Chi_Square_Tests_Titanic_Dataset.ipynb

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      "name": "Chi Square Tests - Titanic Dataset.ipynb",
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        "<a href=\"https://colab.research.google.com/github/mukul-mschauhan/Data-Science-Projects/blob/master/Chi_Square_Tests_Titanic_Dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# About Chi Square Tests\n",
        "\n",
        "1. A Chi Square test is a test of hypothesis where two samples are tested for Independence.\n",
        "\n",
        "2. It means whether the samples are independent of each other or not.\n",
        "\n",
        "3. Here we dont compare means or variance. Here, we have Categorical variables on which we test the hypothesis of Independence.\n",
        "\n",
        "4. Here, we check whether the variables are independent of each other or there is a pattern of dependence between them.\n",
        "\n",
        "5. If there is a Dependence, researcher claims that two variables have statistical relationship.\n",
        "\n",
        "6. Since, it is a test on categories, hence the test is highly dependent on Frequencies. The Sample Of Data received is called \"Observed Frequencies\" and we find out the expected frequencies basis the data received.\n",
        "\n",
        "7. The Chi Square Test is based on the difference between the Observed and the Expected Values. sum[(Obs - Exp)**2/Exp]\n",
        "\n",
        "Q. A University conducted a survey of its recent graduates to collect demographic and health information for future planning purposes as well as to assess students' satisfaction with their undergraduate experiences. The survey revealed that a substantial proportion of students were not engaging in regular exercise, many felt their nutrition was poor and a substantial number were smoking. In response to a question on regular exercise, 60% of all graduates reported getting no regular exercise, 25% reported exercising sporadically and 15% reported exercising regularly as undergraduates. \n",
        "\n",
        "The next year the University launched a health promotion campaign on campus in an attempt to increase health behaviors among undergraduates. The program included modules on exercise, nutrition and smoking cessation. To evaluate the impact of the program, the University again surveyed graduates and asked the same questions. The survey was completed by 470 graduates and the following data were collected on the exercise question:\n",
        "\n",
        "The Distribution of the responses for the last year have been (60%, 25%, 15%).\n",
        "\n",
        "Based on the data, is there evidence of a shift in the distribution of responses to the exercise question following the implementation of the health promotion campaign on campus? Run the test at a 5% level of significance.\n",
        "\n",
        "### Null Hypothesis:\n",
        "\n",
        "The Responses are 60%, 25% and 15%\n",
        "\n",
        "### Alternate Hypothesis:\n",
        "\n",
        "Responses have changed meaning H0 is False.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UChy23NnLvYh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from scipy import stats"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "69OpnZiKLwnh",
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      "source": [
        "dict_ex = {\"No Regular Exercise\": [255], \"Irregular\": [125], \"Regular\": [90]}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
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        "outputId": "332eb4c8-2e84-4e5f-9ebb-854d2dcd298f"
      },
      "source": [
        "dict_ex"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Irregular': [125], 'No Regular Exercise': [255], 'Regular': [90]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mFsZ6iiGslyl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.DataFrame({\"Observed\": [255, 125, 90]})\n",
        "total = df.Observed.sum()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "id": "9lv5rsZrsy2d",
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      "source": [
        "# proportion = [i for i in df[\"Observed\"]/total]\n",
        "proportion = [0.60, 0.25, 0.15]\n",
        "\n",
        "expected =[]\n",
        "for i in proportion:\n",
        "  expected.append(total * i)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3JNEzrjls2ar",
        "colab_type": "code",
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      "source": [
        "df[\"Expected\"] = expected"
      ],
      "execution_count": 0,
      "outputs": []
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    {
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        "id": "vBW0vaWQtGC2",
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          "height": 34
        },
        "outputId": "2d84586a-02b4-4dd6-f0d9-9683d32aef4e"
      },
      "source": [
        "stats.chisquare([255, 125, 90], [282, 117.5, 70.5])"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Power_divergenceResult(statistic=8.457446808510639, pvalue=0.014570979931304122)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ll1Fs3ngtKs5",
        "colab_type": "code",
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      },
      "source": [
        "df[\"Obs-Exp**2\"] = (df.Observed - df.Expected)**2/df.Expected"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b15OPPebtnko",
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        "outputId": "7ab3c337-2e73-4015-d0a7-706d3c6c683b"
      },
      "source": [
