yogeshchappewar/Linear_Regression1_updated_main.ipynb

## Linear_Regression1_updated_main.ipynb
{
  "cells": [
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:32.762764Z",
          "end_time": "2023-01-24T07:39:34.383395Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "import pandas as pd \nimport numpy as np\nimport matplotlib.pyplot as plt\n# reading a csv file using pandas library\nwcat=pd.read_csv(\"wc-at.csv\")\nwcat\npd.set_option('display.min_rows', None) \n\n ",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:34.385874Z",
          "end_time": "2023-01-24T07:39:34.399163Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "wcat.shape    ",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": "(109, 2)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:34.401150Z",
          "end_time": "2023-01-24T07:39:34.439483Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "wcat ",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "      Waist      AT\n0     74.75   25.72\n1     72.60   25.89\n2     81.80   42.60\n3     83.95   42.80\n4     74.65   29.84\n5     71.85   21.68\n6     80.90   29.08\n7     83.40   32.98\n8     63.50   11.44\n9     73.20   32.22\n10    71.90   28.32\n11    75.00   43.86\n12    73.10   38.21\n13    79.00   42.48\n14    77.00   30.96\n15    68.85   55.78\n16    75.95   43.78\n17    74.15   33.41\n18    73.80   43.35\n19    75.90   29.31\n20    76.85   36.60\n21    80.90   40.25\n22    79.90   35.43\n23    89.20   60.09\n24    82.00   45.84\n25    92.00   70.40\n26    86.60   83.45\n27    80.50   84.30\n28    86.00   78.89\n29    82.50   64.75\n..      ...     ...\n79   109.00  192.00\n80   103.50  132.00\n81   110.00  126.00\n82   110.00  153.00\n83   112.00  158.00\n84   108.50  183.00\n85   104.00  184.00\n86   111.00  121.00\n87   108.50  159.00\n88   121.00  245.00\n89   109.00  137.00\n90    97.50  165.00\n91   105.50  152.00\n92    98.00  181.00\n93    94.50   80.95\n94    97.00  137.00\n95   105.00  125.00\n96   106.00  241.00\n97    99.00  134.00\n98    91.00  150.00\n99   102.50  198.00\n100  106.00  151.00\n101  109.10  229.00\n102  115.00  253.00\n103  101.00  188.00\n104  100.10  124.00\n105   93.30   62.20\n106  101.80  133.00\n107  107.90  208.00\n108  108.50  208.00\n\n[109 rows x 2 columns]",
            "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>Waist</th>\n      <th>AT</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>74.75</td>\n      <td>25.72</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>72.60</td>\n      <td>25.89</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>81.80</td>\n      <td>42.60</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>83.95</td>\n      <td>42.80</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>74.65</td>\n      <td>29.84</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>71.85</td>\n      <td>21.68</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>80.90</td>\n      <td>29.08</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>83.40</td>\n      <td>32.98</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>63.50</td>\n      <td>11.44</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>73.20</td>\n      <td>32.22</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>71.90</td>\n      <td>28.32</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>75.00</td>\n      <td>43.86</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>73.10</td>\n      <td>38.21</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>79.00</td>\n      <td>42.48</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>77.00</td>\n      <td>30.96</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>68.85</td>\n      <td>55.78</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>75.95</td>\n      <td>43.78</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>74.15</td>\n      <td>33.41</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>73.80</td>\n      <td>43.35</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>75.90</td>\n      <td>29.31</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>76.85</td>\n      <td>36.60</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>80.90</td>\n      <td>40.25</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>79.90</td>\n      <td>35.43</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>89.20</td>\n      <td>60.09</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>82.00</td>\n      <td>45.84</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>92.00</td>\n      <td>70.40</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>86.60</td>\n      <td>83.45</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>80.50</td>\n      <td>84.30</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>86.00</td>\n      <td>78.89</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>82.50</td>\n      <td>64.75</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>79</th>\n      <td>109.00</td>\n      <td>192.00</td>\n    </tr>\n    <tr>\n      <th>80</th>\n      <td>103.50</td>\n      <td>132.00</td>\n    </tr>\n    <tr>\n      <th>81</th>\n      <td>110.00</td>\n      <td>126.00</td>\n    </tr>\n    <tr>\n      <th>82</th>\n      <td>110.00</td>\n      <td>153.00</td>\n    </tr>\n    <tr>\n      <th>83</th>\n      <td>112.00</td>\n      <td>158.00</td>\n    </tr>\n    <tr>\n      <th>84</th>\n      <td>108.50</td>\n      <td>183.00</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>104.00</td>\n      <td>184.00</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>111.00</td>\n      <td>121.00</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>108.50</td>\n      <td>159.00</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>121.00</td>\n      <td>245.00</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>109.00</td>\n      <td>137.00</td>\n    </tr>\n    <tr>\n      <th>90</th>\n      <td>97.50</td>\n      <td>165.00</td>\n    </tr>\n    <tr>\n      <th>91</th>\n      <td>105.50</td>\n      <td>152.00</td>\n    </tr>\n    <tr>\n      <th>92</th>\n      <td>98.00</td>\n      <td>181.00</td>\n    </tr>\n    <tr>\n      <th>93</th>\n      <td>94.50</td>\n      <td>80.95</td>\n    </tr>\n    <tr>\n      <th>94</th>\n      <td>97.00</td>\n      <td>137.00</td>\n    </tr>\n    <tr>\n      <th>95</th>\n      <td>105.00</td>\n      <td>125.00</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>106.00</td>\n      <td>241.00</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>99.00</td>\n      <td>134.00</td>\n    </tr>\n    <tr>\n      <th>98</th>\n      <td>91.00</td>\n      <td>150.00</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>102.50</td>\n      <td>198.00</td>\n    </tr>\n    <tr>\n      <th>100</th>\n      <td>106.00</td>\n      <td>151.00</td>\n    </tr>\n    <tr>\n      <th>101</th>\n      <td>109.10</td>\n      <td>229.00</td>\n    </tr>\n    <tr>\n      <th>102</th>\n      <td>115.00</td>\n      <td>253.00</td>\n    </tr>\n    <tr>\n      <th>103</th>\n      <td>101.00</td>\n      <td>188.00</td>\n    </tr>\n    <tr>\n      <th>104</th>\n      <td>100.10</td>\n      <td>124.00</td>\n    </tr>\n    <tr>\n      <th>105</th>\n      <td>93.30</td>\n      <td>62.20</td>\n    </tr>\n    <tr>\n      <th>106</th>\n      <td>101.80</td>\n      <td>133.00</td>\n    </tr>\n    <tr>\n      <th>107</th>\n      <td>107.90</td>\n      <td>208.00</td>\n    </tr>\n    <tr>\n      <th>108</th>\n      <td>108.50</td>\n      <td>208.00</td>\n    </tr>\n  </tbody>\n</table>\n<p>109 rows × 2 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:34.439990Z",
          "end_time": "2023-01-24T07:39:34.459887Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "wcat.describe()   ",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": "            Waist          AT\ncount  109.000000  109.000000\nmean    91.901835  101.894037\nstd     13.559116   57.294763\nmin     63.500000   11.440000\n25%     80.000000   50.880000\n50%     90.800000   96.540000\n75%    104.000000  137.000000\nmax    121.000000  253.000000",
            "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>Waist</th>\n      <th>AT</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>109.000000</td>\n      <td>109.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>91.901835</td>\n      <td>101.894037</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>13.559116</td>\n      <td>57.294763</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>63.500000</td>\n      <td>11.440000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>80.000000</td>\n      <td>50.880000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>90.800000</td>\n      <td>96.540000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>104.000000</td>\n      <td>137.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>121.000000</td>\n      <td>253.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:34.462300Z",
          "end_time": "2023-01-24T07:39:34.488836Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "wcat.info() ",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 109 entries, 0 to 108\nData columns (total 2 columns):\n #   Column  Non-Null Count  Dtype  \n---  ------  --------------  -----  \n 0   Waist   109 non-null    float64\n 1   AT      109 non-null    float64\ndtypes: float64(2)\nmemory usage: 1.8 KB\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:34.492350Z",
