04: Hypothesis_Testing_ttest_Chi_Anova.ipynb

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      "display_name": "Python 3",
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    "colab": {
      "name": "Hypothesis_Testing.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "daiTF4CbZ8wC"
      },
      "source": [
        "Help : https://www.youtube.com/watch?v=4-rxTA_5_xA",
        "## T Test\n",
        "\n",
        "A t-test is a type of inferential statistic which is used to determine if there is a significant difference between the means of two groups which may be related in certain features\n",
        "\n",
        "T-test has 2 types : 1. one sampled t-test 2. two-sampled t-test."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3XMSsCY0Z8wD"
      },
      "source": [
        "##  One-sample T-test with Python\n",
        "\n",
        "The test will tell us whether means of the sample and the population are different\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wJsuT5PdZ8wE"
      },
      "source": [
        "ages=[10,20,35,50,28,40,55,18,16,55,30,25,43,18,30,28,14,24,16,17,32,35,26,27,65,18,43,23,21,20,19,70]"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a_6GAdoAZ8wE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e94cc183-b56a-4f4b-c016-0fe63c025efa"
      },
      "source": [
        "len(ages)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "32"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uh2paiPMZ8wF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7a994ba3-8040-42ef-e30f-d198387ae16a"
      },
      "source": [
        "import numpy as np\n",
        "ages_mean=np.mean(ages)\n",
        "print(ages_mean)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "30.34375\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qhx5xRxgZ8wF"
      },
      "source": [
        "## Lets take sample\n",
        "\n",
        "sample_size=10\n",
        "age_sample=np.random.choice(ages,sample_size)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x-mPN91FZ8wG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50607e14-b848-4736-a80d-8ad10726518d"
      },
      "source": [
        "age_sample, np.mean(age_sample)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([43, 50, 43, 35, 24, 28, 35, 24, 25, 10]), 31.7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f8lJEGFrZ8wH"
      },
      "source": [
        "from scipy.stats import ttest_1samp"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vyzMEbW-Z8wH"
      },
      "source": [
        "ttest,p_value=ttest_1samp(age_sample,30)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SiYmOn0GhxKa",
        "outputId": "8d61b89c-88f4-4edc-bff9-c36e1033db17"
      },
      "source": [
        "ttest"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4543260827960123"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LGamImRrZ8wI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "19bf9929-890e-411d-95bf-28756273e5db"
      },
      "source": [
        "print(p_value)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.6603529102274395\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "41G8o1ykZ8wI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "77c0e867-e982-4cea-da5e-1accfa5b257f"
      },
      "source": [
        "if p_value < 0.05:    # alpha value is 0.05 or 5%\n",
        "    print(\" we are rejecting null hypothesis\")\n",
        "else:\n",
        "    print(\"we are accepting null hypothesis\")"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "we are accepting null hypothesis\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x8WJws16Z8wI"
      },
      "source": [
        "## Some More Examples\n",
        "Consider the age of students in a college and in Class A"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_hAOABesZ8wK"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import scipy.stats as stats\n",
        "import math\n",
        "np.random.seed(6)\n",
        "school_ages=stats.poisson.rvs(loc=18,mu=35,size=1500)\n",
        "classA_ages=stats.poisson.rvs(loc=18,mu=30,size=60)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ppWITukpi1WI",
        "outputId": "a5f8e9b0-7704-4ab4-8f12-9bb4bd323d17"
      },
      "source": [
        "school_ages"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([62, 59, 44, ..., 45, 52, 50])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Arrffxbhjqhm",
        "outputId": "ab92c1c6-a1d1-4b4b-9737-e1bc781fb703"
      },
      "source": [
        "school_ages.mean()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "53.303333333333335"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yPkvMFAxi3FL",
        "outputId": "c7d26351-f4ee-4f3a-9f67-b880889bbd94"
      },
      "source": [
        "classA_ages"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([52, 46, 40, 40, 47, 50, 51, 45, 44, 52, 46, 53, 43, 44, 51, 50, 54,\n",
