startups.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.api as sm\n",
    "import pylab\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IMPORTING DATA SET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv(\"C:\\\\Users\\\\kumar\\\\Python Assign\\\\MLR\\\\Dataset\\\\50_startups.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>California</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Florida</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>New York</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Florida</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>131876.90</td>\n",
       "      <td>99814.71</td>\n",
       "      <td>362861.36</td>\n",
       "      <td>New York</td>\n",
       "      <td>156991.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>134615.46</td>\n",
       "      <td>147198.87</td>\n",
       "      <td>127716.82</td>\n",
       "      <td>California</td>\n",
       "      <td>156122.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>130298.13</td>\n",
       "      <td>145530.06</td>\n",
       "      <td>323876.68</td>\n",
       "      <td>Florida</td>\n",
       "      <td>155752.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>120542.52</td>\n",
       "      <td>148718.95</td>\n",
       "      <td>311613.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>152211.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>123334.88</td>\n",
       "      <td>108679.17</td>\n",
       "      <td>304981.62</td>\n",
       "      <td>California</td>\n",
       "      <td>149759.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>101913.08</td>\n",
       "      <td>110594.11</td>\n",
       "      <td>229160.95</td>\n",
       "      <td>Florida</td>\n",
       "      <td>146121.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100671.96</td>\n",
       "      <td>91790.61</td>\n",
       "      <td>249744.55</td>\n",
       "      <td>California</td>\n",
       "      <td>144259.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>93863.75</td>\n",
       "      <td>127320.38</td>\n",
       "      <td>249839.44</td>\n",
       "      <td>Florida</td>\n",
       "      <td>141585.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>91992.39</td>\n",
       "      <td>135495.07</td>\n",
       "      <td>252664.93</td>\n",
       "      <td>California</td>\n",
       "      <td>134307.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>119943.24</td>\n",
       "      <td>156547.42</td>\n",
       "      <td>256512.92</td>\n",
       "      <td>Florida</td>\n",
       "      <td>132602.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>114523.61</td>\n",
       "      <td>122616.84</td>\n",
       "      <td>261776.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>129917.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>78013.11</td>\n",
       "      <td>121597.55</td>\n",
       "      <td>264346.06</td>\n",
       "      <td>California</td>\n",
       "      <td>126992.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>94657.16</td>\n",
       "      <td>145077.58</td>\n",
       "      <td>282574.31</td>\n",
       "      <td>New York</td>\n",
       "      <td>125370.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>91749.16</td>\n",
       "      <td>114175.79</td>\n",
       "      <td>294919.57</td>\n",
       "      <td>Florida</td>\n",
       "      <td>124266.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>86419.70</td>\n",
       "      <td>153514.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>122776.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>76253.86</td>\n",
       "      <td>113867.30</td>\n",
       "      <td>298664.47</td>\n",
       "      <td>California</td>\n",
       "      <td>118474.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>78389.47</td>\n",
       "      <td>153773.43</td>\n",
       "      <td>299737.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>111313.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>73994.56</td>\n",
       "      <td>122782.75</td>\n",
       "      <td>303319.26</td>\n",
       "      <td>Florida</td>\n",
       "      <td>110352.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>67532.53</td>\n",
       "      <td>105751.03</td>\n",
       "      <td>304768.73</td>\n",
       "      <td>Florida</td>\n",
       "      <td>108733.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>77044.01</td>\n",
       "      <td>99281.34</td>\n",
       "      <td>140574.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>108552.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>64664.71</td>\n",
       "      <td>139553.16</td>\n",
       "      <td>137962.62</td>\n",
       "      <td>California</td>\n",
       "      <td>107404.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>75328.87</td>\n",
       "      <td>144135.98</td>\n",
       "      <td>134050.07</td>\n",
       "      <td>Florida</td>\n",
       "      <td>105733.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>72107.60</td>\n",
       "      <td>127864.55</td>\n",
       "      <td>353183.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>105008.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>66051.52</td>\n",
       "      <td>182645.56</td>\n",
       "      <td>118148.20</td>\n",
       "      <td>Florida</td>\n",
       "      <td>103282.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>65605.48</td>\n",
       "      <td>153032.06</td>\n",
       "      <td>107138.38</td>\n",
       "      <td>New York</td>\n",
       "      <td>101004.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>61994.48</td>\n",
       "      <td>115641.28</td>\n",
       "      <td>91131.24</td>\n",
       "      <td>Florida</td>\n",
       "      <td>99937.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>61136.38</td>\n",
       "      <td>152701.92</td>\n",
       "      <td>88218.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>97483.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>63408.86</td>\n",
       "      <td>129219.61</td>\n",
       "      <td>46085.25</td>\n",
       "      <td>California</td>\n",
       "      <td>97427.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>55493.95</td>\n",
       "      <td>103057.49</td>\n",
       "      <td>214634.81</td>\n",
       "      <td>Florida</td>\n",
       "      <td>96778.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>46426.07</td>\n",
       "      <td>157693.92</td>\n",
       "      <td>210797.67</td>\n",
       "      <td>California</td>\n",
       "      <td>96712.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>46014.02</td>\n",
       "      <td>85047.44</td>\n",
       "      <td>205517.64</td>\n",
       "      <td>New York</td>\n",
       "      <td>96479.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>28663.76</td>\n",
       "      <td>127056.21</td>\n",
       "      <td>201126.82</td>\n",
       "      <td>Florida</td>\n",
       "      <td>90708.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>44069.95</td>\n",
       "      <td>51283.14</td>\n",
       "      <td>197029.42</td>\n",
       "      <td>California</td>\n",
       "      <td>89949.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>20229.59</td>\n",
       "      <td>65947.93</td>\n",
       "      <td>185265.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>81229.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>38558.51</td>\n",
       "      <td>82982.09</td>\n",
       "      <td>174999.30</td>\n",
       "      <td>California</td>\n",
       "      <td>81005.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>28754.33</td>\n",
       "      <td>118546.05</td>\n",
       "      <td>172795.67</td>\n",
       "      <td>California</td>\n",
       "      <td>78239.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>27892.92</td>\n",
       "      <td>84710.77</td>\n",
       "      <td>164470.71</td>\n",
       "      <td>Florida</td>\n",
       "      <td>77798.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>23640.93</td>\n",
       "      <td>96189.63</td>\n",
       "      <td>148001.11</td>\n",
       "      <td>California</td>\n",
       "      <td>71498.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>15505.73</td>\n",
       "      <td>127382.30</td>\n",
       "      <td>35534.17</td>\n",
       "      <td>New York</td>\n",
       "      <td>69758.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>22177.74</td>\n",
       "      <td>154806.14</td>\n",
       "      <td>28334.72</td>\n",
       "      <td>California</td>\n",
       "      <td>65200.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1000.23</td>\n",
       "      <td>124153.04</td>\n",
       "      <td>1903.93</td>\n",
       "      <td>New York</td>\n",
       "      <td>64926.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1315.46</td>\n",
       "      <td>115816.21</td>\n",
       "      <td>297114.46</td>\n",
       "      <td>Florida</td>\n",
       "      <td>49490.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.00</td>\n",
       "      <td>135426.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>California</td>\n",
       "      <td>42559.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>542.05</td>\n",
       "      <td>51743.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>35673.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.00</td>\n",
       "      <td>116983.80</td>\n",
       "      <td>45173.06</td>\n",
       "      <td>California</td>\n",
       "      <td>14681.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    R&D Spend  Administration  Marketing Spend       State     Profit\n",
       "0   165349.20       136897.80        471784.10    New York  192261.83\n",
       "1   162597.70       151377.59        443898.53  California  191792.06\n",
       "2   153441.51       101145.55        407934.54     Florida  191050.39\n",
       "3   144372.41       118671.85        383199.62    New York  182901.99\n",
       "4   142107.34        91391.77        366168.42     Florida  166187.94\n",
       "5   131876.90        99814.71        362861.36    New York  156991.12\n",
       "6   134615.46       147198.87        127716.82  California  156122.51\n",
       "7   130298.13       145530.06        323876.68     Florida  155752.60\n",
       "8   120542.52       148718.95        311613.29    New York  152211.77\n",
       "9   123334.88       108679.17        304981.62  California  149759.96\n",
       "10  101913.08       110594.11        229160.95     Florida  146121.95\n",
       "11  100671.96        91790.61        249744.55  California  144259.40\n",
       "12   93863.75       127320.38        249839.44     Florida  141585.52\n",
       "13   91992.39       135495.07        252664.93  California  134307.35\n",
       "14  119943.24       156547.42        256512.92     Florida  132602.65\n",
       "15  114523.61       122616.84        261776.23    New York  129917.04\n",
       "16   78013.11       121597.55        264346.06  California  126992.93\n",
       "17   94657.16       145077.58        282574.31    New York  125370.37\n",
       "18   91749.16       114175.79        294919.57     Florida  124266.90\n",
       "19   86419.70       153514.11             0.00    New York  122776.86\n",
       "20   76253.86       113867.30        298664.47  California  118474.03\n",
       "21   78389.47       153773.43        299737.29    New York  111313.02\n",
       "22   73994.56       122782.75        303319.26     Florida  110352.25\n",
       "23   67532.53       105751.03        304768.73     Florida  108733.99\n",
       "24   77044.01        99281.34        140574.81    New York  108552.04\n",
       "25   64664.71       139553.16        137962.62  California  107404.34\n",
       "26   75328.87       144135.98        134050.07     Florida  105733.54\n",
       "27   72107.60       127864.55        353183.81    New York  105008.31\n",
       "28   66051.52       182645.56        118148.20     Florida  103282.38\n",
       "29   65605.48       153032.06        107138.38    New York  101004.64\n",
       "30   61994.48       115641.28         91131.24     Florida   99937.59\n",
       "31   61136.38       152701.92         88218.23    New York   97483.56\n",
       "32   63408.86       129219.61         46085.25  California   97427.84\n",
       "33   55493.95       103057.49        214634.81     Florida   96778.92\n",
       "34   46426.07       157693.92        210797.67  California   96712.80\n",
       "35   46014.02        85047.44        205517.64    New York   96479.51\n",
       "36   28663.76       127056.21        201126.82     Florida   90708.19\n",
       "37   44069.95        51283.14        197029.42  California   89949.14\n",
       "38   20229.59        65947.93        185265.10    New York   81229.06\n",
       "39   38558.51        82982.09        174999.30  California   81005.76\n",
       "40   28754.33       118546.05        172795.67  California   78239.91\n",
       "41   27892.92        84710.77        164470.71     Florida   77798.83\n",
       "42   23640.93        96189.63        148001.11  California   71498.49\n",
       "43   15505.73       127382.30         35534.17    New York   69758.98\n",
       "44   22177.74       154806.14         28334.72  California   65200.33\n",
       "45    1000.23       124153.04          1903.93    New York   64926.08\n",
       "46    1315.46       115816.21        297114.46     Florida   49490.75\n",
       "47       0.00       135426.92             0.00  California   42559.73\n",
       "48     542.05        51743.15             0.00    New York   35673.41\n",
       "49       0.00       116983.80         45173.06  California   14681.40"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['R&D Spend', 'Administration', 'Marketing Spend', 'State', 'Profit'], dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ASSIGNING COLUMNS TO LISTS AND ROW DATA TO LIST VARIABLES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "RDSpend=dataset['R&D Spend']\n",
    "Administration=dataset['Administration']\n",
    "MarketingSpend=dataset['Marketing Spend']\n",
    "State=dataset['State']\n",
    "Profit=dataset['Profit']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EXPLORATORY DATA ANALYSIS\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1ST BUSINESS MOMENT DECISION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "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>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>73721.615600</td>\n",
       "      <td>121344.639600</td>\n",
       "      <td>211025.097800</td>\n",
       "      <td>112012.639200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>45902.256482</td>\n",
       "      <td>28017.802755</td>\n",
       "      <td>122290.310726</td>\n",
       "      <td>40306.180338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>51283.140000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14681.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>39936.370000</td>\n",
       "      <td>103730.875000</td>\n",
       "      <td>129300.132500</td>\n",
       "      <td>90138.902500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>73051.080000</td>\n",
       "      <td>122699.795000</td>\n",
       "      <td>212716.240000</td>\n",
       "      <td>107978.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>101602.800000</td>\n",
       "      <td>144842.180000</td>\n",
       "      <td>299469.085000</td>\n",
       "      <td>139765.977500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>165349.200000</td>\n",
       "      <td>182645.560000</td>\n",
       "      <td>471784.100000</td>\n",
       "      <td>192261.830000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           R&D Spend  Administration  Marketing Spend         Profit\n",
       "count      50.000000       50.000000        50.000000      50.000000\n",
       "mean    73721.615600   121344.639600    211025.097800  112012.639200\n",
       "std     45902.256482    28017.802755    122290.310726   40306.180338\n",
       "min         0.000000    51283.140000         0.000000   14681.400000\n",
       "25%     39936.370000   103730.875000    129300.132500   90138.902500\n",
       "50%     73051.080000   122699.795000    212716.240000  107978.190000\n",
       "75%    101602.800000   144842.180000    299469.085000  139765.977500\n",
