Hypothesis_Testing.ipynb

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  "metadata": {
    "colab": {
      "name": "Hypothesis_Testing.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyP1RC+KmDa9fSKUhFCzSjaf",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Chandradithya8/Machine-Learning/blob/master/Hypothesis_Testing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PcH-8Qzp01hZ",
        "colab_type": "text"
      },
      "source": [
        "T-Test: One Sample"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VOOFl15j05Yl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f1ac0d23-14c2-4a28-93f2-35a6730dfb9e"
      },
      "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]\n",
        "len(ages)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "32"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0cS3lOcx1C7y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "10231f9e-6a6d-40d4-a838-2e22d166cfa7"
      },
      "source": [
        "import numpy as np\n",
        "pop_mean=np.mean(ages)\n",
        "pop_mean"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30.34375"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_qTwtrbM1KUw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "cd704f8c-3917-437b-fedd-200040135318"
      },
      "source": [
        "sample_size=10\n",
        "age_sample=np.random.choice(ages,sample_size)\n",
        "age_sample"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([30, 50, 28, 24, 18, 16, 70, 40, 27, 18])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7WV3-kJa1e_v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "eeb5b3c6-a2bb-4de9-d413-c344c3c7bd86"
      },
      "source": [
        "from scipy.stats import ttest_1samp\n",
        "pvalue=ttest_1samp(age_sample,30)\n",
        "print(pvalue)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Ttest_1sampResult(statistic=0.39154610826793385, pvalue=0.7045026029020472)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o3qeR3P027Wa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1a8da78f-652f-4bcd-d604-05365c1a8f1d"
      },
      "source": [
        "ttest,pvalue=ttest_1samp(age_sample,30)\n",
        "print(pvalue)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7045026029020472\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H2yLN18H2cql",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "3acdc948-f8f8-4b3b-c8b1-4b5a6520c9ba"
      },
      "source": [
        "if pvalue<0.05:\n",
        "  print(\"reject null hypothesis\")\n",
        "else:\n",
        "  print(\"accept null hypothesis\")  \n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "accept null hypothesis\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4dkQCCGq5P8c",
        "colab_type": "text"
      },
      "source": [
        "T-Test: Two Sample"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tuidlRwr5NG9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import scipy.stats as stats\n",
        "np.random.seed(6)\n",
        "classA_ages=stats.poisson.rvs(loc=18,mu=30,size=60)\n",
        "classB_ages=stats.poisson.rvs(loc=18,mu=33,size=60)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KxIhlqVZ6pkX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "80333cbb-b01d-4d39-b1d2-035adc7a60ad"
      },
      "source": [
        "_,p_value=stats.ttest_ind(classA_ages,classB_ages)\n",
        "p_value"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.003180905180078275"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wkn_YdfM68Cb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e9a62834-b8ee-4e60-c642-6a6bc41c2c27"
      },
      "source": [
        "if p_value<0.05:\n",
        "  print(\" reject null hypothesis\")\n",
        "else:\n",
        "  print(\"accept null hypothesis\")  "
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " reject null hypothesis\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kz94ID6p-Enw",
        "colab_type": "text"
      },
      "source": [
        "Correlation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Phv-kr4i-IUb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "8f1d1e20-3a7f-43e0-8293-69f905c2fcf3"
      },
      "source": [
        "import seaborn as sns\n",
        "df=sns.load_dataset(\"iris\")\n",
        "df.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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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>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": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ypPKDXG_U3v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "outputId": "60e51f12-7826-4bb9-c962-c6fc4b9464a9"
      },
      "source": [
        "df.corr()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\n",
              "\n",
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              "        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": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8lmyXucd_ZxK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 766
        },
        "outputId": "cb5d2073-1ab2-47ac-b7a6-9cf50519ac2c"
      },
      "source": [
        "sns.pairplot(df)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f72d04abb38>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "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": "9ZmpySOL_wdm",
        "colab_type": "text"
      },
      "source": [
        "Chi-Square Test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s6Euv_cv_0JZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "84073382-d811-46b2-afda-0449ffd7d5b5"
      },
      "source": [
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "df=sns.load_dataset(\"tips\")\n",
        "df.head()"
      ],
      "execution_count": 14,
      "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": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7W1OaPvuqIdz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        },
        "outputId": "afff41ba-4cbd-49c6-bf1c-602d290ba927"
      },
      "source": [
        "data_table=pd.crosstab(df['sex'],df['smoker'])\n",
        "data_table"
      ],
      "execution_count": 15,
      "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>smoker</th>\n",
              "      <th>Yes</th>\n",
              "      <th>No</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Male</th>\n",
              "      <td>60</td>\n",
              "      <td>97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Female</th>\n",
              "      <td>33</td>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "smoker  Yes  No\n",
              "sex            \n",
              "Male     60  97\n",
              "Female   33  54"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wQk8t0aaq9vW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "da5474d1-ec7e-4ad1-fad9-04a1fa19b67a"
      },
      "source": [
        "observed_value=data_table.values\n",
        "observed_value"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[60, 97],\n",
              "       [33, 54]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kW9eiYaNqYar",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "ba79e737-efa0-471a-8a73-28169e7bf2b4"
      },
      "source": [
        "import scipy.stats as stats\n",
        "a=stats.chi2_contingency(data_table)\n",
        "a"
      ],
      "execution_count": 16,
      "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": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wLRUe8GGq4ui",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "bd9e423e-995a-4ca4-f9bb-adac08323dbe"
      },
      "source": [
        "expected_value=a[3]\n",
        "expected_value"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[59.84016393, 97.15983607],\n",
              "       [33.15983607, 53.84016393]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ZTSgazbrKHx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "c840ed6b-5574-4dfc-f749-ed1549b998ef"
      },
      "source": [
        "rows=len(data_table.iloc[0:2,0])\n",
        "columns=len(data_table.iloc[0,0:2])\n",
        "print(rows)\n",
        "print(columns)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2\n",
            "2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wlPlArwkrkwI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "728b5886-ca5c-4fda-d600-2630b227b9f6"
      },
      "source": [
        "alpha=0.05\n",
        "ddof=(rows-1)*(columns-1)\n",
        "ddof # degree of freedom"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tIChw1DNr0AI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b58760c2-2956-4fb3-9e0d-20a5ead2a856"
      },
      "source": [
        "from scipy.stats import chi2\n",
        "chi_square=sum([(o-e)**2./e for o,e in zip(observed_value,expected_value)])\n",
        "chi_square_statistic=chi_square[0]+chi_square[1]\n",
        "chi_square_statistic"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.001934818536627623"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-sgdZyu-scRn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8999c6b6-4e18-46be-c315-357553107bec"
      },
      "source": [
        "critical_value=chi2.ppf(q=1-alpha,df=ddof)\n",
        "print('critical_value:',critical_value)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "critical_value: 3.841458820694124\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SvnOpaJystpP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "ce0c356d-6821-4d8e-b92a-d1c0199dd8fe"
      },
      "source": [
        "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": 28,
      "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": "tM7c_z_EswFX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "12d0bd70-c983-4885-fb21-1c18c41a7a36"
      },
      "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": 29,
      "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"
        }
      ]
    }
  ]
}