pree62/Linear_Regression1.ipynb

## Linear_Regression1.ipynb
{
  "cells": [
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:44.584530Z",
          "start_time": "2023-01-07T13:21:44.575530Z"
        },
        "trusted": false
      },
      "id": "b455a4d6",
      "cell_type": "code",
      "source": "import pandas as pd \nimport matplotlib.pyplot as plt\nimport numpy as np\nimport seaborn as sns\nimport statsmodels.formula.api as smf",
      "execution_count": 221,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:44.615533Z",
          "start_time": "2023-01-07T13:21:44.587530Z"
        },
        "trusted": false
      },
      "id": "f79b5aa0",
      "cell_type": "code",
      "source": "df=pd.read_csv('delivery_time.csv')\ndf",
      "execution_count": 222,
      "outputs": [
        {
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Delivery Time</th>\n      <th>Sorting Time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>21.00</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>13.50</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>19.75</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>24.00</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>29.00</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>15.35</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>19.00</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>9.50</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>17.90</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>18.75</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>19.83</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>10.75</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>16.68</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>11.50</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>12.03</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>14.88</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>13.75</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>18.11</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>8.00</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>17.83</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>21.50</td>\n      <td>5</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "    Delivery Time  Sorting Time\n0           21.00            10\n1           13.50             4\n2           19.75             6\n3           24.00             9\n4           29.00            10\n5           15.35             6\n6           19.00             7\n7            9.50             3\n8           17.90            10\n9           18.75             9\n10          19.83             8\n11          10.75             4\n12          16.68             7\n13          11.50             3\n14          12.03             3\n15          14.88             4\n16          13.75             6\n17          18.11             7\n18           8.00             2\n19          17.83             7\n20          21.50             5"
          },
          "execution_count": 222,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:44.630534Z",
          "start_time": "2023-01-07T13:21:44.617535Z"
        },
        "trusted": false
      },
      "id": "f6946655",
      "cell_type": "code",
      "source": "df.info()",
      "execution_count": 223,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 21 entries, 0 to 20\nData columns (total 2 columns):\n #   Column         Non-Null Count  Dtype  \n---  ------         --------------  -----  \n 0   Delivery Time  21 non-null     float64\n 1   Sorting Time   21 non-null     int64  \ndtypes: float64(1), int64(1)\nmemory usage: 464.0 bytes\n"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:44.662534Z",
          "start_time": "2023-01-07T13:21:44.635567Z"
        },
        "trusted": false
      },
      "id": "ab811437",
      "cell_type": "code",
      "source": "df.describe()",
      "execution_count": 224,
      "outputs": [
        {
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Delivery Time</th>\n      <th>Sorting Time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>21.000000</td>\n      <td>21.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>16.790952</td>\n      <td>6.190476</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>5.074901</td>\n      <td>2.542028</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>8.000000</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>13.500000</td>\n      <td>4.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>17.830000</td>\n      <td>6.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>19.750000</td>\n      <td>8.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>29.000000</td>\n      <td>10.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       Delivery Time  Sorting Time\ncount      21.000000     21.000000\nmean       16.790952      6.190476\nstd         5.074901      2.542028\nmin         8.000000      2.000000\n25%        13.500000      4.000000\n50%        17.830000      6.000000\n75%        19.750000      8.000000\nmax        29.000000     10.000000"
          },
          "execution_count": 224,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:44.678537Z",
