcMvhMrTYcAYKK0KrnjL+xzsmJ3MDo3jgBrn3IowxFRgIrCw3TYTgdvC5enAnRaM4zURmOqc2wusNLOa8Pu9lsiAC9/ZTm39LgAc7f9gc/uX961yDlx4L1jet/7dP07et02777FvO9fum7oOP8d1sr6ltY36Xc007G5m0449LN3YyKq6nbS0OXoW5nLj2UO4fvxgyor1UaJE26kDS3ngs+NYvGE7f3xrPc8s2MAPZi4GgjG3K0oKGVhWxDE9C+hRmEv3ghx6FOSSn5tFTpaRnZVFbraRnWXkZBnQdW/3Q+kQfyh95g9neMH+JQWM7N/zkLePkMgfs2lYB+vfAtodYA97maN47mEuN++G3Vth11ZoWAubFsOOd4LH+oyAj90NoyereBF/yqrgkp/A+f8JS54OJpx5ayrMvS94PDsfSgYGt6JeUFgKhSWQ1w2y84LxtLPzwltO0LwJeM+Rdf/x07q43+F5qZJXHFzNT6BkFtcVwNp292uB0w+0jXOuxcwagF7h+r93eG5Fxx9gZjcANwAMHDjwsAM+PHs1j85ec9jP86FHQQ69u+UzuLwbF4zsy5lDejN2UBl5OTooS3o5vl8Pju/Xg29ccBybd+xl/pp6qmsbWLN1F2u27uJvy+rYsaeZnWnaCXLy2Er++/LRvmMciaQfs+Eoj9urX4E/XH94z/EtKycoSrr3C07gfU+EoR+G8uM0HJpER0HPYHz1kyZDawtsXgzr5gYzhtavCv443FIDe7bBngbfaROr93CYMieh3zKth+Jzzt0L3AvBx4uH+/ybzh3KJ8a9e3A3Awv/ajJ797j3nnXttqXT9faebdo/9z0/J1xhvPfnvLsc/JOTlUWPghxyslVES/yUd8/ngpH9uGBkv/c91tLaRuPeFppa2mhuc7S2Olra2mhtczS3dv3r7jiEbZLQKq6kKDfx3zRGjuq4PfTDcMOLwbK1+/TiqJdJwvc0yMkPhkFTES3pJDsH+p0Y3DrT1gpNO6GtBVqbobUpvDXzvk+J9n9U7w5+/z3rUiynIPHfMuHf8V3rgPaDdQ4I13W2Ta2Z5QA9gS2H+NyjVlFSqCHrRCIqJzuLkiI1d0qhyB+zKSoLbiLiT1Z2MDmNHFAyL4fOAYaZWZWZ5QGTgRkdtpkBXBsuTwJmuaBx8gxgctgzvQoYBryexKwiIplOx2wRkQRI2pXrsD3eFOBZIBu43zm3wMy+A8x1zs0A7gMeCju/bCU4mBNuN42gI00LcFNXvc5FROTI6ZgtIpIYSRuKL9UyckgnEYkN30Px+aDjtoikq4Mds9VLTkREREQkQVRci4iIiIgkiIprEREREZEEUXEtIiIiIpIgKq5FRERERBJExbWIiIiISIKouBYRERERSZDYjHNtZpuB1eHd3kCdxzidiVom5ela1DJFLQ9EL1M65znWOVeezDBR0+G4nSpRe48kShxfl15TesjU13TAY3Zsiuv2zGxu1CZjiFom5ela1DJFLQ9EL5PySFfi+n8Sx9el15Qe9JreT81CREREREQSRMW1iIiIiEiCxLW4vtd3gE5ELZPydC1qmaKWB6KXSXmkK3H9P4nj69JrSg96TR3Ess21iIiIiIgPcb1yLSIiIiKScrEqrs3sCjNbYGZtZjamw2P/ZmY1ZrbEzC7wkO02M1tnZm+Gt4tTnaFdlgvD/VBjZjf7ytEuzyozqw73y1xPGe43s01m9o9268rM7DkzWxZ+LfWcx9t7yMwqzewFM1sY/o59JVzvZR8dJI/PfVRgZq+b2Vthpm+H66vMbHb4+/a4meWlKpN0LkrH46MVteN5IkThnJAIUTuvJELUzk2JkIzzW6yKa+AfwMeBl9qvNLMRwGRgJHAh8P/NLDv18bjDOXdyeJvp4ecTvu67gIuAEcDV4f7x7dxwv/gazuc3BO+N9m4G/uKcGwb8JbzvMw/4ew+1AP/inBsBnAHcFL5vfO2jA+UBf/toL3Cec+4k4GTgQjM7A7g9zDQUqAeuS2EmOTDvx+OjFeHjeSL4Pickwm+I1nklEX5DtM5NiZDw81usimvn3CLn3JJOHpoITHXO7XXOrQRqgHGpTRcZ44Aa59wK51wTMJVg/2Q059xLwNYOqycCD4bLDwIf85zHG+fceufc/HB5B7AIqMDTPjpIHm9coDG8mxveHHAeMD1cn9L3kcSejucRFrXzSiJE7dyUCMk4v8WquD6ICmBtu/u1+DkRTzGzt8OPVXx9FBSVfdGeA/5sZvPM7AbPWdrr65xbHy5vAPr6DBPy/h4ys0HAKcBsIrCPOuQBj/vIzLLN7E1gE/AcsBzY5pxrCTeJwu+bBLz/LiVAFI/niRDVc0IieD9mJkkcfp8Sdn5Lu+LazJ43s390cvP+13oX2e4GhhB8XLwe+KnXsNHyIefcqQQfbd5kZmf5DtSRC4bV8T20jvf3kJl1A34PfNU5t739Yz72USd5vO4j51yrc+5kYADBVcXjU/nz5V06Hqe1yJ8TEiEi55VEiMXvUyLPbzkJzpZ0zrkPH8HT1gGV7e4PCNcl1KFmM7NfAX9M9M8/RCnZF4fDObcu/LrJzJ4gKEpeOvizUmKjmR3jnFtvZscQXI30xjm3cd+yj/eQmeUSHHgecc79IVztbR91lsf3PtrHObfNzF4APgCUmFlOePXa++9bpkiT4/HRitzxPBEifE5IhEidVxIhKsfdo5Ho81vaXbk+QjOAyWaWb2ZVwDDg9VQGCP9j9rmMoPOlD3OAYeEIBnkEHT1neMqCmRWbWfd9y8A/4W/fdDQDuDZcvhZ4ymMWr+8hMzPgPmCRc+5n7R7yso8OlMfzPio3s5JwuRCYQNB27wVgUriZ9/eRROp4fLQidTxPhIifExIhUueVREj336dknN9iNYmMmV0G/BIoB7YBbzrnLggf+3fgcwS9Qr/qnHs6xdkeIvjIxAGrgBvbteVJqXCYnJ8D2cD9zrnv+8gRZhkMPBHezQEe9ZHHzB4DzgF6AxuB/wKeBKYBA4HVwJXOuZR05DhAnnPw9B4ysw8BfwOqgbZw9S0E7dJSvo8Okuc4AlrDAAAAk0lEQVRq/O2j0QSdXrIJLlxMc859J3yPTwXKgDeATznn9qYik3QuSsfjoxWl43kiROWckAhRO68kQtTOTYmQjPNbrIprERERERGfMqVZiIiIiIhI0qm4FhERERFJEBXXIiIiIiIJouJaRERERCRBVFyLiIiIiCSIimsRERERkQRRcS0iIiIikiAqrkVEREREEuT/AFFCvaA1Hd/JAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0q50OYR6LEkN",
        "outputId": "d310a2af-3252-4209-8641-dad46431f908"
      },
      "source": [
        "# Kurtosis for skewness\r\n",
        "pf1_data.skew()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Elimination Week Number     -0.103362\n",
              "Number of Evictions Faced    0.805509\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mRTcgY4dLHNY",
        "outputId": "4b1515ef-3700-4a49-9487-38f015b08cc6"
      },
      "source": [
        "pf1_data.kurt()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Elimination Week Number     -0.722060\n",
              "Number of Evictions Faced   -0.076087\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "id": "POpYEjvyLJNO",