        "df[\"Obs-Exp**2\"].sum()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8.457446808510639"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ar9kLzrIvR4u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6e71f220-1c6a-4814-91b5-073ce8d159df"
      },
      "source": [
        "# Chi square Critical Value - Needs LOS and DF\n",
        "stats.chi2.ppf(0.95, 2)"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.991464547107979"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
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      "cell_type": "markdown",
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      "source": [
        "### Conclusion\n",
        "\n",
        "Since the Test Statistic 8.45 is greater than Chi Square Critical Value 5.99 and hence we  reject the Ho meaning that the responses towards exercise has changed.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PZKFljyDyzRB",
        "colab_type": "text"
      },
      "source": [
        "The National Center for Health Statistics (NCHS) provided data on the distribution of weight (in categories) among Americans in 2002. The distribution was based on specific values of body mass index (BMI) computed as weight in kilograms over height in meters squared.\n",
        "\n",
        "Underweight was defined as BMI< 18.5, Normal weight as BMI between 18.5 and 24.9, overweight as BMI between 25 and 29.9 and obese as BMI of 30 or greater.\n",
        "\n",
        "Americans in 2002 were distributed as follows: 2% Underweight, 39% Normal Weight, 36% Overweight, and 23% Obese.\n",
        "\n",
        "Suppose we want to assess whether the distribution of BMI is different in the Framingham Offspring sample. Using data from the n=3,326 participants who attended the seventh examination of the Offspring in the Framingham Heart Study we created the BMI categories as defined and observed the following:\n"
      ]
    },
    {
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        "id": "0CIgSgKDw0Lh",
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      "source": [
        "weights = {\"Underweight\":20, \"Normal\": 932, \"Overweight\": 1374,\n",
        "           \"Obese\": 1000}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TD7hHf_7zRj-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.DataFrame({\"Observed\": [20, 932, 1374, 1000]})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "11AxVhmZzXrr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Find the Expected - 2, 39, 36, 23\n",
        "prop = [0.02, 0.39, 0.36, 0.23]\n",
        "exp = []\n",
        "for i in prop:\n",
        "  exp.append(i*df.Observed.sum())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gE9u6ByXzYgG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df[\"Expected\"] = exp"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bYIJJadEzvyp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df[\"Obs-Exp**2\"] = (df.Observed - df.Expected)**2/df.Expected"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5hgSjpHBz4Vk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tstats = df[\"Obs-Exp**2\"].sum() # Test Statistic"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sdu5bdb7z836",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bcaa8bab-a2f7-45a1-ed97-5ebfaf22ed51"
      },
      "source": [
        "# Find the Critical Value\n",
        "stats.chi2.ppf(0.95, 3)"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7.814727903251179"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lykspFFN0QWE",
        "colab_type": "text"
      },
      "source": [
        "## Conclusion\n",
        "\n",
        "Since the Test Statistic 223 is greater than Chi Square Critical Value 7.81 and hence we reject the Ho meaning that the distribution towards BMI has changed.\\\n",
        "\n",
        "\n",
        "## Null Hypothesis: Twitter Question:\n",
        "The Opinion Percentage is Still Same - 0.45, 0.21, 0.17, 0.17\n",
        "\n",
        "## Alternate Hypothesis: Twitter Question:\n",
        "The Opinion Percentage is Not Same.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "11ilkaiH0Gj8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.DataFrame({\"Observed\": [374, 183, 127, 116]})\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WjNQIZJn0i2Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Find the Expected - 2, 39, 36, 23\n",
        "prop = [0.45, 0.21, 0.17, 0.17]\n",
        "exp = []\n",
        "for i in prop:\n",
        "  exp.append(i*df.Observed.sum())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lrh815A57IKq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df[\"Expected\"] = exp\n",
        "df[\"Obs-Exp**2\"] = (df.Observed - df.Expected)**2/df.Expected\n",
        "tstats = df[\"Obs-Exp**2\"].sum()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XxvIVwaE7OWu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a197f6a3-04ed-4b93-c3dc-9027700a0904"
      },
      "source": [
        "# Find the Critical Value\n",
        "stats.chi2.ppf(1-0.025, 3), tstats"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9.348403604496148, 5.420494864612512)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uMuN2SzG7eAX",
        "colab_type": "text"
      },
      "source": [
        "# Conclusion\n",
        "\n",
        "The value of the test statistic is 5.4204 & Critical Value is 9.34\n",
        "and it falls in the nonrejection region. Hence, we fail to reject the null hypothesis, and state that the current percentage distribution of opinions is the same as for 2009."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NdZcA4OV7U2V",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a14ceedf-1dab-4daa-a360-6067564d35c9"
