pree62/Linear_Regression2.ipynb

## Linear_Regression2.ipynb
{
  "cells": [
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-09T05:06:42.814528Z",
          "start_time": "2023-01-09T05:06:42.798422Z"
        },
        "trusted": false
      },
      "id": "5915e2f6",
      "cell_type": "code",
      "source": "import pandas as pd  \nimport numpy as np \nimport matplotlib.pyplot as plt \nimport seaborn as sns \nimport statsmodels.formula.api as smf",
      "execution_count": 16,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-09T04:44:11.541273Z",
          "start_time": "2023-01-09T04:44:11.475800Z"
        },
        "trusted": false
      },
      "id": "ce13163e",
      "cell_type": "code",
      "source": "df=pd.read_csv('Salary_Data.csv')\ndf.head()",
      "execution_count": 3,
      "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>YearsExperience</th>\n      <th>Salary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.1</td>\n      <td>39343.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.3</td>\n      <td>46205.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1.5</td>\n      <td>37731.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2.0</td>\n      <td>43525.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2.2</td>\n      <td>39891.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   YearsExperience   Salary\n0              1.1  39343.0\n1              1.3  46205.0\n2              1.5  37731.0\n3              2.0  43525.0\n4              2.2  39891.0"
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-09T04:45:19.976987Z",
          "start_time": "2023-01-09T04:45:19.948628Z"
        },
        "scrolled": true,
        "trusted": false
      },
      "id": "39180b4b",
      "cell_type": "code",
      "source": "df.info()",
      "execution_count": 7,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 30 entries, 0 to 29\nData columns (total 2 columns):\n #   Column           Non-Null Count  Dtype  \n---  ------           --------------  -----  \n 0   YearsExperience  30 non-null     float64\n 1   Salary           30 non-null     float64\ndtypes: float64(2)\nmemory usage: 608.0 bytes\n"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-09T04:45:49.349161Z",
          "start_time": "2023-01-09T04:45:49.316885Z"
        },
        "scrolled": true,
        "trusted": false
      },
      "id": "a70de1f2",
      "cell_type": "code",
      "source": "df.describe()",
      "execution_count": 9,
      "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>YearsExperience</th>\n      <th>Salary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>30.000000</td>\n      <td>30.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>5.313333</td>\n      <td>76003.000000</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>2.837888</td>\n      <td>27414.429785</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.100000</td>\n      <td>37731.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>3.200000</td>\n      <td>56720.750000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>4.700000</td>\n      <td>65237.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>7.700000</td>\n      <td>100544.750000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>10.500000</td>\n      <td>122391.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       YearsExperience         Salary\ncount        30.000000      30.000000\nmean          5.313333   76003.000000\nstd           2.837888   27414.429785\nmin           1.100000   37731.000000\n25%           3.200000   56720.750000\n50%           4.700000   65237.000000\n75%           7.700000  100544.750000\nmax          10.500000  122391.000000"
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-09T04:46:03.743780Z",
          "start_time": "2023-01-09T04:46:03.734270Z"
        },
        "trusted": false
      },
      "id": "fb5ea15d",
      "cell_type": "code",
      "source": "df.columns",
      "execution_count": 10,
      "outputs": [
        {
          "data": {
            "text/plain": "Index(['YearsExperience', 'Salary'], dtype='object')"
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-09T04:47:19.443368Z",
          "start_time": "2023-01-09T04:47:19.426731Z"
        },
        "trusted": false
      },
      "id": "ac7ceeda",
      "cell_type": "code",
      "source": "df=df.rename({'YearsExperience':'exp','Salary':'sal'},axis=1)",
      "execution_count": 12,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-09T05:06:55.722077Z",
          "start_time": "2023-01-09T05:06:55.371390Z"
        },
        "trusted": false
      },
      "id": "bc9d3344",
      "cell_type": "code",
      "source": "plt.boxplot(df.exp)",
      "execution_count": 18,
      "outputs": [
        {
          "data": {
            "text/plain": "{'whiskers': [<matplotlib.lines.Line2D at 0x1bb99af08e0>,\n  <matplotlib.lines.Line2D at 0x1bb99af02b0>],\n 'caps': [<matplotlib.lines.Line2D at 0x1bb99ae6490>,\n  <matplotlib.lines.Line2D at 0x1bb99ae6640>],\n 'boxes': [<matplotlib.lines.Line2D at 0x1bb99af04f0>],\n 'medians': [<matplotlib.lines.Line2D at 0x1bb99ae6e80>],\n 'fliers': [<matplotlib.lines.Line2D at 0x1bb99ae6850>],\n 'means': []}"
          },
          "execution_count": 18,
          "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": "2023-01-09T05:07:16.176038Z",
          "start_time": "2023-01-09T05:07:16.075255Z"
        },
        "trusted": false
      },
      "id": "8a7d54a3",
      "cell_type": "code",
      "source": "plt.boxplot(df.sal)",