          "start_time": "2023-01-07T13:21:44.665540Z"
        },
        "trusted": false
      },
      "id": "33e9551c",
      "cell_type": "code",
      "source": "df.columns",
      "execution_count": 225,
      "outputs": [
        {
          "data": {
            "text/plain": "Index(['Delivery Time', 'Sorting Time'], dtype='object')"
          },
          "execution_count": 225,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:44.694560Z",
          "start_time": "2023-01-07T13:21:44.679531Z"
        },
        "trusted": false
      },
      "id": "3e397a1b",
      "cell_type": "code",
      "source": "df=df.rename({'Delivery Time':'DT','Sorting Time':'ST'},axis=1)",
      "execution_count": 226,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:44.774530Z",
          "start_time": "2023-01-07T13:21:44.696531Z"
        },
        "trusted": false
      },
      "id": "4269e679",
      "cell_type": "code",
      "source": "plt.boxplot(df.DT) \nplt.show()",
      "execution_count": 227,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:44.886611Z",
          "start_time": "2023-01-07T13:21:44.776530Z"
        },
        "trusted": false
      },
      "id": "70044002",
      "cell_type": "code",
      "source": "plt.hist(df.DT)\nplt.show()",
      "execution_count": 228,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:45.014606Z",
          "start_time": "2023-01-07T13:21:44.887376Z"
        },
        "trusted": false
      },
      "id": "9d5002f0",
      "cell_type": "code",
      "source": "sns.distplot(df.DT)",
      "execution_count": 229,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": "C:\\Users\\preet\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n  warnings.warn(msg, FutureWarning)\n"
        },
        {
          "data": {
            "text/plain": "<AxesSubplot:xlabel='DT', ylabel='Density'>"
          },
          "execution_count": 229,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:45.142544Z",
          "start_time": "2023-01-07T13:21:45.018538Z"
        },
        "trusted": false
      },
      "id": "dfa78bce",
      "cell_type": "code",
      "source": "sns.distplot(df.ST)",
      "execution_count": 230,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": "C:\\Users\\preet\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n  warnings.warn(msg, FutureWarning)\n"
        },
        {
          "data": {
            "text/plain": "<AxesSubplot:xlabel='ST', ylabel='Density'>"
          },
          "execution_count": 230,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:45.254330Z",
          "start_time": "2023-01-07T13:21:45.144542Z"
        },
        "trusted": false
      },
      "id": "71dbc2e6",
      "cell_type": "code",
      "source": "plt.plot(df.ST,df.DT,\"bo\")\nplt.xlabel(\"ST\")\nplt.ylabel(\"DT\")\nplt.title(\"ScatterPlot\")   ",
      "execution_count": 231,
      "outputs": [
        {
          "data": {
            "text/plain": "Text(0.5, 1.0, 'ScatterPlot')"
          },
          "execution_count": 231,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:45.270322Z",
          "start_time": "2023-01-07T13:21:45.256254Z"
        },
        "trusted": false
      },
      "id": "4f48debc",
      "cell_type": "code",
      "source": "df.corr()",
      "execution_count": 232,
      "outputs": [
        {
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>DT</th>\n      <th>ST</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>DT</th>\n      <td>1.000000</td>\n      <td>0.825997</td>\n    </tr>\n    <tr>\n      <th>ST</th>\n      <td>0.825997</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "          DT        ST\nDT  1.000000  0.825997\nST  0.825997  1.000000"
          },
          "execution_count": 232,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:45.302367Z",
          "start_time": "2023-01-07T13:21:45.272255Z"
        },
        "trusted": false
      },
      "id": "1c64658b",
      "cell_type": "code",
      "source": "model1=smf.ols('DT~ST',data=df).fit()\nmodel1.params",
      "execution_count": 233,
      "outputs": [
        {
          "data": {
            "text/plain": "Intercept    6.582734\nST           1.649020\ndtype: float64"
          },
          "execution_count": 233,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:45.333362Z",
          "start_time": "2023-01-07T13:21:45.303367Z"
        },
        "trusted": false
      },
      "id": "09bcea62",
      "cell_type": "code",
      "source": "model1.summary()",
      "execution_count": 234,
      "outputs": [
        {
          "data": {