              "       42, 54, 45, 61, 53, 49, 46, 47, 41, 45, 51, 43, 45, 48, 50, 40, 52,\n",
              "       44, 55, 54, 40, 45, 46, 54, 42, 46, 35, 51, 51, 46, 48, 47, 35, 52,\n",
              "       52, 39, 44, 48, 40, 42, 46, 47, 45])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ed_5acqMZ8wK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ca08d8d0-6ff8-407f-fba6-b9456c50a026"
      },
      "source": [
        "classA_ages.mean()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "46.9"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LxfJ_jzpZ8wM"
      },
      "source": [
        "_,p_value=stats.ttest_1samp(a=classA_ages,popmean=school_ages.mean())"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9C19XuhsZ8wM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5b0c72e8-54dc-4411-9166-facdf5890ba7"
      },
      "source": [
        "p_value"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.139027071016194e-13"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NCVzPiKhZ8wN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b2599753-afd9-4e7a-e3b8-567b37419575"
      },
      "source": [
        "school_ages.mean()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "53.303333333333335"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GcPon11dZ8wO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e138ee4b-648d-46f7-f5b9-cd2048ebf179"
      },
      "source": [
        "if p_value < 0.05:    # alpha value is 0.05 or 5%\n",
        "    print(\" we are rejecting null hypothesis\")\n",
        "else:\n",
        "    print(\"we are accepting null hypothesis\")"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " we are rejecting null hypothesis\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PQRO4uVKZ8wO"
      },
      "source": [
        "## Two-sample T-test With Python\n",
        "The Independent Samples t Test or 2-sample t-test compares the means of two independent groups in order to determine whether there is statistical evidence that the associated population means are significantly different. The Independent Samples t Test is a parametric test. This test is also known as: Independent t Test\n",
        "\n",
        "![image.png](attachment:image.png)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N29wQE6EZ8wP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4bf66ee1-e1ff-4f46-e6db-ab088dde3751"
      },
      "source": [
        "np.random.seed(12)\n",
        "ClassB_ages=stats.poisson.rvs(loc=18,mu=33,size=60)\n",
        "ClassB_ages.mean()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "50.63333333333333"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BUPscjdvZ8wP"
      },
      "source": [
        "_,p_value=stats.ttest_ind(a=classA_ages,b=ClassB_ages,equal_var=False)"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7SNJoJKmkd1j",
        "outputId": "7a6c41f9-4b2c-400e-ac99-150cd7717004"
      },
      "source": [
        "p_value"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00039942095100859375"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4KtDaNV7Z8wQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "173be9bc-72d4-4e04-cdb7-d897785100c4"
      },
      "source": [
        "if p_value < 0.05:    # alpha value is 0.05 or 5%\n",
        "    print(\" we are rejecting null hypothesis\")\n",
        "else:\n",
        "    print(\"we are accepting null hypothesis\")"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " we are rejecting null hypothesis\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2PGk2WvlZ8wQ"
      },
      "source": [
        "## Paired T-test With Python\n",
        "When you want to check how different samples from the same group are, you can go for a paired T-test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bUI1IOgPZ8wR"
      },
      "source": [
        "weight1=[25,30,28,35,28,34,26,29,30,26,28,32,31,30,45]\n",
        "weight2=weight1+stats.norm.rvs(scale=5,loc=-1.25,size=15)"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7-kkqr6IZ8wR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a33b2cf4-bf78-43b5-b7dd-d2b7c8b0f060"
      },
      "source": [
        "print(weight1)\n",
        "print(weight2)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[25, 30, 28, 35, 28, 34, 26, 29, 30, 26, 28, 32, 31, 30, 45]\n",
            "[30.57926457 34.91022437 29.00444617 30.54295091 19.86201983 37.57873174\n",
            " 18.3299827  21.3771395  36.36420881 32.05941216 26.93827982 29.519014\n",
            " 26.42851213 30.50667769 41.32984284]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kWiaoA-JZ8wU"
      },
      "source": [
        "weight_df=pd.DataFrame({\"weight_10\":np.array(weight1),\n",
        "                         \"weight_20\":np.array(weight2),\n",