      "execution_count": 20,
      "outputs": [
        {
          "data": {
            "text/plain": "{'whiskers': [<matplotlib.lines.Line2D at 0x1bb99435460>,\n  <matplotlib.lines.Line2D at 0x1bb9945d3a0>],\n 'caps': [<matplotlib.lines.Line2D at 0x1bb9945d0d0>,\n  <matplotlib.lines.Line2D at 0x1bb9945d7f0>],\n 'boxes': [<matplotlib.lines.Line2D at 0x1bb99435b20>],\n 'medians': [<matplotlib.lines.Line2D at 0x1bb9945dd60>],\n 'fliers': [<matplotlib.lines.Line2D at 0x1bb9945df10>],\n 'means': []}"
          },
          "execution_count": 20,
          "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": "2023-01-09T05:10:06.977819Z",
          "start_time": "2023-01-09T05:10:06.865953Z"
        },
        "trusted": false
      },
      "id": "0673b279",
      "cell_type": "code",
      "source": "plt.hist(df.exp)\nplt.show()",
      "execution_count": 22,
      "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": "2023-01-09T05:10:38.330351Z",
          "start_time": "2023-01-09T05:10:38.213117Z"
        },
        "trusted": false
      },
      "id": "6f0c68bd",
      "cell_type": "code",
      "source": "plt.hist(df.sal)\nplt.show()",
      "execution_count": 24,
      "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": "2023-01-09T05:11:17.995567Z",
          "start_time": "2023-01-09T05:11:17.849359Z"
        },
        "trusted": false
      },
      "id": "a2727d5e",
      "cell_type": "code",
      "source": "sns.distplot(df.exp)",
      "execution_count": 26,
      "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='exp', ylabel='Density'>"
          },
          "execution_count": 26,
          "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": "2023-01-09T05:11:44.335667Z",
          "start_time": "2023-01-09T05:11:44.194362Z"
        },
        "trusted": false
      },
      "id": "1ea9ba68",
      "cell_type": "code",
      "source": "sns.distplot(df.sal)",
      "execution_count": 28,
      "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='sal', ylabel='Density'>"
          },
          "execution_count": 28,
          "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": "2023-01-09T05:14:23.712287Z",
          "start_time": "2023-01-09T05:14:23.577605Z"
        },
        "trusted": false
      },
      "id": "33e99cc2",
      "cell_type": "code",
      "source": "plt.plot(df.exp,df.sal,\"bo\")\nplt.xlabel(\"exp\")\nplt.ylabel(\"sal\")\nplt.title(\"Scatterplot\")",
      "execution_count": 32,
      "outputs": [
        {
          "data": {
            "text/plain": "Text(0.5, 1.0, 'Scatterplot')"
          },
          "execution_count": 32,
          "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": "2023-01-09T05:14:38.348521Z",
          "start_time": "2023-01-09T05:14:38.331004Z"
        },
        "trusted": false
      },
      "id": "8e9dc6d9",
      "cell_type": "code",
      "source": "df.corr()",
      "execution_count": 34,
      "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>exp</th>\n      <th>sal</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>exp</th>\n      <td>1.000000</td>\n      <td>0.978242</td>\n    </tr>\n    <tr>\n      <th>sal</th>\n      <td>0.978242</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "          exp       sal\nexp  1.000000  0.978242\nsal  0.978242  1.000000"
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-09T05:19:37.070189Z",
          "start_time": "2023-01-09T05:19:37.030704Z"
        },
        "trusted": false
      },
      "id": "8492d50f",
      "cell_type": "code",
      "source": "model1=smf.ols('sal~exp',data=df).fit()\nmodel1.summary()",
      "execution_count": 38,
      "outputs": [
        {
          "data": {
            "text/html": "<table class=\"simpletable\">\n<caption>OLS Regression Results</caption>\n<tr>\n  <th>Dep. Variable:</th>           <td>sal</td>       <th>  R-squared:         </th> <td>   0.957</td>\n</tr>\n<tr>\n  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.955</td>\n</tr>\n<tr>\n  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   622.5</td>\n</tr>\n<tr>\n  <th>Date:</th>             <td>Mon, 09 Jan 2023</td> <th>  Prob (F-statistic):</th> <td>1.14e-20</td>\n</tr>\n<tr>\n  <th>Time:</th>                 <td>10:49:37</td>     <th>  Log-Likelihood:    </th> <td> -301.44</td>\n</tr>\n<tr>\n  <th>No. Observations:</th>      <td>    30</td>      <th>  AIC:               </th> <td>   606.9</td>\n</tr>\n<tr>\n  <th>Df Residuals:</th>          <td>    28</td>      <th>  BIC:               </th> <td>   609.7</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> 2.579e+04</td> <td> 2273.053</td> <td>   11.347</td> <td> 0.000</td> <td> 2.11e+04</td> <td> 3.04e+04</td>\n</tr>\n<tr>\n  <th>exp</th>       <td> 9449.9623</td> <td>  378.755</td> <td>   24.950</td> <td> 0.000</td> <td> 8674.119</td> <td> 1.02e+04</td>\n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n  <th>Omnibus:</th>       <td> 2.140</td> <th>  Durbin-Watson:     </th> <td>   1.648</td>\n</tr>\n<tr>\n  <th>Prob(Omnibus):</th> <td> 0.343</td> <th>  Jarque-Bera (JB):  </th> <td>   1.569</td>\n</tr>\n<tr>\n  <th>Skew:</th>          <td> 0.363</td> <th>  Prob(JB):          </th> <td>   0.456</td>\n</tr>\n<tr>\n  <th>Kurtosis:</th>      <td> 2.147</td> <th>  Cond. No.          </th> <td>    13.2</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:                    sal   R-squared:                       0.957\nModel:                            OLS   Adj. R-squared:                  0.955\nMethod:                 Least Squares   F-statistic:                     622.5\nDate:                Mon, 09 Jan 2023   Prob (F-statistic):           1.14e-20\nTime:                        10:49:37   Log-Likelihood:                -301.44\nNo. Observations:                  30   AIC:                             606.9\nDf Residuals:                      28   BIC:                             609.7\nDf Model:                           1                                         \nCovariance Type:            nonrobust                                         \n==============================================================================\n                 coef    std err          t      P>|t|      [0.025      0.975]\n------------------------------------------------------------------------------\nIntercept   2.579e+04   2273.053     11.347      0.000    2.11e+04    3.04e+04\nexp         9449.9623    378.755     24.950      0.000    8674.119    1.02e+04\n==============================================================================\nOmnibus:                        2.140   Durbin-Watson:                   1.648\nProb(Omnibus):                  0.343   Jarque-Bera (JB):                1.569\nSkew:                           0.363   Prob(JB):                        0.456\nKurtosis:                       2.147   Cond. No.                         13.2\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n\"\"\""
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-09T05:20:22.688356Z",
          "start_time": "2023-01-09T05:20:22.677117Z"
        },
        "trusted": false
      },
      "id": "992c373d",
      "cell_type": "code",
      "source": "model1.params",
      "execution_count": 41,
      "outputs": [
        {
          "data": {
            "text/plain": "Intercept    25792.200199\nexp           9449.962321\ndtype: float64"
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-09T06:16:48.699617Z",
          "start_time": "2023-01-09T06:16:48.688918Z"
        },
        "scrolled": true,
        "trusted": false
      },
      "id": "f61d7096",
      "cell_type": "code",
      "source": "pred1=model1.predict(df.exp)\n#pred1",
      "execution_count": 64,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-09T05:24:40.142942Z",
          "start_time": "2023-01-09T05:24:40.123288Z"
        },
        "trusted": false
      },
      "id": "a0fcfbd5",
      "cell_type": "code",
      "source": "rmse_lin=np.sqrt(np.mean((np.array(df['sal'])-np.array(pred1))**2))\nrmse_lin",
      "execution_count": 44,
      "outputs": [
        {
          "data": {
            "text/plain": "5592.043608760662"
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-09T05:37:12.866112Z",
          "start_time": "2023-01-09T05:37:12.728883Z"
        },
        "trusted": false
      },
      "id": "3b8ddc1c",
      "cell_type": "code",
      "source": "plt.scatter(x=df['exp'],y=df['sal'],color='red')\nplt.plot(df['exp'],pred1,color='black')\nplt.xlabel('exp')\nplt.ylabel('sal')",
      "execution_count": 49,
      "outputs": [
        {
          "data": {
            "text/plain": "Text(0, 0.5, 'sal')"
          },
          "execution_count": 49,
          "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": "2023-01-09T05:38:24.465437Z",
          "start_time": "2023-01-09T05:38:24.337811Z"
        },
        "trusted": false
      },
      "id": "d86d12f9",
      "cell_type": "code",
      "source": "plt.plot(model1.resid_pearson,'o')\nplt.axhline(y=0,color='green')\nplt.xlabel(\"Observation Number\")\nplt.ylabel(\"Standardized Residual\")",
      "execution_count": 50,
      "outputs": [
        {
          "data": {
            "text/plain": "Text(0, 0.5, 'Standardized Residual')"
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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XxIz33nuvRxMzlpWVyWq1qrS01KdTCgCAGTmrDP18wdp6Z5SvmRpjw73D6D6DRzz9/T7rhKilIyECAN/ZtP+Iblq6ucF2b04ZRA0gPOLp77fHNUQPPfTQGY8/+OCDnr4lAMCkWF4HgcLjhGjFihVu2ydOnFB+fr5atWqlHj16kBABABqN5XUQKDxOiLZv315rX1lZmSZPnqzrrrvOK0EBAMyB5XUQKDweZVaX9u3b66GHHtIDDzzgjbcDAJgEy+sgUHglIZKko0ePqrS01FtvBwAwCZbXQSDwuMvsmWeecds2DEMOh0Ovv/66UlJSvBYYAMA8WF4H/ubxsPuaZTtqhISEqGvXrho2bJhmz56tyMhIrwbobwy7BwAg+Ph82H1+fn6TAgMAAAhUXqshAgAACFaNekL0i1/8otFv+M477zQ5GAAAAH9o1BMiq9XqerVv315r1qxRTk6O6/i2bdu0Zs0aWa1WnwUKAADgK416QvTyyy+7/nzvvffqhhtu0JIlSxQaGipJcjqduuOOOyg6BgAAQcnjUWZdu3bVhg0bdOGFF7rt37t3rwYPHqwjR454NUB/Y5QZAADBx9Pfb4+Lqk+ePKk9e/bU2r9nzx5VVVV5+nYAAAB+5/Gw+9tuu0233367/vWvf2nQoEGSpM2bN+uxxx7Tbbfd5vUAAQAAfM3jhOiJJ56QzWbT008/LYfDIUmy2+265557NHPmTK8HCAAA4Gse1xCdqqysTJJadG0NNUQAAAQfn89UfSoSBAAA0BI0KiFKSkrSmjVr1LFjR/Xr108WS/2L7eXm5notOAAAgObQqIRozJgxCg8PlySNHTvWl/EAAAA0u7OqITIDaogAAAg+Pp+HqLCwUN98841re8uWLZo+fbqef/55T98KAAAgIHicEE2YMEHr1q2TJBUVFemqq67Sli1bdP/99+uhhx7yeoAAAAC+5nFCtHv3bg0cOFCStGzZMl188cXauHGj/vKXv+iVV17xdnwAAAA+53FCdOLECVeB9T//+U9de+21kqSLLrrINVEjAABAMPE4Ierdu7eWLFmiTz75RKtXr1ZKSook6dtvv1Xnzp29HiAAAICveZwQLViwQH/+8581dOhQ3XTTTerbt68k6d1333V1pQEAAASTJg27dzqdKisrU8eOHV37Dh48qHPOOUdRUVFeDdDfGHYPAEDwaZalOwzD0LZt27R//35NmDBBkZGRCgsL0znnnNOUtwMaxVllaEt+iYqPlSsqMkID4zspNKT+WdMBAGgsjxOir7/+WikpKSooKFBFRYWGDx+uyMhIPf744yovL9eSJUt8ESdMbuVuh+Zl5clRWu7aZ7dGaE5qglIS7X6MDADQEnhcQ3T33XdrwIAB+u6779SmTRvX/uuuu05r1qzxanCAVJ0MpWXmuiVDklRUWq60zFyt3M3oRgDA2fH4CdGGDRv06aefKiwszG1/XFycDh065LXAAKm6m2xeVp7qKnQzJFkkzcvK0/AEG91nAIAm8/gJUVVVlZxOZ63933zzjSIjI70SFFBjS35JrSdDpzIkOUrLtSW/pPmCAgC0OB4nRMOHD9fChQtd2xaLRd9//73mzJmj0aNHezO2Fs1ZZWjT/iP6x45D2rT/iJxVrLFbl+Jj9SdDTWkHAEBdPO4ye/rpp3XFFVcoISFB5eXlmjBhgvbt26cuXbrozTff9EWMLQ4Fwo0