       "max    165349.200000   182645.560000    471784.100000  192261.830000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2ND BUSINESS MOMENT DECISION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2-->Variance,Standard Deviation and Range:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variances:\n",
      "-------------------------------\n",
      "R&D Spend          2.064877e+09\n",
      "Administration     7.692973e+08\n",
      "Marketing Spend    1.465582e+10\n",
      "Profit             1.592096e+09\n",
      "dtype: float64\n",
      "\n",
      "Standard Deviation:\n",
      "-------------------------------\n",
      "R&D Spend           45440.915563\n",
      "Administration      27736.209651\n",
      "Marketing Spend    121061.231183\n",
      "Profit              39901.082817\n",
      "dtype: float64\n",
      "\n",
      "Range\n",
      "-------------------------------\n",
      "R&D Spend:    165349.2\n",
      "Administration:    131362.41999999998\n",
      "Marketing Spend:    471784.1\n",
      "Profit:    177580.43\n"
     ]
    }
   ],
   "source": [
    "print(\"Variances:\")\n",
    "print(\"-------------------------------\")\n",
    "print(np.var(dataset))\n",
    "print(\"\\nStandard Deviation:\")\n",
    "print(\"-------------------------------\")\n",
    "print(np.std(dataset))\n",
    "print(\"\\nRange\")\n",
    "print(\"-------------------------------\")\n",
    "for c in dataset.columns:\n",
    "    if c == \"State\":\n",
    "        continue\n",
    "    else:\n",
    "        print(c+\":    \"+str(max(dataset[c])-min(dataset[c])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3RD BUSINESS MOMENT DECISION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    " # 3 -->Skewnes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R&D Spend:    0.15904052321503395\n",
      "Administration:    -0.4742300698920047\n",
      "Marketing Spend:    -0.04506631617666136\n",
      "Profit:    0.02258638356958943\n"
     ]
    }
   ],
   "source": [
    "for c in dataset.columns:\n",
    "    if c == \"State\":\n",
    "        continue\n",
    "    else:\n",
    "        print(c+\":    \"+ str(stats.skew(dataset[c])))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4TH BUSINESS MOMENT DECISION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4 -->Kurtosis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R&D Spend:    -0.8050676431519039\n",
      "Administration:    0.08553840791984113\n",
      "Marketing Spend:    -0.7240327111087308\n",
      "Profit:    -0.17529637679752152\n"
     ]
    }
   ],
   "source": [
    "for c in dataset.columns:\n",
    "    if c == \"State\":\n",
    "        continue\n",
    "    else:\n",
    "        print(c+\":    \"+ str(stats.kurtosis(dataset[c])))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### GRAPHS "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distributions based on the variable \"State\" and Independent Variables "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 443.875x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 443.875x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 443.875x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for c in dataset.columns:\n",
    "    if c == \"State\" or c==\"Profit\":\n",
    "        continue\n",
    "    else:\n",
    "        sns.FacetGrid(dataset,hue=\"State\",height=5)        .map(sns.distplot,c)        .add_legend()\n",
    "        plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Box Plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SpAlZyu2pVQx/8z8NeBnwQoa3kuaqg10Os+1I6/ONZXOS6STTs7Oziw1dkjSmpdye+kXgW1U1W1U/BD4HvAk4sd2uAlgN7GvLM8AagLb9RcD+0fqcPoerH6KqtlbVoKoGU1NTSzglSdJClhIaDwLrk7ygPZt4G/AAcDtwfmuzCbi5Le9o67TtX6yqavUL2uyq04B1wJeAe4B1bTbWcQwflu9YwnglSUu0cvEm86uqu5PcBHwZeBL4CrAV+DxwQ5KPtto1rcs1wGeS7GV4hXFB28/9SW5kGDhPAu+uqqcAkrwH2MlwZta2qrp/3PFKkpYuw1/2l4/BYFDT09OTHoZ0iCQst/9vWj6S7K6qwWLt/ES4JKmboSFJ6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqduSQiPJiUluSvK/kvxlkjcmOSnJriR72vuq1jZJrkqyN8nXkpwxsp9Nrf2eJJtG6mcmubf1uSpJljJeSdLSLPVK4xPAF6rqp4HXAn8JXArcVlXrgNvaOsC5wLr22gxcDZDkJGAL8AbgLGDLwaBpbTaP9NuwxPFKkpZg7NBIcgLw88A1AFX1RFU9CmwEtrdm24Hz2vJG4Loaugs4MclLgXOAXVW1v6oOALuADW3bCVV1Z1UVcN3IviRJE7CUK41/AswC/zXJV5J8OskLgZdU1cMA7f2U1v5U4KGR/jOttlB9Zp66JGlClhIaK4EzgKur6p8D/5cf34qaz3zPI2qM+qE7TjYnmU4yPTs7u/CoJUljW0pozAAzVXV3W7+JYYh8p91aor0/MtJ+zUj/1cC+Reqr56kfoqq2VtWgqgZTU1NLOCVJ0kLGDo2q+lvgoSSvaqW3AQ8AO4CDM6A2ATe35R3ARW0W1XrgsXb7aidwdpJV7QH42cDOtu3xJOvbrKmLRvYlSZqAlUvs/1vAZ5McB3wTeBfDILoxycXAg8A7WttbgLcDe4HvtbZU1f4kHwHuae0uq6r9bfkS4FrgeODW9pIkTUiGE5OWj8FgUNPT05MehnSIJCy3/29aPpLsrqrBYu38RLgkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG5L/US49Jw1zneCjdPHDwTqmcTQkMbkD3M9F3l7SpLUzdCQJHUzNCRJ3QwNSVI3Q0OS1M3QkCR1MzQkSd0MDUlSt2X3da9JZoG/nvQ4pHmcDHx30oOQDuMVVTW1WKNlFxrSM1WS6Z7vYJaeybw9JUnqZmhIkroZGtLTZ+ukByAtlc80JEndvNKQJHUzNKRjLMm2JI8kuW/SY5GWytCQjr1rgQ2THoR0NBga0jFWVXcA+yc9DuloMDQkSd0MDUlSN0NDktTN0JAkdTM0pGMsyfXAncCrkswkuXjSY5LG5SfCJUndvNKQJHUzNCRJ3QwNSVI3Q0OS1M3QkCR1MzS07CX51SSV5KcPs/3aJOcfwf5eluSmjna3JDlxge3vS/KC3uOO9Pv1JC8bWf90ktOPdD/SOAwNPRdcCPw5cMHR2FlV7auqRUOmqt5eVY8u0OR9wLyhkWTFAv1+Hfj/oVFV/6aqHlhsPNLRYGhoWUvyD4F/AVxMC40M/V6SB5J8HjhlpP23k/ynJHcmmU5yRpKdSb6R5Ddbm7UHvxuj/db/uSRfSLInycfm7OvkJC9M8vkkX01yX5J3Jnkvwx/8tye5vbX/uySXJbkbeGOSDyW5p/XZ2sZ9PjAAPpvkL5Icn+RPkwzaPi5Mcm/rc8XIWP4uyX9sY7gryUuO6T+8li1DQ8vdecAXqurrwP4kZwC/CrwK+BngN4A3zenzUFW9EfifDL8L43xgPXDZYY7xOuCdbX/vTLJmzvYNwL6qem1VvaaN5ypgH/CWqnpLa/dC4L6qekNV/Tnwe1X1+tbneOCXq+omYBr4tap6XVX9/cGDtFtWVwBvbWN6fZLzRvZ9V1W9Frijnbd0xAwNLXcXAje05Rva+s8D11fVU1W1D/jinD472vu9wN1V9XhVzQLfP8wzituq6rGq+j7wAPCKOdvvBX4xyRVJfq6qHjvMWJ8C/nhk/S1J7k5yL8MgePUi5/p64E+raraqngQ+284V4Angf7Tl3cDaRfYlzWvlpAcgHStJXszwh+1rkhSwAijgv7X3w/lBe//RyPLB9fn+z4y2eWpum6r6epIzgbcD/znJn1TVfFct36+qp9rYnw98ChhU1UNJPgw8f4ExA2SBbT+sH//NoEPGKPXySkPL2fnAdVX1iqpaW1VrgG8x/Ba9C5KsSPJS4C0L7mWJ2m2j71XVHwAfB85omx4H/tFhuh0MiO+25zKjD94P1+9u4Bfac5QVDK+q/myp45dG+duGlrMLgcvn1P4Y+GfAHoa3jb7Osf/B+jPA7yb5EfBD4JJW3wrcmuThkecaAFTVo0l+v43x28A9I5uvBf5Lkr8H3jjS5+EkHwRuZ3jVcUtV3XxsTknPVf6VW0lSN29PSZK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqZuhIUnq9v8AZvm9OIKqWOMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for c in dataset.columns:\n",
    "    if c == \"State\":\n",
    "        continue\n",
    "    else:\n",
    "        plt.boxplot(dataset[c])\n",
    "        plt.xlabel(c)\n",
    "        plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CHECKING ASSUMPTIONS "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LINEARITY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#PLOTTING PAIRPLOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x215226c9278>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(start.iloc[:,:])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OBSERVATIONS:\n",
    "#####  1. ALL INDEPENDENT VARIABLES ARE IN LINEAR RELATIONSHIP WITH DEPENDENT VARIABLE.\n",
    "#####   2. ALL ARE NOT UNIFORMLY DISTRIBUTED AND MAY CONTAIN SOME OUTLIERS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "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>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.241955</td>\n",
       "      <td>0.724248</td>\n",
       "      <td>0.972900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Administration</th>\n",
       "      <td>0.241955</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.032154</td>\n",
       "      <td>0.200717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marketing Spend</th>\n",
       "      <td>0.724248</td>\n",
       "      <td>-0.032154</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.747766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Profit</th>\n",
       "      <td>0.972900</td>\n",
       "      <td>0.200717</td>\n",
       "      <td>0.747766</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 R&D Spend  Administration  Marketing Spend    Profit\n",
       "R&D Spend         1.000000        0.241955         0.724248  0.972900\n",
       "Administration    0.241955        1.000000        -0.032154  0.200717\n",
       "Marketing Spend   0.724248       -0.032154         1.000000  0.747766\n",
       "Profit            0.972900        0.200717         0.747766  1.000000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-->ADMINISTRATION HAS LOW CORRELATION WITH PROFIT IT MEANS IT SIGNIFICANTLY DOES NOT INFLUENCE IT.                              \n",
    "--> MARKETING SPEND AND RD SPEND ARE CORRELATED THAT MEANS MULTI COLLINEARITY EXISTS."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### BUILDING MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept            50125.343832\n",
       "State[T.Florida]       198.788793\n",
       "State[T.New York]      -41.887019\n",
       "RDSpend                  0.806023\n",
       "Administration          -0.027004\n",
       "MarketingSpend           0.026980\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1 = smf.ols('Profit~RDSpend+Administration+State+MarketingSpend',data=start).fit()\n",
    "model1.params\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Profit</td>      <th>  R-squared:         </th> <td>   0.951</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.945</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   169.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 04 Jun 2019</td> <th>  Prob (F-statistic):</th> <td>1.34e-27</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:07:27</td>     <th>  Log-Likelihood:    </th> <td> -525.38</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    50</td>      <th>  AIC:               </th> <td>   1063.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    44</td>      <th>  BIC:               </th> <td>   1074.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</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> 5.013e+04</td> <td> 6884.820</td> <td>    7.281</td> <td> 0.000</td> <td> 3.62e+04</td> <td>  6.4e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>State[T.Florida]</th>  <td>  198.7888</td> <td> 3371.007</td> <td>    0.059</td> <td> 0.953</td> <td>-6595.030</td> <td> 6992.607</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>State[T.New York]</th> <td>  -41.8870</td> <td> 3256.039</td> <td>   -0.013</td> <td> 0.990</td> <td>-6604.003</td> <td> 6520.229</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RDSpend</th>           <td>    0.8060</td> <td>    0.046</td> <td>   17.369</td> <td> 0.000</td> <td>    0.712</td> <td>    0.900</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Administration</th>    <td>   -0.0270</td> <td>    0.052</td> <td>   -0.517</td> <td> 0.608</td> <td>   -0.132</td> <td>    0.078</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MarketingSpend</th>    <td>    0.0270</td> <td>    0.017</td> <td>    1.574</td> <td> 0.123</td> <td>   -0.008</td> <td>    0.062</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>14.782</td> <th>  Durbin-Watson:     </th> <td>   1.283</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.001</td> <th>  Jarque-Bera (JB):  </th> <td>  21.266</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.948</td> <th>  Prob(JB):          </th> <td>2.41e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.572</td> <th>  Cond. No.          </th> <td>1.45e+06</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.45e+06. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 Profit   R-squared:                       0.951\n",
       "Model:                            OLS   Adj. R-squared:                  0.945\n",
       "Method:                 Least Squares   F-statistic:                     169.9\n",
       "Date:                Tue, 04 Jun 2019   Prob (F-statistic):           1.34e-27\n",
       "Time:                        13:07:27   Log-Likelihood:                -525.38\n",
       "No. Observations:                  50   AIC:                             1063.\n",
       "Df Residuals:                      44   BIC:                             1074.\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "Intercept          5.013e+04   6884.820      7.281      0.000    3.62e+04     6.4e+04\n",
       "State[T.Florida]    198.7888   3371.007      0.059      0.953   -6595.030    6992.607\n",
       "State[T.New York]   -41.8870   3256.039     -0.013      0.990   -6604.003    6520.229\n",
       "RDSpend               0.8060      0.046     17.369      0.000       0.712       0.900\n",
       "Administration       -0.0270      0.052     -0.517      0.608      -0.132       0.078\n",
       "MarketingSpend        0.0270      0.017      1.574      0.123      -0.008       0.062\n",
       "==============================================================================\n",
       "Omnibus:                       14.782   Durbin-Watson:                   1.283\n",
       "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               21.266\n",
       "Skew:                          -0.948   Prob(JB):                     2.41e-05\n",
       "Kurtosis:                       5.572   Cond. No.                     1.45e+06\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.45e+06. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OBSERVATIONS:\n",
    "##### 1. P VALUE FOR ADMINISTRATION>0.05 IT IS INSIGNIFICNAT FOR THE MODEL. BUT WE WILL NOT REMOVE HERE AND REMOVE THE VARIABLE IF ITS VIF VALUE SARE HIGH.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>31589.493310</td>\n",
       "      <td>68661.194354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State[T.Florida]</th>\n",
       "      <td>-8876.900421</td>\n",
       "      <td>9274.478006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State[T.New York]</th>\n",
       "      <td>-8808.050396</td>\n",
       "      <td>8724.276357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RDSpend</th>\n",
       "      <td>0.681083</td>\n",
       "      <td>0.930964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Administration</th>\n",
       "      <td>-0.167626</td>\n",
       "      <td>0.113618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MarketingSpend</th>\n",
       "      <td>-0.019172</td>\n",
       "      <td>0.073131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              0             1\n",
       "Intercept          31589.493310  68661.194354\n",
       "State[T.Florida]   -8876.900421   9274.478006\n",
       "State[T.New York]  -8808.050396   8724.276357\n",
       "RDSpend                0.681083      0.930964\n",
       "Administration        -0.167626      0.113618\n",
       "MarketingSpend        -0.019172      0.073131"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.conf_int(0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VARIABLE REGRSSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Profit</td>      <th>  R-squared:         </th> <td>   0.040</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   2.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 04 Jun 2019</td> <th>  Prob (F-statistic):</th>  <td> 0.162</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:18:18</td>     <th>  Log-Likelihood:    </th> <td> -599.63</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    50</td>      <th>  AIC:               </th> <td>   1203.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    48</td>      <th>  BIC:               </th> <td>   1207.</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> 7.697e+04</td> <td> 2.53e+04</td> <td>    3.040</td> <td> 0.004</td> <td> 2.61e+04</td> <td> 1.28e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Administration</th> <td>    0.2887</td> <td>    0.203</td> <td>    1.419</td> <td> 0.162</td> <td>   -0.120</td> <td>    0.698</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.126</td> <th>  Durbin-Watson:     </th> <td>   0.099</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.939</td> <th>  Jarque-Bera (JB):  </th> <td>   0.110</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.093</td> <th>  Prob(JB):          </th> <td>   0.947</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.866</td> <th>  Cond. No.          </th> <td>5.59e+05</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 5.59e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 Profit   R-squared:                       0.040\n",
       "Model:                            OLS   Adj. R-squared:                  0.020\n",
       "Method:                 Least Squares   F-statistic:                     2.015\n",