            "text/html": "<table class=\"simpletable\">\n<caption>OLS Regression Results</caption>\n<tr>\n  <th>Dep. Variable:</th>           <td>DT</td>        <th>  R-squared:         </th> <td>   0.682</td>\n</tr>\n<tr>\n  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.666</td>\n</tr>\n<tr>\n  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   40.80</td>\n</tr>\n<tr>\n  <th>Date:</th>             <td>Sat, 07 Jan 2023</td> <th>  Prob (F-statistic):</th> <td>3.98e-06</td>\n</tr>\n<tr>\n  <th>Time:</th>                 <td>18:51:45</td>     <th>  Log-Likelihood:    </th> <td> -51.357</td>\n</tr>\n<tr>\n  <th>No. Observations:</th>      <td>    21</td>      <th>  AIC:               </th> <td>   106.7</td>\n</tr>\n<tr>\n  <th>Df Residuals:</th>          <td>    19</td>      <th>  BIC:               </th> <td>   108.8</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>    6.5827</td> <td>    1.722</td> <td>    3.823</td> <td> 0.001</td> <td>    2.979</td> <td>   10.186</td>\n</tr>\n<tr>\n  <th>ST</th>        <td>    1.6490</td> <td>    0.258</td> <td>    6.387</td> <td> 0.000</td> <td>    1.109</td> <td>    2.189</td>\n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n  <th>Omnibus:</th>       <td> 3.649</td> <th>  Durbin-Watson:     </th> <td>   1.248</td>\n</tr>\n<tr>\n  <th>Prob(Omnibus):</th> <td> 0.161</td> <th>  Jarque-Bera (JB):  </th> <td>   2.086</td>\n</tr>\n<tr>\n  <th>Skew:</th>          <td> 0.750</td> <th>  Prob(JB):          </th> <td>   0.352</td>\n</tr>\n<tr>\n  <th>Kurtosis:</th>      <td> 3.367</td> <th>  Cond. No.          </th> <td>    18.3</td>\n</tr>\n</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.",
            "text/plain": "<class 'statsmodels.iolib.summary.Summary'>\n\"\"\"\n                            OLS Regression Results                            \n==============================================================================\nDep. Variable:                     DT   R-squared:                       0.682\nModel:                            OLS   Adj. R-squared:                  0.666\nMethod:                 Least Squares   F-statistic:                     40.80\nDate:                Sat, 07 Jan 2023   Prob (F-statistic):           3.98e-06\nTime:                        18:51:45   Log-Likelihood:                -51.357\nNo. Observations:                  21   AIC:                             106.7\nDf Residuals:                      19   BIC:                             108.8\nDf Model:                           1                                         \nCovariance Type:            nonrobust                                         \n==============================================================================\n                 coef    std err          t      P>|t|      [0.025      0.975]\n------------------------------------------------------------------------------\nIntercept      6.5827      1.722      3.823      0.001       2.979      10.186\nST             1.6490      0.258      6.387      0.000       1.109       2.189\n==============================================================================\nOmnibus:                        3.649   Durbin-Watson:                   1.248\nProb(Omnibus):                  0.161   Jarque-Bera (JB):                2.086\nSkew:                           0.750   Prob(JB):                        0.352\nKurtosis:                       3.367   Cond. No.                         18.3\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n\"\"\""
          },
          "execution_count": 234,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:45.348363Z",
          "start_time": "2023-01-07T13:21:45.335363Z"
        },
        "scrolled": true,
        "trusted": false
      },
      "id": "e58dafff",
      "cell_type": "code",
      "source": "model1.resid",
      "execution_count": 235,
      "outputs": [
        {
          "data": {
            "text/plain": "0    -2.072933\n1     0.321186\n2     3.273147\n3     2.576087\n4     5.927067\n5    -1.126853\n6     0.874127\n7    -2.029794\n8    -5.172933\n9    -2.673913\n10    0.055107\n11   -2.428814\n12   -1.445873\n13   -0.029794\n14    0.500206\n15    1.701186\n16   -2.726853\n17   -0.015873\n18   -1.880774\n19   -0.295873\n20    6.672167\ndtype: float64"
          },
          "execution_count": 235,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:45.379454Z",
          "start_time": "2023-01-07T13:21:45.351364Z"
        },
        "scrolled": true,
        "trusted": false
      },
      "id": "8e81cc53",
      "cell_type": "code",
      "source": "pred1=model1.predict(df.ST)\npred1",
      "execution_count": 236,
      "outputs": [
        {
          "data": {
            "text/plain": "0     23.072933\n1     13.178814\n2     16.476853\n3     21.423913\n4     23.072933\n5     16.476853\n6     18.125873\n7     11.529794\n8     23.072933\n9     21.423913\n10    19.774893\n11    13.178814\n12    18.125873\n13    11.529794\n14    11.529794\n15    13.178814\n16    16.476853\n17    18.125873\n18     9.880774\n19    18.125873\n20    14.827833\ndtype: float64"
          },
          "execution_count": 236,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:45.394446Z",
          "start_time": "2023-01-07T13:21:45.381454Z"
        },
        "trusted": false
      },
      "id": "51a38543",
      "cell_type": "code",
      "source": "model1.resid_pearson",
      "execution_count": 237,
      "outputs": [
        {
          "data": {
            "text/plain": "array([-0.70630355,  0.10943679,  1.11524837,  0.87774154,  2.01950985,\n       -0.383949  ,  0.2978383 , -0.69160484, -1.76255624, -0.9110735 ,\n        0.01877637, -0.82756157, -0.49264758, -0.0101515 ,  0.17043364,\n        0.5796396 , -0.92911167, -0.00540844, -0.64082979, -0.10081191,\n        2.27338512])"