        "                       \"weight_change\":np.array(weight2)-np.array(weight1)})"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O7niZ273Z8wV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "outputId": "7114b44c-ba80-49e0-e403-a3e1e5a32c53"
      },
      "source": [
        "weight_df"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>weight_10</th>\n",
              "      <th>weight_20</th>\n",
              "      <th>weight_change</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>25</td>\n",
              "      <td>30.579265</td>\n",
              "      <td>5.579265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>30</td>\n",
              "      <td>34.910224</td>\n",
              "      <td>4.910224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>29.004446</td>\n",
              "      <td>1.004446</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>35</td>\n",
              "      <td>30.542951</td>\n",
              "      <td>-4.457049</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>28</td>\n",
              "      <td>19.862020</td>\n",
              "      <td>-8.137980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>34</td>\n",
              "      <td>37.578732</td>\n",
              "      <td>3.578732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>26</td>\n",
              "      <td>18.329983</td>\n",
              "      <td>-7.670017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>29</td>\n",
              "      <td>21.377139</td>\n",
              "      <td>-7.622861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>30</td>\n",
              "      <td>36.364209</td>\n",
              "      <td>6.364209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>26</td>\n",
              "      <td>32.059412</td>\n",
              "      <td>6.059412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>28</td>\n",
              "      <td>26.938280</td>\n",
              "      <td>-1.061720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>32</td>\n",
              "      <td>29.519014</td>\n",
              "      <td>-2.480986</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>31</td>\n",
              "      <td>26.428512</td>\n",
              "      <td>-4.571488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>30</td>\n",
              "      <td>30.506678</td>\n",
              "      <td>0.506678</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>45</td>\n",
              "      <td>41.329843</td>\n",
              "      <td>-3.670157</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    weight_10  weight_20  weight_change\n",
              "0          25  30.579265       5.579265\n",
              "1          30  34.910224       4.910224\n",
              "2          28  29.004446       1.004446\n",
              "3          35  30.542951      -4.457049\n",
              "4          28  19.862020      -8.137980\n",
              "5          34  37.578732       3.578732\n",
              "6          26  18.329983      -7.670017\n",
              "7          29  21.377139      -7.622861\n",
              "8          30  36.364209       6.364209\n",
              "9          26  32.059412       6.059412\n",
              "10         28  26.938280      -1.061720\n",
              "11         32  29.519014      -2.480986\n",
              "12         31  26.428512      -4.571488\n",
              "13         30  30.506678       0.506678\n",
              "14         45  41.329843      -3.670157"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "25HOx35jZ8wW"
      },
      "source": [
        "_,p_value=stats.ttest_rel(a=weight1,b=weight2)"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oR4ZjGWkZ8wW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fdce3f9b-7087-4b12-957c-58300a0724e4"
      },
      "source": [
        "print(p_value)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.5732936534411279\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z_k6c61VZ8wW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "47bf5315-6488-4660-e69a-a091e4448461"
      },
      "source": [
        "if p_value < 0.05:    # alpha value is 0.05 or 5%\n",
        "    print(\" we are rejecting null hypothesis\")\n",
        "else:\n",
        "    print(\"we are accepting null hypothesis\")"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "we are accepting null hypothesis\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w6uaY7ROZ8wX"
      },
      "source": [
        "## Correlation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L9pvFq1dZ8wX"
      },
      "source": [
        "import seaborn as sns\n",
        "df=sns.load_dataset('iris')"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qDCPQ-5aZ8wY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "426c1362-3a86-46dc-c02e-6af0670a8aae"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QGGUqinVZ8wY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "0efaf6db-1c94-4444-ce4a-e2dc1fed75a7"