        "outputId": "046b547a-136c-4cb4-e6ff-df2230688fa8"
      },
      "source": [
        "pf1_data.corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Elimination Week Number</th>\n",
              "      <th>Number of Evictions Faced</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Elimination Week Number</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.651989</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Number of Evictions Faced</th>\n",
              "      <td>0.651989</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                           Elimination Week Number  Number of Evictions Faced\n",
              "Elimination Week Number                   1.000000                   0.651989\n",
              "Number of Evictions Faced                 0.651989                   1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "yZ06kmOmLMD6",
        "outputId": "4375fefc-0f53-4f14-a2f4-7a511c37dcf4"
      },
      "source": [
        "sns.heatmap(pf1_data.corr())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f22b26fd1d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZdlGIKkMLQQR",
        "outputId": "6300cde5-eb7b-4ae1-8e7c-ca52d4eae205"
      },
      "source": [
        "# Shapiro\r\n",
        "\r\n",
        "test, p_val = stats.shapiro(pf1_data[\"Number of Evictions Faced\"])\r\n",
        "print('P value for Shapiro: ', p_val1)\r\n",
        "\r\n",
        "test, p_val2 = stats.shapiro(pf1_data[\"Elimination Week Number\"])\r\n",
        "print('P value for Shapiro: ',p_val2)\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "P value for Shapiro:  1.0\n",
            "P value for Shapiro:  1.0998514599069864e-11\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "k1_vvAxOL4EH",
        "outputId": "50e1efb1-fd2c-4aa3-e711-23c088d0428c"
      },
      "source": [
        "# For Annova hypotheses\r\n",
        "sns.boxplot(x = \"Number of Evictions Faced\", y = \"Elimination Week Number\", data = pf1_data,palette=['blue','red','black'])\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEGCAYAAAB/+QKOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3de5wcVZ338c83CWOQcJUIIxCCDuImPDDgLOJlJYoi8CCsLrqBxIV4iSgu4LM+XtZn1HXU9b64wipICLoJoK6i6CrCsiaRdaOGMEASVEYuQpxAopLAcmmT/J4/qpp0OtXdNTNd3XP5vl+vfk1X1elf/aamp0+fqjrnKCIwMzOrNqndCZiZ2ejkCsLMzDK5gjAzs0yuIMzMLJMrCDMzyzSl3Qk00/777x8zZ85sdxpmZmPGrbfeuikipmdtG1cVxMyZM1m1alW70zAzGzMk3V9rm08xmZlZJlcQZmaWyRWEmZllcgVhZmaZXEGY2ZjS39/PPvvswx133NHuVMa9wioISYdI+rGkdZLWSrowXb+fpJsk3Z3+3LfG689Jy9wt6Zyi8jSzsWX+/Pls3ryZs88+u92pjHtFtiC2An8XEbOA44HzJc0C3g/cHBGHAzenyzuRtB/wYeBFwHHAh2tVJGY2cfT397N27VoA1q5d61ZEwQrrBxERg8Bg+vxRSXcBBwFnAHPSYl8FlgHvq3r5a4CbIuIPAJJuAk4GrikqXzMbuosuuoj+/v6d1g0MDADQ1dW10/ru7m4uvvjiEe1v/vz5Oy2fffbZrFmzZtjx8uY/3NyLjl+0lnSUkzQTOAb4GXBAWnkAbAAOyHjJQcADFcsPpuuyYi8EFgLMmDGjOQmb2bA99thjhcUutx5qLTdDkfm3In4zqegJgyRNA5YDH4+Ib0t6JCL2qdj+x4jYt+o17wGmRsTH0uVe4ImI+Gy9ffX09IR7Upu115w5cwBYtmxZ02MfeeSRO1UKs2fPHlELIkuR+bci/lBJujUierK2FXoXk6TdgG8BSyPi2+nqhyR1pts7gYczXroeOKRi+eB0nZlNYEuWLNlp+eqrr25TJhNDkXcxCVgE3BURn6/YdD1QvivpHOC7GS//EXCSpH3Ti9MnpevMbALr7u5m9uzZQNJ6OOqoo9qc0fhWZAvipcCbgFdK6k8fpwKfBF4t6W7gVekyknokXQGQXpzuA36RPj5avmBtZhPbkiVL2Hvvvd16aIEi72K6BVCNzSdmlF8FvLVi+UrgymKyM7Oxqru7m0ceeaTdaUwI7kltZmaZXEGYmVkmVxBmZpbJFYSZmWVyBWFmZplcQZiZWSZXEGZmlskVhJmZZXIFYWZmmVxBmJlZJlcQZmaWyRWEmZllcgVhZmaZXEGYmVmmlsxJbWbZ8k5qD6N3Ynsbv1xBmI0yY2lSexvfXEGYtVFWi2C0TWpvE1dhFYSkK4HTgIcj4sh03deBI9Ii+wCPRER3xmvvAx4FtgFbI6KnqDzNzCxbkS2Iq4BLgK+VV0TEX5efS/ocsLnO618REZsKy87MzOoqck7qFZJmZm2TJOCNwCuL2r+ZmY1Mu25z/QvgoYi4u8b2AG6UdKukhfUCSVooaZWkVRs3bmx6omZmE1W7KoizgGvqbH9ZRBwLnAKcL+nltQpGxOUR0RMRPdOnT292nmZmE1bLKwhJU4DXA1+vVSYi1qc/HwauA45rTXZmZlbWjhbEq4BfRsSDWRsl7SFpz/Jz4CRgTQvzMzMzir3N9RpgDrC/pAeBD0fEImAuVaeXJD0HuCIiTgUOAK5LrmMzBbg6Im4oKk+z8cw9tW0kiryL6awa68/NWPc74NT0+T3A0UXlZTbRuae25eWe1GbjmHtq20h4NFczM8vkCsLMzDK5gjAzs0x1KwhJkyV9tlXJmJnZ6FG3goiIbcDLWpSLmZmNInnuYrpN0vXAN4H/Ka+MiG8XlpWZmbVdngpiKvB7dh55NQBXEGZm41jDCiIiFrQiETMzG10