XFRnh1XYAANSlSfMQ/fDDD3rzzTeVm5urqqoqJSUl6eabb3Yrsm4pvD0PUU2B8OkXvab6ZfHEJJKiUzirDP18wVoVlZbXWUdkkWSzRmjDvcOoIQIAuHj6+92khKguDodD8+fP17PPPuuNtwsY3kyIan7c66uJ4ce9bjVJpCS3pIgkEgBQH09/vz2qIcrLy9Nzzz2n559/XkePHpUkHT58WDNmzND555+vtWvXNilos6BAuGlSEu1aPDFJNqt7t5jNGkEyBADwikbXEL333nu6/vrrdeLECUnS448/rqVLl+qGG25QYmKi/vrXv+qaa67xWaAtAQXCTZeSaNfwBBszVQMAfKLRT4jmz5+vqVOnqqysTE888YQOHDigqVOnavny5Vq3bl2zJUOLFi1SfHy8IiIi1L9/f33yySf1ts3OzpbFYqn1+vLLL5sl1tNRIHx2QkMsSu7RWWMuOVfJPTqTDAEAvKbRCdGePXt05513ql27drrrrrsUEhKihQsX6vLLL/dlfG7efvttTZ8+Xb///e+1fft2XXbZZRo1apQKCgrOeN7evXvlcDhcr5/85CfNFLG7gfGdZLdGqL6fcYuqR5sNjO/UnGEBAGB6jU6IysrK1KFDB0lSq1at1KZNG/Xs2dNXcdXpqaee0i9/+Uv96le/Uq9evbRw4ULFxsZq8eLFZzwvKipKNpvN9QoNDW2miN2Fhlg0JzVBkmolRTXbc1ITePIBAEAz82geory8PBUVFUmqXvF+7969On78uFubPn36eC+6U1RWVmrbtm2677773PaPGDFCGzduPOO5/fr1U3l5uRISEvR///d/uuKKK+ptW1FRoYqKCtd2WVnZ2QV+mpoC4dPnIbIxDxEAAH7jUUJ05ZVX6tRR+jV1QxaLRYZhyGKx1LmshzccPnxYTqdT0dHRbvujo6NdSdrp7Ha7nn/+efXv318VFRV6/fXXdeWVVyo7O7verr6MjAzNmzfP6/GfigJhAAACS6MTovz8fF/G0WgWi3vSUJOI1eXCCy/UhRde6NpOTk5WYWGhnnjiiXoTotmzZys9Pd21XVZWptjYWC9E7q6mQBgAAPhfoxOiuLg4X8bRoC5duig0NLTW06Di4uJaT43OZNCgQcrMzKz3eHh4uMLDw5scJwAACD4eL+7qL2FhYerfv79Wr17ttn/16tUaPHhwo99n+/btstup0wEAAD/yeHFXf0pPT9ctt9yiAQMGKDk5Wc8//7wKCgo0depUSdXdXYcOHdJrr70mSVq4cKG6d++u3r17q7KyUpmZmVq+fLmWL1/uz68BAAACTFAlROPHj9eRI0f00EMPyeFwKDExUR988IGrO8/hcLjNSVRZWalZs2bp0KFDatOmjXr37q33339fo0eP9tdXAAAAAchri7u2VN5e7R4AAPieTxd3BQAAaIka1WXWr1+/eoe2ny43N/esAgIAAGhujUqIxo4d6/pzeXm5Fi1apISEBCUnJ0uSNm/erC+++EJ33HGHT4IEAADwpUYlRHPmzHH9+Ve/+pXuuusuPfzww7XaFBYWejc6AACAZuBxUbXValVOTk6tFeP37dunAQMGqLS01KsB+htF1QAABB+fF1W3adNGGzZsqLV/w4YNioiI8PTtAAAA/M7jeYimT5+utLQ0bdu2TYMGDZJUXUP00ksv6cEHH/R6gAAAAL7mcUJ033336fzzz9cf//hH/eUvf5Ek9erVS6+88opuuOEGrwcIAADga0zM2ABqiAAACD7NMjHj0aNH9cILL+j+++9XSUmJpOr5hw4dOtSUtwMAtBDOKkOb9h/RP3Yc0qb9R+Ss4t/cCA4ed5nt3LlTV111laxWqw4ePKhf/epX6tSpk1asWKGvv/7atbAqAMBcVu52aF5Wnhyl5a59dmuE5qQmKCXR7sfIgIZ5/IQoPT1dkydP1r59+9xGlY0aNUoff/yxV4MDAASHlbsdSsvMdUuGJKmotFxpmblaudvhp8iAxvE4Idq6dat+85vf1Np/7rnnqqioyCtBAQCCh7PK0LysPNXVOVazb15WHt1nCGgeJ0QREREqKyurtX/v3r3q2rWrV4ICAASPLfkltZ4MncqQ5Cgt15b8kuYLqplROxX8PK4hGjNmjB566CEtW7ZMkmSxWFRQUKD77rtP119/vdcDBAAEtuJj9SdDTWkXbPxdO+WsMrQlv0TFx8oVFRmhgfGdFBrSuAXZ8SOPE6InnnhCo0ePVlRUlH744QcNGTJERUVFSk5O1vz5830RIwAggEVFNm6Vgsa2CyY1tVOnPw+qqZ1aPDHJp0mRv5OxlsTjhKh9+/basGGD1q5dq9zcXFVVVSkpKUlXXXWVL+IDAAS4gfGdZLdGqKi0vM46Ioskm7X6yUVL0lDtlEXVtVPDE2w+eWLj72SspfE4ISooKFB0dLSGDRumYcOGufYbhqHCwkJ169bNqwECAAJbaIhFc1ITlJaZK4vk9gNdkwbMSU1ocd04ntROJffo7NXP9ncy1hJ5XFTdvXt3JSUlaf/+/W77i4uLFR8f77XAAADBIyXRrsUTk2SzuneL2awRLfZJhT9rpyhk9z6PnxBJ1WuXDRw4UMuWLdOVV17p2s8qIABgXimJdg1PsJmmwNeftVNmL2T3BY+fEFksFi1atEj/93//p6uvvlrPPPOM2zEAgHmFhliU3KOzxlxyrpJ7dG6xyZD0Y+1Ufd/QouoCZ1/UTpm5kN1XPE6Iap4CzZgxQytWrNCDDz6oX/3qV6qoqPB6cAAABKqa2ilJtZIiX9dO+TMZa6matLhrjVGjRmnjxo3Kzs7WNddc462YAAAICv6qnfJnMtZSeVxDNGTIEIWFhbm2ExIStGXLFl133XXUEAEA/MZfExT6q3aqJhk7fR4iG/MQNYnFIIs5o7KyMlmtVpWWlqp9+/b+DgcAUAczT1DITNV18/T3u1EJUVlZmevN6lrH7FQtLWkgIQKAwFbfBIU1KUFLHfaPM/P097tRXWYdO3aUw+FQVFSUOnToUOdoMsMwZLFY5HQ6PY8aAIAmYIJCeEujEqK1a9eqU6fqSvV169b5NCAAABrLn7NFo2VpVEI0ZMiQOv8MIPBRX4CWjAkK4S2NSoh27tzZ6Dfs06dPk4MB4F1mLjSFOTBBIbylUQnRJZdcIovF4qoTOhNqiIDAwErYMIOaCQqLSsvrrCOyqHoYOhMUoiGNmpgxPz9fBw4cUH5+vpYvX674+HgtWrRI27dv1/bt27Vo0SL16NFDy5cv93W8ABqhoUJTqbrQ1FnFrBsIbkxQCG9p1BOiuLg415/HjRunZ555RqNHj3bt69Onj2JjY/XAAw9o7NixXg8SgGcoNIWZMEEhvMHjmap37dql+Pj4Wvvj4+OVl5fnlaAAnB0KTWE2/potGi2Hx2uZ9erVS4888ojKy3/8i7SiokKPPPKIevXq5dXgADQNhaYwo9AQi5J7dNaYS85Vco/OJEPwiMdPiJYsWaLU1FTFxsaqb9++kqTPP/9cFotF7733ntcDBOA5Ck0BwDMeJ0QDBw5Ufn6+MjMz9eWXX8owDI0fP14TJkxQ27ZtfREjAA/VFJqmZebKIrklRRSaAkBtHi3ueuLECV144YV67733lJCQ4Mu4AgZrmSGYMQ8RALPyyVpmNVq3bq2KiooG5yICEBgoNAWAxvG4qPq3v/2tFixYoJMnT/oiHgBeRqEpADTM44Tos88+0zvvvKNu3bpp5MiR+sUvfuH28rVFixYpPj5eERER6t+/vz755JMztl+/fr369++viIgInX/++VqyZInPYwQAAMHF46LqDh066Prrr/dFLA16++23NX36dC1atEg/+9nP9Oc//1mjRo1SXl6eunXrVqt9fn6+Ro8erSlTpigzM1Offvqp7rjjDnXt2tXj73C88rhCK0O99VUAwCecVYZyvi7Rf45VqGtkuAbE0UUKczpeedyj9h4VVfvbpZdeqqSkJC1evNi1r1evXho7dqwyMjJqtb/33nv17rvvas+ePa59U6dO1eeff65NmzbV+RkVFRWqqKhwbZeVlSk2Nla6TxJTtgAAEBzKJT2mRhdVe9xl5i+VlZXatm2bRowY4bZ/xIgR2rhxY53nbNq0qVb7kSNHKicnRydOnKjznIyMDFmtVtcrNjbWO18AAAAELI+7zCTpb3/7m5YtW6aCggJVVla6HcvNzfVKYKc7fPiwnE6noqOj3fZHR0erqKioznOKiorqbH/y5EkdPnxYdnvtYcezZ89Wenq6a7vmCdG3M79l2D2AgOSsMnTVU9kqKquo87hFUnT7cP0zfSjdZzCNsrIyxTwW0+j2HidEzzzzjH7/+99r0qRJ+sc//qHbbrtN+/fv19atW3XnnXd6+nYeO33Iv2EYZ5wGoK72de2vER4ervDw8Fr724a1VdswJp4EEHg27T+i4jKLQs7Qr19cJn1xqILFfGEazjCnR+097jJbtGiRnn/+eT377LMKCwvTPffco9WrV+uuu+5SaWmpp2/XaF26dFFoaGitp0HFxcW1ngLVsNlsdbZv1aqVOnfmLwUALQOL+QJnz+OEqKCgQIMHD5YktWnTRseOHZMk3XLLLXrzzTe9G90pwsLC1L9/f61evdpt/+rVq13xnC45OblW+1WrVmnAgAFq3bq1z2IFgObEYr7A2fM4IbLZbDpy5IgkKS4uTps3b5ZUPcTd1wPW0tPT9cILL+ill17Snj17NGPGDBUUFGjq1KmSqut/br31Vlf7qVOn6uuvv1Z6err27Nmjl156SS+++KJmzZrl0zgBoDnVLOZbX/GARdVLtrCYL1A/j2uIhg0bpqysLCUlJemXv/ylZsyYob/97W/Kycnx+cSM48eP15EjR/TQQw/J4XAoMTFRH3zwgeLi4iRJDodDBQUFrvbx8fH64IMPNGPGDD333HOKiYnRM88847d5lADAF1jMFzh7Hs9DVFVVpaqqKrVqVZ1LLVu2TBs2bNAFF1ygqVOnKiwszCeB+guLuwIIFizmC/zI09/voJqY0R9IiAAEE2eVwWK+aDaBfL/5ZLX7nTt3NjqAPn36NLotAMC7ahbzBXytpT2RbNQTopCQEFkslgbn/JEkp9Ozcf+BjidEAAC4W7nbobTMXJ2eQNRkCIsnJvk9KfL097tRo8zy8/N14MAB5efna/ny5YqPj9eiRYu0fft2bd++XYsWLVKPHj20fPnys/4CAAAgcDmrDM3LyquVDEk/FvTPy8qTsyq4KnIa1WVWM4pLksaNG6dnnnlGo0ePdu3r06ePYmNj9cADD2js2LFeDxIAAASGLfklbt1kpzMkOUrLtSW/JKi6bz2eh2jXrl2Kj4+vtT8+Pl55eXleCQoAAASmljozuscJUa9evfTII4+ovPzHL1pRUaFHHnlEvXr18mpwAAAgsLTUmdE9nphxyZIlSk1NVWxsrPr27StJ+vzzz2WxWPTee+95PUAElkAeYgkA8L2amdGLSsvrrCOySLIF4czoHidEAwcOVH5+vjIzM/Xll1/KMAyNHz9eEyZMUNu2rAbfkrW0IZYAAM+11