          "end_time": "2023-01-24T07:39:34.505463Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "wcat.columns ",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 6,
          "data": {
            "text/plain": "Index(['Waist', 'AT'], dtype='object')"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:34.505968Z",
          "end_time": "2023-01-24T07:39:34.667817Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.boxplot(wcat.Waist) \nplt.show() ",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:34.669141Z",
          "end_time": "2023-01-24T07:39:34.832866Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.hist(wcat.Waist) \nplt.show() ",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:34.834892Z",
          "end_time": "2023-01-24T07:39:35.919932Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "import warnings\nwarnings.filterwarnings('ignore')\nimport seaborn as sns\nsns.distplot(wcat['Waist']) \nplt.show() ",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:35.926283Z",
          "end_time": "2023-01-24T07:39:36.050136Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.boxplot(wcat.AT) \nplt.xlabel(\"AT\") \nplt.ylabel(\"No of Observations\")\nplt.title(\"Boxplot of AT\") \nplt.show()    ",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:36.051810Z",
          "end_time": "2023-01-24T07:39:36.190093Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.hist(wcat.AT)\nplt.show()   ",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:36.192128Z",
          "end_time": "2023-01-24T07:39:36.355802Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "import warnings\nwarnings.filterwarnings('ignore')\nimport seaborn as sns\nsns.distplot(wcat['AT']) \nplt.show()  ",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:36.359233Z",
          "end_time": "2023-01-24T07:39:36.486516Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.plot(wcat.Waist,wcat.AT,\"rs\")\nplt.xlabel(\"Waist\")\nplt.ylabel(\"AT\")\nplt.title(\"ScatterPlot\")   \nhelp(plt.plot) ",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Help on function plot in module matplotlib.pyplot:\n\nplot(*args, scalex=True, scaley=True, data=None, **kwargs)\n    Plot y versus x as lines and/or markers.\n    \n    Call signatures::\n    \n        plot([x], y, [fmt], *, data=None, **kwargs)\n        plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)\n    \n    The coordinates of the points or line nodes are given by *x*, *y*.\n    \n    The optional parameter *fmt* is a convenient way for defining basic\n    formatting like color, marker and linestyle. It's a shortcut string\n    notation described in the *Notes* section below.\n    \n    >>> plot(x, y)        # plot x and y using default line style and color\n    >>> plot(x, y, 'bo')  # plot x and y using blue circle markers\n    >>> plot(y)           # plot y using x as index array 0..N-1\n    >>> plot(y, 'r+')     # ditto, but with red plusses\n    \n    You can use `.Line2D` properties as keyword arguments for more\n    control on the appearance. Line properties and *fmt* can be mixed.\n    The following two calls yield identical results:\n    \n    >>> plot(x, y, 'go--', linewidth=2, markersize=12)\n    >>> plot(x, y, color='green', marker='o', linestyle='dashed',\n    ...      linewidth=2, markersize=12)\n    \n    When conflicting with *fmt*, keyword arguments take precedence.\n    \n    \n    **Plotting labelled data**\n    \n    There's a convenient way for plotting objects with labelled data (i.e.\n    data that can be accessed by index ``obj['y']``). Instead of giving\n    the data in *x* and *y*, you can provide the object in the *data*\n    parameter and just give the labels for *x* and *y*::\n    \n    >>> plot('xlabel', 'ylabel', data=obj)\n    \n    All indexable objects are supported. This could e.g. be a `dict`, a\n    `pandas.DataFrame` or a structured numpy array.\n    \n    \n    **Plotting multiple sets of data**\n    \n    There are various ways to plot multiple sets of data.\n    \n    - The most straight forward way is just to call `plot` multiple times.\n      Example:\n    \n      >>> plot(x1, y1, 'bo')\n      >>> plot(x2, y2, 'go')\n    \n    - If *x* and/or *y* are 2D arrays a separate data set will be drawn\n      for every column. If both *x* and *y* are 2D, they must have the\n      same shape. If only one of them is 2D with shape (N, m) the other\n      must have length N and will be used for every data set m.\n    \n      Example:\n    \n      >>> x = [1, 2, 3]\n      >>> y = np.array([[1, 2], [3, 4], [5, 6]])\n      >>> plot(x, y)\n    \n      is equivalent to:\n    \n      >>> for col in range(y.shape[1]):\n      ...     plot(x, y[:, col])\n    \n    - The third way is to specify multiple sets of *[x]*, *y*, *[fmt]*\n      groups::\n    \n      >>> plot(x1, y1, 'g^', x2, y2, 'g-')\n    \n      In this case, any additional keyword argument applies to all\n      datasets. Also this syntax cannot be combined with the *data*\n      parameter.\n    \n    By default, each line is assigned a different style specified by a\n    'style cycle'. The *fmt* and line property parameters are only\n    necessary if you want explicit deviations from these defaults.\n    Alternatively, you can also change the style cycle using\n    :rc:`axes.prop_cycle`.\n    \n    \n    Parameters\n    ----------\n    x, y : array-like or scalar\n        The horizontal / vertical coordinates of the data points.\n        *x* values are optional and default to ``range(len(y))``.\n    \n        Commonly, these parameters are 1D arrays.\n    \n        They can also be scalars, or two-dimensional (in that case, the\n        columns represent separate data sets).\n    \n        These arguments cannot be passed as keywords.\n    \n    fmt : str, optional\n        A format string, e.g. 'ro' for red circles. See the *Notes*\n        section for a full description of the format strings.\n    \n        Format strings are just an abbreviation for quickly setting\n        basic line properties. All of these and more can also be\n        controlled by keyword arguments.\n    \n        This argument cannot be passed as keyword.\n    \n    data : indexable object, optional\n        An object with labelled data. If given, provide the label names to\n        plot in *x* and *y*.\n    \n        .. note::\n            Technically there's a slight ambiguity in calls where the\n            second label is a valid *fmt*. ``plot('n', 'o', data=obj)``\n            could be ``plt(x, y)`` or ``plt(y, fmt)``. In such cases,\n            the former interpretation is chosen, but a warning is issued.\n            You may suppress the warning by adding an empty format string\n            ``plot('n', 'o', '', data=obj)``.\n    \n    Returns\n    -------\n    list of `.Line2D`\n        A list of lines representing the plotted data.\n    \n    Other Parameters\n    ----------------\n    scalex, scaley : bool, default: True\n        These parameters determine if the view limits are adapted to the\n        data limits. The values are passed on to `autoscale_view`.\n    \n    **kwargs : `.Line2D` properties, optional\n        *kwargs* are used to specify properties like a line label (for\n        auto legends), linewidth, antialiasing, marker face color.\n        Example::\n    \n        >>> plot([1, 2, 3], [1, 2, 3], 'go-', label='line 1', linewidth=2)\n        >>> plot([1, 2, 3], [1, 4, 9], 'rs', label='line 2')\n    \n        If you specify multiple lines with one plot call, the kwargs apply\n        to all those lines. In case the label object is iterable, each\n        element is used as labels for each set of data.\n    \n        Here is a list of available `.Line2D` properties:\n    \n        Properties:\n        agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array\n        alpha: scalar or None\n        animated: bool\n        antialiased or aa: bool\n        clip_box: `.Bbox`\n        clip_on: bool\n        clip_path: Patch or (Path, Transform) or None\n        color or c: color\n        dash_capstyle: `.CapStyle` or {'butt', 'projecting', 'round'}\n        dash_joinstyle: `.JoinStyle` or {'miter', 'round', 'bevel'}\n        dashes: sequence of floats (on/off ink in points) or (None, None)\n        data: (2, N) array or two 1D arrays\n        drawstyle or ds: {'default', 'steps', 'steps-pre', 'steps-mid', 'steps-post'}, default: 'default'\n        figure: `.Figure`\n        fillstyle: {'full', 'left', 'right', 'bottom', 'top', 'none'}\n        gid: str\n        in_layout: bool\n        label: object\n        linestyle or ls: {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}\n        linewidth or lw: float\n        marker: marker style string, `~.path.Path` or `~.markers.MarkerStyle`\n        markeredgecolor or mec: color\n        markeredgewidth or mew: float\n        markerfacecolor or mfc: color\n        markerfacecoloralt or mfcalt: color\n        markersize or ms: float\n        markevery: None or int or (int, int) or slice or list[int] or float or (float, float) or list[bool]\n        path_effects: `.AbstractPathEffect`\n        picker: float or callable[[Artist, Event], tuple[bool, dict]]\n        pickradius: float\n        rasterized: bool\n        sketch_params: (scale: float, length: float, randomness: float)\n        snap: bool or None\n        solid_capstyle: `.CapStyle` or {'butt', 'projecting', 'round'}\n        solid_joinstyle: `.JoinStyle` or {'miter', 'round', 'bevel'}\n        transform: unknown\n        url: str\n        visible: bool\n        xdata: 1D array\n        ydata: 1D array\n        zorder: float\n    \n    See Also\n    --------\n    scatter : XY scatter plot with markers of varying size and/or color (\n        sometimes also called bubble chart).