          },
          "execution_count": 237,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:45.410367Z",
          "start_time": "2023-01-07T13:21:45.396449Z"
        },
        "trusted": false
      },
      "id": "c62d271c",
      "cell_type": "code",
      "source": "rmse_lin=np.sqrt(np.mean((np.array(df['DT'])-np.array(pred1))**2))\nrmse_lin",
      "execution_count": 238,
      "outputs": [
        {
          "data": {
            "text/plain": "2.7916503270617654"
          },
          "execution_count": 238,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:45.536389Z",
          "start_time": "2023-01-07T13:21:45.412355Z"
        },
        "trusted": false
      },
      "id": "7e598886",
      "cell_type": "code",
      "source": "plt.scatter(x=df['ST'],y=df['DT'],color='red')\nplt.plot(df['ST'],pred1,color='black')\nplt.xlabel('ST')\nplt.ylabel('DT')   ",
      "execution_count": 239,
      "outputs": [
        {
          "data": {
            "text/plain": "Text(0, 0.5, 'DT')"
          },
          "execution_count": 239,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:45.551950Z",
          "start_time": "2023-01-07T13:21:45.537951Z"
        },
        "trusted": false
      },
      "id": "51641a44",
      "cell_type": "code",
      "source": "model2=smf.ols('DT~np.log(ST)',data=df).fit()\nmodel2.params",
      "execution_count": 240,
      "outputs": [
        {
          "data": {
            "text/plain": "Intercept     1.159684\nnp.log(ST)    9.043413\ndtype: float64"
          },
          "execution_count": 240,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:45.583901Z",
          "start_time": "2023-01-07T13:21:45.554920Z"
        },
        "trusted": false
      },
      "id": "598eb76b",
      "cell_type": "code",
      "source": "model2.summary()",
      "execution_count": 241,
      "outputs": [
        {
          "data": {
            "text/html": "<table class=\"simpletable\">\n<caption>OLS Regression Results</caption>\n<tr>\n  <th>Dep. Variable:</th>           <td>DT</td>        <th>  R-squared:         </th> <td>   0.695</td>\n</tr>\n<tr>\n  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.679</td>\n</tr>\n<tr>\n  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   43.39</td>\n</tr>\n<tr>\n  <th>Date:</th>             <td>Sat, 07 Jan 2023</td> <th>  Prob (F-statistic):</th> <td>2.64e-06</td>\n</tr>\n<tr>\n  <th>Time:</th>                 <td>18:51:45</td>     <th>  Log-Likelihood:    </th> <td> -50.912</td>\n</tr>\n<tr>\n  <th>No. Observations:</th>      <td>    21</td>      <th>  AIC:               </th> <td>   105.8</td>\n</tr>\n<tr>\n  <th>Df Residuals:</th>          <td>    19</td>      <th>  BIC:               </th> <td>   107.9</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>    1.1597</td> <td>    2.455</td> <td>    0.472</td> <td> 0.642</td> <td>   -3.978</td> <td>    6.297</td>\n</tr>\n<tr>\n  <th>np.log(ST)</th> <td>    9.0434</td> <td>    1.373</td> <td>    6.587</td> <td> 0.000</td> <td>    6.170</td> <td>   11.917</td>\n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n  <th>Omnibus:</th>       <td> 5.552</td> <th>  Durbin-Watson:     </th> <td>   1.427</td>\n</tr>\n<tr>\n  <th>Prob(Omnibus):</th> <td> 0.062</td> <th>  Jarque-Bera (JB):  </th> <td>   3.481</td>\n</tr>\n<tr>\n  <th>Skew:</th>          <td> 0.946</td> <th>  Prob(JB):          </th> <td>   0.175</td>\n</tr>\n<tr>\n  <th>Kurtosis:</th>      <td> 3.628</td> <th>  Cond. No.          </th> <td>    9.08</td>\n</tr>\n</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.",
            "text/plain": "<class 'statsmodels.iolib.summary.Summary'>\n\"\"\"\n                            OLS Regression Results                            \n==============================================================================\nDep. Variable:                     DT   R-squared:                       0.695\nModel:                            OLS   Adj. R-squared:                  0.679\nMethod:                 Least Squares   F-statistic:                     43.39\nDate:                Sat, 07 Jan 2023   Prob (F-statistic):           2.64e-06\nTime:                        18:51:45   Log-Likelihood:                -50.912\nNo. Observations:                  21   AIC:                             105.8\nDf Residuals:                      19   BIC:                             107.9\nDf Model:                           1                                         \nCovariance Type:            nonrobust                                         \n==============================================================================\n                 coef    std err          t      P>|t|      [0.025      0.975]\n------------------------------------------------------------------------------\nIntercept      1.1597      2.455      0.472      0.642      -3.978       6.297\nnp.log(ST)     9.0434      1.373      6.587      0.000       6.170      11.917\n==============================================================================\nOmnibus:                        5.552   Durbin-Watson:                   1.427\nProb(Omnibus):                  0.062   Jarque-Bera (JB):                3.481\nSkew:                           0.946   Prob(JB):                        0.175\nKurtosis:                       3.628   Cond. No.                         9.08\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n\"\"\""