       "Date:                Tue, 04 Jun 2019   Prob (F-statistic):              0.162\n",
       "Time:                        13:18:18   Log-Likelihood:                -599.63\n",
       "No. Observations:                  50   AIC:                             1203.\n",
       "Df Residuals:                      48   BIC:                             1207.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==================================================================================\n",
       "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "Intercept       7.697e+04   2.53e+04      3.040      0.004    2.61e+04    1.28e+05\n",
       "Administration     0.2887      0.203      1.419      0.162      -0.120       0.698\n",
       "==============================================================================\n",
       "Omnibus:                        0.126   Durbin-Watson:                   0.099\n",
       "Prob(Omnibus):                  0.939   Jarque-Bera (JB):                0.110\n",
       "Skew:                           0.093   Prob(JB):                        0.947\n",
       "Kurtosis:                       2.866   Cond. No.                     5.59e+05\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 5.59e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m1_a =smf.ols('Profit~Administration',data=dataset).fit()\n",
    "m1_a.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Profit</td>      <th>  R-squared:         </th> <td>   0.947</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.945</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   849.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 04 Jun 2019</td> <th>  Prob (F-statistic):</th> <td>3.50e-32</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:18:56</td>     <th>  Log-Likelihood:    </th> <td> -527.44</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    50</td>      <th>  AIC:               </th> <td>   1059.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    48</td>      <th>  BIC:               </th> <td>   1063.</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> 4.903e+04</td> <td> 2537.897</td> <td>   19.320</td> <td> 0.000</td> <td> 4.39e+04</td> <td> 5.41e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RDSpend</th>   <td>    0.8543</td> <td>    0.029</td> <td>   29.151</td> <td> 0.000</td> <td>    0.795</td> <td>    0.913</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>13.727</td> <th>  Durbin-Watson:     </th> <td>   1.116</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.001</td> <th>  Jarque-Bera (JB):  </th> <td>  18.536</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.911</td> <th>  Prob(JB):          </th> <td>9.44e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.361</td> <th>  Cond. No.          </th> <td>1.65e+05</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.65e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 Profit   R-squared:                       0.947\n",
       "Model:                            OLS   Adj. R-squared:                  0.945\n",
       "Method:                 Least Squares   F-statistic:                     849.8\n",
       "Date:                Tue, 04 Jun 2019   Prob (F-statistic):           3.50e-32\n",
       "Time:                        13:18:56   Log-Likelihood:                -527.44\n",
       "No. Observations:                  50   AIC:                             1059.\n",
       "Df Residuals:                      48   BIC:                             1063.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept   4.903e+04   2537.897     19.320      0.000    4.39e+04    5.41e+04\n",
       "RDSpend        0.8543      0.029     29.151      0.000       0.795       0.913\n",
       "==============================================================================\n",
       "Omnibus:                       13.727   Durbin-Watson:                   1.116\n",
       "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               18.536\n",
       "Skew:                          -0.911   Prob(JB):                     9.44e-05\n",
       "Kurtosis:                       5.361   Cond. No.                     1.65e+05\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.65e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m1_r =smf.ols('Profit~RDSpend',data=dataset).fit()\n",
    "m1_r.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Profit</td>      <th>  R-squared:         </th> <td>   0.559</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.550</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   60.88</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 04 Jun 2019</td> <th>  Prob (F-statistic):</th> <td>4.38e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:19:45</td>     <th>  Log-Likelihood:    </th> <td> -580.18</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    50</td>      <th>  AIC:               </th> <td>   1164.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    48</td>      <th>  BIC:               </th> <td>   1168.</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>     6e+04</td> <td> 7684.530</td> <td>    7.808</td> <td> 0.000</td> <td> 4.46e+04</td> <td> 7.55e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MarketingSpend</th> <td>    0.2465</td> <td>    0.032</td> <td>    7.803</td> <td> 0.000</td> <td>    0.183</td> <td>    0.310</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 4.420</td> <th>  Durbin-Watson:     </th> <td>   1.178</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.110</td> <th>  Jarque-Bera (JB):  </th> <td>   3.882</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.336</td> <th>  Prob(JB):          </th> <td>   0.144</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.188</td> <th>  Cond. No.          </th> <td>4.89e+05</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 4.89e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 Profit   R-squared:                       0.559\n",
       "Model:                            OLS   Adj. R-squared:                  0.550\n",
       "Method:                 Least Squares   F-statistic:                     60.88\n",
       "Date:                Tue, 04 Jun 2019   Prob (F-statistic):           4.38e-10\n",
       "Time:                        13:19:45   Log-Likelihood:                -580.18\n",
       "No. Observations:                  50   AIC:                             1164.\n",
       "Df Residuals:                      48   BIC:                             1168.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==================================================================================\n",
       "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "Intercept           6e+04   7684.530      7.808      0.000    4.46e+04    7.55e+04\n",
       "MarketingSpend     0.2465      0.032      7.803      0.000       0.183       0.310\n",
       "==============================================================================\n",
       "Omnibus:                        4.420   Durbin-Watson:                   1.178\n",
       "Prob(Omnibus):                  0.110   Jarque-Bera (JB):                3.882\n",
       "Skew:                          -0.336   Prob(JB):                        0.144\n",
       "Kurtosis:                       4.188   Cond. No.                     4.89e+05\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 4.89e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m1_m =smf.ols('Profit~MarketingSpend',data=dataset).fit()\n",
    "m1_m.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# though administration individually correlated with profit but when kept with another variables has less significance.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sm.graphics.influence_plot(model1)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "infl = model1.get_influence()\n",
    "sm_fr = infl.summary_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>dfb_Intercept</th>\n",
       "      <th>dfb_State[T.Florida]</th>\n",
       "      <th>dfb_State[T.New York]</th>\n",
       "      <th>dfb_RDSpend</th>\n",
       "      <th>dfb_Administration</th>\n",
       "      <th>dfb_MarketingSpend</th>\n",
       "      <th>cooks_d</th>\n",
       "      <th>standard_resid</th>\n",
       "      <th>hat_diag</th>\n",
       "      <th>dffits_internal</th>\n",
       "      <th>student_resid</th>\n",
       "      <th>dffits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.002627</td>\n",
       "      <td>0.001137</td>\n",
       "      <td>-0.002293</td>\n",
       "      <td>-0.000680</td>\n",
       "      <td>-0.001084</td>\n",
       "      <td>-0.002943</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>-0.014975</td>\n",
       "      <td>0.170451</td>\n",
       "      <td>-0.006788</td>\n",
       "      <td>-0.014804</td>\n",
       "      <td>-0.006710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.049608</td>\n",
       "      <td>-0.082437</td>\n",
       "      <td>-0.070955</td>\n",
       "      <td>0.020841</td>\n",
       "      <td>0.046268</td>\n",
       "      <td>0.058280</td>\n",
       "      <td>0.003960</td>\n",
       "      <td>0.319946</td>\n",
       "      <td>0.188385</td>\n",
       "      <td>0.154143</td>\n",
       "      <td>0.316658</td>\n",
       "      <td>0.152559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.080109</td>\n",
       "      <td>0.153248</td>\n",
       "      <td>-0.028648</td>\n",
       "      <td>0.197811</td>\n",
       "      <td>-0.174765</td>\n",
       "      <td>-0.013702</td>\n",
       "      <td>0.027895</td>\n",
       "      <td>1.003787</td>\n",
       "      <td>0.142447</td>\n",
       "      <td>0.409108</td>\n",
       "      <td>1.003876</td>\n",
       "      <td>0.409144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.035947</td>\n",
       "      <td>-0.047849</td>\n",
       "      <td>0.163879</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>-0.049701</td>\n",
       "      <td>0.073783</td>\n",
       "      <td>0.023571</td>\n",
       "      <td>1.048610</td>\n",
       "      <td>0.113958</td>\n",
       "      <td>0.376063</td>\n",
       "      <td>1.049827</td>\n",
       "      <td>0.376499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.104342</td>\n",
       "      <td>-0.115870</td>\n",
       "      <td>0.017478</td>\n",
       "      <td>-0.153851</td>\n",
       "      <td>0.163418</td>\n",
       "      <td>0.047737</td>\n",
       "      <td>0.014023</td>\n",
       "      <td>-0.698507</td>\n",
       "      <td>0.147083</td>\n",
       "      <td>-0.290067</td>\n",
       "      <td>-0.694385</td>\n",
       "      <td>-0.288355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.046292</td>\n",
       "      <td>0.027220</td>\n",
       "      <td>-0.115816</td>\n",
       "      <td>-0.081405</td>\n",
       "      <td>0.105665</td>\n",
       "      <td>-0.033055</td>\n",
       "      <td>0.011710</td>\n",
       "      <td>-0.730587</td>\n",
       "      <td>0.116320</td>\n",
       "      <td>-0.265065</td>\n",
       "      <td>-0.726658</td>\n",
       "      <td>-0.263639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.021299</td>\n",
       "      <td>0.037236</td>\n",
       "      <td>0.050562</td>\n",
       "      <td>-0.095187</td>\n",
       "      <td>0.004676</td>\n",
       "      <td>0.082074</td>\n",
       "      <td>0.002493</td>\n",
       "      <td>-0.235897</td>\n",
       "      <td>0.211863</td>\n",
       "      <td>-0.122306</td>\n",
       "      <td>-0.233349</td>\n",
       "      <td>-0.120985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.056695</td>\n",
       "      <td>-0.079214</td>\n",
       "      <td>0.007763</td>\n",
       "      <td>-0.045130</td>\n",
       "      <td>-0.041183</td>\n",
       "      <td>0.001538</td>\n",
       "      <td>0.004165</td>\n",
       "      <td>-0.490128</td>\n",
       "      <td>0.094234</td>\n",
       "      <td>-0.158090</td>\n",
       "      <td>-0.485855</td>\n",
       "      <td>-0.156712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.010483</td>\n",
       "      <td>-0.002030</td>\n",
       "      <td>0.010515</td>\n",
       "      <td>0.000992</td>\n",
       "      <td>0.008446</td>\n",
       "      <td>0.005091</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.064286</td>\n",
       "      <td>0.095764</td>\n",
       "      <td>0.020921</td>\n",
       "      <td>0.063555</td>\n",
       "      <td>0.020683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.073333</td>\n",
       "      <td>0.113458</td>\n",
       "      <td>0.114051</td>\n",
       "      <td>-0.087676</td>\n",
       "      <td>0.064819</td>\n",
       "      <td>0.009105</td>\n",
       "      <td>0.006314</td>\n",
       "      <td>-0.567773</td>\n",
       "      <td>0.105163</td>\n",
       "      <td>-0.194641</td>\n",
       "      <td>-0.563352</td>\n",
       "      <td>-0.193125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.116978</td>\n",
       "      <td>0.227283</td>\n",
       "      <td>-0.009802</td>\n",
       "      <td>0.186811</td>\n",
       "      <td>-0.137400</td>\n",
       "      <td>-0.159583</td>\n",
       "      <td>0.022139</td>\n",
       "      <td>1.161151</td>\n",
       "      <td>0.089686</td>\n",
       "      <td>0.364465</td>\n",
       "      <td>1.165882</td>\n",
       "      <td>0.365950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.226735</td>\n",
       "      <td>-0.180498</td>\n",
       "      <td>-0.191722</td>\n",
       "      <td>0.152695</td>\n",
       "      <td>-0.197500</td>\n",
       "      <td>-0.063320</td>\n",
       "      <td>0.019351</td>\n",
       "      <td>0.979338</td>\n",
       "      <td>0.107986</td>\n",
       "      <td>0.340745</td>\n",
       "      <td>0.978873</td>\n",
       "      <td>0.340583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.017490</td>\n",
       "      <td>0.246939</td>\n",
       "      <td>-0.005732</td>\n",
       "      <td>0.069420</td>\n",
       "      <td>0.010085</td>\n",
       "      <td>-0.048411</td>\n",
       "      <td>0.021326</td>\n",
       "      <td>1.348432</td>\n",
       "      <td>0.065747</td>\n",
       "      <td>0.357712</td>\n",
       "      <td>1.361448</td>\n",
       "      <td>0.361165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.007538</td>\n",
       "      <td>-0.144106</td>\n",
       "      <td>-0.139683</td>\n",
       "      <td>0.005568</td>\n",
       "      <td>0.054245</td>\n",
       "      <td>0.040526</td>\n",
       "      <td>0.007401</td>\n",
       "      <td>0.756216</td>\n",
       "      <td>0.072056</td>\n",
       "      <td>0.210726</td>\n",
       "      <td>0.752479</td>\n",
       "      <td>0.209685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.267421</td>\n",
       "      <td>-0.359607</td>\n",
       "      <td>0.022591</td>\n",
       "      <td>-0.221204</td>\n",
       "      <td>-0.257240</td>\n",
       "      <td>0.142195</td>\n",
       "      <td>0.072017</td>\n",
       "      <td>-1.914478</td>\n",
       "      <td>0.105459</td>\n",
       "      <td>-0.657343</td>\n",
       "      <td>-1.976719</td>\n",
       "      <td>-0.678713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-0.002577</td>\n",
       "      <td>0.021586</td>\n",
       "      <td>-0.300602</td>\n",
       "      <td>-0.208289</td>\n",
       "      <td>0.066627</td>\n",
       "      <td>0.071114</td>\n",
       "      <td>0.043416</td>\n",
       "      <td>-1.787748</td>\n",
       "      <td>0.075363</td>\n",
       "      <td>-0.510387</td>\n",
       "      <td>-1.835225</td>\n",
       "      <td>-0.523941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.037230</td>\n",
       "      <td>-0.217604</td>\n",
       "      <td>-0.202959</td>\n",
       "      <td>-0.055403</td>\n",
       "      <td>0.029857</td>\n",
       "      <td>0.116725</td>\n",
       "      <td>0.015759</td>\n",
       "      <td>1.114271</td>\n",
       "      <td>0.070766</td>\n",
       "      <td>0.307497</td>\n",
       "      <td>1.117415</td>\n",
       "      <td>0.308365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.081487</td>\n",
       "      <td>0.013546</td>\n",
       "      <td>-0.089993</td>\n",
       "      <td>0.035255</td>\n",
       "      <td>-0.075381</td>\n",
       "      <td>-0.061166</td>\n",
       "      <td>0.004331</td>\n",
       "      <td>-0.522727</td>\n",
