_Q_Q_Plot_Normality_Test.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "# Q-Q Plot Normality Test.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/vinay10949/AnalyticsAndML/blob/master/_Q_Q_Plot_Normality_Test.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "iFj-30_rGmL4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Q-Q Plot Normality Test"
      ]
    },
    {
      "metadata": {
        "id": "t0AyLDDeGmL6",
        "colab_type": "code",
        "outputId": "2e9d0383-94f3-487f-e8ed-3661e17f4b5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1734
        }
      },
      "cell_type": "code",
      "source": [
        "#Q-Q plot\n",
        "import numpy as np \n",
        "import pylab \n",
        "import scipy.stats as stats\n",
        "\n",
        "# N(0,1)\n",
        "std_normal = np.random.normal(loc = 0, scale = 1, size=1000)\n",
        "\n",
        "# 0 to 100th percentiles of std-normal\n",
        "for i in range(0,101):\n",
        "    print(i, np.percentile(std_normal,i))\n",
        "\n",
        "\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 -3.4752637961950192\n",
            "1 -2.2934434732687143\n",
            "2 -1.9481416624582102\n",
            "3 -1.8164859868955767\n",
            "4 -1.7057393242461438\n",
            "5 -1.6384850662614565\n",
            "6 -1.5395368010145103\n",
            "7 -1.4860414671959072\n",
            "8 -1.411650088826292\n",
            "9 -1.3220486046686022\n",
            "10 -1.257748569595862\n",
            "11 -1.229778998159037\n",
            "12 -1.163859538927824\n",
            "13 -1.1240456233174467\n",
            "14 -1.0839960941748188\n",
            "15 -1.047836994538193\n",
            "16 -1.0143646626371634\n",
            "17 -0.9948863437418627\n",
            "18 -0.9622509483272028\n",
            "19 -0.910633724281054\n",
            "20 -0.8793385485935473\n",
            "21 -0.862293626448637\n",
            "22 -0.8219578927769828\n",
            "23 -0.7904266880394626\n",
            "24 -0.769755160056737\n",
            "25 -0.7437686495569906\n",
            "26 -0.7013689888181651\n",
            "27 -0.6390240251526212\n",
            "28 -0.6031826960377297\n",
            "29 -0.5803068888399542\n",
            "30 -0.5624364583388024\n",
            "31 -0.5374391472867049\n",
            "32 -0.5240862583693713\n",
            "33 -0.4853208285815708\n",
            "34 -0.4685977457839521\n",
            "35 -0.44236854283981614\n",
            "36 -0.41781530692176005\n",
            "37 -0.36107866467779315\n",
            "38 -0.3160998592046046\n",
            "39 -0.2958820876986795\n",
            "40 -0.261170427099462\n",
            "41 -0.2435474357810485\n",
            "42 -0.22227084404019226\n",
            "43 -0.202862508783306\n",
            "44 -0.18468693443518341\n",
            "45 -0.15458656778101512\n",
            "46 -0.12441564524035037\n",
            "47 -0.08212427242555616\n",
            "48 -0.04590757282992462\n",
            "49 -0.0040491705050391515\n",
            "50 0.011690203934142326\n",
            "51 0.03062200584344983\n",
            "52 0.0604590448483363\n",
            "53 0.08230202022578892\n",
            "54 0.10500308294868976\n",
            "55 0.12518749055863496\n",
            "56 0.1479451146299402\n",
            "57 0.1697874752252777\n",
            "58 0.1969151886289836\n",
            "59 0.22152541196653758\n",
            "60 0.2413487740283184\n",
            "61 0.2669014167471152\n",
            "62 0.2932704239850875\n",
            "63 0.3173868527386526\n",
            "64 0.3393009583390632\n",
            "65 0.35648247845974257\n",
            "66 0.39138290196264414\n",
            "67 0.42988086081451166\n",
            "68 0.4695884165240697\n",
            "69 0.48486111200016374\n",
            "70 0.5151520791650803\n",
            "71 0.545417590876475\n",
            "72 0.582310188695884\n",
            "73 0.612118371412174\n",
            "74 0.6402545242241876\n",
            "75 0.6645809394851878\n",
            "76 0.6903914874374558\n",
            "77 0.7112514038065293\n",
            "78 0.7530340881641517\n",
            "79 0.7666203697548477\n",
            "80 0.8000353551828823\n",
            "81 0.8516438536612178\n",
            "82 0.8933875937167034\n",
            "83 0.9423161878480041\n",
            "84 0.984183220195053\n",
            "85 1.0344239268161313\n",
            "86 1.0718339817388793\n",
            "87 1.0923184477093792\n",
            "88 1.1343530913764004\n",
            "89 1.1669129191136711\n",
            "90 1.2289580171587644\n",
            "91 1.2781757011686903\n",
            "92 1.392223401234692\n",
            "93 1.4447692555364076\n",
            "94 1.5034370521540417\n",
            "95 1.5628739319129206\n",
            "96 1.6364462145041205\n",