          },
          "execution_count": 241,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:45.615910Z",
          "start_time": "2023-01-07T13:21:45.589904Z"
        },
        "trusted": false
      },
      "id": "6375cdb9",
      "cell_type": "code",
      "source": "pred2=model2.predict(pd.DataFrame(df['ST']))\npred2",
      "execution_count": 242,
      "outputs": [
        {
          "data": {
            "text/plain": "0     21.982913\n1     13.696517\n2     17.363305\n3     21.030094\n4     21.982913\n5     17.363305\n6     18.757354\n7     11.094889\n8     21.982913\n9     21.030094\n10    19.964933\n11    13.696517\n12    18.757354\n13    11.094889\n14    11.094889\n15    13.696517\n16    17.363305\n17    18.757354\n18     7.428100\n19    18.757354\n20    15.714496\ndtype: float64"
          },
          "execution_count": 242,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:45.647901Z",
          "start_time": "2023-01-07T13:21:45.622901Z"
        },
        "trusted": false
      },
      "id": "19297065",
      "cell_type": "code",
      "source": "rmse_log=np.sqrt(np.mean((np.array(df['DT'])-np.array(pred2))**2))\nrmse_log",
      "execution_count": 243,
      "outputs": [
        {
          "data": {
            "text/plain": "2.7331714766820663"
          },
          "execution_count": 243,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:45.791923Z",
          "start_time": "2023-01-07T13:21:45.651901Z"
        },
        "trusted": false
      },
      "id": "5b23286a",
      "cell_type": "code",
      "source": "plt.scatter(x=df['ST'],y=df['DT'],color='red')\nplt.plot(df['ST'],pred2,color='black')\nplt.xlabel('ST')\nplt.ylabel('DT')   ",
      "execution_count": 244,
      "outputs": [
        {
          "data": {
            "text/plain": "Text(0, 0.5, 'DT')"
          },
          "execution_count": 244,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:45.919220Z",
          "start_time": "2023-01-07T13:21:45.792939Z"
        },
        "trusted": false
      },
      "id": "9fbcab1a",
      "cell_type": "code",
      "source": "plt.scatter(x=df['ST'],y=df['DT'],color='red')\nplt.plot(df['ST'],pred2,color='black')\nplt.xlabel('ST')\nplt.ylabel('DT')   ",
      "execution_count": 245,
      "outputs": [
        {
          "data": {
            "text/plain": "Text(0, 0.5, 'DT')"
          },
          "execution_count": 245,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:45.935187Z",
          "start_time": "2023-01-07T13:21:45.921229Z"
        },
        "trusted": false
      },
      "id": "5ba0368d",
      "cell_type": "code",
      "source": "student_resid = model2.resid_pearson \nstudent_resid",
      "execution_count": 246,
      "outputs": [
        {
          "data": {
            "text/plain": "array([-0.34207012, -0.06839109,  0.83061001,  1.0335774 ,  2.44206469,\n       -0.70066414,  0.08444506, -0.55504813, -1.42092236, -0.79351107,\n       -0.046959  , -1.02543743, -0.72295404,  0.14098557,  0.3254345 ,\n        0.41187217, -1.2574911 , -0.22528994,  0.19903082, -0.32273466,\n        2.01345289])"
          },
          "execution_count": 246,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:45.967186Z",
          "start_time": "2023-01-07T13:21:45.937180Z"
        },
        "trusted": false
      },
      "id": "b03af98b",
      "cell_type": "code",
      "source": "model3 = smf.ols('np.log(DT)~ST',data=df).fit()\nmodel3.params\nmodel3.summary()",
      "execution_count": 247,
      "outputs": [
        {
          "data": {
            "text/html": "<table class=\"simpletable\">\n<caption>OLS Regression Results</caption>\n<tr>\n  <th>Dep. Variable:</th>       <td>np.log(DT)</td>    <th>  R-squared:         </th> <td>   0.711</td>\n</tr>\n<tr>\n  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.696</td>\n</tr>\n<tr>\n  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   46.73</td>\n</tr>\n<tr>\n  <th>Date:</th>             <td>Sat, 07 Jan 2023</td> <th>  Prob (F-statistic):</th> <td>1.59e-06</td>\n</tr>\n<tr>\n  <th>Time:</th>                 <td>18:51:45</td>     <th>  Log-Likelihood:    </th> <td>  7.7920</td>\n</tr>\n<tr>\n  <th>No. Observations:</th>      <td>    21</td>      <th>  AIC:               </th> <td>  -11.58</td>\n</tr>\n<tr>\n  <th>Df Residuals:</th>          <td>    19</td>      <th>  BIC:               </th> <td>  -9.495</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>    2.1214</td> <td>    0.103</td> <td>   20.601</td> <td> 0.000</td> <td>    1.906</td> <td>    2.337</td>\n</tr>\n<tr>\n  <th>ST</th>        <td>    0.1056</td> <td>    0.015</td> <td>    6.836</td> <td> 0.000</td> <td>    0.073</td> <td>    0.138</td>\n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n  <th>Omnibus:</th>       <td> 1.238</td> <th>  Durbin-Watson:     </th> <td>   1.325</td>\n</tr>\n<tr>\n  <th>Prob(Omnibus):</th> <td> 0.538</td> <th>  Jarque-Bera (JB):  </th> <td>   0.544</td>\n</tr>\n<tr>\n  <th>Skew:</th>          <td> 0.393</td> <th>  Prob(JB):          </th> <td>   0.762</td>\n</tr>\n<tr>\n  <th>Kurtosis:</th>      <td> 3.067</td> <th>  Cond. No.          </th> <td>    18.3</td>\n</tr>\n</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.",
            "text/plain": "<class 'statsmodels.iolib.summary.Summary'>\n\"\"\"\n                            OLS Regression Results                            \n==============================================================================\nDep. Variable:             np.log(DT)   R-squared:                       0.711\nModel:                            OLS   Adj. R-squared:                  0.696\nMethod:                 Least Squares   F-statistic:                     46.73\nDate:                Sat, 07 Jan 2023   Prob (F-statistic):           1.59e-06\nTime:                        18:51:45   Log-Likelihood:                 7.7920\nNo. Observations:                  21   AIC:                            -11.58\nDf Residuals:                      19   BIC:                            -9.495\nDf Model:                           1                                         \nCovariance Type:            nonrobust                                         \n==============================================================================\n                 coef    std err          t      P>|t|      [0.025      0.975]\n------------------------------------------------------------------------------\nIntercept      2.1214      0.103     20.601      0.000       1.906       2.337\nST             0.1056      0.015      6.836      0.000       0.073       0.138\n==============================================================================\nOmnibus:                        1.238   Durbin-Watson:                   1.325\nProb(Omnibus):                  0.538   Jarque-Bera (JB):                0.544\nSkew:                           0.393   Prob(JB):                        0.762\nKurtosis:                       3.067   Cond. No.                         18.3\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n\"\"\""
          },
          "execution_count": 247,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:45.983181Z",
          "start_time": "2023-01-07T13:21:45.972190Z"
        },
        "trusted": false
      },
      "id": "4a95b19c",
      "cell_type": "code",
      "source": "pred_log=model3.predict(pd.DataFrame(df['ST']))",
      "execution_count": 248,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:45.999181Z",
          "start_time": "2023-01-07T13:21:45.985181Z"
        },
        "trusted": false
      },
      "id": "1fa27c31",
      "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": 249,
      "outputs": [
        {
          "data": {
            "text/plain": "0     23.972032\n1     12.725123\n2     15.716034\n3     21.570707\n4     23.972032\n5     15.716034\n6     17.465597\n7     11.450423\n8     23.972032\n9     21.570707\n10    19.409927\n11    12.725123\n12    17.465597\n13    11.450423\n14    11.450423\n15    12.725123\n16    15.716034\n17    17.465597\n18    10.303411\n19    17.465597\n20    14.141728\ndtype: float64"
          },
          "execution_count": 249,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:46.015191Z",
          "start_time": "2023-01-07T13:21:46.001181Z"
        },
        "trusted": false
      },
      "id": "0df97db6",
      "cell_type": "code",
      "source": "rmse_exp=np.sqrt(np.mean((np.array(df['DT'])-np.array(pred3))**2))\nrmse_exp",
      "execution_count": 250,
      "outputs": [
        {
          "data": {
            "text/plain": "2.9402503230562007"
          },
          "execution_count": 250,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:46.158382Z",
          "start_time": "2023-01-07T13:21:46.018182Z"
        },
        "trusted": false
      },
      "id": "8d280932",
      "cell_type": "code",
      "source": "plt.scatter(x=df['ST'],y=df['DT'],color='red')\nplt.plot(df['ST'],pred3,color='black')\nplt.xlabel('ST')\nplt.ylabel('DT')   ",
      "execution_count": 251,
      "outputs": [
        {
          "data": {
            "text/plain": "Text(0, 0.5, 'DT')"
          },
          "execution_count": 251,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:46.174413Z",
          "start_time": "2023-01-07T13:21:46.160327Z"
        },
        "trusted": false
      },
      "id": "1262b43f",
      "cell_type": "code",
      "source": "student_resid = model3.resid_pearson \nstudent_resid",
      "execution_count": 252,
      "outputs": [
        {
          "data": {
            "text/plain": "array([-0.75408795,  0.33675889,  1.3016092 ,  0.60797197,  1.08475764,\n       -0.13425573,  0.47972269, -1.06383166, -1.66402537, -0.79839415,\n        0.12198057, -0.9609301 , -0.26219179,  0.02461332,  0.28130094,\n        0.89124014, -0.7613643 ,  0.20640995, -1.44153627,  0.11763994,\n        2.38661208])"
          },
          "execution_count": 252,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:46.301415Z",
          "start_time": "2023-01-07T13:21:46.176415Z"
        },