aVhCSng98CTggIo6UdBRwekR8rPDsrHBjfSiGovMf68dnrMt7/PMc+6xYWcplyh0Ka5kIf+88p5i+Avxf4DKAiLhD0tWAK4hxaqwPxVB0/mP9+Ix1wz3+/f39LF++PHf5oZQdr/JUEM+MiJ+ng+eVbS0oH2uxsT4UQ9H5j/XjM9b5+LdXno5ymyQ9j+TCNJLOBAYLzcrMzNouTwvifOBy4AWS1gP3AvMKzcrMzNouz11M9wCvSifvmRQRjxaflpmZtVvDU0ySniXpn4GfAMskfUHSs4pPzczM2inPNYhrgY3AXwFnps9rzic9VgwODnLCCSewYcOGdqdiZjYq5bkG0RkRfRXLH5P0141eJOlK4DTg4Yg4Ml33EeBtJJUMwN9HxA8yXnsy8AVgMslUpJ/MkeeQ9PX1ccstt9DX18ell17a7PBmNkTupzD65KkgbpQ0F/hGunwm8KMcr7sKuAT4WtX6f4qImiPESpoMXAq8GngQ+IWk6yNiXY595jI4OMjixYvZvn07ixcvpre3lwMPPLBZ4c1sGPr7+1m9YgWzOjrqltt9a3KX/ZMrV9Yss65UampuE1XNCkLSoyS3tgq4CFiSbpoEPAa8p17giFghaeYwcjoOGEgvjiPpWuAMoGkVRF9fH9u3bwdg27ZtbkUUyD2Rd/A35PrK74tGDp2Sb6bkvPGaZTz+fWse6YjYs6B9vkvS3wCrgL+LiD9WbT8IeKBi+UHgRbWCSVoILASYMWNGrgSWLl1KKf2GUSqVWLJkiSuIFpqoPZH9DXl86+/vZ8WK1XR0zKpbbuvW3QFYufLJmmVKpaZ9Hx6RXFVxOv7SzMrywxzu+0tAH0nLpA/4HPDmYcR5WkRcTtJPg56ensjzmnnz5rFo0SJKpRIdHR3Mnz9/JClYHe4Ju7NZHR1c29k54jhzB8dfX9Wuri6e3LSpacdnalULtRU6OmbR2XntiOMMDs5tQjYjl2ewviuBo4C1wPZ09bCG+46IhyrifgX4fkax9cAhFcsHp+uapre3l8WLFwMwefJkent7mxnezIZpXanUsPK7P21h1TvVtK5U4timZjYx5WlBHB8R9dtMOUnqjIjyX/91wJqMYr8ADpd0GEnFMBc4uxn7L+vs7GTBggVcdtllLFiwwBeozUaB7u7uXOWeSM/hT61T/tghxLPa8lQQ/y1p1lDvIpJ0DTAH2F/Sg8CHgTmSuklaIPcBb0/LPofkdtZTI2KrpHeR3Ck1GbgyItYOZd959Pb2snbtWrcezEaJvBdkJ/IpylbLU0F8jaSS2AA8RXJXU0TEUfVeFBFnZaxeVKPs74BTK5Z/AOzSP6KZOjs7PZyvmVkdeSqIRcCbgDvZcQ3CzMzGuTxDbWyMiOsj4t6IuL/8KDyzgnmoDTOz+vJUELdJulrSWZJeX34UnlnBKofaMDOzXeWpIHYnufZwEvDa9HFakUkVrXqoDbcizMx2lWc+iAWtSKSVxtpQG9Vd+CfqUBWQbziDvEMZwM7HrNlDJVTHHxgYYEuO+/zzWFcqsVfVUBLHHHMM9957b8PXlnuy77PPPnXLHXbYYdx2220ti58l62+SdfzzvPebPfRGO4byuOqqq3Za9+ijjz79WVbPpEmT2HPPnQfHOPfccxseszwd5RaTTjdaKSJG1AO6ncb6UBsTdagKGNrE80O9S62ZQyVA64dL2LhxI5s3b85dvlHZjRs37rK8efNjSM+s+7qI5MTEli21P7giHt8lfl7Tpk0b1uts6PLcxVTZ23kqSQe33xWTTmuMtaE2qmt53wdenGYNlQC7DpdQ9FASXV1drF/fvEEHqluoXV1dbNp0cNOGkujqmtqwXDNbxEUfn6JdfPHFLT9D0PAaRER8q+KxFHgj0FN8asXp7e1l0qTkV/dQG2Zm2fJcpK52OPDsZifSSuWhNiZNmuShNszMashzDaI8L0TZBuB9hWXUIh5qw8ysvjx3MRU1L0RbeagNM7P66s0oV3f2nYj4bfPTMTOz0aJeC+Lf2THlaFkA00muQUwuMC8zM2uzelOO/q/K5XR+6fcBrwI+UWhWZmbWdnkuUh8OfJBkXujPARdExJ+KTqyZsnpjZvVGngg9ka22gYEBSqUtTZvusVRax8DAXk2JZdYO9a5BHElSMcwGPg28JSK2tSqxok3k3shmZnnUa0HcDjxAci3iOOA4acfliIi4oNjUmierVeDeyFYt6Sn8ZFN7UufpLWw2WtWrIEY01pKkK0lGfX04Io5M132GZDTYEvAbYEFEPJLx2vuAR4FtwNaIGNM9t83MxqJ6F6m/OsLYVwGXkExZWnYT8IF03ulPAR+gdqe7V0TEphHmYGZmwzScoTZyiYgVwB+q1t0YEVvTxZXAwUXt38zMRqawCiKHNwM/rLEtgBsl3SppYb0gkhZKWiVp1XCHDzYzs101rCAk7Zex7rCR7FTSB4GtwNIaRV4WEccCpwDnS3p5rVgRcXlE9EREz/Tp00eSlpmZVcjTgviepKdv5pY0C/jecHco6VySi9fzImKXiYgAImJ9+vNh4DqSu6jMzKyF8lQQnyCpJKZJeiHwTWBYM+xIOhl4L3B6RDxeo8wekvYsPyeZC3vNcPZnZmbDl2c013+XtBtwI7An8LqI+HWj10m6BpgD7C/pQeDDJHctPQO4Ke1TsTIizpP0HOCKiDgVOAC4Lt0+Bbg6Im4Yzi/XKp4zeoci54yGsT+vsNlYUq8n9RfZMQ+EgL1J+i68S1LDjnIRcVbG6kU1yv4OODV9fg9wdOPUR6+J3Eu7v7+f1StWMKujo2aZ3bcmN7I9uXJl3Vjr0nnDLZ/u7u5c5coVdKPyeePZ+FWvBbGqwbKlPGf0zmZ1dDRt3uVqY31e4SLlbZ1O9Pen5Zero5yk3YEZEfGrlmRlZmZtl+c219cC/cAN6XK3pOuLTszMzNqr4UVq4CMkt5kuA4iIfknPLTAnM7Mxp5nDxY+WoeLz3Ob6p4jYXLVuexHJmJnZ6JGnBbFW0tnA5HTyoAuAnxablpnZ2NLM4eJHy1DxeVoQf0syadBTwNXAZuCiIpMyM7P2y9NR7nHgg5I+Xqv3s5mZjT955qR+CXAFMA2YIelo4O0R8c6ikxuOPD15IX9v3onQ+9nMLEueaxD/BLwGuB4gIm6vN7pqu/X397NixWo6OmbVLbd16+4ArFz5ZM0ypdK6puZmtq5UyuwAWOn+tKf5oVNq/3uuK5U4tqmZme0qTwVBRDxQOR81yVSgo1ZHx6ymXSgya5a8Q1c8kbZup9Ypf+wQ4pkNV54K4oH0NFOkg/ZdCNxVbFpm44+HwrCxJs9dTOcB5wMHAeuB7nTZzMzGsXqjuX4X+K/0sSAiPLSmmdkEUq8F8RVgH+DjwAZJP5X0WUmvk3RAa9IzM7N2qTea6/eB7wNImgwcQzIB0GeAw4DJLcjPzMzapO5Fakn7Ay9JH8cDU4H/AP67+NTMzKydap5iknQ3yRDfs4EfAadFxPERcV7lXBH1SLpS0sOS1lSs20/STZLuTn/uW+O156Rl7pZ0ztB+LTMzG6l61yCuJLlr6a+AtwELJPWkp5vyugo4uWrd+4GbI+Jw4OZ0eSeS9iOZw/pFJEONf7hWRWJmZsWodw3iH8vPJT2f5DTT24CXSdoUESc0Ch4RKyTNrFp9Bsm1DICvkswz8b6qMq8BboqIP6T7v4mkormm0T6tvjxDkeQdhgQ8FInZeJZnLKbnknyLfxHJdYhnA/eOYJ8HRER5rIENQNYdUQcBD1QsP5iuy8pvIbAQYMaMGUybNm0EqY1/eYYiyTMMCXgoErPxrl4/iOtIKoUtJPM//BT454hoWi/qiAhJMcIYlwOXA/T09Iwo1kThoUjMLI96LYjFwNsiYlOT9/mQpM6IGJTUCTycUWY9O05DARxMOuWpmZm1Rr1rENcXtM/rgXOAT6Y/v5tR5kfAJyouTJ8EfKCgfMwsp1JpXcOW49at9wMwZcqhdePg8WhHvVyjuQ6XpGtIWgL7S3qQ5M6kTwLfkPQW4H7gjWnZHuC8iHhrRPxBUh/wizTUR8sXrM2sPfKOHtvf/0Ravt6Umcd6NNoxoNAKIiLOqrHpxIyyq4C3VixfSXKrrZmNAqNlNNr+/n7mzJnDihUrOOqoowrZhyVyVRCSDgIOrSwfESuKSsrMrJb58+ezefNmzj77bNasWdP4BTZseW5z/RTw18A6dkwUFIArCDNrqf7+ftauXQvA2rVrueOOO9yKKFCeFsRfAkdExFNFJ9MMAwMDPPXUBu6/f8ebJuJJYHuOV09C2nHeNOJxBgYObH6SdYz1jmwDAwNsyTGtZh7rSiX2GhhoQlb5NesibDlWuy/EZr2far1/hvNeKTp+tfnz5++0PNpaEdXvn61b72f79v9p+LpJk/bY6f00Gt47kK+CuAfYDRgTFcT06dN57LHHdlr3xBNiW45JUidPFrvvXjn6yDSmT5/e3AQb6O/vZ/ny5bnK5i03nuS5sFn+gMpTtrJMcy/Cwmi9EFt0Z9Ii45dbD7WW68n6WwwMDOzyeZFl2rRpdHV11Y2XHX8yjz3WeF62adMm09VV+X4aHe+dPBXE40C/pJupqCQi4oLCshqB2267rd0pjGoDAwOUSlua0smtVFrHwMBeO63r6uriyU2buLazc8Tx5w4OMrXqnzLPN9DhXiQdLRdhm6no1mOrh1mZPXv2TpXC7Nmzc792vB2LVshTQVyfPszM2mrJkiUcc8wxTy9fffXVbcxm/GtYQUTEVyV1AM9PV/0qIv5UbFpWlK6uLjZterJpQ23s3Cw2K1Z3d/fTrYjZs2f7AnXBGp4ckzQHuBu4FPgX4NeSXl5wXmZmmZYsWcLee+/t1kML5DnF9DngpIj4FTw99Pc1wAuLTMzMLEt3dzePPPJIu9OYEBpfXofdypUDQET8muSuJjMzG8fytCBWSboCWJIuzwNWFZeSmZmNBnlaEO8g6UV9QfpYl64zszFocHCQE044gQ0bNrQ7FRvlGlYQEfFURHw+Il6fPv5prPSqNrNd9fX1ccstt9DX19fuVGyUq1lBSPpG+vNOSXdUP1qXopk1y+DgIIsXL2b79u0sXrzYrQirq941iAvTn6e1IpGxYqyPldQK6xqMxXT/1q0AHDql/iWwdaXSKBiNplitHsuor6+P7duTccm2bdtGX18fl1566Yhi2vhVb0a58n/4OyPifZXb0hFe37frq8a//v5+Vq9YwayOjppldk8/AJ9cubJurHWlUlNzGw3yjB/zRPoBOLVB2WNzxhtvihzLaOnSpZTS912pVGLJkiWuIKymPHcxvZpdK4NTMtZNGLM6Opo21tB4U+RYSeNRq1uP8+bNY9GiRZRKJTo6OnYZHdWsUr1rEO+QdCdwRNX1h3uBYV+DkHSEpP6KxxZJF1WVmSNpc0WZDw13f2a2Q29vL5MmJf/2kydPpre3t80Z2WhWrwVxNfBD4B+B91esf3Qk80Onne66ASRNBtYD12UU/UlE+PqHWRN1dnayYMECLrvsMhYsWMCBB7Z2vhMbW+pdg9gMbAbOApD0bGAqME3StIj4bRP2fyLwm4i4vwmxzCyH3t5e1q5d69aDNZRnsL7XSrobuBdYDtxH0rJohrkk4zplebGk2yX9UFLNQd8lLZS0StKqjRs3Nikts/Grs7OT5cuXu/VgDeXpSf0x4Hjg1xFxGMm3/vq35+SQDiF+OvDNjM2rgUMj4mjgi8B3asWJiMsjoicielo9+5vZWOSe1JZXngriTxHxe2CSpEkR8WOgpwn7PgVYHREPVW+IiC0R8Vj6/AfAbpL2b8I+zSY896S2vPJUEI9ImgasAJZK+gLQeBbuxs6ixuklSQdKUvr8uDTP3zdhn2YTmntS21Dk6QdxBvAk8G6SkVz3Bj46kp1K2oOkf8XbK9adBxARXwbOBN4haSvwBDA3ImIk+xwrBgYGRnW8iabVPZ2L5p7UNhR5phytbC18tRk7TWM+q2rdlyueXwJc0ox9mTVbkT2di+ae1DYUDSsISa8HPgU8G1D6iIjYq+DcJqSuri7Wr1/f1Hg2fKO9RTBU7kltQ5HnGsSngdMjYu+I2Csi9nTlYDY2uSe1DUWeCuKhiLir8EzMrHDlntSTJk1yT2prKO+Uo18n6Yvw9ERBEfHtwrIys8K4J7XllaeC2At4HDipYl0AriDMxqByT2qzRvLcxbSgFYmYmdnoUrOCkPTeiPi0pC+StBh2EhEXFJqZmZm1Vb0WRPnC9KpWJGJmZqNLveG+v5f+bErnODMzG1vydJTrAT4IHFpZPiKOKjAvK1CptI7Bwbk1t2/dmkzPMWXKoQ3jJDNHm9l4lOcupqXA/wXuBLYXm44Vrbu7u2GZ/v4n0rJTG5Q8Nlc8Mxub8lQQGyPi+sIzsZbIM3REeRC6ZcuWFZuMmY1qeSqID0u6ArgZd5QzM5sw8lQQC4AXALux4xSTO8qZmY1zeSqIP4+IIwrPxMzMRpU8g/X9VNKswjMxM7NRJU8L4nigX9K9JNcgyvNB+DZXM7NxLE8FcXIRO5Z0H/AosA3YGhE9VdsFfAE4lWSwwHMjYnURuZiZ2a7qjcW0V0RsIfkQL8orImJTjW2nAIenjxcBX0p/mplZC9RrQVwNnAbcSnLXkiq2BfDcAvMCOAP4WkQEsFLSPpI6I2Kw4P2amRn1x2I6Lf15WEH7DuBGSQFcFhGXV20/CHigYvnBdN1OFYSkhcBCgBkzZhSUqpnZxFPvFFPdQXaacD3gZRGxXtKzgZsk/TIiVgw1SFqxXA7Q09Ozy7DkZmY2PPVOMX2uzrYAXjmSHUfE+vTnw5KuA44DKiuI9cAhFcsHp+vMzKwF6p1iekVRO5W0BzApIh5Nn58EfLSq2PXAuyRdS3JxerOvP5iZtU7NjnKS3lvx/A1V2z4xwv0eANwi6Xbg58C/R8QNks6TdF5a5gfAPcAA8BXgnSPcp5mZDUG9U0xzgU+nzz8AfLNi28nA3w93pxFxD3B0xvovVzwP4Pzh7sPMzEam3lAbqvE8a9nMzMaZehVE1HietWxmZuNMvVNMR0vaQtJa2D19TrrcaKoxMzMb4+rdxTS5lYmYmdnokmewPqswMDDAllKJuYMjv+N2XanEXgMDTchq4rrooovo7+/faV15uTx1all3d3euKVfNLOEKYpTp7u5uWKb8AZinbJ4y4820adPanYLZuOAKYoi6urp4ctMmru3sHHGsuYODTO3q2mldnm+45W/Gy5YtG3EOY51bBGbFyTOjnJmZTUCuIMzMLJMrCDMzy+QKwszMMrmCMDOzTK4gzMwskysIMzPL5H4QVij3dDYbu1xBWMu5p7PZ2OAKwgrlFoHZ2NXyaxCSDpH0Y0nrJK2VdGFGmTmSNkvqTx8fanWeZmYTXTtaEFuBv4uI1ZL2BG6VdFNErKsq95OIOK0N+ZmZGW1oQUTEYESsTp8/CtwFHNTqPMzMrL623uYqaSZwDPCzjM0vlnS7pB9Kml0nxkJJqySt2rhxY0GZmplNPG2rICRNA74FXBQRW6o2rwYOjYijgS8C36kVJyIuj4ieiOiZPn16cQmbmU0wbakgJO1GUjksjYhvV2+PiC0R8Vj6/AfAbpL2b3GaZmYTWjvuYhKwCLgrIj5fo8yBaTkkHUeS5+9bl6WZmbXjLqaXAm8C7pRU7mL798AMgIj4MnAm8A5JW4EngLkREW3I1cxswmp5BRERtwBqUOYS4JLWZDR060ol5g4O1tx+/9atABw6pf7hXVcqcWyO/VUPV9HMoSo8FIaZ1eKe1EPU3d3dsMwT6Qfs1AZlj80Zr1rRQ1V4KAwzA9B4OnPT09MTq1atancaT3/zXrZsWVvzMDNrRNKtEdGTtc3DfZuZWSZXEGZmlskVhJmZZXIFYWZmmSZsBTE4OMgJJ5zAhg0b2p2KmdmoNGEriL6+Pm655Rb6+vranYqZ2ag0ISuIwcFBFi9ezPbt21m8eLFbEWZmGSZkR7m+vj62b98OwLZt2+jr6+PSSy8ddrwiezqbmbXLhGxBLF26lFKpBECpVGLJkiVNjT9t2jT3RjazMW9CtiDmzZvHokWLKJVKdHR0MH/+/BHFc6vAzMajCdmC6O3tZdKk5FefPHkyvb29bc7IzGz0mZAVRGdnJwsWLGDSpEksWLCAAw88sN0pmZmNOhPyFBMkrYi1a9e69WBmVsOErSA6OztZvnx5u9MwMxu1JuQpJjMza8wVhJmZZXIFYWZmmVxBmJlZpnE15aikjcD9Q3jJ/sCmgtIpMrbjO77jO36zYh8aEdOzNoyrCmKoJK2qNRfraI7t+I7v+I7fitg+xWRmZplcQZiZWaaJXkFcPkZjO77jO77jFx57Ql+DMDOz2iZ6C8LMzGpwBWFmZpnGfQUh6WRJv5I0IOn9GdufIenr6fafSZo5xPhXSnpY0poa2yXpn9P4d0g6dgixD5H0Y0nrJK2VdGGT40+V9HNJt6fx/yGjzIiOTxpjsqTbJH2/2fEl3SfpTkn9klZlbB/28Ulfv4+kf5P0S0l3SXpxs+JLOiLNu/zYIumiJuf/7vRvu0bSNZKmVm0f9vGXdGEad2113sPNPev/SdJ+km6SdHf6c98arz0nLXO3pHOGEP8N6e+wXVLN20PV4LOkTvzPpO+fOyRdJ2mf4cSvEbsvjdsv6UZJzxnusckUEeP2AUwGfgM8F+gAbgdmVZV5J/Dl9Plc4OtD3MfLgWOBNTW2nwr8EBBwPPCzIcTuBI5Nn+8J/Doj/5HEFzAtfb4b8DPg+GYen/R1/we4Gvh+xraRHv/7gP3rbB/28Ulf/1XgrenzDmCfZsaveq9uIOm01Ky/70HAvcDu6fI3gHObcfyBI4E1wDNJRoX+D6BrpLln/T8Bnwbenz5/P/CpjNftB9yT/tw3fb5vzvh/BhwBLAN66vx96n6W1Il/EjAlff6pGvnn+azKir1XxfMLyn/L4RybrMd4b0EcBwxExD0RUQKuBc6oKnMGyYcAwL8BJ0pS3h1ExArgD3WKnAF8LRIrgX0kdeaMPRgRq9PnjwJ3kfzTNyt+RMRj6eJu6aP6roURHR9JBwP/G7iiRpERxc9h2MdH0t4k/5SLACKiFBGPNCt+lROB30RE9W3JIRUAAAifSURBVEgAI40/Bdhd0hSSD/PfZcQfzvH/M5IP/McjYiuwHHj9SHOv8f9UmeNXgb/MeOlrgJsi4g8R8UfgJuDkPPEj4q6I+FW9vMj3WVIr/o3pMQJYCRw8nPg1Ym+pWNyDXf9/IeexyTLeK4iDgAcqlh9k1w/Yp8ukf8TNwLNanENDadP/GJJv+U2Lr+T0Tz/wMMmbqGb8YR6fi4H3AttrbB9p/ABulHSrpIX14qeGcnwOAzYCi5WcIrtC0h5NjF9pLnBNxvphx4+I9cBngd8Cg8DmiLixVvwhHv81wF9IepakZ5K0Fg5pVu5VDoiIwfT5BuCAjDLN2lctzYr/ZpJWVdPiS/q4pAeAecCHmhl7vFcQ44KkacC3gIuqvjGMWERsi4hukm81x0k6slmxJZ0GPBwRtzYrZoaXRcSxwCnA+ZJe3sTYU0ia9F+KiGOA/yE5xdFUkjqA04FvNjnuviTfQg8DngPsIWl+M2JHxF0kp0tuBG4A+oFtzYjdYL9B9rfkUU/SB4GtwNJmxo2ID0bEIWncdzUz9nivINaz87eag9N1mWXSZvjewO9bnENNknYjqRyWRsS3mx2/LD118mN2bXqO5Pi8FDhd0n0kTeZXSlrSxPjlb8lExMPAdSRN9cz4qaEcnweBBytaVf9GUmE0K37ZKcDqiHgoY9tI4r8KuDciNkbEn4BvAy+pFX+oxz8iFkXECyPi5cAfSa6RNSv3Sg+VT02lPx/OKNOsfdUy0v/jc4HTgHlpJdfU+KmlwF81M/Z4ryB+ARwu6bD0W9pc4PqqMtcD5av6ZwL/WeMPOFzXA3+T3tFxPEkzf7DRiyC5C4Tk/PddEfH5AuJPL99RIWl34NXALzPiD+v4RMQHIuLgiJhJcuz/MyKqv8EOO76kPSTtWX5OcjGw+m6yYR+fiNgAPCDpiHTVicC6ZsWvcBbZp5dGGv+3wPGSnpm+l04kuY5VHX+4x//Z6c8ZJNcfrm5i7rVyPAf4bkaZHwEnSdo3bTmdlK5rljyfJZkknUxymvX0iHi8mfElHV6xeAa7/v/CSI5NnivZY/lBcm701yR3CHwwXffR9I8FMJWkaT8A/Bx47hDjX0NyfvdPJN843wKcB5yXbhdwabr/O6lxl0SN2C8jaU7fQdKE709/n2bFPwq4LY2/BvhQs49Pxb7mkN7F1Kz4JHd83J4+1lb8fZtyfNLXdwOr0mP0HZK7QJoZfw+Sb+x7V6xrZvx/IPnQWAP8K/CMJh7/n5BUmLcDJzYj9xr/T88CbgbuJrlbar+0bA9wRcVr35z+HgPAgiHEf136/CngIeBHadnnAD+oeO0unyU54w+QXAMo/w9/eTjxa8T+Vvq3vQP4HnDQcI9N1sNDbZiZWabxforJzMyGyRWEmZllcgVhZmaZXEGYmVkmVxBmZpbJFYQVTlJI+lzF8nskfaRJsa+SdGYzYjXYzxuUjOb646r1MyU9oZ1HZf2bBrF+2mD7uZWjcqZDfMwa2W9Qc19Z+XcUtK/HGpey0WRKuxOwCeEp4PWS/jEiNrU7mTJJU2LHIGqNvAV4W0TckrHtN5EMV5JLRFT3Zq52Lsm97b9Ly781b+xhGlL+NnG4BWGtsJVkntx3V2+obgGUv2VKmiNpuaTvSrpH0iclzVMyf8Wdkp5XEeZVklZJ+nU6/lN5EMLPSPqFkvHy314R9yeSrmfXXtFIOiuNv0bSp9J1HyLptLhI0mfy/MKSzqssm7YKLqn8HdPn70v3d3v6O55J0slpafptfndJy5TOU5CVXzmmkkHbbpe0UtIB6fo3pGVvl7QiZ+4z02O0On28pGLbTvmm654n6QYlAyb+RNIL0vWHSfrvtPzH8uzbRpmh9oj1w4+hPoDHgL1I5m7YG3gP8JF021XAmZVl059zgEdI5sR4BsnYMf+QbrsQuLji9TeQfNk5nKSH6VRgIfD/0jLPIOkNfVga93+AwzLyfA7J8BTTSVrX/wn8ZbptGRk9gYGZwBPs6CXbD/xFGmOgotwPSQYWrPwdTwF+CjwzXd4va1/l5Qb5BfDa9PmnK373O9nRu3afHPlfSjIs+NR0++HAqgb53gwcnj5/EclwHZAOtZE+P7/8e/sxdh4+xWQtERFbJH2NZFKTJ3K+7BeRjt0j6TckI4dC8qH3iopy34iI7cDdku4BXkAy3sxRFa2TvUk+7ErAzyPi3oz9/TmwLCI2pvtcSjIfxHca5Jl5iiZt+RxPMkTEC4D/qiryKmBxpOPzRES9eUUa5VcCyjP23UoyrhbpPq+S9A2Swfoa5q9kHoxLJHWTjND6/Fr5Khlp+CXAN7VjGolnpD9fyo7B4/6VZPRXG0NcQVgrXQysBhZXrNtKeqpT0iSS2bTKnqp4vr1ieTs7v3erx4sJknGA/jYidhqUTNIckhZEK1wLvJFkLKTrIv0qXZA/VcTfRnp8IuI8SS8imbTpVkkvjIhGo7W+m2RMoqNJ/jZP1ik7CXgkq4JMeSyfMczXIKxl0m/I3yC54Ft2H/DC9PnpJLPaDdUbJE1Kr0s8F/gVyWiV71AyXDqSnq9dJ/up9nPgBEn7S5pMMsrq8mHkU3YdyQibZ5FUFtVuAhYomXAHSful6x8lmWJ2xPlJel5E/CwiPkQy+VH1pD5Z9gYG01bZm0imw8zMN5L5Se6V9IZ0nSQdnZb/L5JRSSGZzMbGGFcQ1mqfA/avWP4KyYfe7cCLGd63+9+SfHj+kGQk0SdJpjhdB6xWMsn7ZTRoMaens95PMi/G7cCtEZE1tHS151XdJnpBGu+PJMNrHxoRP8/Y3w0k5+lXKZnV7z3ppquAL5cvUo8wv8+UL2qTXD+4Pcfv8y/AOenf5AWkf5M6+c4D3pKWX8uOqTIvJJnE6U6aO7ubtYhHczUzs0xuQZiZWSZXEGZmlskVhJmZZXIFYWZmmVxBmJlZJlcQZmaWyRWEmZll+v9/Rj+RHBligwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MYxKrPHbMHeN"