JnRmZixAQy7rxYMQywBAM0n0P+R7JOJGU/31VdfKTs7W8XFxaqqqnI79uCDDzblLRHAGhpiaVH1EMvhCbag+xcBAKBpUhLtGp5gazFlFB4nREuXLlVaWpq6dOkim83mNlGjxWIhIWqBWuoQSwDA2WlJM6N7nBA98sgjmj9/vu69915fxIMA1FKHWAIAUMPjYfffffedxo0b54tYEKBa6hBLAABqeJwQjRs3TqtWrfJFLAhQNUMs6+sVtqi6kC7YhlgCAFDD4y6zCy64QA888IA2b96siy++WK1bt3Y7ftddd3ktOASGljrE0hPMvwQALZvHw+7rWrbD9WYWiw4cOHDWQQUSht3/KNCHWPqKWb83AAQzT3+/mYeoASRE7sz2pIT5lwAgODXLPEQwr5Y0xLIhzL8EAObRpITom2++0bvvvquCggJVVla6HXvqqae8Ehjgb8y/BADm4XFCtGbNGl177bWKj4/X3r17lZiYqIMHD8owDCUlJfkiRsAvmH8JAMzD42H3s2fP1syZM7V7925FRERo+fLlKiws1JAhQ5ifCC0K8y8BgHl4nBDt2bNHkyZNkiS1atVKP/zwg9q1a6eHHnpICxYs8HqAgL8w/xIAmIfHCVHbtm1VUVEhSYqJidH+/ftdxw4fPuy9yAA/q5l/SVKtpMgs8y8BgFl4nBANGjRIn376qSTp6quv1syZMzV//nzdfvvtGjRokNcDBPwpJdGuxROTZLO6d4vZrBEMuQeAFsTjeYgOHDig77//Xn369NF///tfzZo1Sxs2bNAFF1ygp59+WnFxcb6K1S+YhwiS+eZfAoBgx8SMXtYSEyJ+3AEALZ3PJ2Y8//zztXXrVnXu7D7vytGjR5WUlNTilu5oaViGAgCA2jyuITp48KCcTmet/RUVFTp06JBXgoJv1CxDcfpkg0Wl5UrLzNXK3Q4/RQYAgH81+gnRu+++6/rzRx99JKvV6tp2Op1as2aNunfv7tXg4D0sQwEAQP0anRCNHTtWUvWK9jXzENVo3bq1unfvrieffNKrwcF7WIYCAID6NTohqqqqkiTFx8dr69at6tKli8+CgvexDAUAAPXzuKg6Pz/fF3HAx1iGAgCA+jW6qPqzzz7Thx9+6LbvtddeU3x8vKKiovTrX//aNYM1Ag/LUAAAUL9GJ0Rz587Vzp07Xdu7du3SL3/5S1111VW67777lJWVpYyMDJ8EibPHMhQAANSv0QnRjh07dOWVV7q233rrLV166aVaunSp0tPT9cwzz2jZsmU+CRLewTIUAADUrdE1RN99952io6Nd2+vXr1dKSopr+6c//akKCwu9Gx28LiXRruEJNmaqBgDgFI1+QhQdHe0qqK6srFRubq6Sk5Ndx48dO6bWrVt7P0J4XWiIRck9OmvMJecquUdnkiEAgOk1OiFKSUnRfffdp08++USzZ8/WOeeco8suu8x1fOfOnerRo4dPggQAAPClRidEjzzyiEJDQzVkyBAtXbpUS5cuVVhYmOv4Sy+9pBEjRvgkSKm6y+6WW26R1WqV1WrVLbfcoqNHj57xnMmTJ8tisbi9Bg0a5LMYAQBAcPJ4tfvS0lK1a9dOoaGhbvtLSkrUrl07tyTJm0aNGqVvvvlGzz//vCTp17/+tbp3766srKx6z5k8ebL+/e9/6+WXX3btCwsLU6dOjR9a3hJXuwcAoKXz+Wr3p65hdipPkgxP7dmzRytXrtTmzZt16aWXSpKWLl2q5ORk7d27VxdeeGG954aHh8tmszX6syoqKtzmUyorK2t64AAAICh4vNq9P2zatElWq9WVDEnSoEGDZLVatXHjxjOem52draioKPXs2VNTpkxRcXHxGdtnZGS4uuWsVqtiY2O98h0AAMCPnFWGNu0/on/sOKRN+4/IWeVRh5XXefyEyB+KiooUFRVVa39UVJSKiorqPW/UqFEaN26c4uLilJ+frwceeEDDhg3Ttm3bFB4eXuc5s2fPVnp6umu7rKyMpAgAAC9auduheVl5bouO260RmpOa4Lc58fz6hGju3Lm1ip5Pf+Xk5EiSLJbaQ8MNw6hzf43x48fr6quvVmJiolJTU/Xhhx/qq6++0vvvv1/vOeHh4Wrfvr3bCwAAeMfK3Q6lZea6JUOSVFRarrTMXK3c7fBLXH59QjRt2jTdeOONZ2zTvXt37dy5U//+979rHfvPf/7jNllkQ+x2u+Li4rRv3z6PYwUAAGfHWWVoXlae6uocM1S9lNS8rDwNT7A1+xx5fk2IunTpoi5dujTYLjk5WaWlpdqyZYsGDhwoqXqx2dLSUg0ePLjRn3fkyBEVFhbKbmeJCgAAmtuW/JJaT4ZOZUhylJZrS36Jknt0br7AFCRF1b169VJKSoqmTJmizZs3a/PmzZoyZYquueYatxFmF110kVasWCFJ+v777zVr1ixt2rRJBw8eVHZ2tlJTU9WlSxddd911/voqAACYVvGx+pOhprTzpqBIiCTpjTfe0MUXX6wRI0ZoxIgR6tOnj15//XW3Nnv37lVpaakkKTQ0VLt27dKYMWPUs2dPTZo0ST179tSmTZsUGRnpj68AAICpRUVGNNzIg3beFBSjzKTqeY4yMzPP2ObUOSbbtGmjjz76yNdhAc3CWWWwIC+AoDcwvpPs1ggVlZbXWUdkkWSzVv8d19yCJiECzCoQh6cCQFOEhlg0JzVBaZm5skhuSVHNP/HmpCb45R98QdNlBphRoA5PBYCmSkm0a/HEJNms7t1iNmuEFk9M8ts/9HhCBASoQB6eCgBnIyXRruEJtoAqBSAhAgJUIA9PBYCzFRpiCai/u0iIgAAVyMNTAbQMDNj4EQkREKACeXgqgODHgA13FFUDAapmeGp9/1azqPovL38MTwUQ3BiwURsJERCgaoanSqqVFPl7eCqA4NXQgA2pesCGs6quFi0XCREQwAJ1eCqA4OXJgA0zoYYICHCBODzVDCg2RUvFgI26kRABQSDQhqe2dBSboiVjwEbd6DIDgFNQbIqWjgEbdSMhAoD/odgUZsCAjbqREAHA/1BsCrNgwEZt1BABwP9QbAozYcCGOxIiAPgfik1hNgzY+BFdZgDwPxSbAuZFQgQA/0OxKWBeJEQAcAqKTQFzooYIAE5DsSlgPiREAFAHik0Bc6HLDAAAmB4JEQAAMD0SIgAAYHokRAAAwPRIiAAAgOmREAEAANMjIQIAAKbHPEQAAPiRs8pgEtAAQEIEAICfrNzt0LysPDlKy1377NYIzUlNYJmYZkaXGQAAfrByt0NpmbluyZAkFZWWKy0zVyt3O/wUmTmREAEA0MycVYbmZeXJqONYzb55WXlyVtXVAr5AQgQAQDPbkl9S68nQqQxJjtJybckvab6gTI6ECACAZlZ8rP5kqCntcPZIiAAAaGZRkRFebYezR0IEAEAzGxjfSXZrhOobXG9R9WizgfGdmjMsUyMhAgCgmYWGWDQnNUGSaiVFNdtzUhOYj6gZkRABAOAHKYl2LZ6YJJvVvVvMZo3Q4olJzEPUzJiYEQAAP0lJtGt4go2ZqgNA0Dwhmj9/vgYPHqxzzjlHHTp0aNQ5hmFo7ty5iomJUZs2bTR06FB98cUXvg0UAAAPhIZYlNyjs8Zccq6Se3QmGfKToEmIKisrNW7cOKWlpTX6nMcff1xPPfWUnn32WW3dulU2m03Dhw/XsWPHfBgpAAAINhbDMIJqGsxXXnlF06dP19GjR8/YzjAMxcTEaPr06br33nslSRUVFYqOjtaCBQv0m9/8ps7zKioqVFFR4douKytTbGysSktL1b59e699DwAA4DtlZWWyWq2N/v0OmidEnsrPz1dRUZFGjBjh2hceHq4hQ4Zo48aN9Z6XkZEhq9XqesXGxjZHuAAAwI9abEJUVFQkSYqOjnbbHx0d7TpWl9mzZ6u0tNT1Kiws9GmcAADA//yaEM2dO1cWi+WMr5ycnLP6DIvFvTjNMIxa+04VHh6u9u3bu70AAEDL5tdh99OmTdONN954xjbdu3dv0nvbbDZJ1U+K7PYf53IoLi6u9dQIAACYm18Toi5duqhLly4+ee/4+HjZbDatXr1a/fr1k1Q9Um39+vVasGCBTz4TAAAEp6CpISooKNCOHTtUUFAgp9OpHTt2aMeOHfr+++9dbS666CKtWLFCUnVX2fTp0/Xoo49qxYoV2r17tyZPnqxzzjlHEyZM8NfXAAAAAShoZqp+8MEH9eqrr7q2a576rFu3TkOHDpUk7d27V6Wlpa4299xzj3744Qfdcccd+u6773TppZdq1apVioyMbNbYAQBAYAu6eYiam6fzGAAAAP9jHiIAAAAPkRABAADTC5oaIgDBx1llsIo3gKBAQoRmxQ+keazc7dC8rDw5Sstd++zWCM1JTVBKov0MZwJA8yMhQrPhB9I8Vu52KC0zV6eP2CgqLVdaZq4WT0zivzmAgEINEZpFzQ/kqcmQ9OMP5MrdDj9FBm9zVhmal5VXKxmS5No3LytPzioGuAIIHCRE8Dl+IM1lS35JrcT3VIYkR2m5tuSXNF9QANAAEiL4HD+Q5lJ8rP7/1k1pBwDNgYQIPscPpLlERUZ4tR0ANAcSIvgcP5DmMjC+k+zWCNU3dtCi6mL6gfGdmjMsADgjEiL4HD+Q5hIaYtGc1ARJqvXfvGZ7TmoC0y0ACCgkRPA5fiDNJyXRrsUTk2Szuj/1s1kjGHIPICCxuGsDWNzVe5iHyHz8OREnk4AC5ubp7zcJUQNIiLyLHyk0B5JvACREXkZCBASX+mbJrkm76bIDzMHT329qiAC0GEwCCqCpSIgABCxnlaFN+4/oHzsOadP+Iw0mMkwCCqCpWNwVQEBqSh0Qk4ACaCqeEAEIOE1dDJhJQAE0FQkRgIByNnVATAIKoKlIiAAElLOpA2ISUABNRUIEIKCcbR0Qs2QDaAqKqgGcUXNPpumNOqCURLuGJ9iYBBRAo5EQAaiXP2Z8rqkDKiotr7OOyKLqpz0N1QGFhliU3KOzT2IE0PLQZQagTk0d6XW2qAMC4A8kRABq8feMz8FeB+TphJIA/I8uMwC1eDLSy1fdUsFaB8TCskBwIiECUEugzPgcbHVA9S0sW9PNGAxPtwCzossMQC3M+Ow5f3czAjg7JEQAamHGZ8+xsCwQ3EiIANTCSC/PBUo3I4CmISECUKdgH+nV3OhmBIIbRdUA6hWsI738wVsTSgLwDxIiAGcUbCO9/KWmmzEtM1cWyS0popsRCHx0mQGAl9DNCAQvnhABgBfRzQgEJxIiAPAyuhmB4EOXGQAAMD0SIgAAYHpBkxDNnz9fgwcP1jnnnKMOHTo06pzJkyfLYrG4vQYNGuTbQAEAQNAJmoSosrJS48aNU1pamkfnpaSkyOFwuF4ffPCBjyIEAADBKmiKqufNmydJeuWVVzw6Lzw8XDabrdHtKyoqVFFR4douKyvz6PMAAEDwCZonRE2VnZ2tqKgo9ezZU1OmTFFxcfEZ22dkZMhqtbpesbGxzRQpAADwlxadEI0aNUpvvPGG1q5dqyeffFJbt27VsGHD3J4AnW727NkqLS11vQoLC5sxYgAA4A9+TYjmzp1bq+j59FdOTk6T33/8+PG6+uqrlZiYqNTUVH344Yf66quv9P7779d7Tnh4uNq3b+/2AgAALZtfa4imTZumG2+88Yxtunfv7rXPs9vtiouL0759+7z2ngAAIPj5NSHq0qWLunTp0myfd+TIERUWFspub/x6QoZRvUQjxdUAAASPmt/tmt/xhgTNKLOCggKVlJSooKBATqdTO3bskCRdcMEFateunSTpoosuUkZGhq677jp9//33mjt3rq6//nrZ7XYdPHhQ999/v7p06aLrrruu0Z977NgxSaK4GgCAIHTs2DFZrdYG2wVNQvTggw/q1VdfdW3369dPkrRu3ToNHTpUkrR3716VlpZKkkJDQ7Vr1y699tprOnr0qOx2u6644gq9/fbbioyMbPTnxsTEqLCwUJGRkbJYvLs4Y1lZmWJjY1VYWEitUiNxzZqG69Y0XLem4bp5jmvWNGe6boZh6NixY4qJiWnUe1mMxj5LgteVlZXJarWqtLSU/wM0EtesabhuTcN1axqum+e4Zk3jzevWoofdAwAANAYJEQAAMD0SIj8KDw/XnDlzFB4e7u9QggbXrGm4bk3DdWsarpvnuGZN483rRg0RAAAwPZ4QAQAA0yMhAgAApkdCBAAATI+ECAAAmB4JkZ8sWrRI8fHxioiIUP/+/fXJJ5/4O6SANnfuXFksFreXzWbzd1gB5+OPP1ZqaqpiYmJksVj097//3e24YRiaO3euYmJi1KZNGw0dOlRffPGFf4INEA1ds8mTJ9e69wYNGuSfYANIRkaGfvrTnyoyMlJRUVEaO3as9u7d69aG+81dY64Z91ttixcvVp8+fdS+fXu1b99eycnJ+vDDD13HvXWfkRD5wdtvv63p06fr97//vbZv367LLrtMo0aNUkFBgb9DC2i9e/eWw+FwvXbt2uXvkALO8ePH1bdvXz377LN1Hn/88cf11FNP6dlnn9XWrVtls9k0fPhw15p9ZtTQNZOklJQUt3vvgw8+aMYIA9P69et15513avPmzVq9erVOnjypESNG6Pjx46423G/uGnPNJO6305133nl67LHHlJOTo5ycHA0bNkxjxoxxJT1eu88MNLuBAwcaU6dOddt30UUXGffdd5+fIgp8c+bMMfr27evvMIKKJGPFihWu7aqqKsNmsxmPPfaYa195eblhtVqNJUuW+CHCwHP6NTMMw5g0aZIxZswYv8QTTIqLiw1Jxvr16w3D4H5rjNOvmWFwvzVWx44djRdeeMGr9xlPiJpZZWWltm3bphEjRrjtHzFihDZu3OinqILDvn37FBMTo/j4eN144406cOCAv0MKKvn5+SoqKnK798LDwzVkyBDuvQZkZ2crKipKPXv21JQpU1RcXOzvkAJOzcLanTp1ksT91hinX7Ma3G/1czqdeuutt3T8+HElJyd79T4jIWpmhw8fltPpVHR0tNv+6OhoFRUV+SmqwHfppZfqtdde00cffaSlS5eqqKhIgwcP1pEjR/wdWtCoub+49zwzatQovfHGG1q7dq2efPJJbd26VcOGDVNFRYW/QwsYhmEoPT1dP//5z5WYmCiJ+60hdV0zifutPrt27VK7du0UHh6uqVOnasWKFUpISPDqfdbKa9HCIxaLxW3bMIxa+/CjUaNGuf588cUXKzk5WT169NCrr76q9PR0P0YWfLj3PDN+/HjXnxMTEzVgwADFxcXp/fff1y9+8Qs/RhY4pk2bpp07d2rDhg21jnG/1a2+a8b9VrcLL7xQO3bs0NGjR7V8+XJNmjRJ69evdx33xn3GE6Jm1qVLF4WGhtbKXIuLi2tluKhf27ZtdfHFF2vfvn3+DiVo1IzK4947O3a7XXFxcdx7//Pb3/5W7777rtatW6fzzjvPtZ/7rX71XbO6cL9VCwsL0wUXXKABAwYoIyNDffv21R//+Eev3mckRM0sLCxM/fv31+rVq932r169WoMHD/ZTVMGnoqJCe/bskd1u93coQSM+Pl42m83t3qusrNT69eu59zxw5MgRFRYWmv7eMwxD06ZN0zvvvKO1a9cqPj7e7Tj3W20NXbO6cL/VzTAMVVRUePc+81LBNzzw1ltvGa1btzZefPFFIy8vz5g+fbrRtm1b4+DBg/4OLWDNnDnTyM7ONg4cOGBs3rzZuOaaa4zIyEiu2WmOHTtmbN++3di+fbshyXjqqaeM7du3G19//bVhGIbx2GOPGVar1XjnnXeMXbt2GTfddJNht9uNsrIyP0fuP2e6ZseOHTNmzpxpbNy40cjPzzfWrVtnJCcnG+eee66pr5lhGEZaWpphtVqN7Oxsw+FwuF7//e9/XW2439w1dM243+o2e/Zs4+OPPzby8/ONnTt3Gvfff78REhJirFq1yjAM791nJER+8txzzxlxcXFGWFiYkZSU5DbsErWNHz/esNvtRuvWrY2YmBjjF7/4hfHFF1/4O6yAs27dOkNSrdekSZMMw6geCj1nzhzDZrMZ4eHhxuWXX27s2rXLv0H72Zmu2X//+19jxIgRRteuXY3WrVsb3bp1MyZNmmQUFBT4O2y/q+uaSTJefvllVxvuN3cNXTPut7rdfvvtrt/Lrl27GldeeaUrGTIM791nFsMwjCY+sQIAAGgRqCECAACmR0IEAABMj4QIAACYHgkRAAAwPRIiAABgeiREAADA9EiIAACA6ZEQAQAA0yMhAuCme/fuWrhwob/D8Jrs7GxZLBYdPXrU36H41MGDB2WxWLRjxw5/hwIEJRIiwCQKCwv1y1/+UjExMQoLC1NcXJzuvvtuHTlyxN+hec3QoUM1ffp0t32DBw+Ww+GQ1Wr16WdPnjxZFotFjz32mNv+v//977JYLD79bABnj4QIMIEDBw5owIAB+uqrr/Tmm2/qX//6l5YsWaI1a9YoOTlZJSUlfovN6XSqqqrKZ+8fFhYmm83WLElJRESEFixYoO+++87nn9VcKisr/R0C0CxIiAATuPPOOxUWFqZVq1ZpyJAh6tatm0aNGqV//vOfOnTokH7/+9+7tT927JgmTJigdu3aKSYmRn/605/cjs+dO1fdunVTeHi4YmJidNddd7mOVVZW6p577tG5556rtm3b6tJLL1V2drbr+CuvvKIOHTrovffeU0JCgsLDw7V06VJFRETU6ta66667NGTIEEnSkSNHdNNNN+m8887TOeeco4svvlhvvvmmq+3kyZO1fv16/fGPf5TFYpHFYtHBgwfr7DJbvny5evfurfDwcHXv3l1PPvmk2+d2795djz76qG6//XZFRkaqW7duev755xu8zldddZVsNpsyMjLqbTN37lxdcsklbvsWLlyo7t27u32XsWPH6tFHH1V0dLQ6dOigefPm6eTJk/rd736nTp066bzzztNLL71U6/2//PJLDR48WBEREerdu7fbtZekvLw8jR49Wu3atVN0dLRuueUWHT582HV86NChmjZtmtLT09WlSxcNHz68we8NtAQkREALV1JSoo8++kh33HGH2rRp43bMZrPp5ptv1ttvv61T13n+wx/+oD59+ig3N1ezZ8/WjBkztHr1aknS3/72Nz399NP685//rH379unvf/+7Lr74Yte5t912mz799FO99dZb2rlzp8aNG6eUlBTt27fP1ea///2vMjIy9MILL+iLL77QxIkT1aFDBy1fvtzVxul0atmyZbr55pslSeXl5erfv7/ee+897d69W7/+9a91yy236LPPPpMk/fGPf1RycrKmTJkih8Mhh8Oh2NjYWtdj27ZtuuGGG3TjjTdq165dmjt3rh544AG98sorbu2efPJJDRgwQNu3b9cdd9yhtLQ0ffnll2e81qGhoXr00Uf1pz/9Sd98880Z2zZk7dq1+vbbb/Xxxx/rqaee0ty5c3XNNdeoY8eO+uyzzzR16lRNnTpVhYWFbuf97ne/08yZM7V9+3YNHjxY1157ratb1OFwaMiQIbrkkkuUk5OjlStX6t///rduuOEGt/d49dVX1apVK3366af685//fFbfAwgaBoAWbfPmzYYkY8WKFXUef+qppwxJxr///W/DMAwjLi7OSElJcWszfvx4Y9SoUYZhGMaTTz5p9OzZ06isrKz1Xv/6178Mi8ViHDp0yG3/lVdeacyePdswDMN4+eWXDUnGjh073NrcddddxrBhw1zbH330kREWFmaUlJTU+91Gjx5tzJw507U9ZMgQ4+6773Zrs27dOkOS8d133xmGYRgTJkwwhg8f7tbmd7/7nZGQkODajouLMyZOnOjarqqqMqKioozFixfXG8ukSZOMMWPGGIZhGIMGDTJuv/12wzAMY8WKFcapf9XOmTPH6Nu3r9u5Tz/9tBEXF+f2XnFxcYbT6XTtu/DCC43LLrvMtX3y5Emjbdu2xptvvmkYhmHk5+cbkozHHnvM1ebEiRPGeeedZyxYsMAwDMN44IEHjBEjRrh9dmFhoSHJ2Lt3r2EY1dfwkksuqfd7Ai0VT4gAkzP+92To1Bqb5ORktzbJycnas2ePJGncuHH64YcfdP7552vKlClasWKFTp48KUnKzc2VYRjq2bOn2rVr53qtX79e+/fvd71fWFiY+vTp4/YZN998s7Kzs/Xtt99Kkt544w2NHj1aHTt2lFT9xGj+/Pnq06ePOnfurHbt2mnVqlUqKCjw6Pvu2bNHP/vZz9z2/exnP9O+ffvkdDpd+06Nz2KxyGazqbi4uFGfsWDBAr366qvKy8vzKLZT9e7dWyEhP/4VHR0d7fYkLjQ0VJ07d64V06n/7Vq1aqUBAwa4/ttt27ZN69atc/tvc9FFF0mS23+fAQMGNDluIFi18ncAAHzrggsukMViUV5ensaOHVvr+JdffqmOHTuqS5cuZ3yfmoQpNjZWe/fu1erVq/XPf/5Td9xxh/7whz9o/fr1qqqqUmhoqLZt26bQ0FC389u1a+f6c5s2bWoVOQ8cOFA9evTQW2+9pbS0NK1YsUIvv/yy6/iTTz6pp59+WgsXLtTFF1+stm3bavr06R4X/RqGUeuzjVO6C2u0bt261vdvbPH35ZdfrpEjR+r+++/X5MmT3Y6FhITU+rwTJ0406vObGlPN962qqlJqaqoWLFhQq43dbnf9uW3btg2+J9DSkBABLVznzp01fPhwLVq0SDNmzHCrIyoqKtIbb7yhW2+91S1J2Lx5s9t7bN682fUkQapOaK699lpde+21uvPOO3XRRRdp165d6tevn5xOp4qLi3XZZZd5HOuECRP0xhtv6LzzzlNISIiuvvpq17FPPvlEY8aM0cSJEyVV/7jv27dPvXr1crUJCwtze8pTl4SEBG3YsMFt38aNG9WzZ89aSdzZyMjIUL9+/dSzZ0+3/V27dlVRUZFbYubNuYM2b96syy+/XJJ08uRJbdu2TdOmTZMkJSUlafny5erevbtateKvf+BUdJkBJvDss8+qoqJCI0eO1Mcff6zCwkKtXLlSw4cP17nnnqv58+e7tf/000/1+OOP66uvvtJzzz2nv/71r7r77rslVY8Se/HFF7V7924