\n    \n    Notes\n    -----\n    **Format Strings**\n    \n    A format string consists of a part for color, marker and line::\n    \n        fmt = '[marker][line][color]'\n    \n    Each of them is optional. If not provided, the value from the style\n    cycle is used. Exception: If ``line`` is given, but no ``marker``,\n    the data will be a line without markers.\n    \n    Other combinations such as ``[color][marker][line]`` are also\n    supported, but note that their parsing may be ambiguous.\n    \n    **Markers**\n    \n    =============   ===============================\n    character       description\n    =============   ===============================\n    ``'.'``         point marker\n    ``','``         pixel marker\n    ``'o'``         circle marker\n    ``'v'``         triangle_down marker\n    ``'^'``         triangle_up marker\n    ``'<'``         triangle_left marker\n    ``'>'``         triangle_right marker\n    ``'1'``         tri_down marker\n    ``'2'``         tri_up marker\n    ``'3'``         tri_left marker\n    ``'4'``         tri_right marker\n    ``'8'``         octagon marker\n    ``'s'``         square marker\n    ``'p'``         pentagon marker\n    ``'P'``         plus (filled) marker\n    ``'*'``         star marker\n    ``'h'``         hexagon1 marker\n    ``'H'``         hexagon2 marker\n    ``'+'``         plus marker\n    ``'x'``         x marker\n    ``'X'``         x (filled) marker\n    ``'D'``         diamond marker\n    ``'d'``         thin_diamond marker\n    ``'|'``         vline marker\n    ``'_'``         hline marker\n    =============   ===============================\n    \n    **Line Styles**\n    \n    =============    ===============================\n    character        description\n    =============    ===============================\n    ``'-'``          solid line style\n    ``'--'``         dashed line style\n    ``'-.'``         dash-dot line style\n    ``':'``          dotted line style\n    =============    ===============================\n    \n    Example format strings::\n    \n        'b'    # blue markers with default shape\n        'or'   # red circles\n        '-g'   # green solid line\n        '--'   # dashed line with default color\n        '^k:'  # black triangle_up markers connected by a dotted line\n    \n    **Colors**\n    \n    The supported color abbreviations are the single letter codes\n    \n    =============    ===============================\n    character        color\n    =============    ===============================\n    ``'b'``          blue\n    ``'g'``          green\n    ``'r'``          red\n    ``'c'``          cyan\n    ``'m'``          magenta\n    ``'y'``          yellow\n    ``'k'``          black\n    ``'w'``          white\n    =============    ===============================\n    \n    and the ``'CN'`` colors that index into the default property cycle.\n    \n    If the color is the only part of the format string, you can\n    additionally use any  `matplotlib.colors` spec, e.g. full names\n    (``'green'``) or hex strings (``'#008000'``).\n\n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:36.486516Z",
          "end_time": "2023-01-24T07:39:36.502622Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "wcat.corr() ",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 14,
          "data": {
            "text/plain": "          Waist        AT\nWaist  1.000000  0.818558\nAT     0.818558  1.000000",
            "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>Waist</th>\n      <th>AT</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Waist</th>\n      <td>1.000000</td>\n      <td>0.818558</td>\n    </tr>\n    <tr>\n      <th>AT</th>\n      <td>0.818558</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:36.503703Z",
          "end_time": "2023-01-24T07:39:36.511117Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "#wcat.AT.corr(wcat.Waist) ",
      "execution_count": 15,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:36.513196Z",
          "end_time": "2023-01-24T07:39:36.526780Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "wcat.corr()  ",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 16,
          "data": {
            "text/plain": "          Waist        AT\nWaist  1.000000  0.818558\nAT     0.818558  1.000000",
            "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>Waist</th>\n      <th>AT</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Waist</th>\n      <td>1.000000</td>\n      <td>0.818558</td>\n    </tr>\n    <tr>\n      <th>AT</th>\n      <td>0.818558</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:36.526780Z",
          "end_time": "2023-01-24T07:39:36.682168Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "import statsmodels.formula.api as smf\nmodel=smf.ols(\"AT~Waist\",data=wcat).fit()\nmodel.params ",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 17,
          "data": {
            "text/plain": "Intercept   -215.981488\nWaist          3.458859\ndtype: float64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:36.682168Z",
          "end_time": "2023-01-24T07:39:36.722623Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "model.summary() ",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 18,
          "data": {
            "text/plain": "<class 'statsmodels.iolib.summary.Summary'>\n\"\"\"\n                            OLS Regression Results                            \n==============================================================================\nDep. Variable:                     AT   R-squared:                       0.670\nModel:                            OLS   Adj. R-squared:                  0.667\nMethod:                 Least Squares   F-statistic:                     217.3\nDate:                Tue, 24 Jan 2023   Prob (F-statistic):           1.62e-27\nTime:                        13:09:36   Log-Likelihood:                -534.99\nNo. Observations:                 109   AIC:                             1074.\nDf Residuals:                     107   BIC:                             1079.\nDf Model:                           1                                         \nCovariance Type:            nonrobust                                         \n==============================================================================\n                 coef    std err          t      P>|t|      [0.025      0.975]\n------------------------------------------------------------------------------\nIntercept   -215.9815     21.796     -9.909      0.000    -259.190    -172.773\nWaist          3.4589      0.235     14.740      0.000       2.994       3.924\n==============================================================================\nOmnibus:                        3.960   Durbin-Watson:                   1.560\nProb(Omnibus):                  0.138   Jarque-Bera (JB):                4.596\nSkew:                           0.104   Prob(JB):                        0.100\nKurtosis:                       3.984   Cond. No.                         639.\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n\"\"\"",
            "text/html": "<table class=\"simpletable\">\n<caption>OLS Regression Results</caption>\n<tr>\n  <th>Dep. Variable:</th>           <td>AT</td>        <th>  R-squared:         </th> <td>   0.670</td>\n</tr>\n<tr>\n  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.667</td>\n</tr>\n<tr>\n  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   217.3</td>\n</tr>\n<tr>\n  <th>Date:</th>             <td>Tue, 24 Jan 2023</td> <th>  Prob (F-statistic):</th> <td>1.62e-27</td>\n</tr>\n<tr>\n  <th>Time:</th>                 <td>13:09:36</td>     <th>  Log-Likelihood:    </th> <td> -534.99</td>\n</tr>\n<tr>\n  <th>No. Observations:</th>      <td>   109</td>      <th>  AIC:               </th> <td>   1074.</td>\n</tr>\n<tr>\n  <th>Df Residuals:</th>          <td>   107</td>      <th>  BIC:               </th> <td>   1079.</td>\n</tr>\n<tr>\n  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n</tr>\n<tr>\n  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n</tr>\n<tr>\n  <th>Intercept</th> <td> -215.9815</td> <td>   21.796</td> <td>   -9.909</td> <td> 0.000</td> <td> -259.190</td> <td> -172.773</td>\n</tr>\n<tr>\n  <th>Waist</th>     <td>    3.4589</td> <td>    0.235</td> <td>   14.740</td> <td> 0.000</td> <td>    2.994</td> <td>    3.924</td>\n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n  <th>Omnibus:</th>       <td> 3.960</td> <th>  Durbin-Watson:     </th> <td>   1.560</td>\n</tr>\n<tr>\n  <th>Prob(Omnibus):</th> <td> 0.138</td> <th>  Jarque-Bera (JB):  </th> <td>   4.596</td>\n</tr>\n<tr>\n  <th>Skew:</th>          <td> 0.104</td> <th>  Prob(JB):          </th> <td>   0.100</td>\n</tr>\n<tr>\n  <th>Kurtosis:</th>      <td> 3.984</td> <th>  Cond. No.          </th> <td>    639.</td>\n</tr>\n</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:36.724663Z",
          "end_time": "2023-01-24T07:39:36.737706Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "model.resid  ",