      },
      "source": [
        "#Sample of n=50\r\n",
        "\r\n",
        "ptype1=pf1_data[pf1_data[\"Number of Evictions Faced\"]<5].sample(50,random_state=2)\r\n",
        "ptype2=pf1_data[pf1_data[\"Number of Evictions Faced\"]>6].sample(50,random_state=2)\r\n",
        "ptype3=pf1_data[pf1_data[\"Number of Evictions Faced\"]<2].sample(50,random_state=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-0SrXWA_MOmX",
        "outputId": "681ce30c-6176-44b6-b227-ca565391fde4"
      },
      "source": [
        "# Normality of data at 95% confidence level\r\n",
        "\r\n",
        "print(stats.shapiro(ptype1[\"Elimination Week Number\"]))\r\n",
        "print(stats.shapiro(ptype2[\"Elimination Week Number\"]))\r\n",
        "print(stats.shapiro(ptype2[\"Elimination Week Number\"]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(0.9497615694999695, 0.03335428610444069)\n",
            "(0.8540376424789429, 1.9935261661885306e-05)\n",
            "(0.8540376424789429, 1.9935261661885306e-05)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HHrct4zgMUfV"
      },
      "source": [
        "Since p-value is less than 0.05 level of significance we have enough evidence to conclude that the data is not normally distributed"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bkocHsGmMUBc",
        "outputId": "bd6fb72e-5c22-4fbe-9e48-fc4281c9bf67"
      },
      "source": [
        "# Finding the F-stat and F-critical values\r\n",
        "\r\n",
        "crit_val = stats.f.isf(0.05, dfn = 2, dfd = 500)\r\n",
        "print(\"F critical value with 95% confidence:\",crit_val)\r\n",
        "\r\n",
        "#Calculating f_statistic and p-value:\r\n",
        "f_stat, p_val = stats.f_oneway(ptype1[\"Elimination Week Number\"],ptype2[\"Elimination Week Number\"],ptype3[\"Elimination Week Number\"])\r\n",
        "\r\n",
        "print(\"F-statistic:\",f_stat)\r\n",
        "\r\n",
        "#Calculating p-value\r\n",
        "print(\"P-value:\",p_val)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "F critical value with 95% confidence: 3.013753005713494\n",
            "F-statistic: 86.19964526803996\n",
            "P-value: 1.6954265896568623e-25\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lnJRAt3ZMeRu"
      },
      "source": [
        "## Since F - statistic is greater than F critical value with 95% confidence it falls in the rejection region.So null hypothes is rejected .\r\n",
        "## Thus Elimination Week Number does not influence Number of Evictions Faced by a candidate: H1\r\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c_uTTvWQMmLm"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vsj0kP1sHq03"
      },
      "source": [
        "# 7. Does being a \"Finalist\" depend on the \"Number of times elected as Captain\".\r\n",
        "## H0 : 'Yes' being a \"Finalist\" depends on the \"Number of times elected as Captain\" for house mates\r\n",
        "\r\n",
        "## H1: 'No' being a Finalist depends on the Number of times elected as Captain for house mates"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 913
        },
        "id": "rElkFuDYHn29",
        "outputId": "3ce3f90b-e80c-4597-90eb-f4b68cfc0dcb"
      },
      "source": [
        "# Fisrt understanding the data distribution for \"Finalist\" & \"Number of times elected as Captain\" are they normally distributed?\r\n",
        "import random\r\n",
        "\r\n",
        "## For Finalist\r\n",
        "def mean_dist_for_finalist(sample_size,sample_text):\r\n",
        "    m={}\r\n",
        "    for i in range(sample_size):\r\n",
        "        j = np.unique(np.random.randint(0, len(pf1), size = sample_size))\r\n",
        "        m[i] = pf1[\"Finalist\"][j].mean()   \r\n",
        "        df_x = pd.DataFrame()\r\n",
        "        df_x[\"sample\"] = m.keys()   \r\n",