      },
      "source": [
        "stats.chisquare(df.Observed, df.Expected)"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Power_divergenceResult(statistic=5.420494864612512, pvalue=0.14347199699959678)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "goc3Z-2A7xkV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "outputId": "9f6ebb40-f9fc-48d8-a4f0-7fc2169e1852"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')\n",
        "%cd /gdrive"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /gdrive\n",
            "/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3cRWy0t38C9L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "titanic = pd.read_csv(\"/gdrive/My Drive/Colab Notebooks/TitanicTrain.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lQDddr0YA2A_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "9d592828-9f89-429f-df33-77019a3c5594"
      },
      "source": [
        "titanic.head()"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
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              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
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              "      <th>Titles</th>\n",
              "      <th>Family</th>\n",
              "      <th>Family_On_Board</th>\n",
              "      <th>Fare_Per_Person</th>\n",
              "      <th>SocioEcoStats</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>7.2500</td>\n",
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              "      <td>No Cabin Found</td>\n",
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              "      <th>1</th>\n",
              "      <td>1</td>\n",
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              "      <td>38.0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>Cabin Allotted</td>\n",
              "      <td>Mrs</td>\n",
              "      <td>1</td>\n",
              "      <td>Singles</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>Upper</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
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              "      <td>Miss</td>\n",
              "      <td>1</td>\n",
              "      <td>Singles</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>Low</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>Cabin Allotted</td>\n",
              "      <td>Mrs</td>\n",
              "      <td>1</td>\n",
              "      <td>Singles</td>\n",
              "      <td>53.1000</td>\n",
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              "      <td>35.0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>No Cabin Found</td>\n",
              "      <td>Mr</td>\n",
              "      <td>1</td>\n",
              "      <td>Singles</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass     Sex  ...  Family_On_Board  Fare_Per_Person SocioEcoStats\n",
              "0         0       3    male  ...          Singles           7.2500           Low\n",
              "1         1       1  female  ...          Singles          71.2833         Upper\n",
              "2         1       3  female  ...          Singles           7.9250           Low\n",
              "3         1       1  female  ...          Singles          53.1000         Upper\n",
              "4         0       3    male  ...          Singles           8.0500           Low\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BhM1uF8IBBXO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Does the Survival Depends on Gender\n",
        "# Ho: Gender and Survival and Independent of Each Other meaning Survival Doesn't Depend on Gender\n",
        "teststats, pvalue, df, exp_freq = stats.chi2_contingency(pd.crosstab(titanic.Sex, titanic.Survived))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6uAv39n3BYaB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "0ee18864-fa89-4c8e-b878-3e223bc67f77"
      },
      "source": [
        "print(teststats)\n",
        "print(pvalue)\n",
        "\n",
        "# Con: Since P Value is Less than 0, it means that the results are statistically significant meaning that these variables are dep."
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "260.71702016732104\n",
            "1.1973570627755645e-58\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EX-7XqX8B5oe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "fc46e60b-34eb-49e8-b067-53ec6c634372"
      },
      "source": [
        "# Lets Check how it is Distributed\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.countplot(x = \"SocioEcoStats\", hue = \"Survived\", data = titanic)"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5bb7e3ea20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 131
        },
        {
          "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": {
        "id": "LVJs9WWrBoPu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "a27372e5-9ffb-40d1-81fa-d2f2189aed6f"
      },
      "source": [
        "# Check if Survival is Also Independent of SES\n",
        "# Ho: Survival is Independent of SocioEcoStatus\n",
        "# H1: Null is False meaning that Survival depends on whether you are rich or not.\n",
        "teststats, pvalue, df, exp_freq = stats.chi2_contingency(pd.crosstab(titanic.SocioEcoStats, titanic.Survived))\n",
        "\n",
        "print(teststats)\n",
        "print(pvalue)\n",
        "\n",
        "# Again the P Value is 0 meaning that the Survival is Dependent on the SocioEcoStatus."