       "      <td>0.086835</td>\n",
       "      <td>-0.161194</td>\n",
       "      <td>-0.518365</td>\n",
       "      <td>-0.159849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.008655</td>\n",
       "      <td>-0.089991</td>\n",
       "      <td>0.002396</td>\n",
       "      <td>-0.003027</td>\n",
       "      <td>0.019777</td>\n",
       "      <td>-0.017913</td>\n",
       "      <td>0.003440</td>\n",
       "      <td>-0.535579</td>\n",
       "      <td>0.067125</td>\n",
       "      <td>-0.143666</td>\n",
       "      <td>-0.531193</td>\n",
       "      <td>-0.142489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.009492</td>\n",
       "      <td>0.056267</td>\n",
       "      <td>0.163205</td>\n",
       "      <td>0.252210</td>\n",
       "      <td>0.039342</td>\n",
       "      <td>-0.342025</td>\n",
       "      <td>0.032891</td>\n",
       "      <td>0.857080</td>\n",
       "      <td>0.211760</td>\n",
       "      <td>0.444236</td>\n",
       "      <td>0.854447</td>\n",
       "      <td>0.442872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.009204</td>\n",
       "      <td>-0.043026</td>\n",
       "      <td>-0.038229</td>\n",
       "      <td>-0.018937</td>\n",
       "      <td>0.001269</td>\n",
       "      <td>0.034093</td>\n",
       "      <td>0.000703</td>\n",
       "      <td>0.210978</td>\n",
       "      <td>0.086587</td>\n",
       "      <td>0.064958</td>\n",
       "      <td>0.208672</td>\n",
       "      <td>0.064248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.162458</td>\n",
       "      <td>0.024662</td>\n",
       "      <td>-0.123303</td>\n",
       "      <td>0.134008</td>\n",
       "      <td>-0.163215</td>\n",
       "      <td>-0.156986</td>\n",
       "      <td>0.011700</td>\n",
       "      <td>-0.670741</td>\n",
       "      <td>0.134979</td>\n",
       "      <td>-0.264956</td>\n",
       "      <td>-0.666491</td>\n",
       "      <td>-0.263277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.028443</td>\n",
       "      <td>-0.080087</td>\n",
       "      <td>-0.001184</td>\n",
       "      <td>0.057093</td>\n",
       "      <td>-0.025665</td>\n",
       "      <td>-0.065599</td>\n",
       "      <td>0.003525</td>\n",
       "      <td>-0.494810</td>\n",
       "      <td>0.079524</td>\n",
       "      <td>-0.145439</td>\n",
       "      <td>-0.490522</td>\n",
       "      <td>-0.144178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-0.003001</td>\n",
       "      <td>-0.025200</td>\n",
       "      <td>-0.000520</td>\n",
       "      <td>0.017345</td>\n",
       "      <td>0.004924</td>\n",
       "      <td>-0.019954</td>\n",
       "      <td>0.000369</td>\n",
       "      <td>-0.153957</td>\n",
       "      <td>0.085385</td>\n",
       "      <td>-0.047041</td>\n",
       "      <td>-0.152239</td>\n",
       "      <td>-0.046515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-0.087996</td>\n",
       "      <td>-0.013268</td>\n",
       "      <td>-0.093171</td>\n",
       "      <td>-0.077455</td>\n",
       "      <td>0.089257</td>\n",
       "      <td>0.087987</td>\n",
       "      <td>0.005070</td>\n",
       "      <td>-0.529011</td>\n",
       "      <td>0.098048</td>\n",
       "      <td>-0.174419</td>\n",
       "      <td>-0.524636</td>\n",
       "      <td>-0.172976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.012690</td>\n",
       "      <td>-0.091010</td>\n",
       "      <td>-0.099577</td>\n",
       "      <td>0.012139</td>\n",
       "      <td>0.046367</td>\n",
       "      <td>-0.028528</td>\n",
       "      <td>0.004164</td>\n",
       "      <td>0.571987</td>\n",
       "      <td>0.070941</td>\n",
       "      <td>0.158057</td>\n",
       "      <td>0.567564</td>\n",
       "      <td>0.156835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.018763</td>\n",
       "      <td>-0.120147</td>\n",
       "      <td>-0.002104</td>\n",
       "      <td>-0.047078</td>\n",
       "      <td>-0.040934</td>\n",
       "      <td>0.086363</td>\n",
       "      <td>0.005794</td>\n",
       "      <td>-0.561674</td>\n",
       "      <td>0.099259</td>\n",
       "      <td>-0.186453</td>\n",
       "      <td>-0.557256</td>\n",
       "      <td>-0.184987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.169919</td>\n",
       "      <td>0.054174</td>\n",
       "      <td>-0.198665</td>\n",
       "      <td>0.271462</td>\n",
       "      <td>-0.146112</td>\n",
       "      <td>-0.339679</td>\n",
       "      <td>0.032503</td>\n",
       "      <td>-1.063541</td>\n",
       "      <td>0.147057</td>\n",
       "      <td>-0.441609</td>\n",
       "      <td>-1.065166</td>\n",
       "      <td>-0.442284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-0.041421</td>\n",
       "      <td>0.037581</td>\n",
       "      <td>0.001794</td>\n",
       "      <td>-0.004892</td>\n",
       "      <td>0.054512</td>\n",
       "      <td>-0.014043</td>\n",
       "      <td>0.001074</td>\n",
       "      <td>0.171270</td>\n",
       "      <td>0.180166</td>\n",
       "      <td>0.080289</td>\n",
       "      <td>0.169369</td>\n",
       "      <td>0.079398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.007881</td>\n",
       "      <td>-0.001871</td>\n",
       "      <td>-0.014675</td>\n",
       "      <td>-0.001210</td>\n",
       "      <td>-0.011523</td>\n",
       "      <td>0.007045</td>\n",
       "      <td>0.000115</td>\n",
       "      <td>-0.079884</td>\n",
       "      <td>0.097336</td>\n",
       "      <td>-0.026232</td>\n",
       "      <td>-0.078976</td>\n",
       "      <td>-0.025934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.004519</td>\n",
       "      <td>0.008083</td>\n",
       "      <td>0.000222</td>\n",
       "      <td>0.004911</td>\n",
       "      <td>-0.003183</td>\n",
       "      <td>-0.008237</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.034788</td>\n",
       "      <td>0.116933</td>\n",
       "      <td>0.012659</td>\n",
       "      <td>0.034391</td>\n",
       "      <td>0.012515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.001322</td>\n",
       "      <td>-0.000431</td>\n",
       "      <td>-0.002779</td>\n",
       "      <td>-0.000298</td>\n",
       "      <td>-0.002100</td>\n",
       "      <td>0.001610</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>-0.014956</td>\n",
       "      <td>0.102554</td>\n",
       "      <td>-0.005056</td>\n",
       "      <td>-0.014785</td>\n",
       "      <td>-0.004998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>-0.025767</td>\n",
       "      <td>0.023004</td>\n",
       "      <td>0.031218</td>\n",
       "      <td>-0.029851</td>\n",
       "      <td>0.005201</td>\n",
       "      <td>0.042128</td>\n",
       "      <td>0.000655</td>\n",
       "      <td>-0.175538</td>\n",
       "      <td>0.113141</td>\n",
       "      <td>-0.062698</td>\n",
       "      <td>-0.173593</td>\n",
       "      <td>-0.062003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>-0.012256</td>\n",
       "      <td>-0.026781</td>\n",
       "      <td>-0.001052</td>\n",
       "      <td>0.005720</td>\n",
       "      <td>0.010764</td>\n",
       "      <td>-0.000214</td>\n",
       "      <td>0.000270</td>\n",
       "      <td>-0.141261</td>\n",
       "      <td>0.074972</td>\n",
       "      <td>-0.040216</td>\n",
       "      <td>-0.139679</td>\n",
       "      <td>-0.039765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>-0.138400</td>\n",
       "      <td>-0.173841</td>\n",
       "      <td>-0.153489</td>\n",
       "      <td>-0.196836</td>\n",
       "      <td>0.242310</td>\n",
       "      <td>0.173390</td>\n",
       "      <td>0.020989</td>\n",
       "      <td>0.883449</td>\n",
       "      <td>0.138939</td>\n",
       "      <td>0.354876</td>\n",
       "      <td>0.881203</td>\n",
       "      <td>0.353973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.099727</td>\n",
       "      <td>0.001307</td>\n",
       "      <td>0.126044</td>\n",
       "      <td>-0.049889</td>\n",
       "      <td>-0.102221</td>\n",
       "      <td>0.034443</td>\n",
       "      <td>0.008322</td>\n",
       "      <td>0.676102</td>\n",
       "      <td>0.098473</td>\n",
       "      <td>0.223451</td>\n",
       "      <td>0.671874</td>\n",
       "      <td>0.222054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>-0.107545</td>\n",
       "      <td>0.334034</td>\n",
       "      <td>0.033735</td>\n",
       "      <td>-0.379353</td>\n",
       "      <td>0.189523</td>\n",
       "      <td>0.218405</td>\n",
       "      <td>0.059247</td>\n",
       "      <td>1.714446</td>\n",
       "      <td>0.107892</td>\n",
       "      <td>0.596224</td>\n",
       "      <td>1.754467</td>\n",
       "      <td>0.610141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.018397</td>\n",
       "      <td>-0.007621</td>\n",
       "      <td>-0.008050</td>\n",
       "      <td>0.001322</td>\n",
       "      <td>-0.016087</td>\n",
       "      <td>-0.000850</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>0.043591</td>\n",
       "      <td>0.185086</td>\n",
       "      <td>0.020774</td>\n",
       "      <td>0.043094</td>\n",
       "      <td>0.020537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.320201</td>\n",
       "      <td>0.010955</td>\n",
       "      <td>0.273511</td>\n",
       "      <td>-0.189819</td>\n",
       "      <td>-0.313449</td>\n",
       "      <td>0.109261</td>\n",
       "      <td>0.055802</td>\n",
       "      <td>1.341034</td>\n",
       "      <td>0.156954</td>\n",
       "      <td>0.578628</td>\n",
       "      <td>1.353659</td>\n",
       "      <td>0.584076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>-0.074335</td>\n",
       "      <td>0.049682</td>\n",
       "      <td>0.051171</td>\n",
       "      <td>0.011576</td>\n",
       "      <td>0.050925</td>\n",
       "      <td>-0.005414</td>\n",
       "      <td>0.001608</td>\n",
       "      <td>-0.298786</td>\n",
       "      <td>0.097549</td>\n",
       "      <td>-0.098233</td>\n",
       "      <td>-0.295671</td>\n",
       "      <td>-0.097209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.021070</td>\n",
       "      <td>-0.066547</td>\n",
       "      <td>-0.063116</td>\n",
       "      <td>-0.063137</td>\n",
       "      <td>0.020142</td>\n",
       "      <td>0.044268</td>\n",
       "      <td>0.002271</td>\n",
       "      <td>0.384941</td>\n",
       "      <td>0.084220</td>\n",
       "      <td>0.116736</td>\n",
       "      <td>0.381184</td>\n",
       "      <td>0.115597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.059567</td>\n",
       "      <td>0.066841</td>\n",
       "      <td>0.005162</td>\n",
       "      <td>-0.022659</td>\n",
       "      <td>-0.051009</td>\n",
       "      <td>-0.006525</td>\n",
       "      <td>0.002195</td>\n",
       "      <td>0.319929</td>\n",
       "      <td>0.114008</td>\n",
       "      <td>0.114764</td>\n",
       "      <td>0.316641</td>\n",
       "      <td>0.113585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.018523</td>\n",
       "      <td>-0.016530</td>\n",
       "      <td>-0.016757</td>\n",
       "      <td>-0.010791</td>\n",
       "      <td>-0.008202</td>\n",
       "      <td>0.004902</td>\n",
       "      <td>0.000166</td>\n",
       "      <td>0.102281</td>\n",
       "      <td>0.087009</td>\n",
       "      <td>0.031575</td>\n",
       "      <td>0.101124</td>\n",
       "      <td>0.031218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.024294</td>\n",
       "      <td>0.047181</td>\n",
       "      <td>0.220181</td>\n",
       "      <td>-0.090858</td>\n",
       "      <td>0.058906</td>\n",
       "      <td>-0.085052</td>\n",
       "      <td>0.023399</td>\n",
       "      <td>1.082788</td>\n",
       "      <td>0.106940</td>\n",
       "      <td>0.374691</td>\n",
       "      <td>1.084965</td>\n",
       "      <td>0.375445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>-0.001883</td>\n",
       "      <td>-0.009633</td>\n",
       "      <td>-0.011309</td>\n",
       "      <td>-0.005257</td>\n",
       "      <td>0.013394</td>\n",
       "      <td>-0.004732</td>\n",
       "      <td>0.000117</td>\n",
       "      <td>0.069710</td>\n",
       "      <td>0.125938</td>\n",
       "      <td>0.026461</td>\n",
       "      <td>0.068917</td>\n",
       "      <td>0.026160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.095382</td>\n",
       "      <td>0.108210</td>\n",
       "      <td>0.428337</td>\n",
       "      <td>-0.212843</td>\n",
       "      <td>0.091394</td>\n",
       "      <td>-0.189969</td>\n",
       "      <td>0.094395</td>\n",
       "      <td>1.966666</td>\n",
       "      <td>0.127729</td>\n",
       "      <td>0.752575</td>\n",
       "      <td>2.035721</td>\n",
       "      <td>0.779000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.106828</td>\n",
       "      <td>-0.144471</td>\n",
       "      <td>-0.025892</td>\n",
       "      <td>0.434369</td>\n",
       "      <td>-0.142646</td>\n",
       "      <td>-0.364064</td>\n",
       "      <td>0.042323</td>\n",
       "      <td>-0.838336</td>\n",
       "      <td>0.265420</td>\n",
       "      <td>-0.503925</td>\n",
       "      <td>-0.835454</td>\n",
       "      <td>-0.502192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>-0.029301</td>\n",
       "      <td>0.056945</td>\n",
       "      <td>0.068742</td>\n",
       "      <td>0.046774</td>\n",
       "      <td>-0.046937</td>\n",
       "      <td>0.031389</td>\n",
       "      <td>0.004482</td>\n",
       "      <td>-0.441686</td>\n",
       "      <td>0.121136</td>\n",
       "      <td>-0.163979</td>\n",
       "      <td>-0.437609</td>\n",
       "      <td>-0.162466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>-0.783828</td>\n",
       "      <td>-0.124090</td>\n",
       "      <td>-0.373999</td>\n",
       "      <td>-0.112734</td>\n",
       "      <td>0.701599</td>\n",
       "      <td>0.418630</td>\n",
       "      <td>0.156376</td>\n",
       "      <td>-1.651796</td>\n",
       "      <td>0.255887</td>\n",
       "      <td>-0.968637</td>\n",
       "      <td>-1.686029</td>\n",
       "      <td>-0.988712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>-0.566028</td>\n",
       "      <td>0.626360</td>\n",
       "      <td>0.703325</td>\n",
       "      <td>0.578956</td>\n",
       "      <td>-0.114232</td>\n",
       "      <td>0.080954</td>\n",
       "      <td>0.263959</td>\n",
       "      <td>-3.744509</td>\n",
       "      <td>0.101490</td>\n",
       "      <td>-1.258474</td>\n",
       "      <td>-4.484594</td>\n",
       "      <td>-1.507205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    dfb_Intercept  dfb_State[T.Florida]  dfb_State[T.New York]  dfb_RDSpend  \\\n",
       "0        0.002627              0.001137              -0.002293    -0.000680   \n",
       "1       -0.049608             -0.082437              -0.070955     0.020841   \n",
       "2        0.080109              0.153248              -0.028648     0.197811   \n",
       "3       -0.035947             -0.047849               0.163879     0.110000   \n",
       "4       -0.104342             -0.115870               0.017478    -0.153851   \n",
       "5       -0.046292              0.027220              -0.115816    -0.081405   \n",
       "6       -0.021299              0.037236               0.050562    -0.095187   \n",
       "7        0.056695             -0.079214               0.007763    -0.045130   \n",
       "8       -0.010483             -0.002030               0.010515     0.000992   \n",
       "9       -0.073333              0.113458               0.114051    -0.087676   \n",
       "10       0.116978              0.227283              -0.009802     0.186811   \n",
       "11       0.226735             -0.180498              -0.191722     0.152695   \n",
       "12      -0.017490              0.246939              -0.005732     0.069420   \n",
       "13      -0.007538             -0.144106              -0.139683     0.005568   \n",
       "14       0.267421             -0.359607               0.022591    -0.221204   \n",
       "15      -0.002577              0.021586              -0.300602    -0.208289   \n",
       "16       0.037230             -0.217604              -0.202959    -0.055403   \n",