      },
      "source": [
        "df.corr()"
      ],
      "execution_count": 49,
      "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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sepal_length</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.117570</td>\n",
              "      <td>0.871754</td>\n",
              "      <td>0.817941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sepal_width</th>\n",
              "      <td>-0.117570</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.428440</td>\n",
              "      <td>-0.366126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>petal_length</th>\n",
              "      <td>0.871754</td>\n",
              "      <td>-0.428440</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.962865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>petal_width</th>\n",
              "      <td>0.817941</td>\n",
              "      <td>-0.366126</td>\n",
              "      <td>0.962865</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              sepal_length  sepal_width  petal_length  petal_width\n",
              "sepal_length      1.000000    -0.117570      0.871754     0.817941\n",
              "sepal_width      -0.117570     1.000000     -0.428440    -0.366126\n",
              "petal_length      0.871754    -0.428440      1.000000     0.962865\n",
              "petal_width       0.817941    -0.366126      0.962865     1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-6RW2fhPZ8wZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 763
        },
        "outputId": "c23a5236-0eb3-4556-9d82-6dbf2a453b43"
      },
      "source": [
        "sns.pairplot(df)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f4700974e48>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sAklfUFpZ8vw"
      },
      "source": [
        "## Chi-Square Test- \n",
        "\n",
        "The test is applied when you have two categorical variables from a single population. It is used to determine whether there is a significant association between the two variables."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dHjGTwQKZ8vy"
      },
      "source": [
        "import scipy.stats as stats"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0X-cCjTZZ8v0"
      },
      "source": [
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "dataset=sns.load_dataset('tips')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TdnZ8Sh1Z8v0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "f3bb1f2b-e345-4f9a-f998-bff5b365e473"
      },
      "source": [
        "dataset.head()"
      ],
      "execution_count": 3,
      "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>total_bill</th>\n",
              "      <th>tip</th>\n",
              "      <th>sex</th>\n",
              "      <th>smoker</th>\n",
              "      <th>day</th>\n",
              "      <th>time</th>\n",
              "      <th>size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>16.99</td>\n",
              "      <td>1.01</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.34</td>\n",
              "      <td>1.66</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>21.01</td>\n",
              "      <td>3.50</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>23.68</td>\n",
              "      <td>3.31</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>24.59</td>\n",
              "      <td>3.61</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   total_bill   tip     sex smoker  day    time  size\n",
              "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
              "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
              "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
              "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
              "4       24.59  3.61  Female     No  Sun  Dinner     4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ib6i_n6rZ8v3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9a6f4f48-e3f3-4846-fbba-a216ad8e9d56"
      },
      "source": [
        "dataset_table=pd.crosstab(dataset['sex'],dataset['smoker'])\n",
        "print(dataset_table)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "smoker  Yes  No\n",
            "sex            \n",
            "Male     60  97\n",
            "Female   33  54\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EN_3C87LZ8v7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "47600f87-3ae8-42b5-d8bc-7ecf72bafbc5"
      },
      "source": [
        "dataset_table.values "
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[60, 97],\n",
              "       [33, 54]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NCP4ALmMZ8v7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e2cdfc0-eda9-4278-ae16-a80b8d9f4dd8"
      },
      "source": [
        "#Observed Values\n",