        "        df_x[\"mean\"] = m.values() \r\n",
        "    plt.figure(figsize= (7,3))\r\n",
        "    plt.subplot(1,2,2)\r\n",
        "    sns.distplot(df_x[\"mean\"],color='darkgreen')\r\n",
        "    plt.title(\"Sampling Distribution for Mean of Finalist\")\r\n",
        "    plt.xlabel(sample_text)\r\n",
        "    plt.ylabel('freq')\r\n",
        "\r\n",
        "mean_dist_for_finalist(50,sample_text=\"Sample\")  # For sample size \r\n",
        "mean_dist_for_finalist(632,sample_text=\"Population\") # For population change sample size to population size \r\n",
        "\r\n",
        "i=0\r\n",
        "j=0\r\n",
        "## For \"Number of times elected as Captain\" \r\n",
        "def mean_dist_for_nof_elect_cap(sample_size,sample_text):\r\n",
        "    n={}\r\n",
        "    for i in range(sample_size):\r\n",
        "        j = np.unique(np.random.randint(0, len(pf1), size = sample_size))\r\n",
        "        n[i] = pf1[\"Number of times elected as Captain\"][j].mean()   \r\n",
        "        df_y = pd.DataFrame()\r\n",
        "        df_y[\"sample\"] = n.keys()   \r\n",
        "        df_y[\"mean\"] = n.values() \r\n",
        "    plt.figure(figsize= (7,3))\r\n",
        "    plt.subplot(1,2,2)\r\n",
        "    sns.distplot(df_y[\"mean\"],color='purple')\r\n",
        "    plt.title(\"Sampling Distribution for Mean of Number of times elected as Captain\")\r\n",
        "    plt.xlabel(sample_text)\r\n",
        "    plt.ylabel('freq')\r\n",
        "\r\n",
        "mean_dist_for_nof_elect_cap(50,sample_text=\"Sample\")  # For sample size\r\n",
        "mean_dist_for_nof_elect_cap(632,sample_text=\"Population\") # For population change sample size to population size \r\n",
        "plt.show()\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 504x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 504x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 504x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 504x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9_moEnGoIR-s"
      },
      "source": [
        "### If  sample size is increased to Population the data is normally distributed for both \"Number of times elected as Captain\" and \"Finalist\". \r\n",
        "\r\n",
        "### For discrete varibales we can consider a non-parametric hyptothesis test like Chi-Square test\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "24UWH2GNIYu9",
        "outputId": "4d492090-9e37-459e-87a6-f657e816da00"
      },
      "source": [
        "# Checking for normality of data at 95% confidence level \r\n",
        "test, p_val1 = stats.shapiro(pf1[\"Finalist\"])\r\n",
        "test, p_val2 = stats.shapiro(pf1[\"Number of times elected as Captain\"])\r\n",
        "\r\n",
        "print(' p_val for \"Finalist\": ',p_val1,'\\n','p_val for \"Number of times elected as Captain\": ',p_val2)\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " p_val for \"Finalist\":  4.831595269161653e-38 \n",
            " p_val for \"Number of times elected as Captain\":  1.0208765095392441e-32\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FEw7tTz3NEqv"
      },
      "source": [
        "## Since p-value is less than 0.05 level of significance we have enough evidence to conclude that the data is not normally distributed."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "hqvzFLjcIhuZ",
        "outputId": "6ca1ac71-aa19-401d-eff7-e61588d216da"
      },
      "source": [
        "sns.boxplot(x = \"Number of times elected as Captain\", y = \"Finalist\", data = pf1,palette=['purple','violet','grey','pink'])\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GfOAE07OIm52",
        "outputId": "298e0ee5-332b-4b1d-ef15-4fbcac685d2e"
      },
      "source": [
        "# Correlation for proving Hypothesis\r\n",
        "pf1[\"Number of times elected as Captain\"].corr(pf1[\"Finalist\"])  # Postive correlation exists"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.37243257805632163"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_jMcrfutNfgo"