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "102.88898875696057\n",
            "4.5492517112987287e-23\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XYU4o582CaMf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Does the Fare Same for all the PClasses\n",
        "import statsmodels.api         as     sm\n",
        "from   statsmodels.formula.api import ols\n",
        "from   statsmodels.stats.anova import anova_lm"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qjQG95xkEKrm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "59157460-0f9f-4728-c6be-6910673b7b1f"
      },
      "source": [
        "formula = 'Pclass ~ Fare'\n",
        "model = ols(formula, titanic).fit() # Fitting the Anova Table\n",
        "aov_table = anova_lm(model) # Generating the Anova Table\n",
        "print(aov_table)\n",
        "\n",
        "# Conclusion - Mean Fare Paid across Categories is not Same. Avg Fare is Different for each category"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "             df      sum_sq     mean_sq           F        PR(>F)\n",
            "Fare        1.0  187.850073  187.850073  384.547434  1.967386e-71\n",
            "Residual  889.0  434.273383    0.488496         NaN           NaN\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xvqaWR2BEUTW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "e7ed8afd-25c4-4f5d-f924-8c66120904ed"
      },
      "source": [
        "# Does the Survival Depends on the Family Count\n",
        "teststats, pvalue, df, exp_freq = stats.chi2_contingency(pd.crosstab(titanic.Family, titanic.Survived))\n",
        "print(teststats)\n",
        "print(pvalue)\n",
        "\n",
        "# Again the P Value is 0 meaning that the Survival is Dependent on the Family Count. The Same can be seen in the Plot Too."
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "48.13393377928416\n",
            "3.352263602998165e-08\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CdFFemZrEdnv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "d0f074ee-54a9-45e9-fe5b-2d302f407876"
      },
      "source": [
        "sns.countplot(x = \"Family\", hue = \"Survived\", data = titanic)"
      ],
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5bad3de2e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 141
        },
        {
          "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": {
        "id": "kwMSpASeE_tK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5aacd3b7-2b76-4c81-8ece-1234f91a7244"
      },
      "source": [
        "# Is the Fare Same Across the Survived Class Too.\n",
        "stats.ttest_ind(titanic.loc[titanic.Survived==1, \"Fare\"], titanic.loc[titanic.Survived==0, \"Fare\"])"
      ],
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=7.939191660871055, pvalue=6.120189341924198e-15)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QJZI6p4OFfdu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "3b40652b-20d2-4268-dd9d-dd138950f858"
      },
      "source": [
        "# Does the Survival Depend on the Boarding Point\n",
        "teststats, pvalue, df, exp_freq = stats.chi2_contingency(pd.crosstab(titanic.Embarked, titanic.Survived))\n",
        "print(teststats)\n",
        "print(pvalue)\n",
        "\n",
        "# Again the P Value is 0 meaning that the Survival is Dependent on the Location of Boarding"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "25.964452881874784\n",
            "2.3008626481449577e-06\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fa2cdmDlFim2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "4f5b6ee4-21b1-49e7-ee72-d8b40f1a26e5"
      },
      "source": [
        "sns.countplot(titanic.Embarked, hue = \"Survived\", data = titanic)\n",
        "# Southampton People have better Survival Count"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5badb864a8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        },
        {
          "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": {
        "id": "fyEC5Zo9HVQO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "e2409cc3-494f-42f2-d628-bfdcd6dc511d"
      },
      "source": [
        "sns.countplot(titanic.Cabin_Status, hue = \"Survived\", data = titanic)\n",
        "# No Cabin Found have better Survival Count"
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5bad924208>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 154
        },
        {
          "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": {
        "id": "_QrnvY0xHiHq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "558f0f59-c4e5-48bb-9d33-bb6b3153afeb"
      },
      "source": [
        "# Does the Survival Depend on the Boarding Point\n",
        "teststats, pvalue, df, exp_freq = stats.chi2_contingency(pd.crosstab(titanic.Cabin_Status, titanic.Survived))\n",
        "print(teststats)\n",
        "print(pvalue)\n",
        "\n",
        "# Again the P Value is 0 meaning that the Survival is Dependent on the Cabin Status. The People Had no Cabin had better survival chances."
      ],
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "87.94148561238097\n",
            "6.7419704360811776e-21\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dH-Y2Qv4HtMC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "215eb977-e056-4be6-88b3-ad297a825169"
      },
      "source": [
        "# Does the Survival Depend on the Boarding Point\n",
        "teststats, pvalue, df, exp_freq = stats.chi2_contingency(pd.crosstab(titanic.Titles, titanic.Survived))\n",
        "print(teststats)\n",
        "print(pvalue)\n",
        "\n",
        "# Again the P Value is 0 meaning that the Survival is Dependent on the Titles. \n",
        "# Miss & Mrs Found have better Survival Count"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "284.6667057498796\n",
            "1.9783487591671835e-59\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PXCmITEuH7N_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "5952a796-58b9-4ad5-cdc1-c8fe6a99cd2e"
      },
      "source": [
        "sns.countplot(titanic.Titles, hue = \"Survived\", data = titanic)\n",
        "# Miss & Mrs Found have better Survival Count"
      ],
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5bad369c50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        },
        {
          "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": {
        "id": "pbOep7gOIB_n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}