        "Observed_Values = dataset_table.values \n",
        "print(\"Observed Values :-\\n\",Observed_Values)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Observed Values :-\n",
            " [[60 97]\n",
            " [33 54]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4UOJAcVpZ8v8"
      },
      "source": [
        "val=stats.chi2_contingency(dataset_table)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1pZIxEK9Z8v9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "00ba4a48-ce23-4d94-d206-1433da03a465"
      },
      "source": [
        "val"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.008763290531773594, 0.925417020494423, 1, array([[59.84016393, 97.15983607],\n",
              "        [33.15983607, 53.84016393]]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nf8s999DZ8v-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "59f5b1a8-1f8b-4007-8510-7c9168f10fca"
      },
      "source": [
        "Expected_Values=val[3]\r\n",
        "Expected_Values"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[59.84016393, 97.15983607],\n",
              "       [33.15983607, 53.84016393]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iGMSJRFnZ8v-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f2bac1b0-94f3-433b-b9cf-bcdd6b3cc6be"
      },
      "source": [
        "no_of_rows=len(dataset_table.iloc[0:2,0])\n",
        "no_of_columns=len(dataset_table.iloc[0,0:2])\n",
        "ddof=(no_of_rows-1)*(no_of_columns-1)\n",
        "print(\"Degree of Freedom:-\",ddof)\n",
        "alpha = 0.05"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Degree of Freedom:- 1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rBea9vtYH_Fm"
      },
      "source": [
        "![Chi_squre.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAANkAAACECAYAAAAdro5fAAATo0lEQVR4Ae1a/2ob17bOK/QZ+gp9hz6B6GXAMAQEbUzBFI4pNoWagDglIkQUjgMVpSRQ54/6QEwaHKgCuSoH9xJ0W3wgKNdVeo1KkK8vupgJ4QyG7/LNzIq3x6MfI83Ys0drg5jRaGbttb+1vrXWXqNr0KEIKAK5InAtV+kqXBFQBKAkUydQBHJGQEmWM8AqXhFQkqkPKAI5I6AkyxlgFa8IKMnUBxSBnBFQkuUMsIpXBJRk6gOKQM4IKMlyBljFKwJKMvUBRSBnBJRkOQOs4hUBJZn6gCKQMwJKspwBVvGKgJJMfUARyBkBJVnOAKt4RUBJpj6gCOSMgJIsZ4CzFP/777/jq6++yuyztraGDz/88N2H33Vkj4CSLHtMc5N4fHyM9957D9euXcvt0263c9PfNsHE++7du/jggw/e4f3RRx/h119/TbUUJVkquK7+5q2trXcGF7IxA82a4fjs+++//06mkiy0MQlGXEgyDiEcMWegSzOUZGnQKsi9ZmSl0ect8+hAQtiCLPHK1WDQIibxwfKa19MEo4tS4lL1e+EQoIGFFHLkfm2eIc4zj4wyPfvjjz8GGYuVgzmIk2YyE5ESn3/yySfniMa9wjxDSTYZPQYyBjUpISc/Ed5R+EzGqE2HMvcNLJcYaRZ50ODxJkiaEiaOHUnGzyyDjQCSfN6ydZa5Z3mGJOFa0+DFkpp+R19MOwpLMi5KDE9CERAezf1IPJWnXbzt98u+QUpGYjPrEKzTPk8dSHbbbEF/YuCehjRCsLQZTLAsLMlodDoPFxgfQrS0tXFcju3fiY2Z4YnXrM5OvNOWnMxctEHalnZRcBfyjCMa10Z/S5P14usrLMkkOieRjBFFfo8vaNG+k1SCBY8kXRJmWeNCp+N8s5I6a31mlUcScR1JWYpZmoGHv/Fcsj2PaUhXWJIxSnLxXFx8iGPRoXQgMD6xkk8SZlnjREcrC/7MZMzIZnAiuQTPpOO47BfHurAkiytqfucCufDLcCZz3qKeS1YRZ4g7TNZ6j4v+SXOxScPASHsxK8z7uiFpjqRr3MNzTn7GNcr4G7FLymZJctNes45k0kbN25HSAnnV90vgEaKlibRpdZcqY5qSiQ5OnZj5eM4jv+dZZlI2/UPmlD38uO6n6JgWi2nut4pkslElILZutqcxyiz3JLX088oYQpRJegrx44QSp89DPwkAJLQM+g19hsQbNUSnUb/Pcz0VyQgKAeNCCLREilkVkMhGOUzVZk2cJHOU0ZLuXcRrkjXoULlG5gkOS+xFl6SOpfzGY5ZDyr74nPRb4kEijRoSOPIg/lQkIxjxVrEYksdZwBKgRQ6jzKgFknwEgfdoBhvlJuGfWImRYMrjNCXdaInJv1Au7TFq0F6iR5K9xPZxv+H3NJ/42sRHTT/iuWSpcfsyIVlc5qg1prk+FcmkhSkAyGLEmAR0UhYylTKNQBnjyMN7CRI/JnimPD0/Q4CVhtiFx3HR++ypdGeUO45kklFGdR/FoeMkM/We5tx8nuTgM/QlXmfVI+TicRzBuHrR6cpIlmSCeIszTWdGyj4BMl6zy3xcMEGLb1h5nR8lnSB1/ijONQnf809N/41yx5GMTs57aOekIUE67vh8Ls3HJITMSb1ERhofKSTJ6OBiRB5HRa04yNL+lWfjBJL7BTT+LntAecY88j4d5xGgc5kYTWub81JGfxPZo+4Q2yXZRnRj8ExT/YyaS66Pm1PuGXcUkuURuKcqF0cpN21GMp+XxdBQPE8a8ZJHjJp0HEXSJLnjrsWjf9Jcs14btc5x+sz7m4kz9U5y+FnnENmjnh/n8PJslvpQj3Fz8vdJ5BH7j1rTPNfnIlk8m01yJqnVafSsI9k8IPDZWQk07XPz6pf2+bhtssSbgY3rNss1Uz/JVnRcc0jwjF8375n1XGTHfZA4cL74dXMeZlSuZ9w95v1pz+ciGSeLZ7NRwPNeqcXHGSjtArK6XyLZtKRJc19expu0diGD6JpV9hASjasiZG6unfPySD14Pcsy0cRAfJH9As7J7/S5SeuW4J+mr2DOO+l8bpIJ4GJILixpcAFyz6RFJz2v19IjQGdmBhPc4++P0ks8e4LOO2mvx/03bc0PM82kku1M+uxnJIzMyfNpCE1cssz0ce3nJhkFSpQSY8bBNI2dR6kQX5R+P0NAojttk/TO6uzOdGcSXPOK/um0mf1uWUeegT8Tkkk9LCSLlxFSOjBaZGno2aFdjCcZ7CST5eFEtKvNNmXwZzbOMsMneVYmJKNgKiskM1Ov2bK3PeolAVjka1Jh0DbTlE2zrIXkpb1tsy0zGPVmps8LG8EzM5LFs5mALobmUcflISCbeQY+OlSeg/KZDeIVTJ5zziObvkl/zBsX0TEzkpn7LhqW0VOIx4gR36eJAnrMHgHTFnmXQtlrXz6JmZGM0LB0kJLRPEpWKx98xVyRNDvMsr2Ymi6GVpmSjBHUJBfPtUy8XEdiCSQ20OB2udiPmi1TknESiaJiaJaMNoyyvIyWBlQewU1sugjHLH02c5LFS0Zb9gR5O06WRhsly8Q+j1cleWNUJPmjMJ7leqYkM9v1JmA2ND1sz2R5vxObxbn0mRCBTEkmjspSxSQZS0gd+SLAioGY5/lOLN8VlFd6ZiSTUkU6WkI4IVveL/zKa6LJKzObHXw/pqNYCGRCMtPI0uiQd2RCMpJQRz4ISLPDlv1vPigUV+rcJGOGEiPHO1pynUSTDFdcKOzUzKwgbNj72onyfFrPTTL58y+JFO9oiQNINpMsN5/K+rQgoM0OQaLYx7lIZv4/Lqm5Yf69h0RjZtORHQJmsyMrqaxG4hVJVrIXVc7MJDMJNK4UjL+c1o15Nq5m7oOzxJQNKyVZNjYSKTOTjIaQMnBcU4MljdzHoxpQoJ/vKPvdLPFk4KSNbPk3/XwIXt7TM5GM/4kT4kxTAkpZI88wCuuYHQFzr5tls0M6wuOC5uxaL+6TqUkW/1fHNM0Ms7TRbDafs5nNjqR98KzSaVeW/bSP/rF4VhSTn0tFMrNdn5Ys8ZfT8U5ksnp6NY6AWRWwrGPWYck47YfP8xnzY8qkXdU2cdTn+56KZHFjpCn7pBShEdMSdL4llufpeEUgWGZ5nKb8vyxEuV5ma8mwPNq4X5yaZHGSkHBph2zWxSni5Q4zZZZ7jLT6Ff3+OH6CY5bHouzHqAfXRVLRL/iRaijLbupl2Hwqkpn1uhh0FjLEiUpZjE4sdQTAWch7GUBd9RwmdkmlIXGLl4BJ90lWEDvKkdeLQjBprMWzlhDPtj3jRJKZEUQMEs9AaRxQyCSy5MjrBI/z6VhcBBi86RPxspV+IZl8lgB/lYhOJBmzGCMIUzRr5HlJwOdJJkZeRiqe2wbaVRqs7HMzgJNkkq3oczwXgjGj2zYmksy2Bam+diMgZJIKh99Z9tocjJVkdvtk6bQXcpVpYUqyMlmzBGtRkpXAiLqEYiMgJIvv/fmd+zUbX5RrJiu2zy2cdmyGkWhmB5tNN+7N2CyLk88GgJRkNlhpwXQk0eR9Ho8kF7uMtg4lma2WU72tQUBJZo2pVFFbEVCS2Wo51dsaBJRk1phKFbUVASWZrZZTva1BQElmjalUUVsRUJLZajnV2xoElGTWmEoVtRUBJZmtllO9rUFASWaNqVRRWxFQktlqOdXbGgSUZNaYShW1FQElma2WU72tQUBJZo2pVFFbEVCS2Wo51dsaBJRk1phKFbUVASWZrZZTva1BQElmjalUUVsRUJLZajnV2xoElGTWmEoVtRUBJZmtllO9rUFASWaNqVRRWxFQktlqOdXbGgSUZNaYShW1FQElma2WU72tQUBJZo2pVFFbEVCS2Wo51dsaBJRk1phKFbUVASUZAH/YR+9wAO/UVjMm6P12iP5BD71XA3h+wu95Xjr1MDjsoT+87InzXNTssheKZP7RPlr3G6jd3MDGzRoa91vonnjYv78CZ20bvbcxIN/2sFNbQe1xH1a5y0kX22sVVCoVVJwaWv3L1d5/3ULtehWNfwxjgC7m14UhmfdiBxtLFVSur2PzwTa2v99E7dYW9v+nj90vHVTvdjCMZ7K3fbS+28T2b3Y5y/AfDVTdDey88oH4mi7Bz4edJqruOnYOL5fcl7C0maZYDJIN93Hvswrc2g56JzGcjvfQ+NjF+v1dbH9TR/1v29iLIr//uoPWk/bFZ0562HvYRL3GbLiLzqEXCPVf76P1fQP1O/fQ6uxj72kbXfLzpIf2kxY6ryOnG3bRfhL9BmB40Mb23Trq3+6i86KD1rN9DHwfg3+20XrWwd6jJuq3mmjRab0+Oo/vofHXOpqPeJ+5Hh+DFy001xxUltbRfBjq7h910f57E427W9h93o/K4gT5fxyh+3ML7ecdtB5son5nC+1DD8NXbWx9XQv060YloHfYQeunPfTDpcM72MPu01Dv3g+rcFbuYZ9Yn3roP99B83Ydmw/30P2tjdZzyyoDE+IZzheAZD4GT2twnXVsH0QeYQDlvdjCCsuqioPVWw2sVytwvtxF/62P/uMNuDc20TETmT9A+46LiruOxrebqK+tovFsgDf9FurVCio3NtC4sw6XMpdqaL/24XWaWF5aDzMLgMGzOtzrdbSPgOHzZjC/u9bAZq0alnhf7KDvDdC+7Ybfq6vYuL2Fzn+/xM5NEmgV9TsbqFZc1H8enK3GH2L/SRPrbgXO55vYfryHP7ot1K9Tr3XUbq7Aqbio/dSHf5og/+A/0LwRlpnLtQbqKyw3XbjuMjbu1LG6VMHK/X148NGNiNQNiDRE5241xO1kgPYtF+7tNgb/8tD9YT3CdjOUV6lg5fsuLlribBllOys/yU6HASmctZ2Ley746D/ZCByv/pSO56H7/Qqcv2yhexI+59Z2cW5L43Wx9RcHy3f38G5ffzrE3t+qqHzaROeYnZSwBK0Ec/oIIvuNZkjWUw/73y0j0OcoyrC3W2FGOmoHhAgc+WQ/dPjPqQvdLgoWJOuBh+E/t7HquKg/M0jG25iZqyTSAD4Dwm0XjsgIsKjCubmLwf/G5QP+wTZWSYLvOhj6od6VyjKanSH8t0ZZzfNaRCSWo2972F5zQgIOO2jecLD+9x7evNrBuuNg41Ev2NMy2FQrVWz+YkatslHq4nrKTzI2L9YcuHfaF/dcp1EE/nwbPYZWkkOc56SLrc8crDyIR10fvSf1IFO5ta2wBBx2sHnDwarcS6f7PNrn/St0dPevEZEifZa/6eDoxRZWHTpxGNf9fgs1NySOf7iD9SWjeeAP0Ko5YWYLMm8F1du7od6GXYPMvLSKrZdeRDgHG9K4YTD4Osw4f/xXTL5kWDdqlESZzvlyB302hKI1rj/qwxciPQzJEzY6DL3dkJisBJxqmLGpYrBXZJBYsL3aApAsjMBOLXJywyHhhWQIS6DQkYIo/LCHN/1dbLijoq6P4Ytd1FlarW3jZff8vd7Lbaz+mxNmE2akTx2s/hBF84PtILrXng7wZ1A21tAK9mrMqjU4TlhWDn5uwL0uvwEIMmgFzhdN7P7SRe8oqeCKMvPHDewdA/5hpNfzKHOcMHM6WP62g1f/HpN/6qHzbZRhSSrO9xn17oZZ6OUWVpeW0fzNCzNedM4M2//J0FvW9Of/ofONIS8os6uorNyLMrNpiHKfl59k8NB9sIpKZQXNn1kSynuxId4EreazkisolyLnGf6yGXTods9FXR+Dl91wsx+UfStBififB2EGqj3twzvex1bQPl8Js0lQApJw0W9fMBuFvwVzLIV7Re/lDmou904sK6PXCixbhUvMsjcdsHwNsq7XR/dFrIEQZWb3ZlTiBqVjlGF9L8jA1aVVbL84Cl9bmPKNDMv3he8IGpV25/aRLPsoh2Xry13Ugj2foXdQGURrYANk6KH/tIFqpRLu1a6g43mVNF4AkrG718XOraipEJVa7td7+PN5E8tGqzl0JDYr3oQb+88MJ6eVgnIrakZEzZKNh114b9kMMeQ7FVSqYTbhfmXni+idFZ3sYweVj6NmynEHzU/lNxfLVXZAWxi8IaHcC68Vhs/vRU2a8Bk2F869dohnZnjoPaqFTZhA3yoaP/XgcU8Vl89gUD0LOO+CDDekzHJGVmJ5WGdACGS6qLoVBOXwibFvOwVYunKPF9y3tIyqE+7VzjVEr9L7L2nuxSBZQJDwXwi9l130XvWDpkXwTw/jHxH+8dk/P7zXPfT6w4svoflvhlc9dA966Jslmz9E/1UPvcNe0AR5l004t/z7oj+Ex3+XUG4Uzf2TAXoHPQw8H95RP5Qp/5g4vuiOgc4HXMPFf6jI3qhhdhxZ+XEt1Fc6NUnyqeOrPgZR5nyHTaCnD6/fQ++1pNWoGqDMYw/e65jeMg/nPuqHur71Mez3MTi5uKZL8vUrm2ZxSHZZEF/IJpcw8amPwWEXew/W4Zj7uEuYWqeYjICSbDJGqe4YlU1SCUl7c9BeZ1nmova4V67/YKbFooD3K8kKaBRVqVwIKMnKZU9dTQERUJIV0CiqUrkQUJKVy566mgIioCQroFFUpXIhoCQrlz11NQVEQElWQKOoSuVCQElWLnvqagqIgJKsgEZRlcqFgJKsXPbU1RQQASVZAY2iKpULASVZueypqykgAkqyAhpFVSoXAkqyctlTV1NABJRkBTSKqlQuBJRk5bKnrqaACCjJCmgUValcCCjJymVPXU0BEVCSFdAoqlK5EPh/xu1EPBbIXrMAAAAASUVORK5CYII=)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DTgnBH_JZ8v_"
      },
      "source": [
        "from scipy.stats import chi2\n",
        "chi_square=sum([(o-e)**2./e for o,e in zip(Observed_Values,Expected_Values)])\n",
        "chi_square_statistic=chi_square[0]+chi_square[1]"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JRwMb9wHZ8wA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "032f9a6b-6c7f-4a94-b644-a2b59b40feec"
      },
      "source": [
        "print(\"chi-square statistic:-\",chi_square_statistic)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "chi-square statistic:- 0.001934818536627623\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U5eui3DCZ8wA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e0ada4b4-1a19-43ba-8aed-dae2b5c4d1d6"
      },
      "source": [
        "critical_value=chi2.ppf(q=1-alpha,df=ddof)\n",
        "print('critical_value:',critical_value)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "critical_value: 3.841458820694124\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wgZ7PTs7Z8wB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a2d1545c-2980-4580-88ef-69a5bac06ac2"
      },
      "source": [
        "#p-value\n",
        "p_value=1-chi2.cdf(x=chi_square_statistic,df=ddof)\n",