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 19,
          "data": {
            "text/plain": "0     -16.848252\n1      -9.241704\n2     -24.353210\n3     -31.589758\n4     -12.382366\n5     -10.857559\n6     -34.760237\n7     -39.507385\n8       7.783917\n9      -4.987020\n10     -4.390502\n11      0.427034\n12      1.348866\n13    -14.788404\n14    -19.390685\n15     33.619019\n16     -2.938883\n17     -7.082936\n18      4.067665\n19    -17.235940\n20    -13.231856\n21    -23.590237\n22    -24.951377\n23    -32.458770\n24    -21.804982\n25    -31.833576\n26     -0.105735\n27     21.843307\n28     -2.590420\n29     -4.624412\n         ...    \n79     30.965814\n80    -10.010459\n81    -38.493045\n82    -11.493045\n83    -13.410764\n84     23.695244\n85     40.260111\n86    -46.951905\n87     -0.304756\n88     42.459502\n89    -24.034186\n90     43.742697\n91      3.071822\n92     58.013268\n93    -29.930725\n94     17.472127\n95    -22.198748\n96     90.342392\n97      7.554408\n98     51.225283\n99     59.448400\n100     0.342392\n101    67.619928\n102    71.212658\n103    54.636689\n104    -6.250337\n105   -44.530093\n106    -3.130398\n107    50.770560\n108    48.695244\nLength: 109, dtype: float64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:36.740427Z",
          "end_time": "2023-01-24T07:39:36.752902Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "pred = model.predict(wcat.Waist)\n#pred = model.predict(wcat.iloc[:,0]) \npred  ",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 20,
          "data": {
            "text/plain": "0       42.568252\n1       35.131704\n2       66.953210\n3       74.389758\n4       42.222366\n5       32.537559\n6       63.840237\n7       72.487385\n8        3.656083\n9       37.207020\n10      32.710502\n11      43.432966\n12      36.861134\n13      57.268404\n14      50.350685\n15      22.160981\n16      46.718883\n17      40.492936\n18      39.282335\n19      46.545940\n20      49.831856\n21      63.840237\n22      60.381377\n23      92.548770\n24      67.644982\n25     102.233576\n26      83.555735\n27      62.456693\n28      81.480420\n29      69.374412\n          ...    \n79     161.034186\n80     142.010459\n81     164.493045\n82     164.493045\n83     171.410764\n84     159.304756\n85     143.739889\n86     167.951905\n87     159.304756\n88     202.540498\n89     161.034186\n90     121.257303\n91     148.928178\n92     122.986732\n93     110.880725\n94     119.527873\n95     147.198748\n96     150.657608\n97     126.445592\n98      98.774717\n99     138.551600\n100    150.657608\n101    161.380072\n102    181.787342\n103    133.363311\n104    130.250337\n105    106.730093\n106    136.130398\n107    157.229440\n108    159.304756\nLength: 109, dtype: float64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:36.755243Z",
          "end_time": "2023-01-24T07:39:36.779883Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "pred = model.predict(wcat.Waist)\npred ",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 21,
          "data": {
            "text/plain": "0       42.568252\n1       35.131704\n2       66.953210\n3       74.389758\n4       42.222366\n5       32.537559\n6       63.840237\n7       72.487385\n8        3.656083\n9       37.207020\n10      32.710502\n11      43.432966\n12      36.861134\n13      57.268404\n14      50.350685\n15      22.160981\n16      46.718883\n17      40.492936\n18      39.282335\n19      46.545940\n20      49.831856\n21      63.840237\n22      60.381377\n23      92.548770\n24      67.644982\n25     102.233576\n26      83.555735\n27      62.456693\n28      81.480420\n29      69.374412\n          ...    \n79     161.034186\n80     142.010459\n81     164.493045\n82     164.493045\n83     171.410764\n84     159.304756\n85     143.739889\n86     167.951905\n87     159.304756\n88     202.540498\n89     161.034186\n90     121.257303\n91     148.928178\n92     122.986732\n93     110.880725\n94     119.527873\n95     147.198748\n96     150.657608\n97     126.445592\n98      98.774717\n99     138.551600\n100    150.657608\n101    161.380072\n102    181.787342\n103    133.363311\n104    130.250337\n105    106.730093\n106    136.130398\n107    157.229440\n108    159.304756\nLength: 109, dtype: float64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:36.782113Z",
          "end_time": "2023-01-24T07:39:36.796585Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": " model.resid_pearson ",
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 22,
          "data": {
            "text/plain": "array([-5.09550501e-01, -2.79501693e-01, -7.36526900e-01, -9.55385603e-01,\n       -3.74486373e-01, -3.28370857e-01, -1.05127206e+00, -1.19484256e+00,\n        2.35413067e-01, -1.50825045e-01, -1.32784262e-01,  1.29149997e-02,\n        4.07944734e-02, -4.47253451e-01, -5.86442653e-01,  1.01675760e+00,\n       -8.88821743e-02, -2.14212943e-01,  1.23020519e-01, -5.21275561e-01,\n       -4.00177964e-01, -7.13451894e-01, -7.54617582e-01, -9.81667583e-01,\n       -6.59459501e-01, -9.62759522e-01, -3.19781076e-03,  6.60618576e-01,\n       -7.83434219e-02, -1.39858512e-01, -8.26468630e-03,  1.71170943e-02,\n       -5.78950111e-01, -2.93076130e-01, -2.97057035e-01,  3.06042549e-01,\n        2.48761552e-01, -3.41178084e-01,  4.02019198e-01, -5.40876395e-02,\n        8.97412236e-03,  1.09123902e-01, -3.50119995e-01,  1.71753428e+00,\n        7.69279067e-01, -1.10275004e+00, -9.64451677e-02,  3.66529597e-01,\n        1.88429083e-01,  6.95115285e-01,  4.95265365e-01,  5.09877715e-01,\n        1.64182014e+00, -9.86663558e-02, -4.07776106e-01,  6.88082435e-02,\n        3.45875024e-02,  2.19210876e+00, -2.71616788e-01,  4.70273747e-02,\n        1.39846029e-01,  6.67000524e-01, -9.70075897e-01, -1.07706214e+00,\n       -3.24477008e+00, -2.80465597e+00,  7.92427406e-01, -8.44646518e-01,\n       -1.51425539e+00,  3.59399916e-01, -1.99989739e+00,  6.24126210e-01,\n       -5.90591697e-01, -2.08814937e-01,  1.79721154e+00,  3.05324971e-01,\n       -9.46048563e-01, -5.06273229e-01, -1.16914228e+00,  9.36515345e-01,\n       -3.02751560e-01, -1.16416533e+00, -3.47590187e-01, -4.05588761e-01,\n        7.16627678e-01,  1.21760764e+00, -1.41999105e+00, -9.21689763e-03,\n        1.28412495e+00, -7.26878474e-01,  1.32293331e+00,  9.29027254e-02,\n        1.75452565e+00, -9.05210587e-01,  5.28418690e-01, -6.71368373e-01,\n        2.73227231e+00,  2.28471924e-01,  1.54923308e+00,  1.79792912e+00,\n        1.03551529e-02,  2.04506492e+00,  2.15372173e+00,  1.65240602e+00,\n       -1.89032222e-01, -1.34674694e+00, -9.46742714e-02,  1.53548064e+00,\n        1.47271578e+00])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:36.796585Z",
          "end_time": "2023-01-24T07:39:36.813273Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "#print(model.conf_int(0.05))",
      "execution_count": 23,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:36.814355Z",
          "end_time": "2023-01-24T07:39:36.828716Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "rmse_lin = np.sqrt(np.mean((np.array(wcat['AT'])-np.array(pred))**2))\nrmse_lin   ",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 24,
          "data": {
            "text/plain": "32.760177495755144"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:36.830830Z",
          "end_time": "2023-01-24T07:39:36.961137Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "#import matplotlib.pylab as plt\nplt.scatter(x=wcat['Waist'],y=wcat['AT'],color='red')\nplt.plot(wcat['Waist'],pred,color='black')\nplt.xlabel('WAIST')\nplt.ylabel('TISSUE') ",
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 25,
          "data": {
            "text/plain": "Text(0, 0.5, 'TISSUE')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:36.961137Z",
          "end_time": "2023-01-24T07:39:36.977209Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "model2 = smf.ols('AT~np.log(Waist)',data=wcat).fit()\n#model2.params\n#model2.resid \n#model2.resid_pearson \n",
      "execution_count": 26,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:36.979302Z",
          "end_time": "2023-01-24T07:39:37.010674Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "model2.summary() ",
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 27,
          "data": {
            "text/plain": "<class 'statsmodels.iolib.summary.Summary'>\n\"\"\"\n                            OLS Regression Results                            \n==============================================================================\nDep. Variable:                     AT   R-squared:                       0.675\nModel:                            OLS   Adj. R-squared:                  0.672\nMethod:                 Least Squares   F-statistic:                     222.6\nDate:                Tue, 24 Jan 2023   Prob (F-statistic):           6.80e-28\nTime:                        13:09:36   Log-Likelihood:                -534.11\nNo. Observations:                 109   AIC:                             1072.\nDf Residuals:                     107   BIC:                             1078.\nDf Model:                           1                                         \nCovariance Type:            nonrobust                                         \n=================================================================================\n                    coef    std err          t      P>|t|      [0.025      0.975]\n---------------------------------------------------------------------------------\nIntercept     -1328.3420     95.923    -13.848      0.000   -1518.498   -1138.186\nnp.log(Waist)   317.1356     21.258     14.918      0.000     274.994     359.277\n==============================================================================\nOmnibus:                        3.317   Durbin-Watson:                   1.599\nProb(Omnibus):                  0.190   Jarque-Bera (JB):                2.908\nSkew:                           0.235   Prob(JB):                        0.234\nKurtosis:                       3.647   Cond. No.                         145.\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n\"\"\"",