      },
      "source": [
        "## Postive correlation exists between being a Finalist and being Elected as a Captain."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "3SWk09WEYuHq",
        "outputId": "c8001390-1629-4c3f-c4ea-e09d71cece93"
      },
      "source": [
        "sns.boxplot(x = \"Finalist\", y = \"Number of times elected as Captain\", data = pf1,palette=['blue','cyan'])\r\n",
        "plt.title(\"Correlation\")\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "_QHEoeaew2ab",
        "outputId": "44121402-2c64-4aa3-c4dd-cda3d479010c"
      },
      "source": [
        "sns.heatmap(pf1[[\"Number of times elected as Captain\", \"Finalist\"]],cmap=\"PiYG\",center=1)\r\n",
        "plt.title(\"Correlation\")\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yq5GTIiWIxBS"
      },
      "source": [
        "## Chi Square 2 test for proving the hypotheses\r\n",
        "##H0 : Yes:  \"Number of times elected as Captain\" determines turning a \"Finalist\" \r\n",
        "##H1 : No:  \"Number of times elected as Captain\" determines turning a \"Finalist\" \r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ai-A6mHIZJRJ",
        "outputId": "10a476fd-207e-4bff-c90d-bb889e1262fb"
      },
      "source": [
        "cross_t = pd.crosstab(index= pf1[\"Number of times elected as Captain\"],columns=pf1[\"Finalist\"])\r\n",
        "chi_stat, p_val, dof, expected = chi2_contingency(cross_t)\r\n",
        "\r\n",
        "print('p_val:',p_val)\r\n",
        "print('chi stat: ', chi_stat)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "p_val: 1.0054696062404451e-20\n",
            "chi stat:  103.4177483533082\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kW5E4E-lZIxv",
        "outputId": "4d0ea4ef-edea-4ed0-8610-4f17d2ff6f1a"
      },
      "source": [
        "critical = chi2.ppf(p_val, dof)\r\n",
        "print('probability= ' , p_val, 'critical= ',critical, 'chi_stat= ', chi_stat)\r\n",
        "\r\n",
        "if abs(chi_stat) >= critical:\r\n",
        "\tprint('(Reject Null H0)')\r\n",
        "else:\r\n",
        "\tprint('(Accept Null H0)')\r\n",
        " \r\n",
        "# interpreting  p_value\r\n",
        "\r\n",
        "alpha = 1.0 - p_val\r\n",
        "print('significance= ',alpha,  'p_valp= ', p_val)\r\n",
        "\r\n",
        "if p_val <= alpha:\r\n",
        "\tprint('Dependent (reject H0)')\r\n",
        "else:\r\n",
        "\tprint('Independent (fail to reject H0)')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "probability=  1.0054696062404451e-20 critical=  3.2404704083705394e-08 chi_stat=  103.4177483533082\n",
            "(Reject Null H0)\n",
            "significance=  1.0 p_valp=  1.0054696062404451e-20\n",
            "Dependent (reject H0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d24_HpxBdAWv"
      },
      "source": [
        "## Since p_val is less than Alpha ( significance) H0 is rejected\r\n",
        "\r\n",
        "## \"Number of times elected as Captain\" does not determine being a \"Finalist\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r_l6iSYH0p-o"
      },
      "source": [
        "Also because being Finalist is necessary for being a Winner, we can say that since the \"Number of times elected as Captain\" does not determine being a Finalist the same could be true for the dependencies between the Winner and the \"Number of times elected as Captain\"\r\n",
        "\r\n",
        "Since there is a strong correlation value between being a \"Finalist\" and \"Winner\""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9lGl4wH20xDS",
        "outputId": "e6e6b8d7-e57e-4e1c-e65d-d5b0689eb2ed"
      },
      "source": [
        "# Correlation for proving Hypothesis\r\n",
        "pf1[\"Winner\"].corr(pf1[\"Finalist\"])  # Strong Postive correlation exists"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.42368872920801176"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    }
  ]
}