        "print('p-value:',p_value)\n",
        "print('Significance level: ',alpha)\n",
        "print('Degree of Freedom: ',ddof)\n",
        "print('p-value:',p_value)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "p-value: 0.964915107315732\n",
            "Significance level:  0.05\n",
            "Degree of Freedom:  1\n",
            "p-value: 0.964915107315732\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h7pFIGKGZ8wC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "34d97bc8-dbde-4fba-90a5-7f4b39d1abb1"
      },
      "source": [
        "if chi_square_statistic>=critical_value:\n",
        "    print(\"Reject H0,There is a relationship between 2 categorical variables\")\n",
        "else:\n",
        "    print(\"Retain H0,There is no relationship between 2 categorical variables\")\n",
        "    \n",
        "if p_value<=alpha:\n",
        "    print(\"Reject H0,There is a relationship between 2 categorical variables\")\n",
        "else:\n",
        "    print(\"Retain H0,There is no relationship between 2 categorical variables\")"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Retain H0,There is no relationship between 2 categorical variables\n",
            "Retain H0,There is no relationship between 2 categorical variables\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "upZ8sr35Z8wZ"
      },
      "source": [
        "## Anova Test(F-Test)\n",
        "The t-test works well when dealing with two groups, but sometimes we want to compare more than two groups at the same time.\n",
        "\n",
        "\n",
        "For example, if we wanted to test whether petal_width age differs based on some categorical variable like species, we have to compare the means of each level or group the variable"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XdR2H2CHZ8wa"
      },
      "source": [
        "### One Way F-test(Anova) :-\n",
        "It tell whether two or more groups are similar or not based on their mean similarity and f-score.\n",
        "\n",
        "Example : there are 3 different category of iris flowers and their petal width and need to check whether all 3 group are similar or not \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "efBnWOQeZ8wa"
      },
      "source": [
        "import seaborn as sns\n",
        "df1=sns.load_dataset('iris')"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DltHeLY_Z8wb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "63fa1c44-b6bb-4a9b-cf93-fb841655ce6c"
      },
      "source": [
        "df1.head()"
      ],
      "execution_count": 17,
      "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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width species\n",
              "0           5.1          3.5           1.4          0.2  setosa\n",
              "1           4.9          3.0           1.4          0.2  setosa\n",
              "2           4.7          3.2           1.3          0.2  setosa\n",
              "3           4.6          3.1           1.5          0.2  setosa\n",
              "4           5.0          3.6           1.4          0.2  setosa"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DrOS1PTRZ8wb"
      },
      "source": [
        "df_anova = df1[['petal_width','species']]"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XBJWMqpvZ8wc"
      },
      "source": [
        "grps = pd.unique(df_anova.species.values)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ikDy5SZZ8wc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d33a47cf-46d9-42b9-d860-773fb4c967fe"
      },
      "source": [
        "grps"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['setosa', 'versicolor', 'virginica'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ib-O10iZ8wd"
      },
      "source": [
        "d_data = {grp:df_anova['petal_width'][df_anova.species == grp] for grp in grps}"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LjlFYfWXZ8wd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cb72fb5f-da1e-4320-ac1d-dc3c0befebd8"
      },
      "source": [
        "d_data"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'setosa': 0     0.2\n",
              " 1     0.2\n",
              " 2     0.2\n",
              " 3     0.2\n",
              " 4     0.2\n",
              " 5     0.4\n",
              " 6     0.3\n",
              " 7     0.2\n",
              " 8     0.2\n",
              " 9     0.1\n",
              " 10    0.2\n",
              " 11    0.2\n",
              " 12    0.1\n",
              " 13    0.1\n",
              " 14    0.2\n",
              " 15    0.4\n",
              " 16    0.4\n",
              " 17    0.3\n",
              " 18    0.3\n",
              " 19    0.3\n",
              " 20    0.2\n",
              " 21    0.4\n",
              " 22    0.2\n",
              " 23    0.5\n",
              " 24    0.2\n",
              " 25    0.2\n",