        "trusted": false
      },
      "id": "b967f718",
      "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": 253,
      "outputs": [
        {
          "data": {
            "text/plain": "Text(0, 0.5, 'Standardized Residual')"
          },
          "execution_count": 253,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:46.317415Z",
          "start_time": "2023-01-07T13:21:46.303417Z"
        },
        "trusted": false
      },
      "id": "eb2e06c0",
      "cell_type": "code",
      "source": "df[\"ST_Sq\"] = df.ST*df.ST\ndf",
      "execution_count": 254,
      "outputs": [
        {
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>DT</th>\n      <th>ST</th>\n      <th>ST_Sq</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>21.00</td>\n      <td>10</td>\n      <td>100</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>13.50</td>\n      <td>4</td>\n      <td>16</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>19.75</td>\n      <td>6</td>\n      <td>36</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>24.00</td>\n      <td>9</td>\n      <td>81</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>29.00</td>\n      <td>10</td>\n      <td>100</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>15.35</td>\n      <td>6</td>\n      <td>36</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>19.00</td>\n      <td>7</td>\n      <td>49</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>9.50</td>\n      <td>3</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>17.90</td>\n      <td>10</td>\n      <td>100</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>18.75</td>\n      <td>9</td>\n      <td>81</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>19.83</td>\n      <td>8</td>\n      <td>64</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>10.75</td>\n      <td>4</td>\n      <td>16</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>16.68</td>\n      <td>7</td>\n      <td>49</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>11.50</td>\n      <td>3</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>12.03</td>\n      <td>3</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>14.88</td>\n      <td>4</td>\n      <td>16</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>13.75</td>\n      <td>6</td>\n      <td>36</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>18.11</td>\n      <td>7</td>\n      <td>49</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>8.00</td>\n      <td>2</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>17.83</td>\n      <td>7</td>\n      <td>49</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>21.50</td>\n      <td>5</td>\n      <td>25</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       DT  ST  ST_Sq\n0   21.00  10    100\n1   13.50   4     16\n2   19.75   6     36\n3   24.00   9     81\n4   29.00  10    100\n5   15.35   6     36\n6   19.00   7     49\n7    9.50   3      9\n8   17.90  10    100\n9   18.75   9     81\n10  19.83   8     64\n11  10.75   4     16\n12  16.68   7     49\n13  11.50   3      9\n14  12.03   3      9\n15  14.88   4     16\n16  13.75   6     36\n17  18.11   7     49\n18   8.00   2      4\n19  17.83   7     49\n20  21.50   5     25"
          },
          "execution_count": 254,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:46.365479Z",
          "start_time": "2023-01-07T13:21:46.320416Z"
        },
        "trusted": false
      },
      "id": "9b00683f",
      "cell_type": "code",
      "source": "model4=smf.ols(\"np.log(DT)~ST+ST_Sq\",data=df).fit()\nmodel4.summary()",
      "execution_count": 255,
      "outputs": [
        {
          "data": {
            "text/html": "<table class=\"simpletable\">\n<caption>OLS Regression Results</caption>\n<tr>\n  <th>Dep. Variable:</th>       <td>np.log(DT)</td>    <th>  R-squared:         </th> <td>   0.765</td>\n</tr>\n<tr>\n  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.739</td>\n</tr>\n<tr>\n  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   29.28</td>\n</tr>\n<tr>\n  <th>Date:</th>             <td>Sat, 07 Jan 2023</td> <th>  Prob (F-statistic):</th> <td>2.20e-06</td>\n</tr>\n<tr>\n  <th>Time:</th>                 <td>18:51:46</td>     <th>  Log-Likelihood:    </th> <td>  9.9597</td>\n</tr>\n<tr>\n  <th>No. Observations:</th>      <td>    21</td>      <th>  AIC:               </th> <td>  -13.92</td>\n</tr>\n<tr>\n  <th>Df Residuals:</th>          <td>    18</td>      <th>  BIC:               </th> <td>  -10.79</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>    1.6997</td> <td>    0.228</td> <td>    7.441</td> <td> 0.000</td> <td>    1.220</td> <td>    2.180</td>\n</tr>\n<tr>\n  <th>ST</th>        <td>    0.2659</td> <td>    0.080</td> <td>    3.315</td> <td> 0.004</td> <td>    0.097</td> <td>    0.434</td>\n</tr>\n<tr>\n  <th>ST_Sq</th>     <td>   -0.0128</td> <td>    0.006</td> <td>   -2.032</td> <td> 0.057</td> <td>   -0.026</td> <td>    0.000</td>\n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n  <th>Omnibus:</th>       <td> 2.548</td> <th>  Durbin-Watson:     </th> <td>   1.369</td>\n</tr>\n<tr>\n  <th>Prob(Omnibus):</th> <td> 0.280</td> <th>  Jarque-Bera (JB):  </th> <td>   1.777</td>\n</tr>\n<tr>\n  <th>Skew:</th>          <td> 0.708</td> <th>  Prob(JB):          </th> <td>   0.411</td>\n</tr>\n<tr>\n  <th>Kurtosis:</th>      <td> 2.846</td> <th>  Cond. No.          </th> <td>    373.</td>\n</tr>\n</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.",