            "text/html": "<table class=\"simpletable\">\n<caption>OLS Regression Results</caption>\n<tr>\n  <th>Dep. Variable:</th>           <td>AT</td>        <th>  R-squared:         </th> <td>   0.675</td>\n</tr>\n<tr>\n  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.672</td>\n</tr>\n<tr>\n  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   222.6</td>\n</tr>\n<tr>\n  <th>Date:</th>             <td>Tue, 24 Jan 2023</td> <th>  Prob (F-statistic):</th> <td>6.80e-28</td>\n</tr>\n<tr>\n  <th>Time:</th>                 <td>13:09:36</td>     <th>  Log-Likelihood:    </th> <td> -534.11</td>\n</tr>\n<tr>\n  <th>No. Observations:</th>      <td>   109</td>      <th>  AIC:               </th> <td>   1072.</td>\n</tr>\n<tr>\n  <th>Df Residuals:</th>          <td>   107</td>      <th>  BIC:               </th> <td>   1078.</td>\n</tr>\n<tr>\n  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n</tr>\n<tr>\n  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n        <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n</tr>\n<tr>\n  <th>Intercept</th>     <td>-1328.3420</td> <td>   95.923</td> <td>  -13.848</td> <td> 0.000</td> <td>-1518.498</td> <td>-1138.186</td>\n</tr>\n<tr>\n  <th>np.log(Waist)</th> <td>  317.1356</td> <td>   21.258</td> <td>   14.918</td> <td> 0.000</td> <td>  274.994</td> <td>  359.277</td>\n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n  <th>Omnibus:</th>       <td> 3.317</td> <th>  Durbin-Watson:     </th> <td>   1.599</td>\n</tr>\n<tr>\n  <th>Prob(Omnibus):</th> <td> 0.190</td> <th>  Jarque-Bera (JB):  </th> <td>   2.908</td>\n</tr>\n<tr>\n  <th>Skew:</th>          <td> 0.235</td> <th>  Prob(JB):          </th> <td>   0.234</td>\n</tr>\n<tr>\n  <th>Kurtosis:</th>      <td> 3.647</td> <th>  Cond. No.          </th> <td>    145.</td>\n</tr>\n</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:37.014855Z",
          "end_time": "2023-01-24T07:39:37.043326Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "pred2 = model2.predict(pd.DataFrame(wcat['Waist'])) \npred2  ",
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 28,
          "data": {
            "text/plain": "0       39.828156\n1       30.572779\n2       68.410960\n3       76.638769\n4       39.403610\n5       27.279545\n6       64.902356\n7       74.554213\n8      -11.899606\n9       33.182963\n10      27.500161\n11      40.887040\n12      32.749422\n13      57.365321\n14      49.233194\n15      13.753591\n16      44.878862\n17      37.272314\n18      35.771839\n19      44.670014\n20      48.614795\n21      64.902356\n22      60.957833\n23      95.876103\n24      69.185406\n25     105.677983\n26      86.494845\n27      63.330429\n28      84.289954\n29      71.113288\n          ...    \n79     159.451269\n80     143.031172\n81     162.347504\n82     162.347504\n83     168.061813\n84     157.993171\n85     144.559539\n86     165.217529\n87     157.993171\n88     192.573752\n89     159.451269\n90     124.092079\n91     149.100938\n92     125.714259\n93     114.180786\n94     122.461558\n95     147.594352\n96     150.600399\n97     128.933937\n98     102.211986\n99     139.952162\n100    150.600399\n101    159.742085\n102    176.444739\n103    135.276860\n104    132.438233\n105    110.127880\n106    137.778929\n107    156.234559\n108    157.993171\nLength: 109, dtype: float64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:37.043326Z",
          "end_time": "2023-01-24T07:39:37.060059Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "pred2\nrmse_log = np.sqrt(np.mean((np.array(wcat['AT'])-np.array(pred2))**2))\nrmse_log   ",
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 29,
          "data": {
            "text/plain": "32.496884909321274"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:37.062251Z",
          "end_time": "2023-01-24T07:39:37.076220Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "pred2.corr(wcat.AT)",
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 30,
          "data": {
            "text/plain": "0.8217781862645355"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:37.078663Z",
          "end_time": "2023-01-24T07:39:37.198255Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.scatter(x=wcat['Waist'],y=wcat['AT'],color='green')\nplt.plot(wcat['Waist'],pred2,color='blue')\nplt.xlabel('WAIST')\nplt.ylabel('TISSUE') ",
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 31,
          "data": {
            "text/plain": "Text(0, 0.5, 'TISSUE')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:37.199915Z",
          "end_time": "2023-01-24T07:39:37.231754Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "#exponential model\nmodel3 = smf.ols('np.log(AT)~Waist',data=wcat).fit()\nmodel3.params\nmodel3.summary()\n",
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 32,
          "data": {
            "text/plain": "<class 'statsmodels.iolib.summary.Summary'>\n\"\"\"\n                            OLS Regression Results                            \n==============================================================================\nDep. Variable:             np.log(AT)   R-squared:                       0.707\nModel:                            OLS   Adj. R-squared:                  0.704\nMethod:                 Least Squares   F-statistic:                     258.3\nDate:                Tue, 24 Jan 2023   Prob (F-statistic):           2.68e-30\nTime:                        13:09:37   Log-Likelihood:                -40.123\nNo. Observations:                 109   AIC:                             84.25\nDf Residuals:                     107   BIC:                             89.63\nDf Model:                           1                                         \nCovariance Type:            nonrobust                                         \n==============================================================================\n                 coef    std err          t      P>|t|      [0.025      0.975]\n------------------------------------------------------------------------------\nIntercept      0.7410      0.233      3.185      0.002       0.280       1.202\nWaist          0.0403      0.003     16.073      0.000       0.035       0.045\n==============================================================================\nOmnibus:                        2.604   Durbin-Watson:                   1.201\nProb(Omnibus):                  0.272   Jarque-Bera (JB):                2.117\nSkew:                          -0.329   Prob(JB):                        0.347\nKurtosis:                       3.182   Cond. No.                         639.\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n\"\"\"",
            "text/html": "<table class=\"simpletable\">\n<caption>OLS Regression Results</caption>\n<tr>\n  <th>Dep. Variable:</th>       <td>np.log(AT)</td>    <th>  R-squared:         </th> <td>   0.707</td>\n</tr>\n<tr>\n  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.704</td>\n</tr>\n<tr>\n  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   258.3</td>\n</tr>\n<tr>\n  <th>Date:</th>             <td>Tue, 24 Jan 2023</td> <th>  Prob (F-statistic):</th> <td>2.68e-30</td>\n</tr>\n<tr>\n  <th>Time:</th>                 <td>13:09:37</td>     <th>  Log-Likelihood:    </th> <td> -40.123</td>\n</tr>\n<tr>\n  <th>No. Observations:</th>      <td>   109</td>      <th>  AIC:               </th> <td>   84.25</td>\n</tr>\n<tr>\n  <th>Df Residuals:</th>          <td>   107</td>      <th>  BIC:               </th> <td>   89.63</td>\n</tr>\n<tr>\n  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n</tr>\n<tr>\n  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n</tr>\n<tr>\n  <th>Intercept</th> <td>    0.7410</td> <td>    0.233</td> <td>    3.185</td> <td> 0.002</td> <td>    0.280</td> <td>    1.202</td>\n</tr>\n<tr>\n  <th>Waist</th>     <td>    0.0403</td> <td>    0.003</td> <td>   16.073</td> <td> 0.000</td> <td>    0.035</td> <td>    0.045</td>\n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n  <th>Omnibus:</th>       <td> 2.604</td> <th>  Durbin-Watson:     </th> <td>   1.201</td>\n</tr>\n<tr>\n  <th>Prob(Omnibus):</th> <td> 0.272</td> <th>  Jarque-Bera (JB):  </th> <td>   2.117</td>\n</tr>\n<tr>\n  <th>Skew:</th>          <td>-0.329</td> <th>  Prob(JB):          </th> <td>   0.347</td>\n</tr>\n<tr>\n  <th>Kurtosis:</th>      <td> 3.182</td> <th>  Cond. No.          </th> <td>    639.</td>\n</tr>\n</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:37.233896Z",
          "end_time": "2023-01-24T07:39:37.328355Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "pred_log = model3.predict(pd.DataFrame(wcat['Waist']))",
      "execution_count": 33,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:37.328355Z",