            "97 1.788078211257054\n",
            "98 1.97376345645099\n",
            "99 2.3847321042239886\n",
            "100 2.692159983742053\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "WrBS1wWZGmMC",
        "colab_type": "code",
        "outputId": "b5aa0574-face-4275-b74d-29fb37dee5c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "cell_type": "code",
      "source": [
        "# generate 100 sanples from N(20,5)\n",
        "measurements = np.random.normal(loc = 20, scale = 5, size=50000) \n",
        "#try size=1000\n",
        "\n",
        "\n",
        "stats.probplot(measurements, dist=\"norm\", plot=pylab)\n",
        "pylab.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HzKqFvYKGgBaOE8lC0ZPBHZnAUHrwDz7nEc7hCu7gJ+rGbFu9OqxYkYIiJSHJPGuovpnV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\nAhGRHKcgEBHJcQoCEZEcpyAQEclx/w+H7TprlMRylAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "MUJ6w7yUGmMG",
        "colab_type": "code",
        "outputId": "6cc69abf-b27a-4062-c7e0-afcc449459a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "cell_type": "code",
      "source": [
        "# generate 100 sanples from N(20,5)\n",
        "measurements = np.random.uniform(low=-1, high=1, size=10000) \n",
        "#try size=1000\n",
        "\n",
        "stats.probplot(measurements, dist=\"norm\", plot=pylab)\n",
        "pylab.show()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TM9sV+CK1YYlIRdWsCQUF5XtPI2bTh/M5nHcZxSF0oi9fsbsSQDVTZkJw9xeAFxKezwb+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\niEhECUFERAD4fy00yECQfdi6AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Ty-RZjYCF5zl",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "# **Shapiro-Wilk Test**\n",
        "\n",
        "Tests whether a data sample has a **Gaussian distribution.**\n",
        "\n",
        "Assumptions\n",
        "\n",
        "Observations in each sample are independent and identically distributed (iid).\n",
        "Interpretation *italicized text*\n",
        "\n",
        "H0: the sample has a **Gaussian** distribution.\n",
        "H1: the sample does not have a Gaussian distribution.\n"
      ]
    },
    {
      "metadata": {
        "id": "5wBpELnFE3pJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "c36e2028-fb44-4ae5-fdf1-520923c1ce85"
      },
      "cell_type": "code",
      "source": [
        "from scipy.stats import shapiro\n",
        "\n",
        "stat, p = shapiro(measurements)\n",
        "print(stat,p)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9999721050262451 0.9668164253234863\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/scipy/stats/morestats.py:1653: UserWarning: p-value may not be accurate for N > 5000.\n",
            "  warnings.warn(\"p-value may not be accurate for N > 5000.\")\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "HpELUlmaGT_5",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# D’Agostino’s K^2 Test\n",
        "\n",
        "Tests whether a data sample has a Gaussian distribution.\n",
        "\n",
        "Assumptions\n",
        "\n",
        "Observations in each sample are independent and identically distributed (iid).\n",
        "Interpretation\n",
        "\n",
        "H0: the sample has a Gaussian distribution.\n",
        "H1: the sample does not have a Gaussian distribution.\n"
      ]
    },
    {
      "metadata": {
        "id": "6QMcC6qAGmMK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "27d7c611-3577-456f-f6f9-b235be4f64ab"
      },
      "cell_type": "code",
      "source": [
        "from scipy.stats import normaltest\n",
        "stat, p = normaltest(measurements)\n",
        "print(stat,p)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9572954455074391 0.6196207245326539\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "J_aC1_dYGqlE",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Anderson-Darling Test\n",
        "\n",
        "Tests whether a data sample has a Gaussian distribution.\n",
        "\n",
        "Assumptions\n",
        "\n",
        "Observations in each sample are independent and identically distributed (iid).\n",
        "Interpretation\n",
        "\n",
        "H0: the sample has a Gaussian distribution.\n",
        "H1: the sample does not have a Gaussian distribution."
      ]
    },
    {
      "metadata": {
        "id": "X0fd7d4jGfR6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "d789daa9-01d4-48ab-ced7-84da0679e683"
      },
      "cell_type": "code",
      "source": [
        "from scipy.stats import anderson\n",
        "result = anderson(measurements)\n",
        "print(result)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AndersonResult(statistic=0.2663910991832381, critical_values=array([0.576, 0.656, 0.787, 0.918, 1.092]), significance_level=array([15. , 10. ,  5. ,  2.5,  1. ]))\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}