              " 26    0.4\n",
              " 27    0.2\n",
              " 28    0.2\n",
              " 29    0.2\n",
              " 30    0.2\n",
              " 31    0.4\n",
              " 32    0.1\n",
              " 33    0.2\n",
              " 34    0.2\n",
              " 35    0.2\n",
              " 36    0.2\n",
              " 37    0.1\n",
              " 38    0.2\n",
              " 39    0.2\n",
              " 40    0.3\n",
              " 41    0.3\n",
              " 42    0.2\n",
              " 43    0.6\n",
              " 44    0.4\n",
              " 45    0.3\n",
              " 46    0.2\n",
              " 47    0.2\n",
              " 48    0.2\n",
              " 49    0.2\n",
              " Name: petal_width, dtype: float64, 'versicolor': 50    1.4\n",
              " 51    1.5\n",
              " 52    1.5\n",
              " 53    1.3\n",
              " 54    1.5\n",
              " 55    1.3\n",
              " 56    1.6\n",
              " 57    1.0\n",
              " 58    1.3\n",
              " 59    1.4\n",
              " 60    1.0\n",
              " 61    1.5\n",
              " 62    1.0\n",
              " 63    1.4\n",
              " 64    1.3\n",
              " 65    1.4\n",
              " 66    1.5\n",
              " 67    1.0\n",
              " 68    1.5\n",
              " 69    1.1\n",
              " 70    1.8\n",
              " 71    1.3\n",
              " 72    1.5\n",
              " 73    1.2\n",
              " 74    1.3\n",
              " 75    1.4\n",
              " 76    1.4\n",
              " 77    1.7\n",
              " 78    1.5\n",
              " 79    1.0\n",
              " 80    1.1\n",
              " 81    1.0\n",
              " 82    1.2\n",
              " 83    1.6\n",
              " 84    1.5\n",
              " 85    1.6\n",
              " 86    1.5\n",
              " 87    1.3\n",
              " 88    1.3\n",
              " 89    1.3\n",
              " 90    1.2\n",
              " 91    1.4\n",
              " 92    1.2\n",
              " 93    1.0\n",
              " 94    1.3\n",
              " 95    1.2\n",
              " 96    1.3\n",
              " 97    1.3\n",
              " 98    1.1\n",
              " 99    1.3\n",
              " Name: petal_width, dtype: float64, 'virginica': 100    2.5\n",
              " 101    1.9\n",
              " 102    2.1\n",
              " 103    1.8\n",
              " 104    2.2\n",
              " 105    2.1\n",
              " 106    1.7\n",
              " 107    1.8\n",
              " 108    1.8\n",
              " 109    2.5\n",
              " 110    2.0\n",
              " 111    1.9\n",
              " 112    2.1\n",
              " 113    2.0\n",
              " 114    2.4\n",
              " 115    2.3\n",
              " 116    1.8\n",
              " 117    2.2\n",
              " 118    2.3\n",
              " 119    1.5\n",
              " 120    2.3\n",
              " 121    2.0\n",
              " 122    2.0\n",
              " 123    1.8\n",
              " 124    2.1\n",
              " 125    1.8\n",
              " 126    1.8\n",
              " 127    1.8\n",
              " 128    2.1\n",
              " 129    1.6\n",
              " 130    1.9\n",
              " 131    2.0\n",
              " 132    2.2\n",
              " 133    1.5\n",
              " 134    1.4\n",
              " 135    2.3\n",
              " 136    2.4\n",
              " 137    1.8\n",
              " 138    1.8\n",
              " 139    2.1\n",
              " 140    2.4\n",
              " 141    2.3\n",
              " 142    1.9\n",
              " 143    2.3\n",
              " 144    2.5\n",
              " 145    2.3\n",
              " 146    1.9\n",
              " 147    2.0\n",
              " 148    2.3\n",
              " 149    1.8\n",
              " Name: petal_width, dtype: float64}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T8r6_-u3Z8wf"
      },
      "source": [
        "F, p = stats.f_oneway(d_data['setosa'], d_data['versicolor'], d_data['virginica'])"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qAlyDcA2Z8wf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "332a1c4d-f2e6-42ef-ff20-0f1c8c240d49"
      },
      "source": [
        "print(p)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4.169445839443116e-85\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZidD8oztZ8wg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "846cfc13-f4d3-4268-bf0f-1bb982c575ab"
      },
      "source": [
        "if p<0.05:\n",
        "    print(\"reject null hypothesis\")\n",
        "else:\n",
        "    print(\"accept null hypothesis\")"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "reject null hypothesis\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UiIcFqzeJZkz"
      },
      "source": [
        "-----------"
      ]
    }
  ]
}