          "end_time": "2023-01-24T07:39:37.344836Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "pred_log",
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 34,
          "data": {
            "text/plain": "0      3.749889\n1      3.663346\n2      4.033669\n3      4.120211\n4      3.745864\n5      3.633157\n6      3.997441\n7      4.098072\n8      3.297049\n9      3.687498\n10     3.635170\n11     3.759952\n12     3.683472\n13     3.920962\n14     3.840457\n15     3.512400\n16     3.798192\n17     3.725738\n18     3.711649\n19     3.796179\n20     3.834419\n21     3.997441\n22     3.957189\n23     4.331536\n24     4.041719\n25     4.444243\n26     4.226880\n27     3.981340\n28     4.202729\n29     4.061845\n         ...   \n79     5.128534\n80     4.907146\n81     5.168787\n82     5.168787\n83     5.249292\n84     5.108408\n85     4.927272\n86     5.209039\n87     5.108408\n88     5.611563\n89     5.128534\n90     4.665631\n91     4.987651\n92     4.685758\n93     4.544874\n94     4.645505\n95     4.967525\n96     5.007777\n97     4.726010\n98     4.403991\n99     4.866894\n100    5.007777\n101    5.132560\n102    5.370049\n103    4.806515\n104    4.770288\n105    4.496571\n106    4.838717\n107    5.084257\n108    5.108408\nLength: 109, dtype: float64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:37.347072Z",
          "end_time": "2023-01-24T07:39:37.361398Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "pred3=np.exp(pred_log)  # as we have used log(AT) in preparing model so we need to convert it back\npred3",
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 35,
          "data": {
            "text/plain": "0       42.516361\n1       38.991601\n2       56.467684\n3       61.572246\n4       42.345566\n5       37.832062\n6       54.458630\n7       60.224086\n8       27.032754\n9       39.944768\n10      37.908280\n11      42.946367\n12      39.784304\n13      50.448940\n14      46.546737\n15      33.528629\n16      44.620431\n17      41.501829\n18      40.921236\n19      44.530718\n20      46.266541\n21      54.458630\n22      52.310071\n23      76.061058\n24      56.924111\n25      85.135421\n26      68.503177\n27      53.588815\n28      66.868544\n29      58.081384\n          ...    \n79     168.769572\n80     135.252851\n81     175.701533\n82     175.701533\n83     190.431312\n84     165.406833\n85     138.002556\n86     182.918215\n87     165.406833\n88     273.571587\n89     168.769572\n90     106.232649\n91     146.591651\n92     108.392370\n93      94.148585\n94     104.115960\n95     143.670808\n96     149.571875\n97     112.844427\n98      81.776569\n99     129.916714\n100    149.571875\n101    169.450279\n102    214.873357\n103    122.304636\n104    117.953181\n105     89.709020\n106    126.307181\n107    161.459872\n108    165.406833\nLength: 109, dtype: float64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:37.364060Z",
          "end_time": "2023-01-24T07:39:37.377256Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "rmse_exp = np.sqrt(np.mean((np.array(wcat['AT'])-np.array(pred3))**2)) \nrmse_exp ",
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 36,
          "data": {
            "text/plain": "38.52900175807143"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:37.378261Z",
          "end_time": "2023-01-24T07:39:37.393873Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "pred3.corr(wcat.AT)",
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 37,
          "data": {
            "text/plain": "0.7633804583650527"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:37.396182Z",
          "end_time": "2023-01-24T07:39:37.533794Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.scatter(x=wcat['Waist'],y=wcat['AT'],color='green')\nplt.plot(wcat.Waist,pred3,color='blue')\nplt.xlabel('WAIST')\nplt.ylabel('TISSUE')\n ",
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 38,
          "data": {
            "text/plain": "Text(0, 0.5, 'TISSUE')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:37.536200Z",
          "end_time": "2023-01-24T07:39:37.665098Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.plot(wcat['Waist'],wcat['AT'], 'bo')  \nplt.plot(wcat.Waist,pred3,color='red')",
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 39,
          "data": {
            "text/plain": "[<matplotlib.lines.Line2D at 0x2051218b220>]"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:37.668003Z",
          "end_time": "2023-01-24T07:39:37.689496Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "student_resid = model3.resid_pearson \nstudent_resid",
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 40,
          "data": {
            "text/plain": "array([-1.42426841, -1.16036536, -0.79857367, -1.03053611, -0.99183029,\n       -1.57770329, -1.77782943, -1.7063649 , -2.43678253, -0.60898882,\n       -0.82630708,  0.05965107, -0.11441073, -0.48719133, -1.15546702,\n        1.44239166, -0.05388192, -0.61457662,  0.16338349, -1.18519137,\n       -0.66413371, -0.85671265, -1.10408983, -0.66788034, -0.61366891,\n       -0.53854217,  0.559278  ,  1.28377825,  0.46848176,  0.30798929,\n        0.51662718,  0.58049434, -0.07722426,  0.24329581, -0.00731172,\n        0.71101855,  0.76180321, -0.07709724,  0.97229834,  0.26937281,\n        0.53880043,  0.34505495, -0.14698466,  2.03681962,  1.38280011,\n       -0.79731502,  0.38539598,  0.75747349,  0.67811133,  1.31762395,\n        1.00267879,  1.11566466,  2.03975207, -0.12692781, -0.4705888 ,\n        0.44867784,  0.60008028,  2.34805258,  0.21771996,  0.59055788,\n        0.50813615,  1.25820146, -1.76365553, -0.81659309, -2.97779536,\n       -2.56121918,  0.97601914, -0.5436722 , -1.10931613,  0.18123793,\n       -1.61748694,  0.6529884 , -0.11429706,  0.13012811,  0.82634725,\n        0.49697188, -0.55447313, -0.23543731, -0.81972409,  0.36543543,\n       -0.06898352, -0.9422149 , -0.39203734, -0.52903971,  0.28642368,\n        0.81514874, -1.17101758, -0.1119417 , -0.31257029, -0.5909752 ,\n        1.24771245,  0.10266362,  1.45294329, -0.42800746,  0.77777835,\n       -0.39448023,  1.35172552,  0.48691171,  1.71904131,  1.19404088,\n        0.02692794,  0.85340044,  0.4628565 ,  1.218279  ,  0.1416668 ,\n       -1.0377428 ,  0.14630966,  0.71772056,  0.64928288])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:37.689496Z",
          "end_time": "2023-01-24T07:39:37.818694Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.plot(model3.resid_pearson,'o')\nplt.axhline(y=0,color='green')\nplt.xlabel(\"Observation Number\")\nplt.ylabel(\"Standardized Residual\") ",
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 41,
          "data": {
            "text/plain": "Text(0, 0.5, 'Standardized Residual')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:37.819224Z",
          "end_time": "2023-01-24T07:39:37.954741Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.scatter(x=pred3,y=wcat.AT)\nplt.xlabel(\"Predicted\")\nplt.ylabel(\"Actual\")",
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 42,
          "data": {
            "text/plain": "Text(0, 0.5, 'Actual')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:37.954741Z",
          "end_time": "2023-01-24T07:39:37.987702Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "wcat[\"Waist_Sq\"] = wcat.Waist*wcat.Waist\nwcat  ",
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 43,
          "data": {
            "text/plain": "      Waist      AT    Waist_Sq\n0     74.75   25.72   5587.5625\n1     72.60   25.89   5270.7600\n2     81.80   42.60   6691.2400\n3     83.95   42.80   7047.6025\n4     74.65   29.84   5572.6225\n5     71.85   21.68   5162.4225\n6     80.90   29.08   6544.8100\n7     83.40   32.98   6955.5600\n8     63.50   11.44   4032.2500\n9     73.20   32.22   5358.2400\n10    71.90   28.32   5169.6100\n11    75.00   43.86   5625.0000\n12    73.10   38.21   5343.6100\n13    79.00   42.48   6241.0000\n14    77.00   30.96   5929.0000\n15    68.85   55.78   4740.3225\n16    75.95   43.78   5768.4025\n17    74.15   33.41   5498.2225\n18    73.80   43.35   5446.4400\n19    75.90   29.31   5760.8100\n20    76.85   36.60   5905.9225\n21    80.90   40.25   6544.8100\n22    79.90   35.43   6384.0100\n23    89.20   60.09   7956.6400\n24    82.00   45.84   6724.0000\n25    92.00   70.40   8464.0000\n26    86.60   83.45   7499.5600\n27    80.50   84.30   6480.2500\n28    86.00   78.89   7396.0000\n29    82.50   64.75   6806.2500\n..      ...     ...         ...\n79   109.00  192.00  11881.0000\n80   103.50  132.00  10712.2500\n81   110.00  126.00  12100.0000\n82   110.00  153.00  12100.0000\n83   112.00  158.00  12544.0000\n84   108.50  183.00  11772.2500\n85   104.00  184.00  10816.0000\n86   111.00  121.00  12321.0000\n87   108.50  159.00  11772.2500\n88   121.00  245.00  14641.0000\n89   109.00  137.00  11881.0000\n90    97.50  165.00   9506.2500\n91   105.50  152.00  11130.2500\n92    98.00  181.00   9604.0000\n93    94.50   80.95   8930.2500\n94    97.00  137.00   9409.0000\n95   105.00  125.00  11025.0000\n96   106.00  241.00  11236.0000\n97    99.00  134.00   9801.0000\n98    91.00  150.00   8281.0000\n99   102.50  198.00  10506.2500\n100  106.00  151.00  11236.0000\n101  109.10  229.00  11902.8100\n102  115.00  253.00  13225.0000\n103  101.00  188.00  10201.0000\n104  100.10  124.00  10020.0100\n105   93.30   62.20   8704.8900\n106  101.80  133.00  10363.2400\n107  107.90  208.00  11642.4100\n108  108.50  208.00  11772.2500\n\n[109 rows x 3 columns]",