dOHBAr7/+utq0aaO4uDj17NlTN998s2699Va98847ys/P19atW7VgwQJ98MEHDcZ58803Kzc3V/Pnz9f/+3//TxEREa5jF1xwgVavXq2NGzdqz549+s1vfqOioiK387t3767PPvtMBw8e1OHDh+t8ejJz5kytWbNGDz/8sL766iu9+uqrevbZZzVr1qymXNp69enTRzfffHOtEXpDhw7Vf/7zHz3++OPav3+/nnvuOX344Yde+9znnntOK1as0Jdffqk777xT3333nW6//XZJ1aMNS0pKdNNNN2nLli06cOCAVq1apdtvv73BRBJo6UiIABP4yU9+opycHPXo0UPjx49Xjx499Otf/1pXXHGFNm3apE6dOrm1nzlzprZt26Z+/frp4Ycf1pNPPqmRI0dKkjp06KClS5fqZz/7mfr06aM1a9YoKytLnTt3liS9/PLLuvXWWzVz5kxdeOGFuvbaa/XZZ5/VOeKrrjh/+tOfaufOna7RZTUeeOABJSUlaeTIkRo6dKhsNlutLsBZs2YpNDRUCQkJ6tq1a531RUlJSVq2bJneeustJSYm6sEHH9RDDz1Uq2vLGx5++OFa3WO9evXSokWL9Nxzz6lv377asmWLV5Oxxx57TAsWLFDfvn31ySef6B//+IerOzQmJkaffvqpnE6nRo4cqcTERN19992yWq1u9UqAGVmMujrPAQAATIR/EgAAANMjIQIAAKZHQgQAAEyPhAgAAJgeCREAADA9EiIAAGB6JEQAAMD0SIgAAIDpkRABAADTIyECAACmR0IEAABM7/8DS20kIkK+FhoAAAAASUVORK5CYII=\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-09T05:40:00.894792Z",
          "start_time": "2023-01-09T05:40:00.878207Z"
        },
        "trusted": false
      },
      "id": "8730570e",
      "cell_type": "code",
      "source": "# R squared is good enough but for betterment lets try for transformation \nmodel2=smf.ols('sal~np.log(exp)',data=df).fit()\nmodel2.params",
      "execution_count": 54,
      "outputs": [
        {
          "data": {
            "text/plain": "Intercept      14927.97177\nnp.log(exp)    40581.98796\ndtype: float64"
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-09T05:40:07.577742Z",
          "start_time": "2023-01-09T05:40:07.546200Z"
        },
        "trusted": false
      },
      "id": "934e0d9f",
      "cell_type": "code",
      "source": "model2.summary()",
      "execution_count": 55,
      "outputs": [
        {
          "data": {
            "text/html": "<table class=\"simpletable\">\n<caption>OLS Regression Results</caption>\n<tr>\n  <th>Dep. Variable:</th>           <td>sal</td>       <th>  R-squared:         </th> <td>   0.854</td>\n</tr>\n<tr>\n  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.849</td>\n</tr>\n<tr>\n  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   163.6</td>\n</tr>\n<tr>\n  <th>Date:</th>             <td>Mon, 09 Jan 2023</td> <th>  Prob (F-statistic):</th> <td>3.25e-13</td>\n</tr>\n<tr>\n  <th>Time:</th>                 <td>11:10:07</td>     <th>  Log-Likelihood:    </th> <td> -319.77</td>\n</tr>\n<tr>\n  <th>No. Observations:</th>      <td>    30</td>      <th>  AIC:               </th> <td>   643.5</td>\n</tr>\n<tr>\n  <th>Df Residuals:</th>          <td>    28</td>      <th>  BIC:               </th> <td>   646.3</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> 1.493e+04</td> <td> 5156.226</td> <td>    2.895</td> <td> 0.007</td> <td> 4365.921</td> <td> 2.55e+04</td>\n</tr>\n<tr>\n  <th>np.log(exp)</th> <td> 4.058e+04</td> <td> 3172.453</td> <td>   12.792</td> <td> 0.000</td> <td> 3.41e+04</td> <td> 4.71e+04</td>\n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n  <th>Omnibus:</th>       <td> 1.094</td> <th>  Durbin-Watson:     </th> <td>   0.512</td>\n</tr>\n<tr>\n  <th>Prob(Omnibus):</th> <td> 0.579</td> <th>  Jarque-Bera (JB):  </th> <td>   0.908</td>\n</tr>\n<tr>\n  <th>Skew:</th>          <td> 0.156</td> <th>  Prob(JB):          </th> <td>   0.635</td>\n</tr>\n<tr>\n  <th>Kurtosis:</th>      <td> 2.207</td> <th>  Cond. No.          </th> <td>    5.76</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:                    sal   R-squared:                       0.854\nModel:                            OLS   Adj. R-squared:                  0.849\nMethod:                 Least Squares   F-statistic:                     163.6\nDate:                Mon, 09 Jan 2023   Prob (F-statistic):           3.25e-13\nTime:                        11:10:07   Log-Likelihood:                -319.77\nNo. Observations:                  30   AIC:                             643.5\nDf Residuals:                      28   BIC:                             646.3\nDf Model:                           1                                         \nCovariance Type:            nonrobust                                         \n===============================================================================\n                  coef    std err          t      P>|t|      [0.025      0.975]\n-------------------------------------------------------------------------------\nIntercept    1.493e+04   5156.226      2.895      0.007    4365.921    2.55e+04\nnp.log(exp)  4.058e+04   3172.453     12.792      0.000    3.41e+04    4.71e+04\n==============================================================================\nOmnibus:                        1.094   Durbin-Watson:                   0.512\nProb(Omnibus):                  0.579   Jarque-Bera (JB):                0.908\nSkew:                           0.156   Prob(JB):                        0.635\nKurtosis:                       2.207   Cond. No.                         5.76\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n\"\"\""
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-09T05:43:12.670376Z",
          "start_time": "2023-01-09T05:43:12.658326Z"
        },
        "trusted": false
      },
      "id": "a43ba3fa",
      "cell_type": "code",
      "source": "pred2=model2.predict(pd.DataFrame(df['exp']))\n#pred2",
      "execution_count": 60,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-09T06:17:22.645279Z",
          "start_time": "2023-01-09T06:17:22.517714Z"
        },
        "trusted": false
      },
      "id": "f952d79a",
      "cell_type": "code",