       "17       0.081487              0.013546              -0.089993     0.035255   \n",
       "18      -0.008655             -0.089991               0.002396    -0.003027   \n",
       "19       0.009492              0.056267               0.163205     0.252210   \n",
       "20       0.009204             -0.043026              -0.038229    -0.018937   \n",
       "21       0.162458              0.024662              -0.123303     0.134008   \n",
       "22       0.028443             -0.080087              -0.001184     0.057093   \n",
       "23      -0.003001             -0.025200              -0.000520     0.017345   \n",
       "24      -0.087996             -0.013268              -0.093171    -0.077455   \n",
       "25       0.012690             -0.091010              -0.099577     0.012139   \n",
       "26       0.018763             -0.120147              -0.002104    -0.047078   \n",
       "27       0.169919              0.054174              -0.198665     0.271462   \n",
       "28      -0.041421              0.037581               0.001794    -0.004892   \n",
       "29       0.007881             -0.001871              -0.014675    -0.001210   \n",
       "30       0.004519              0.008083               0.000222     0.004911   \n",
       "31       0.001322             -0.000431              -0.002779    -0.000298   \n",
       "32      -0.025767              0.023004               0.031218    -0.029851   \n",
       "33      -0.012256             -0.026781              -0.001052     0.005720   \n",
       "34      -0.138400             -0.173841              -0.153489    -0.196836   \n",
       "35       0.099727              0.001307               0.126044    -0.049889   \n",
       "36      -0.107545              0.334034               0.033735    -0.379353   \n",
       "37       0.018397             -0.007621              -0.008050     0.001322   \n",
       "38       0.320201              0.010955               0.273511    -0.189819   \n",
       "39      -0.074335              0.049682               0.051171     0.011576   \n",
       "40       0.021070             -0.066547              -0.063116    -0.063137   \n",
       "41       0.059567              0.066841               0.005162    -0.022659   \n",
       "42       0.018523             -0.016530              -0.016757    -0.010791   \n",
       "43       0.024294              0.047181               0.220181    -0.090858   \n",
       "44      -0.001883             -0.009633              -0.011309    -0.005257   \n",
       "45       0.095382              0.108210               0.428337    -0.212843   \n",
       "46       0.106828             -0.144471              -0.025892     0.434369   \n",
       "47      -0.029301              0.056945               0.068742     0.046774   \n",
       "48      -0.783828             -0.124090              -0.373999    -0.112734   \n",
       "49      -0.566028              0.626360               0.703325     0.578956   \n",
       "\n",
       "    dfb_Administration  dfb_MarketingSpend   cooks_d  standard_resid  \\\n",
       "0            -0.001084           -0.002943  0.000008       -0.014975   \n",
       "1             0.046268            0.058280  0.003960        0.319946   \n",
       "2            -0.174765           -0.013702  0.027895        1.003787   \n",
       "3            -0.049701            0.073783  0.023571        1.048610   \n",
       "4             0.163418            0.047737  0.014023       -0.698507   \n",
       "5             0.105665           -0.033055  0.011710       -0.730587   \n",
       "6             0.004676            0.082074  0.002493       -0.235897   \n",
       "7            -0.041183            0.001538  0.004165       -0.490128   \n",
       "8             0.008446            0.005091  0.000073        0.064286   \n",
       "9             0.064819            0.009105  0.006314       -0.567773   \n",
       "10           -0.137400           -0.159583  0.022139        1.161151   \n",
       "11           -0.197500           -0.063320  0.019351        0.979338   \n",
       "12            0.010085           -0.048411  0.021326        1.348432   \n",
       "13            0.054245            0.040526  0.007401        0.756216   \n",
       "14           -0.257240            0.142195  0.072017       -1.914478   \n",
       "15            0.066627            0.071114  0.043416       -1.787748   \n",
       "16            0.029857            0.116725  0.015759        1.114271   \n",
       "17           -0.075381           -0.061166  0.004331       -0.522727   \n",
       "18            0.019777           -0.017913  0.003440       -0.535579   \n",
       "19            0.039342           -0.342025  0.032891        0.857080   \n",
       "20            0.001269            0.034093  0.000703        0.210978   \n",
       "21           -0.163215           -0.156986  0.011700       -0.670741   \n",
       "22           -0.025665           -0.065599  0.003525       -0.494810   \n",
       "23            0.004924           -0.019954  0.000369       -0.153957   \n",
       "24            0.089257            0.087987  0.005070       -0.529011   \n",
       "25            0.046367           -0.028528  0.004164        0.571987   \n",
       "26           -0.040934            0.086363  0.005794       -0.561674   \n",
       "27           -0.146112           -0.339679  0.032503       -1.063541   \n",
       "28            0.054512           -0.014043  0.001074        0.171270   \n",
       "29           -0.011523            0.007045  0.000115       -0.079884   \n",
       "30           -0.003183           -0.008237  0.000027        0.034788   \n",
       "31           -0.002100            0.001610  0.000004       -0.014956   \n",
       "32            0.005201            0.042128  0.000655       -0.175538   \n",
       "33            0.010764           -0.000214  0.000270       -0.141261   \n",
       "34            0.242310            0.173390  0.020989        0.883449   \n",
       "35           -0.102221            0.034443  0.008322        0.676102   \n",
       "36            0.189523            0.218405  0.059247        1.714446   \n",
       "37           -0.016087           -0.000850  0.000072        0.043591   \n",
       "38           -0.313449            0.109261  0.055802        1.341034   \n",
       "39            0.050925           -0.005414  0.001608       -0.298786   \n",
       "40            0.020142            0.044268  0.002271        0.384941   \n",
       "41           -0.051009           -0.006525  0.002195        0.319929   \n",
       "42           -0.008202            0.004902  0.000166        0.102281   \n",
       "43            0.058906           -0.085052  0.023399        1.082788   \n",
       "44            0.013394           -0.004732  0.000117        0.069710   \n",
       "45            0.091394           -0.189969  0.094395        1.966666   \n",
       "46           -0.142646           -0.364064  0.042323       -0.838336   \n",
       "47           -0.046937            0.031389  0.004482       -0.441686   \n",
       "48            0.701599            0.418630  0.156376       -1.651796   \n",
       "49           -0.114232            0.080954  0.263959       -3.744509   \n",
       "\n",
       "    hat_diag  dffits_internal  student_resid    dffits  \n",
       "0   0.170451        -0.006788      -0.014804 -0.006710  \n",
       "1   0.188385         0.154143       0.316658  0.152559  \n",
       "2   0.142447         0.409108       1.003876  0.409144  \n",
       "3   0.113958         0.376063       1.049827  0.376499  \n",
       "4   0.147083        -0.290067      -0.694385 -0.288355  \n",
       "5   0.116320        -0.265065      -0.726658 -0.263639  \n",
       "6   0.211863        -0.122306      -0.233349 -0.120985  \n",
       "7   0.094234        -0.158090      -0.485855 -0.156712  \n",
       "8   0.095764         0.020921       0.063555  0.020683  \n",
       "9   0.105163        -0.194641      -0.563352 -0.193125  \n",
       "10  0.089686         0.364465       1.165882  0.365950  \n",
       "11  0.107986         0.340745       0.978873  0.340583  \n",
       "12  0.065747         0.357712       1.361448  0.361165  \n",
       "13  0.072056         0.210726       0.752479  0.209685  \n",
       "14  0.105459        -0.657343      -1.976719 -0.678713  \n",
       "15  0.075363        -0.510387      -1.835225 -0.523941  \n",
       "16  0.070766         0.307497       1.117415  0.308365  \n",
       "17  0.086835        -0.161194      -0.518365 -0.159849  \n",
       "18  0.067125        -0.143666      -0.531193 -0.142489  \n",
       "19  0.211760         0.444236       0.854447  0.442872  \n",
       "20  0.086587         0.064958       0.208672  0.064248  \n",
       "21  0.134979        -0.264956      -0.666491 -0.263277  \n",
       "22  0.079524        -0.145439      -0.490522 -0.144178  \n",
       "23  0.085385        -0.047041      -0.152239 -0.046515  \n",
       "24  0.098048        -0.174419      -0.524636 -0.172976  \n",
       "25  0.070941         0.158057       0.567564  0.156835  \n",
       "26  0.099259        -0.186453      -0.557256 -0.184987  \n",
       "27  0.147057        -0.441609      -1.065166 -0.442284  \n",
       "28  0.180166         0.080289       0.169369  0.079398  \n",
       "29  0.097336        -0.026232      -0.078976 -0.025934  \n",
       "30  0.116933         0.012659       0.034391  0.012515  \n",
       "31  0.102554        -0.005056      -0.014785 -0.004998  \n",
       "32  0.113141        -0.062698      -0.173593 -0.062003  \n",
       "33  0.074972        -0.040216      -0.139679 -0.039765  \n",
       "34  0.138939         0.354876       0.881203  0.353973  \n",
       "35  0.098473         0.223451       0.671874  0.222054  \n",
       "36  0.107892         0.596224       1.754467  0.610141  \n",
       "37  0.185086         0.020774       0.043094  0.020537  \n",
       "38  0.156954         0.578628       1.353659  0.584076  \n",
       "39  0.097549        -0.098233      -0.295671 -0.097209  \n",
       "40  0.084220         0.116736       0.381184  0.115597  \n",
       "41  0.114008         0.114764       0.316641  0.113585  \n",
       "42  0.087009         0.031575       0.101124  0.031218  \n",
       "43  0.106940         0.374691       1.084965  0.375445  \n",
       "44  0.125938         0.026461       0.068917  0.026160  \n",
       "45  0.127729         0.752575       2.035721  0.779000  \n",
       "46  0.265420        -0.503925      -0.835454 -0.502192  \n",
       "47  0.121136        -0.163979      -0.437609 -0.162466  \n",
       "48  0.255887        -0.968637      -1.686029 -0.988712  \n",
       "49  0.101490        -1.258474      -4.484594 -1.507205  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_fr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### FROM GRAPH AND COOKS_d VALUES WE FOUND 49 IS AN INFLENTIAL POINT .SO REMOVE THAT INFLUENTIAL POINT."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Profit_new</td>    <th>  R-squared:         </th> <td>   0.962</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.957</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   216.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 04 Jun 2019</td> <th>  Prob (F-statistic):</th> <td>2.51e-29</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>16:36:48</td>     <th>  Log-Likelihood:    </th> <td> -505.97</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    49</td>      <th>  AIC:               </th> <td>   1024.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    43</td>      <th>  BIC:               </th> <td>   1035.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</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> 5.338e+04</td> <td> 5794.239</td> <td>    9.212</td> <td> 0.000</td> <td> 4.17e+04</td> <td> 6.51e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>State_new[T.Florida]</th>  <td>-1564.2242</td> <td> 2842.017</td> <td>   -0.550</td> <td> 0.585</td> <td>-7295.698</td> <td> 4167.249</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>State_new[T.New York]</th> <td>-1954.0177</td> <td> 2751.932</td> <td>   -0.710</td> <td> 0.482</td> <td>-7503.818</td> <td> 3595.783</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RDSpend_new</th>           <td>    0.7836</td> <td>    0.039</td> <td>   20.056</td> <td> 0.000</td> <td>    0.705</td> <td>    0.862</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Administration_new</th>    <td>   -0.0220</td> <td>    0.044</td> <td>   -0.505</td> <td> 0.616</td> <td>   -0.110</td> <td>    0.066</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MarketingSpend_new</th>    <td>    0.0258</td> <td>    0.014</td> <td>    1.804</td> <td> 0.078</td> <td>   -0.003</td> <td>    0.055</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.051</td> <th>  Durbin-Watson:     </th> <td>   1.667</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.975</td> <th>  Jarque-Bera (JB):  </th> <td>   0.207</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.061</td> <th>  Prob(JB):          </th> <td>   0.902</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.705</td> <th>  Cond. No.          </th> <td>1.46e+06</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.46e+06. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             Profit_new   R-squared:                       0.962\n",
       "Model:                            OLS   Adj. R-squared:                  0.957\n",
       "Method:                 Least Squares   F-statistic:                     216.6\n",
       "Date:                Tue, 04 Jun 2019   Prob (F-statistic):           2.51e-29\n",
       "Time:                        16:36:48   Log-Likelihood:                -505.97\n",
       "No. Observations:                  49   AIC:                             1024.\n",
       "Df Residuals:                      43   BIC:                             1035.\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=========================================================================================\n",
       "                            coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------------\n",
       "Intercept              5.338e+04   5794.239      9.212      0.000    4.17e+04    6.51e+04\n",
       "State_new[T.Florida]  -1564.2242   2842.017     -0.550      0.585   -7295.698    4167.249\n",
       "State_new[T.New York] -1954.0177   2751.932     -0.710      0.482   -7503.818    3595.783\n",
       "RDSpend_new               0.7836      0.039     20.056      0.000       0.705       0.862\n",
       "Administration_new       -0.0220      0.044     -0.505      0.616      -0.110       0.066\n",
       "MarketingSpend_new        0.0258      0.014      1.804      0.078      -0.003       0.055\n",
       "==============================================================================\n",
       "Omnibus:                        0.051   Durbin-Watson:                   1.667\n",
       "Prob(Omnibus):                  0.975   Jarque-Bera (JB):                0.207\n",
       "Skew:                           0.061   Prob(JB):                        0.902\n",
       "Kurtosis:                       2.705   Cond. No.                     1.46e+06\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.46e+06. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dataset = dataset.drop(dataset.index[[49]],axis=0)\n",
    "RDSpend_new=new_dataset['R&D Spend']\n",
    "Administration_new=new_dataset['Administration']\n",
    "MarketingSpend_new=new_dataset['Marketing Spend']\n",
    "State_new=new_dataset['State']\n",
    "Profit_new=new_dataset['Profit']\n",
    "model1_new = smf.ols('Profit_new~RDSpend_new+Administration_new+State_new+MarketingSpend_new',data=new_dataset).fit()\n",
    "sm.graphics.influence_plot(model1_new)\n",
    "plt.show()\n",
    "\n",
    "model1_new.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        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>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>California</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Florida</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>New York</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Florida</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>131876.90</td>\n",
       "      <td>99814.71</td>\n",
       "      <td>362861.36</td>\n",
       "      <td>New York</td>\n",