            "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>Waist</th>\n      <th>AT</th>\n      <th>Waist_Sq</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>74.75</td>\n      <td>25.72</td>\n      <td>5587.5625</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>72.60</td>\n      <td>25.89</td>\n      <td>5270.7600</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>81.80</td>\n      <td>42.60</td>\n      <td>6691.2400</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>83.95</td>\n      <td>42.80</td>\n      <td>7047.6025</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>74.65</td>\n      <td>29.84</td>\n      <td>5572.6225</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>71.85</td>\n      <td>21.68</td>\n      <td>5162.4225</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>80.90</td>\n      <td>29.08</td>\n      <td>6544.8100</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>83.40</td>\n      <td>32.98</td>\n      <td>6955.5600</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>63.50</td>\n      <td>11.44</td>\n      <td>4032.2500</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>73.20</td>\n      <td>32.22</td>\n      <td>5358.2400</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>71.90</td>\n      <td>28.32</td>\n      <td>5169.6100</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>75.00</td>\n      <td>43.86</td>\n      <td>5625.0000</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>73.10</td>\n      <td>38.21</td>\n      <td>5343.6100</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>79.00</td>\n      <td>42.48</td>\n      <td>6241.0000</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>77.00</td>\n      <td>30.96</td>\n      <td>5929.0000</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>68.85</td>\n      <td>55.78</td>\n      <td>4740.3225</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>75.95</td>\n      <td>43.78</td>\n      <td>5768.4025</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>74.15</td>\n      <td>33.41</td>\n      <td>5498.2225</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>73.80</td>\n      <td>43.35</td>\n      <td>5446.4400</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>75.90</td>\n      <td>29.31</td>\n      <td>5760.8100</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>76.85</td>\n      <td>36.60</td>\n      <td>5905.9225</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>80.90</td>\n      <td>40.25</td>\n      <td>6544.8100</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>79.90</td>\n      <td>35.43</td>\n      <td>6384.0100</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>89.20</td>\n      <td>60.09</td>\n      <td>7956.6400</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>82.00</td>\n      <td>45.84</td>\n      <td>6724.0000</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>92.00</td>\n      <td>70.40</td>\n      <td>8464.0000</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>86.60</td>\n      <td>83.45</td>\n      <td>7499.5600</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>80.50</td>\n      <td>84.30</td>\n      <td>6480.2500</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>86.00</td>\n      <td>78.89</td>\n      <td>7396.0000</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>82.50</td>\n      <td>64.75</td>\n      <td>6806.2500</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>79</th>\n      <td>109.00</td>\n      <td>192.00</td>\n      <td>11881.0000</td>\n    </tr>\n    <tr>\n      <th>80</th>\n      <td>103.50</td>\n      <td>132.00</td>\n      <td>10712.2500</td>\n    </tr>\n    <tr>\n      <th>81</th>\n      <td>110.00</td>\n      <td>126.00</td>\n      <td>12100.0000</td>\n    </tr>\n    <tr>\n      <th>82</th>\n      <td>110.00</td>\n      <td>153.00</td>\n      <td>12100.0000</td>\n    </tr>\n    <tr>\n      <th>83</th>\n      <td>112.00</td>\n      <td>158.00</td>\n      <td>12544.0000</td>\n    </tr>\n    <tr>\n      <th>84</th>\n      <td>108.50</td>\n      <td>183.00</td>\n      <td>11772.2500</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>104.00</td>\n      <td>184.00</td>\n      <td>10816.0000</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>111.00</td>\n      <td>121.00</td>\n      <td>12321.0000</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>108.50</td>\n      <td>159.00</td>\n      <td>11772.2500</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>121.00</td>\n      <td>245.00</td>\n      <td>14641.0000</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>109.00</td>\n      <td>137.00</td>\n      <td>11881.0000</td>\n    </tr>\n    <tr>\n      <th>90</th>\n      <td>97.50</td>\n      <td>165.00</td>\n      <td>9506.2500</td>\n    </tr>\n    <tr>\n      <th>91</th>\n      <td>105.50</td>\n      <td>152.00</td>\n      <td>11130.2500</td>\n    </tr>\n    <tr>\n      <th>92</th>\n      <td>98.00</td>\n      <td>181.00</td>\n      <td>9604.0000</td>\n    </tr>\n    <tr>\n      <th>93</th>\n      <td>94.50</td>\n      <td>80.95</td>\n      <td>8930.2500</td>\n    </tr>\n    <tr>\n      <th>94</th>\n      <td>97.00</td>\n      <td>137.00</td>\n      <td>9409.0000</td>\n    </tr>\n    <tr>\n      <th>95</th>\n      <td>105.00</td>\n      <td>125.00</td>\n      <td>11025.0000</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>106.00</td>\n      <td>241.00</td>\n      <td>11236.0000</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>99.00</td>\n      <td>134.00</td>\n      <td>9801.0000</td>\n    </tr>\n    <tr>\n      <th>98</th>\n      <td>91.00</td>\n      <td>150.00</td>\n      <td>8281.0000</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>102.50</td>\n      <td>198.00</td>\n      <td>10506.2500</td>\n    </tr>\n    <tr>\n      <th>100</th>\n      <td>106.00</td>\n      <td>151.00</td>\n      <td>11236.0000</td>\n    </tr>\n    <tr>\n      <th>101</th>\n      <td>109.10</td>\n      <td>229.00</td>\n      <td>11902.8100</td>\n    </tr>\n    <tr>\n      <th>102</th>\n      <td>115.00</td>\n      <td>253.00</td>\n      <td>13225.0000</td>\n    </tr>\n    <tr>\n      <th>103</th>\n      <td>101.00</td>\n      <td>188.00</td>\n      <td>10201.0000</td>\n    </tr>\n    <tr>\n      <th>104</th>\n      <td>100.10</td>\n      <td>124.00</td>\n      <td>10020.0100</td>\n    </tr>\n    <tr>\n      <th>105</th>\n      <td>93.30</td>\n      <td>62.20</td>\n      <td>8704.8900</td>\n    </tr>\n    <tr>\n      <th>106</th>\n      <td>101.80</td>\n      <td>133.00</td>\n      <td>10363.2400</td>\n    </tr>\n    <tr>\n      <th>107</th>\n      <td>107.90</td>\n      <td>208.00</td>\n      <td>11642.4100</td>\n    </tr>\n    <tr>\n      <th>108</th>\n      <td>108.50</td>\n      <td>208.00</td>\n      <td>11772.2500</td>\n    </tr>\n  </tbody>\n</table>\n<p>109 rows × 3 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:37.987702Z",
          "end_time": "2023-01-24T07:39:38.003247Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "model_quad = smf.ols(\"np.log(AT)~Waist+Waist_Sq\",data=wcat).fit()\n#model_quad.params ",
      "execution_count": 44,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:38.003247Z",
          "end_time": "2023-01-24T07:39:38.043885Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "model_quad.summary()",
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 45,
          "data": {
            "text/plain": "<class 'statsmodels.iolib.summary.Summary'>\n\"\"\"\n                            OLS Regression Results                            \n==============================================================================\nDep. Variable:             np.log(AT)   R-squared:                       0.779\nModel:                            OLS   Adj. R-squared:                  0.775\nMethod:                 Least Squares   F-statistic:                     186.8\nDate:                Tue, 24 Jan 2023   Prob (F-statistic):           1.80e-35\nTime:                        13:09:38   Log-Likelihood:                -24.779\nNo. Observations:                 109   AIC:                             55.56\nDf Residuals:                     106   BIC:                             63.63\nDf Model:                           2                                         \nCovariance Type:            nonrobust                                         \n==============================================================================\n                 coef    std err          t      P>|t|      [0.025      0.975]\n------------------------------------------------------------------------------\nIntercept     -7.8241      1.473     -5.312      0.000     -10.744      -4.904\nWaist          0.2289      0.032      7.107      0.000       0.165       0.293\nWaist_Sq      -0.0010      0.000     -5.871      0.000      -0.001      -0.001\n==============================================================================\nOmnibus:                        0.325   Durbin-Watson:                   1.464\nProb(Omnibus):                  0.850   Jarque-Bera (JB):                0.271\nSkew:                           0.119   Prob(JB):                        0.873\nKurtosis:                       2.949   Cond. No.                     4.49e+05\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n[2] The condition number is large, 4.49e+05. This might indicate that there are\nstrong multicollinearity or other numerical problems.\n\"\"\"",