      "source": "plt.scatter(x=df['exp'],y=df['sal'],color='red')\nplt.plot(df['exp'],pred2,color='black')\nplt.xlabel('exp')\nplt.ylabel('sal')",
      "execution_count": 65,
      "outputs": [
        {
          "data": {
            "text/plain": "Text(0, 0.5, 'sal')"
          },
          "execution_count": 65,
          "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": "2023-01-09T06:19:10.077451Z",
          "start_time": "2023-01-09T06:19:10.056144Z"
        },
        "trusted": false
      },
      "id": "a5c84e44",
      "cell_type": "code",
      "source": "# we cannot go forward as R squared value has reduced\nmodel3=smf.ols('np.log(sal)~exp', data=df).fit()\nmodel3.params",
      "execution_count": 67,
      "outputs": [
        {
          "data": {
            "text/plain": "Intercept    10.507402\nexp           0.125453\ndtype: float64"
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-09T06:19:21.618995Z",
          "start_time": "2023-01-09T06:19:21.594996Z"
        },
        "trusted": false
      },
      "id": "ec902c4a",
      "cell_type": "code",
      "source": "model3.summary()",
      "execution_count": 68,
      "outputs": [
        {
          "data": {
            "text/html": "<table class=\"simpletable\">\n<caption>OLS Regression Results</caption>\n<tr>\n  <th>Dep. Variable:</th>       <td>np.log(sal)</td>   <th>  R-squared:         </th> <td>   0.932</td>\n</tr>\n<tr>\n  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.930</td>\n</tr>\n<tr>\n  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   383.6</td>\n</tr>\n<tr>\n  <th>Date:</th>             <td>Mon, 09 Jan 2023</td> <th>  Prob (F-statistic):</th> <td>7.03e-18</td>\n</tr>\n<tr>\n  <th>Time:</th>                 <td>11:49:21</td>     <th>  Log-Likelihood:    </th> <td>  28.183</td>\n</tr>\n<tr>\n  <th>No. Observations:</th>      <td>    30</td>      <th>  AIC:               </th> <td>  -52.37</td>\n</tr>\n<tr>\n  <th>Df Residuals:</th>          <td>    28</td>      <th>  BIC:               </th> <td>  -49.56</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>   10.5074</td> <td>    0.038</td> <td>  273.327</td> <td> 0.000</td> <td>   10.429</td> <td>   10.586</td>\n</tr>\n<tr>\n  <th>exp</th>       <td>    0.1255</td> <td>    0.006</td> <td>   19.585</td> <td> 0.000</td> <td>    0.112</td> <td>    0.139</td>\n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n  <th>Omnibus:</th>       <td> 0.826</td> <th>  Durbin-Watson:     </th> <td>   1.438</td>\n</tr>\n<tr>\n  <th>Prob(Omnibus):</th> <td> 0.661</td> <th>  Jarque-Bera (JB):  </th> <td>   0.812</td>\n</tr>\n<tr>\n  <th>Skew:</th>          <td> 0.187</td> <th>  Prob(JB):          </th> <td>   0.666</td>\n</tr>\n<tr>\n  <th>Kurtosis:</th>      <td> 2.286</td> <th>  Cond. No.          </th> <td>    13.2</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(sal)   R-squared:                       0.932\nModel:                            OLS   Adj. R-squared:                  0.930\nMethod:                 Least Squares   F-statistic:                     383.6\nDate:                Mon, 09 Jan 2023   Prob (F-statistic):           7.03e-18\nTime:                        11:49:21   Log-Likelihood:                 28.183\nNo. Observations:                  30   AIC:                            -52.37\nDf Residuals:                      28   BIC:                            -49.56\nDf Model:                           1                                         \nCovariance Type:            nonrobust                                         \n==============================================================================\n                 coef    std err          t      P>|t|      [0.025      0.975]\n------------------------------------------------------------------------------\nIntercept     10.5074      0.038    273.327      0.000      10.429      10.586\nexp            0.1255      0.006     19.585      0.000       0.112       0.139\n==============================================================================\nOmnibus:                        0.826   Durbin-Watson:                   1.438\nProb(Omnibus):                  0.661   Jarque-Bera (JB):                0.812\nSkew:                           0.187   Prob(JB):                        0.666\nKurtosis:                       2.286   Cond. No.                         13.2\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n\"\"\""
          },
          "execution_count": 68,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-09T06:23:08.042698Z",
          "start_time": "2023-01-09T06:23:08.016444Z"
        },
        "trusted": false
      },
      "id": "417b9ccb",
      "cell_type": "code",
      "source": "pred_log=model3.predict(pd.DataFrame(df['exp']))\npred3=np.exp(pred_log)\n#pred3",
      "execution_count": 78,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-09T06:23:10.051006Z",
          "start_time": "2023-01-09T06:23:09.922246Z"
        },
        "trusted": false
      },
      "id": "f6f4969b",
      "cell_type": "code",
      "source": "plt.scatter(x=df['exp'],y=df['sal'],color='red')\nplt.plot(df['exp'],pred3,color='black')\nplt.xlabel('exp')\nplt.ylabel('sal')",
      "execution_count": 79,
      "outputs": [
        {
          "data": {
            "text/plain": "Text(0, 0.5, 'sal')"
          },
          "execution_count": 79,
          "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": {
    "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_Regression2.ipynb",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}