       "      <td>156991.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>134615.46</td>\n",
       "      <td>147198.87</td>\n",
       "      <td>127716.82</td>\n",
       "      <td>California</td>\n",
       "      <td>156122.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>130298.13</td>\n",
       "      <td>145530.06</td>\n",
       "      <td>323876.68</td>\n",
       "      <td>Florida</td>\n",
       "      <td>155752.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>120542.52</td>\n",
       "      <td>148718.95</td>\n",
       "      <td>311613.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>152211.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>123334.88</td>\n",
       "      <td>108679.17</td>\n",
       "      <td>304981.62</td>\n",
       "      <td>California</td>\n",
       "      <td>149759.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>101913.08</td>\n",
       "      <td>110594.11</td>\n",
       "      <td>229160.95</td>\n",
       "      <td>Florida</td>\n",
       "      <td>146121.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100671.96</td>\n",
       "      <td>91790.61</td>\n",
       "      <td>249744.55</td>\n",
       "      <td>California</td>\n",
       "      <td>144259.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>93863.75</td>\n",
       "      <td>127320.38</td>\n",
       "      <td>249839.44</td>\n",
       "      <td>Florida</td>\n",
       "      <td>141585.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>91992.39</td>\n",
       "      <td>135495.07</td>\n",
       "      <td>252664.93</td>\n",
       "      <td>California</td>\n",
       "      <td>134307.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>119943.24</td>\n",
       "      <td>156547.42</td>\n",
       "      <td>256512.92</td>\n",
       "      <td>Florida</td>\n",
       "      <td>132602.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>114523.61</td>\n",
       "      <td>122616.84</td>\n",
       "      <td>261776.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>129917.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>78013.11</td>\n",
       "      <td>121597.55</td>\n",
       "      <td>264346.06</td>\n",
       "      <td>California</td>\n",
       "      <td>126992.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>94657.16</td>\n",
       "      <td>145077.58</td>\n",
       "      <td>282574.31</td>\n",
       "      <td>New York</td>\n",
       "      <td>125370.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>91749.16</td>\n",
       "      <td>114175.79</td>\n",
       "      <td>294919.57</td>\n",
       "      <td>Florida</td>\n",
       "      <td>124266.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>86419.70</td>\n",
       "      <td>153514.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>122776.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>76253.86</td>\n",
       "      <td>113867.30</td>\n",
       "      <td>298664.47</td>\n",
       "      <td>California</td>\n",
       "      <td>118474.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>78389.47</td>\n",
       "      <td>153773.43</td>\n",
       "      <td>299737.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>111313.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>73994.56</td>\n",
       "      <td>122782.75</td>\n",
       "      <td>303319.26</td>\n",
       "      <td>Florida</td>\n",
       "      <td>110352.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>67532.53</td>\n",
       "      <td>105751.03</td>\n",
       "      <td>304768.73</td>\n",
       "      <td>Florida</td>\n",
       "      <td>108733.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>77044.01</td>\n",
       "      <td>99281.34</td>\n",
       "      <td>140574.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>108552.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>64664.71</td>\n",
       "      <td>139553.16</td>\n",
       "      <td>137962.62</td>\n",
       "      <td>California</td>\n",
       "      <td>107404.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>75328.87</td>\n",
       "      <td>144135.98</td>\n",
       "      <td>134050.07</td>\n",
       "      <td>Florida</td>\n",
       "      <td>105733.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>72107.60</td>\n",
       "      <td>127864.55</td>\n",
       "      <td>353183.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>105008.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>66051.52</td>\n",
       "      <td>182645.56</td>\n",
       "      <td>118148.20</td>\n",
       "      <td>Florida</td>\n",
       "      <td>103282.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>65605.48</td>\n",
       "      <td>153032.06</td>\n",
       "      <td>107138.38</td>\n",
       "      <td>New York</td>\n",
       "      <td>101004.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>61994.48</td>\n",
       "      <td>115641.28</td>\n",
       "      <td>91131.24</td>\n",
       "      <td>Florida</td>\n",
       "      <td>99937.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>61136.38</td>\n",
       "      <td>152701.92</td>\n",
       "      <td>88218.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>97483.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>63408.86</td>\n",
       "      <td>129219.61</td>\n",
       "      <td>46085.25</td>\n",
       "      <td>California</td>\n",
       "      <td>97427.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>55493.95</td>\n",
       "      <td>103057.49</td>\n",
       "      <td>214634.81</td>\n",
       "      <td>Florida</td>\n",
       "      <td>96778.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>46426.07</td>\n",
       "      <td>157693.92</td>\n",
       "      <td>210797.67</td>\n",
       "      <td>California</td>\n",
       "      <td>96712.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>46014.02</td>\n",
       "      <td>85047.44</td>\n",
       "      <td>205517.64</td>\n",
       "      <td>New York</td>\n",
       "      <td>96479.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>28663.76</td>\n",
       "      <td>127056.21</td>\n",
       "      <td>201126.82</td>\n",
       "      <td>Florida</td>\n",
       "      <td>90708.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>44069.95</td>\n",
       "      <td>51283.14</td>\n",
       "      <td>197029.42</td>\n",
       "      <td>California</td>\n",
       "      <td>89949.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>20229.59</td>\n",
       "      <td>65947.93</td>\n",
       "      <td>185265.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>81229.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>38558.51</td>\n",
       "      <td>82982.09</td>\n",
       "      <td>174999.30</td>\n",
       "      <td>California</td>\n",
       "      <td>81005.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>28754.33</td>\n",
       "      <td>118546.05</td>\n",
       "      <td>172795.67</td>\n",
       "      <td>California</td>\n",
       "      <td>78239.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>27892.92</td>\n",
       "      <td>84710.77</td>\n",
       "      <td>164470.71</td>\n",
       "      <td>Florida</td>\n",
       "      <td>77798.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>23640.93</td>\n",
       "      <td>96189.63</td>\n",
       "      <td>148001.11</td>\n",
       "      <td>California</td>\n",
       "      <td>71498.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>15505.73</td>\n",
       "      <td>127382.30</td>\n",
       "      <td>35534.17</td>\n",
       "      <td>New York</td>\n",
       "      <td>69758.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>22177.74</td>\n",
       "      <td>154806.14</td>\n",
       "      <td>28334.72</td>\n",
       "      <td>California</td>\n",
       "      <td>65200.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1000.23</td>\n",
       "      <td>124153.04</td>\n",
       "      <td>1903.93</td>\n",
       "      <td>New York</td>\n",
       "      <td>64926.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1315.46</td>\n",
       "      <td>115816.21</td>\n",
       "      <td>297114.46</td>\n",
       "      <td>Florida</td>\n",
       "      <td>49490.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.00</td>\n",
       "      <td>135426.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>California</td>\n",
       "      <td>42559.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>542.05</td>\n",
       "      <td>51743.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>35673.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    R&D Spend  Administration  Marketing Spend       State     Profit\n",
       "0   165349.20       136897.80        471784.10    New York  192261.83\n",
       "1   162597.70       151377.59        443898.53  California  191792.06\n",
       "2   153441.51       101145.55        407934.54     Florida  191050.39\n",
       "3   144372.41       118671.85        383199.62    New York  182901.99\n",
       "4   142107.34        91391.77        366168.42     Florida  166187.94\n",
       "5   131876.90        99814.71        362861.36    New York  156991.12\n",
       "6   134615.46       147198.87        127716.82  California  156122.51\n",
       "7   130298.13       145530.06        323876.68     Florida  155752.60\n",
       "8   120542.52       148718.95        311613.29    New York  152211.77\n",
       "9   123334.88       108679.17        304981.62  California  149759.96\n",
       "10  101913.08       110594.11        229160.95     Florida  146121.95\n",
       "11  100671.96        91790.61        249744.55  California  144259.40\n",
       "12   93863.75       127320.38        249839.44     Florida  141585.52\n",
       "13   91992.39       135495.07        252664.93  California  134307.35\n",
       "14  119943.24       156547.42        256512.92     Florida  132602.65\n",
       "15  114523.61       122616.84        261776.23    New York  129917.04\n",
       "16   78013.11       121597.55        264346.06  California  126992.93\n",
       "17   94657.16       145077.58        282574.31    New York  125370.37\n",
       "18   91749.16       114175.79        294919.57     Florida  124266.90\n",
       "19   86419.70       153514.11             0.00    New York  122776.86\n",
       "20   76253.86       113867.30        298664.47  California  118474.03\n",
       "21   78389.47       153773.43        299737.29    New York  111313.02\n",
       "22   73994.56       122782.75        303319.26     Florida  110352.25\n",
       "23   67532.53       105751.03        304768.73     Florida  108733.99\n",
       "24   77044.01        99281.34        140574.81    New York  108552.04\n",
       "25   64664.71       139553.16        137962.62  California  107404.34\n",
       "26   75328.87       144135.98        134050.07     Florida  105733.54\n",
       "27   72107.60       127864.55        353183.81    New York  105008.31\n",
       "28   66051.52       182645.56        118148.20     Florida  103282.38\n",
       "29   65605.48       153032.06        107138.38    New York  101004.64\n",
       "30   61994.48       115641.28         91131.24     Florida   99937.59\n",
       "31   61136.38       152701.92         88218.23    New York   97483.56\n",
       "32   63408.86       129219.61         46085.25  California   97427.84\n",
       "33   55493.95       103057.49        214634.81     Florida   96778.92\n",
       "34   46426.07       157693.92        210797.67  California   96712.80\n",
       "35   46014.02        85047.44        205517.64    New York   96479.51\n",
       "36   28663.76       127056.21        201126.82     Florida   90708.19\n",
       "37   44069.95        51283.14        197029.42  California   89949.14\n",
       "38   20229.59        65947.93        185265.10    New York   81229.06\n",
       "39   38558.51        82982.09        174999.30  California   81005.76\n",
       "40   28754.33       118546.05        172795.67  California   78239.91\n",
       "41   27892.92        84710.77        164470.71     Florida   77798.83\n",
       "42   23640.93        96189.63        148001.11  California   71498.49\n",
       "43   15505.73       127382.30         35534.17    New York   69758.98\n",
       "44   22177.74       154806.14         28334.72  California   65200.33\n",
       "45    1000.23       124153.04          1903.93    New York   64926.08\n",
       "46    1315.46       115816.21        297114.46     Florida   49490.75\n",
       "47       0.00       135426.92             0.00  California   42559.73\n",
       "48     542.05        51743.15             0.00    New York   35673.41"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### WE CAN SEE THAT THERE IS A CHANGE IN P VALUE FOR MARKETNG SPEND AFTER REMOVING INFLUENTIAL POINTS BUT NOT IN ADMINISTRATION...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lets drop row-48 and see the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Profit_new2</td>   <th>  R-squared:         </th> <td>   0.963</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.958</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   217.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 04 Jun 2019</td> <th>  Prob (F-statistic):</th> <td>7.02e-29</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>16:54:11</td>     <th>  Log-Likelihood:    </th> <td> -493.23</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    48</td>      <th>  AIC:               </th> <td>   998.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    42</td>      <th>  BIC:               </th> <td>   1010.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</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> 5.939e+04</td> <td> 6093.634</td> <td>    9.746</td> <td> 0.000</td> <td> 4.71e+04</td> <td> 7.17e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>State_new2[T.Florida]</th>  <td>-1150.1897</td> <td> 2712.520</td> <td>   -0.424</td> <td> 0.674</td> <td>-6624.276</td> <td> 4323.896</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>State_new2[T.New York]</th> <td> -665.7875</td> <td> 2678.779</td> <td>   -0.249</td> <td> 0.805</td> <td>-6071.783</td> <td> 4740.208</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RDSpend_new2</th>           <td>    0.7888</td> <td>    0.037</td> <td>   21.160</td> <td> 0.000</td> <td>    0.714</td> <td>    0.864</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Administration_new2</th>    <td>   -0.0621</td> <td>    0.045</td> <td>   -1.381</td> <td> 0.174</td> <td>   -0.153</td> <td>    0.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MarketingSpend_new2</th>    <td>    0.0179</td> <td>    0.014</td> <td>    1.275</td> <td> 0.209</td> <td>   -0.010</td> <td>    0.046</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.267</td> <th>  Durbin-Watson:     </th> <td>   1.835</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.875</td> <th>  Jarque-Bera (JB):  </th> <td>   0.453</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.108</td> <th>  Prob(JB):          </th> <td>   0.797</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.576</td> <th>  Cond. No.          </th> <td>1.60e+06</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.6e+06. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:            Profit_new2   R-squared:                       0.963\n",
       "Model:                            OLS   Adj. R-squared:                  0.958\n",
       "Method:                 Least Squares   F-statistic:                     217.6\n",
       "Date:                Tue, 04 Jun 2019   Prob (F-statistic):           7.02e-29\n",
       "Time:                        16:54:11   Log-Likelihood:                -493.23\n",
       "No. Observations:                  48   AIC:                             998.5\n",
       "Df Residuals:                      42   BIC:                             1010.\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==========================================================================================\n",
       "                             coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------------\n",
       "Intercept               5.939e+04   6093.634      9.746      0.000    4.71e+04    7.17e+04\n",
       "State_new2[T.Florida]  -1150.1897   2712.520     -0.424      0.674   -6624.276    4323.896\n",
       "State_new2[T.New York]  -665.7875   2678.779     -0.249      0.805   -6071.783    4740.208\n",
       "RDSpend_new2               0.7888      0.037     21.160      0.000       0.714       0.864\n",
       "Administration_new2       -0.0621      0.045     -1.381      0.174      -0.153       0.029\n",
       "MarketingSpend_new2        0.0179      0.014      1.275      0.209      -0.010       0.046\n",
       "==============================================================================\n",
       "Omnibus:                        0.267   Durbin-Watson:                   1.835\n",
       "Prob(Omnibus):                  0.875   Jarque-Bera (JB):                0.453\n",
       "Skew:                           0.108   Prob(JB):                        0.797\n",
       "Kurtosis:                       2.576   Cond. No.                     1.60e+06\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.6e+06. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dataset2 = dataset.drop(dataset.index[[49,48]],axis=0)\n",
    "RDSpend_new2=new_dataset2['R&D Spend']\n",
    "Administration_new2=new_dataset2['Administration']\n",
    "MarketingSpend_new2=new_dataset2['Marketing Spend']\n",
    "State_new2=new_dataset2['State']\n",
    "Profit_new2=new_dataset2['Profit']\n",
    "model1_new2 = smf.ols('Profit_new2~RDSpend_new2+Administration_new2+State_new2+MarketingSpend_new2',data=new_dataset2).fit()\n",
    "sm.graphics.influence_plot(model1_new2)\n",
    "plt.show()\n",
    "\n",
    "model1_new2.summary()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### THERE IS NO CHANGE IN ADMINISTRATION AND BY REMOVING 48 POINT WE SEE P VALUE BECOME INSIGNIFICNAT FOR MARKETING SPEND ..... SO KEEP 48 IN DATASET AND REMOVE ADMINISTRATION IF WE HAVE ANY DOUBTS IT CAN BE CLEARED BY CHECKING VUF VALUES..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Profit_new</td>    <th>  R-squared:         </th> <td>   0.962</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.957</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   216.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 04 Jun 2019</td> <th>  Prob (F-statistic):</th> <td>2.51e-29</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>16:54:33</td>     <th>  Log-Likelihood:    </th> <td> -505.97</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    49</td>      <th>  AIC:               </th> <td>   1024.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    43</td>      <th>  BIC:               </th> <td>   1035.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</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> 5.338e+04</td> <td> 5794.239</td> <td>    9.212</td> <td> 0.000</td> <td> 4.17e+04</td> <td> 6.51e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>State_new[T.Florida]</th>  <td>-1564.2242</td> <td> 2842.017</td> <td>   -0.550</td> <td> 0.585</td> <td>-7295.698</td> <td> 4167.249</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>State_new[T.New York]</th> <td>-1954.0177</td> <td> 2751.932</td> <td>   -0.710</td> <td> 0.482</td> <td>-7503.818</td> <td> 3595.783</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RDSpend_new</th>           <td>    0.7836</td> <td>    0.039</td> <td>   20.056</td> <td> 0.000</td> <td>    0.705</td> <td>    0.862</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Administration_new</th>    <td>   -0.0220</td> <td>    0.044</td> <td>   -0.505</td> <td> 0.616</td> <td>   -0.110</td> <td>    0.066</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MarketingSpend_new</th>    <td>    0.0258</td> <td>    0.014</td> <td>    1.804</td> <td> 0.078</td> <td>   -0.003</td> <td>    0.055</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.051</td> <th>  Durbin-Watson:     </th> <td>   1.667</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.975</td> <th>  Jarque-Bera (JB):  </th> <td>   0.207</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.061</td> <th>  Prob(JB):          </th> <td>   0.902</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.705</td> <th>  Cond. No.          </th> <td>1.46e+06</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.46e+06. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             Profit_new   R-squared:                       0.962\n",
       "Model:                            OLS   Adj. R-squared:                  0.957\n",
       "Method:                 Least Squares   F-statistic:                     216.6\n",
       "Date:                Tue, 04 Jun 2019   Prob (F-statistic):           2.51e-29\n",
       "Time:                        16:54:33   Log-Likelihood:                -505.97\n",
       "No. Observations:                  49   AIC:                             1024.\n",
       "Df Residuals:                      43   BIC:                             1035.\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=========================================================================================\n",
       "                            coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------------\n",
       "Intercept              5.338e+04   5794.239      9.212      0.000    4.17e+04    6.51e+04\n",
       "State_new[T.Florida]  -1564.2242   2842.017     -0.550      0.585   -7295.698    4167.249\n",
       "State_new[T.New York] -1954.0177   2751.932     -0.710      0.482   -7503.818    3595.783\n",
       "RDSpend_new               0.7836      0.039     20.056      0.000       0.705       0.862\n",
       "Administration_new       -0.0220      0.044     -0.505      0.616      -0.110       0.066\n",
       "MarketingSpend_new        0.0258      0.014      1.804      0.078      -0.003       0.055\n",
       "==============================================================================\n",
       "Omnibus:                        0.051   Durbin-Watson:                   1.667\n",
       "Prob(Omnibus):                  0.975   Jarque-Bera (JB):                0.207\n",
       "Skew:                           0.061   Prob(JB):                        0.902\n",
       "Kurtosis:                       2.705   Cond. No.                     1.46e+06\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.46e+06. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dataset = dataset.drop(dataset.index[[49]],axis=0)\n",
    "RDSpend_new=new_dataset['R&D Spend']\n",
    "Administration_new=new_dataset['Administration']\n",
    "MarketingSpend_new=new_dataset['Marketing Spend']\n",
    "State_new=new_dataset['State']\n",
    "Profit_new=new_dataset['Profit']\n",
    "model1_new = smf.ols('Profit_new~RDSpend_new+Administration_new+State_new+MarketingSpend_new',data=new_dataset).fit()\n",
    "sm.graphics.influence_plot(model1_new)\n",
    "plt.show()\n",
    "\n",
    "model1_new.summary()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CALACULATING VIF VALUES OF INDEPENDENT VARIABLES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "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>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>California</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Florida</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>New York</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Florida</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>131876.90</td>\n",
       "      <td>99814.71</td>\n",
       "      <td>362861.36</td>\n",
       "      <td>New York</td>\n",
       "      <td>156991.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>134615.46</td>\n",
       "      <td>147198.87</td>\n",
       "      <td>127716.82</td>\n",
       "      <td>California</td>\n",
       "      <td>156122.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>130298.13</td>\n",
       "      <td>145530.06</td>\n",
       "      <td>323876.68</td>\n",
       "      <td>Florida</td>\n",
       "      <td>155752.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>120542.52</td>\n",
       "      <td>148718.95</td>\n",
       "      <td>311613.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>152211.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>123334.88</td>\n",
       "      <td>108679.17</td>\n",
       "      <td>304981.62</td>\n",
       "      <td>California</td>\n",
       "      <td>149759.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>101913.08</td>\n",
       "      <td>110594.11</td>\n",
       "      <td>229160.95</td>\n",
       "      <td>Florida</td>\n",
       "      <td>146121.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100671.96</td>\n",
       "      <td>91790.61</td>\n",
       "      <td>249744.55</td>\n",
       "      <td>California</td>\n",
       "      <td>144259.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>93863.75</td>\n",
       "      <td>127320.38</td>\n",
       "      <td>249839.44</td>\n",
       "      <td>Florida</td>\n",
       "      <td>141585.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>91992.39</td>\n",
       "      <td>135495.07</td>\n",
       "      <td>252664.93</td>\n",
       "      <td>California</td>\n",
       "      <td>134307.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>119943.24</td>\n",
       "      <td>156547.42</td>\n",
       "      <td>256512.92</td>\n",
       "      <td>Florida</td>\n",
       "      <td>132602.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>114523.61</td>\n",
       "      <td>122616.84</td>\n",
       "      <td>261776.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>129917.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>78013.11</td>\n",
       "      <td>121597.55</td>\n",
       "      <td>264346.06</td>\n",
       "      <td>California</td>\n",
       "      <td>126992.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>94657.16</td>\n",
       "      <td>145077.58</td>\n",
       "      <td>282574.31</td>\n",
       "      <td>New York</td>\n",
       "      <td>125370.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>91749.16</td>\n",
       "      <td>114175.79</td>\n",
       "      <td>294919.57</td>\n",
       "      <td>Florida</td>\n",
       "      <td>124266.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>86419.70</td>\n",
       "      <td>153514.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>122776.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>76253.86</td>\n",
       "      <td>113867.30</td>\n",
       "      <td>298664.47</td>\n",
       "      <td>California</td>\n",
       "      <td>118474.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>78389.47</td>\n",
       "      <td>153773.43</td>\n",
       "      <td>299737.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>111313.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>73994.56</td>\n",
       "      <td>122782.75</td>\n",
       "      <td>303319.26</td>\n",
       "      <td>Florida</td>\n",
       "      <td>110352.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>67532.53</td>\n",
       "      <td>105751.03</td>\n",
       "      <td>304768.73</td>\n",
       "      <td>Florida</td>\n",
       "      <td>108733.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>77044.01</td>\n",
       "      <td>99281.34</td>\n",
       "      <td>140574.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>108552.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>64664.71</td>\n",
       "      <td>139553.16</td>\n",
       "      <td>137962.62</td>\n",
       "      <td>California</td>\n",
       "      <td>107404.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>75328.87</td>\n",
       "      <td>144135.98</td>\n",
       "      <td>134050.07</td>\n",
       "      <td>Florida</td>\n",
       "      <td>105733.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>72107.60</td>\n",
       "      <td>127864.55</td>\n",
       "      <td>353183.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>105008.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>66051.52</td>\n",
       "      <td>182645.56</td>\n",
       "      <td>118148.20</td>\n",
       "      <td>Florida</td>\n",
       "      <td>103282.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>65605.48</td>\n",
       "      <td>153032.06</td>\n",
       "      <td>107138.38</td>\n",
       "      <td>New York</td>\n",
       "      <td>101004.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>61994.48</td>\n",
       "      <td>115641.28</td>\n",
       "      <td>91131.24</td>\n",
       "      <td>Florida</td>\n",
       "      <td>99937.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>61136.38</td>\n",
       "      <td>152701.92</td>\n",
       "      <td>88218.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>97483.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>63408.86</td>\n",
       "      <td>129219.61</td>\n",
       "      <td>46085.25</td>\n",
       "      <td>California</td>\n",
       "      <td>97427.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>55493.95</td>\n",
       "      <td>103057.49</td>\n",
       "      <td>214634.81</td>\n",
       "      <td>Florida</td>\n",
       "      <td>96778.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>46426.07</td>\n",
       "      <td>157693.92</td>\n",
       "      <td>210797.67</td>\n",
       "      <td>California</td>\n",
       "      <td>96712.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>46014.02</td>\n",
       "      <td>85047.44</td>\n",
       "      <td>205517.64</td>\n",
       "      <td>New York</td>\n",
       "      <td>96479.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>28663.76</td>\n",
       "      <td>127056.21</td>\n",
       "      <td>201126.82</td>\n",
       "      <td>Florida</td>\n",
       "      <td>90708.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>44069.95</td>\n",
       "      <td>51283.14</td>\n",
       "      <td>197029.42</td>\n",
       "      <td>California</td>\n",
       "      <td>89949.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>20229.59</td>\n",
       "      <td>65947.93</td>\n",
       "      <td>185265.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>81229.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>38558.51</td>\n",
       "      <td>82982.09</td>\n",
       "      <td>174999.30</td>\n",
       "      <td>California</td>\n",
       "      <td>81005.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>28754.33</td>\n",
       "      <td>118546.05</td>\n",
       "      <td>172795.67</td>\n",
       "      <td>California</td>\n",
       "      <td>78239.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>27892.92</td>\n",
       "      <td>84710.77</td>\n",
       "      <td>164470.71</td>\n",
       "      <td>Florida</td>\n",
       "      <td>77798.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>23640.93</td>\n",
       "      <td>96189.63</td>\n",
       "      <td>148001.11</td>\n",
       "      <td>California</td>\n",
       "      <td>71498.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>15505.73</td>\n",
       "      <td>127382.30</td>\n",
       "      <td>35534.17</td>\n",
       "      <td>New York</td>\n",
       "      <td>69758.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>22177.74</td>\n",
       "      <td>154806.14</td>\n",
       "      <td>28334.72</td>\n",
       "      <td>California</td>\n",
       "      <td>65200.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1000.23</td>\n",
       "      <td>124153.04</td>\n",
       "      <td>1903.93</td>\n",
       "      <td>New York</td>\n",
       "      <td>64926.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1315.46</td>\n",
       "      <td>115816.21</td>\n",
       "      <td>297114.46</td>\n",
       "      <td>Florida</td>\n",
       "      <td>49490.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.00</td>\n",
       "      <td>135426.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>California</td>\n",
       "      <td>42559.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>542.05</td>\n",
       "      <td>51743.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>35673.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    R&D Spend  Administration  Marketing Spend       State     Profit\n",
       "0   165349.20       136897.80        471784.10    New York  192261.83\n",
       "1   162597.70       151377.59        443898.53  California  191792.06\n",
       "2   153441.51       101145.55        407934.54     Florida  191050.39\n",