            "text/html": "<table class=\"simpletable\">\n<caption>OLS Regression Results</caption>\n<tr>\n  <th>Dep. Variable:</th>       <td>np.log(AT)</td>    <th>  R-squared:         </th> <td>   0.779</td>\n</tr>\n<tr>\n  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.775</td>\n</tr>\n<tr>\n  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   186.8</td>\n</tr>\n<tr>\n  <th>Date:</th>             <td>Tue, 24 Jan 2023</td> <th>  Prob (F-statistic):</th> <td>1.80e-35</td>\n</tr>\n<tr>\n  <th>Time:</th>                 <td>13:09:38</td>     <th>  Log-Likelihood:    </th> <td> -24.779</td>\n</tr>\n<tr>\n  <th>No. Observations:</th>      <td>   109</td>      <th>  AIC:               </th> <td>   55.56</td>\n</tr>\n<tr>\n  <th>Df Residuals:</th>          <td>   106</td>      <th>  BIC:               </th> <td>   63.63</td>\n</tr>\n<tr>\n  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n</tr>\n<tr>\n  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n</tr>\n<tr>\n  <th>Intercept</th> <td>   -7.8241</td> <td>    1.473</td> <td>   -5.312</td> <td> 0.000</td> <td>  -10.744</td> <td>   -4.904</td>\n</tr>\n<tr>\n  <th>Waist</th>     <td>    0.2289</td> <td>    0.032</td> <td>    7.107</td> <td> 0.000</td> <td>    0.165</td> <td>    0.293</td>\n</tr>\n<tr>\n  <th>Waist_Sq</th>  <td>   -0.0010</td> <td>    0.000</td> <td>   -5.871</td> <td> 0.000</td> <td>   -0.001</td> <td>   -0.001</td>\n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n  <th>Omnibus:</th>       <td> 0.325</td> <th>  Durbin-Watson:     </th> <td>   1.464</td>\n</tr>\n<tr>\n  <th>Prob(Omnibus):</th> <td> 0.850</td> <th>  Jarque-Bera (JB):  </th> <td>   0.271</td>\n</tr>\n<tr>\n  <th>Skew:</th>          <td> 0.119</td> <th>  Prob(JB):          </th> <td>   0.873</td>\n</tr>\n<tr>\n  <th>Kurtosis:</th>      <td> 2.949</td> <th>  Cond. No.          </th> <td>4.49e+05</td>\n</tr>\n</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 4.49e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:38.044602Z",
          "end_time": "2023-01-24T07:39:38.076177Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "pred_quad = model_quad.predict(wcat)\npred4=np.exp(pred_quad)  # as we have used log(AT) in preparing model so we need to convert it back\npred4\n",
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 46,
          "data": {
            "text/plain": "0       36.779400\n1       31.026570\n2       60.145769\n3       68.488132\n4       36.497176\n5       29.174597\n6       56.804132\n7       66.309110\n8       13.610862\n9       32.565621\n10      29.295590\n11      37.491210\n12      32.305552\n13      50.075256\n14      43.505263\n15      22.548823\n16      40.277322\n17      35.107507\n18      34.156014\n19      40.127488\n20      43.034658\n21      56.804132\n22      53.205658\n23      90.409485\n24      60.901036\n25     102.467180\n26      79.346903\n27      55.349922\n28      76.842525\n29      62.808612\n          ...    \n79     155.642311\n80     144.980610\n81     156.625326\n82     156.625326\n83     157.645795\n84     155.034904\n85     146.290109\n86     157.294508\n87     155.034904\n88     146.783920\n89     155.642311\n90     125.093851\n91     149.832482\n92     126.995763\n93     113.052967\n94     123.157825\n95     148.717813\n96     150.878818\n97     130.687397\n98      98.166004\n99     142.179763\n100    150.878818\n101    155.754580\n102    156.780640\n103    137.554384\n104    134.555999\n105    108.012957\n106    140.081911\n107    154.205675\n108    155.034904\nLength: 109, dtype: float64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:38.076177Z",
          "end_time": "2023-01-24T07:39:38.092476Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "rmse_quad = np.sqrt(np.mean((np.array(wcat['AT'])-np.array(pred4))**2))\nrmse_quad ",
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 47,
          "data": {
            "text/plain": "32.24444782776234"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:38.092476Z",
          "end_time": "2023-01-24T07:39:38.237247Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.scatter(wcat.Waist,wcat.AT,c=\"b\")\nplt.plot(wcat.Waist,pred4,\"r\") ",
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 48,
          "data": {
            "text/plain": "[<matplotlib.lines.Line2D at 0x205121d41c0>]"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:38.239412Z",
          "end_time": "2023-01-24T07:39:38.374796Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.plot(np.arange(109),model_quad.resid_pearson)\nplt.axhline(y=0,color='red')\nplt.xlabel(\"Observation Number\")\nplt.ylabel(\"Standardized Residual\")   ",
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 49,
          "data": {
            "text/plain": "Text(0, 0.5, 'Standardized Residual')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:38.377683Z",
          "end_time": "2023-01-24T07:39:38.522618Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.scatter(np.arange(109),model_quad.resid_pearson)\nplt.axhline(y=0,color='red')\nplt.xlabel(\"Observation Number\")\nplt.ylabel(\"Standardized Residual\")  ",
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 50,
          "data": {
            "text/plain": "Text(0, 0.5, 'Standardized Residual')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:38.524793Z",
          "end_time": "2023-01-24T07:39:38.646662Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.hist(model_quad.resid_pearson)\nplt.show() ",
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:38.647960Z",
          "end_time": "2023-01-24T07:39:38.662573Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "data = {\"MODEL\":pd.Series([\"rmse_lin\",\"rmse_log\",\"rmse_exp\",\"rmse_quad\"]),\n        \"RMSE_Values\":pd.Series([rmse_lin,rmse_log,rmse_exp,rmse_quad]),\n        \"Rsquare\":pd.Series([model.rsquared,model2.rsquared,model3.rsquared,model_quad.rsquared])}\ntable=pd.DataFrame(data)\ntable ",
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 52,
          "data": {
            "text/plain": "       MODEL  RMSE_Values   Rsquare\n0   rmse_lin    32.760177  0.670037\n1   rmse_log    32.496885  0.675319\n2   rmse_exp    38.529002  0.707114\n3  rmse_quad    32.244448  0.778982",
            "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>MODEL</th>\n      <th>RMSE_Values</th>\n      <th>Rsquare</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>rmse_lin</td>\n      <td>32.760177</td>\n      <td>0.670037</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>rmse_log</td>\n      <td>32.496885</td>\n      <td>0.675319</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>rmse_exp</td>\n      <td>38.529002</td>\n      <td>0.707114</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>rmse_quad</td>\n      <td>32.244448</td>\n      <td>0.778982</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:38.663620Z",
          "end_time": "2023-01-24T07:39:38.670917Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "print(plt.style.available)  ",
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": "['Solarize_Light2', '_classic_test_patch', '_mpl-gallery', '_mpl-gallery-nogrid', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark', 'seaborn-dark-palette', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'tableau-colorblind10']\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:38.673776Z",
          "end_time": "2023-01-24T07:39:38.687013Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "import matplotlib.pyplot as plt\n>>> plt.style.use('classic')     ",
      "execution_count": 54,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:38.689871Z",
          "end_time": "2023-01-24T07:39:38.826516Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.hist(model_quad.resid_pearson)  \nplt.show() ",
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-24T07:39:38.827551Z",
          "end_time": "2023-01-24T07:39:38.967530Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.scatter(wcat.Waist,wcat.AT,c=\"b\")\nplt.plot(wcat.Waist,pred4,\"r\")",
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 56,
          "data": {
            "text/plain": "[<matplotlib.lines.Line2D at 0x20513f2c8b0>]"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3 (ipykernel)",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.9.13",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "varInspector": {
      "cols": {
        "lenName": 16,
        "lenType": 16,
        "lenVar": 40
      },
      "kernels_config": {
        "python": {
          "delete_cmd_postfix": "",
          "delete_cmd_prefix": "del ",
          "library": "var_list.py",
          "varRefreshCmd": "print(var_dic_list())"
        },
        "r": {
          "delete_cmd_postfix": ") ",
          "delete_cmd_prefix": "rm(",
          "library": "var_list.r",
          "varRefreshCmd": "cat(var_dic_list()) "
        }
      },
      "types_to_exclude": [
        "module",
        "function",
        "builtin_function_or_method",
        "instance",
        "_Feature"
      ],
      "window_display": false
    },
    "gist": {
      "id": "",
      "data": {
        "description": "Linear_Regression1_updated_main.ipynb",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}