            "text/plain": "<class 'statsmodels.iolib.summary.Summary'>\n\"\"\"\n                            OLS Regression Results                            \n==============================================================================\nDep. Variable:             np.log(DT)   R-squared:                       0.765\nModel:                            OLS   Adj. R-squared:                  0.739\nMethod:                 Least Squares   F-statistic:                     29.28\nDate:                Sat, 07 Jan 2023   Prob (F-statistic):           2.20e-06\nTime:                        18:51:46   Log-Likelihood:                 9.9597\nNo. Observations:                  21   AIC:                            -13.92\nDf Residuals:                      18   BIC:                            -10.79\nDf Model:                           2                                         \nCovariance Type:            nonrobust                                         \n==============================================================================\n                 coef    std err          t      P>|t|      [0.025      0.975]\n------------------------------------------------------------------------------\nIntercept      1.6997      0.228      7.441      0.000       1.220       2.180\nST             0.2659      0.080      3.315      0.004       0.097       0.434\nST_Sq         -0.0128      0.006     -2.032      0.057      -0.026       0.000\n==============================================================================\nOmnibus:                        2.548   Durbin-Watson:                   1.369\nProb(Omnibus):                  0.280   Jarque-Bera (JB):                1.777\nSkew:                           0.708   Prob(JB):                        0.411\nKurtosis:                       2.846   Cond. No.                         373.\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n\"\"\""
          },
          "execution_count": 255,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:46.381470Z",
          "start_time": "2023-01-07T13:21:46.367470Z"
        },
        "trusted": false
      },
      "id": "c1572695",
      "cell_type": "code",
      "source": "#R-Squared achieved\npred_quad = model4.predict(df)\npred4=np.exp(pred_quad)  # as we have used log(AT) in preparing model so we need to convert it back\n#pred4",
      "execution_count": 256,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:46.397477Z",
          "start_time": "2023-01-07T13:21:46.383471Z"
        },
        "trusted": false
      },
      "id": "8d7e0c12",
      "cell_type": "code",
      "source": "rmse_quad = np.sqrt(np.mean((np.array(df['DT'])-np.array(pred4))**2))\nrmse_quad",
      "execution_count": 257,
      "outputs": [
        {
          "data": {
            "text/plain": "2.799041988740927"
          },
          "execution_count": 257,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:46.525483Z",
          "start_time": "2023-01-07T13:21:46.400472Z"
        },
        "trusted": false
      },
      "id": "5a6e1e29",
      "cell_type": "code",
      "source": "plt.scatter(x=df['ST'],y=df['DT'],color='red')\nplt.plot(df['ST'],pred4,color='black')\nplt.xlabel('ST')\nplt.ylabel('DT')   ",
      "execution_count": 258,
      "outputs": [
        {
          "data": {
            "text/plain": "Text(0, 0.5, 'DT')"
          },
          "execution_count": 258,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-07T13:21:46.541471Z",
          "start_time": "2023-01-07T13:21:46.527473Z"
        },
        "trusted": false
      },
      "id": "62666cdf",
      "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([model1.rsquared,model2.rsquared,model3.rsquared,model4.rsquared])}\ntable=pd.DataFrame(data)\ntable",
      "execution_count": 259,
      "outputs": [
        {
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>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>2.791650</td>\n      <td>0.682271</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>rmse_log</td>\n      <td>2.733171</td>\n      <td>0.695443</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>rmse_exp</td>\n      <td>2.940250</td>\n      <td>0.710948</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>rmse_quad</td>\n      <td>2.799042</td>\n      <td>0.764867</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       MODEL  RMSE_Values   Rsquare\n0   rmse_lin     2.791650  0.682271\n1   rmse_log     2.733171  0.695443\n2   rmse_exp     2.940250  0.710948\n3  rmse_quad     2.799042  0.764867"
          },
          "execution_count": 259,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    }
  ],
  "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"
    },
    "gist": {
      "id": "",
      "data": {
        "description": "!excelR/assignments/Gists/Linear_Regression1.ipynb",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}