       "3   144372.41       118671.85        383199.62    New York  182901.99\n",
       "4   142107.34        91391.77        366168.42     Florida  166187.94\n",
       "5   131876.90        99814.71        362861.36    New York  156991.12\n",
       "6   134615.46       147198.87        127716.82  California  156122.51\n",
       "7   130298.13       145530.06        323876.68     Florida  155752.60\n",
       "8   120542.52       148718.95        311613.29    New York  152211.77\n",
       "9   123334.88       108679.17        304981.62  California  149759.96\n",
       "10  101913.08       110594.11        229160.95     Florida  146121.95\n",
       "11  100671.96        91790.61        249744.55  California  144259.40\n",
       "12   93863.75       127320.38        249839.44     Florida  141585.52\n",
       "13   91992.39       135495.07        252664.93  California  134307.35\n",
       "14  119943.24       156547.42        256512.92     Florida  132602.65\n",
       "15  114523.61       122616.84        261776.23    New York  129917.04\n",
       "16   78013.11       121597.55        264346.06  California  126992.93\n",
       "17   94657.16       145077.58        282574.31    New York  125370.37\n",
       "18   91749.16       114175.79        294919.57     Florida  124266.90\n",
       "19   86419.70       153514.11             0.00    New York  122776.86\n",
       "20   76253.86       113867.30        298664.47  California  118474.03\n",
       "21   78389.47       153773.43        299737.29    New York  111313.02\n",
       "22   73994.56       122782.75        303319.26     Florida  110352.25\n",
       "23   67532.53       105751.03        304768.73     Florida  108733.99\n",
       "24   77044.01        99281.34        140574.81    New York  108552.04\n",
       "25   64664.71       139553.16        137962.62  California  107404.34\n",
       "26   75328.87       144135.98        134050.07     Florida  105733.54\n",
       "27   72107.60       127864.55        353183.81    New York  105008.31\n",
       "28   66051.52       182645.56        118148.20     Florida  103282.38\n",
       "29   65605.48       153032.06        107138.38    New York  101004.64\n",
       "30   61994.48       115641.28         91131.24     Florida   99937.59\n",
       "31   61136.38       152701.92         88218.23    New York   97483.56\n",
       "32   63408.86       129219.61         46085.25  California   97427.84\n",
       "33   55493.95       103057.49        214634.81     Florida   96778.92\n",
       "34   46426.07       157693.92        210797.67  California   96712.80\n",
       "35   46014.02        85047.44        205517.64    New York   96479.51\n",
       "36   28663.76       127056.21        201126.82     Florida   90708.19\n",
       "37   44069.95        51283.14        197029.42  California   89949.14\n",
       "38   20229.59        65947.93        185265.10    New York   81229.06\n",
       "39   38558.51        82982.09        174999.30  California   81005.76\n",
       "40   28754.33       118546.05        172795.67  California   78239.91\n",
       "41   27892.92        84710.77        164470.71     Florida   77798.83\n",
       "42   23640.93        96189.63        148001.11  California   71498.49\n",
       "43   15505.73       127382.30         35534.17    New York   69758.98\n",
       "44   22177.74       154806.14         28334.72  California   65200.33\n",
       "45    1000.23       124153.04          1903.93    New York   64926.08\n",
       "46    1315.46       115816.21        297114.46     Florida   49490.75\n",
       "47       0.00       135426.92             0.00  California   42559.73\n",
       "48     542.05        51743.15             0.00    New York   35673.41"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x21524afa5f8>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.1751717842324867\n"
     ]
    }
   ],
   "source": [
    "rsq_ad = smf.ols('Administration_new~RDSpend_new+MarketingSpend_new',data=new_dataset).fit().rsquared  \n",
    "vif_ad = 1/(1-(rsq_ad))\n",
    "\n",
    "print(vif_ad)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2399568221016826\n"
     ]
    }
   ],
   "source": [
    "rsq_ms = smf.ols('MarketingSpend_new~RDSpend_new+Administration_new',data=new_dataset).fit().rsquared  \n",
    "vif_ms = 1/(1-(rsq_ms))\n",
    "print(vif_ms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.3777540899785308\n"
     ]
    }
   ],
   "source": [
    "rsq_rds = smf.ols('RDSpend_new~MarketingSpend_new+Administration_new',data=new_dataset).fit().rsquared  \n",
    "vif_rds = 1/(1-(rsq_rds))\n",
    "print(vif_rds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "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>Variables</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RDSpend_new</td>\n",
       "      <td>2.377754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MarketingSpend_new</td>\n",
       "      <td>2.239957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Administration_new</td>\n",
       "      <td>1.175172</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Variables       VIF\n",
       "0         RDSpend_new  2.377754\n",
       "1  MarketingSpend_new  2.239957\n",
       "2  Administration_new  1.175172"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif_dataframe = pd.DataFrame({'Variables':['RDSpend_new','MarketingSpend_new','Administration_new'],'VIF':[vif_rds,vif_ms,vif_ad]})\n",
    "vif_dataframe\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HERE ALL VIF VALUES ARE LESS THAN 5 SO WE NEED TO REMOVE THE VARIABLE WHICH HAS NO SIGNIFICANCE EVEN AFTER REMOVING THE INFLUENCE POINT ..,, SO REMOVE ADMINISTRATION AND BUILD THE FINAL MODEL.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "## An added variable plot (also known as a partial regression plot) shows you the relationship between \n",
    "##the response variable and one of the predictors in the regression model, \n",
    "##after controlling for the presence of the other predictors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sm.graphics.plot_partregress_grid(model1_new)\n",
    "plt.plot()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ADDED VARIABLE PLOT FOR ADMINISTRATION IS NOT SHOWING ANY SIGNIFICANCE.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Profit_new</td>    <th>  R-squared:         </th> <td>   0.961</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.959</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   568.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 04 Jun 2019</td> <th>  Prob (F-statistic):</th> <td>3.74e-33</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:56:44</td>     <th>  Log-Likelihood:    </th> <td> -506.43</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    49</td>      <th>  AIC:               </th> <td>   1019.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    46</td>      <th>  BIC:               </th> <td>   1025.</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> 4.979e+04</td> <td> 2341.584</td> <td>   21.261</td> <td> 0.000</td> <td> 4.51e+04</td> <td> 5.45e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RDSpend_new</th>        <td>    0.7754</td> <td>    0.035</td> <td>   22.136</td> <td> 0.000</td> <td>    0.705</td> <td>    0.846</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MarketingSpend_new</th> <td>    0.0274</td> <td>    0.013</td> <td>    2.104</td> <td> 0.041</td> <td>    0.001</td> <td>    0.054</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.082</td> <th>  Durbin-Watson:     </th> <td>   1.546</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.960</td> <th>  Jarque-Bera (JB):  </th> <td>   0.273</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.051</td> <th>  Prob(JB):          </th> <td>   0.872</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.649</td> <th>  Cond. No.          </th> <td>5.52e+05</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 5.52e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             Profit_new   R-squared:                       0.961\n",
       "Model:                            OLS   Adj. R-squared:                  0.959\n",
       "Method:                 Least Squares   F-statistic:                     568.0\n",
       "Date:                Tue, 04 Jun 2019   Prob (F-statistic):           3.74e-33\n",
       "Time:                        17:56:44   Log-Likelihood:                -506.43\n",
       "No. Observations:                  49   AIC:                             1019.\n",
       "Df Residuals:                      46   BIC:                             1025.\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "======================================================================================\n",
       "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------\n",
       "Intercept           4.979e+04   2341.584     21.261      0.000    4.51e+04    5.45e+04\n",
       "RDSpend_new            0.7754      0.035     22.136      0.000       0.705       0.846\n",
       "MarketingSpend_new     0.0274      0.013      2.104      0.041       0.001       0.054\n",
       "==============================================================================\n",
       "Omnibus:                        0.082   Durbin-Watson:                   1.546\n",
       "Prob(Omnibus):                  0.960   Jarque-Bera (JB):                0.273\n",
       "Skew:                          -0.051   Prob(JB):                        0.872\n",
       "Kurtosis:                       2.649   Cond. No.                     5.52e+05\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 5.52e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2 =smf.ols('Profit_new~ RDSpend_new+MarketingSpend_new',data=new_dataset).fit()\n",
    "model2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept             49785.269847\n",
       "RDSpend_new               0.775384\n",
       "MarketingSpend_new        0.027446\n",
       "dtype: float64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>45071.906084</td>\n",
       "      <td>54498.633611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RDSpend_new</th>\n",
       "      <td>0.704875</td>\n",
       "      <td>0.845893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MarketingSpend_new</th>\n",
       "      <td>0.001192</td>\n",
       "      <td>0.053699</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               0             1\n",
       "Intercept           45071.906084  54498.633611\n",
       "RDSpend_new             0.704875      0.845893\n",
       "MarketingSpend_new      0.001192      0.053699"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.conf_int(0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### APPLYING TRANSFORMATIONS SO THAT WE COULD GET MORE ADJUSTED RSQUARED VALUE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Logarithmic Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2_log1 = smf.ols(\"Profit_new~np.log(RDSpend_new)+np.log(MarketingSpend_new)\",data=new_dataset).fit()\n",
    "model2_log2 = smf.ols(\"Profit_new~np.log(RDSpend_new)+MarketingSpend_new\",data=new_dataset).fit()\n",
    "model2_log3 = smf.ols(\"Profit_new~RDSpend_new+np.log(MarketingSpend_new)\",data=new_dataset).fit()\n",
    "model2_log1.summary()\n",
    "model2_log2.summary()\n",
    "model2_log3.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1)Logarithmic models produce errors as the data contain 0 values in the data.\n",
    "# \n",
    "# 2)This data can be replaced with the mean/median/mode but that may cause errors to pre written models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Exponential Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>np.log(Profit_new)</td> <th>  R-squared:         </th> <td>   0.894</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                    <td>OLS</td>        <th>  Adj. R-squared:    </th> <td>   0.890</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>              <td>Least Squares</td>   <th>  F-statistic:       </th> <td>   194.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>              <td>Tue, 04 Jun 2019</td>  <th>  Prob (F-statistic):</th> <td>3.49e-23</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                  <td>18:04:18</td>      <th>  Log-Likelihood:    </th> <td>  34.529</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>       <td>    49</td>       <th>  AIC:               </th> <td>  -63.06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>           <td>    46</td>       <th>  BIC:               </th> <td>  -57.38</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>   10.9825</td> <td>    0.038</td> <td>  292.267</td> <td> 0.000</td> <td>   10.907</td> <td>   11.058</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RDSpend_new</th>        <td> 7.412e-06</td> <td> 5.62e-07</td> <td>   13.186</td> <td> 0.000</td> <td> 6.28e-06</td> <td> 8.54e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MarketingSpend_new</th> <td>  1.96e-07</td> <td> 2.09e-07</td> <td>    0.936</td> <td> 0.354</td> <td>-2.25e-07</td> <td> 6.17e-07</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>34.029</td> <th>  Durbin-Watson:     </th> <td>   0.650</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  87.452</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-1.925</td> <th>  Prob(JB):          </th> <td>1.02e-19</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 8.292</td> <th>  Cond. No.          </th> <td>5.52e+05</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 5.52e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:     np.log(Profit_new)   R-squared:                       0.894\n",
       "Model:                            OLS   Adj. R-squared:                  0.890\n",
       "Method:                 Least Squares   F-statistic:                     194.8\n",
       "Date:                Tue, 04 Jun 2019   Prob (F-statistic):           3.49e-23\n",
       "Time:                        18:04:18   Log-Likelihood:                 34.529\n",
       "No. Observations:                  49   AIC:                            -63.06\n",
       "Df Residuals:                      46   BIC:                            -57.38\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "======================================================================================\n",
       "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------\n",
       "Intercept             10.9825      0.038    292.267      0.000      10.907      11.058\n",
       "RDSpend_new         7.412e-06   5.62e-07     13.186      0.000    6.28e-06    8.54e-06\n",
       "MarketingSpend_new   1.96e-07   2.09e-07      0.936      0.354   -2.25e-07    6.17e-07\n",
       "==============================================================================\n",
       "Omnibus:                       34.029   Durbin-Watson:                   0.650\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               87.452\n",
       "Skew:                          -1.925   Prob(JB):                     1.02e-19\n",
       "Kurtosis:                       8.292   Cond. No.                     5.52e+05\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 5.52e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2_exp = smf.ols(\"np.log(Profit_new)~RDSpend_new+MarketingSpend_new\",data=new_dataset).fit()\n",
    "model2_exp.summary()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1) The Exponential Model produces less R and R-Squared values when compared to default model.\n",
    "# \n",
    "# 2) Hence we may consider the standard model to be our Final Model.\n",
    "# \n",
    "# 3) In this case we take the standard model as Final Model due to less Complexity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2_pred= model2.predict(new_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     190942.726927\n",
       "1     188043.924520\n",
       "2     179957.312472\n",
       "3     172246.416164\n",
       "4     170022.687457\n",
       "5     161999.405410\n",
       "6     157669.178690\n",
       "7     159705.298181\n",
       "8     151804.380605\n",
       "9     153787.521922\n",
       "10    135096.466868\n",
       "11    134699.049858\n",
       "12    129422.677987\n",
       "13    128049.202655\n",
       "14    149827.450777\n",
       "15    145769.611357\n",
       "16    117530.489091\n",
       "17    130936.300519\n",
       "18    129020.306206\n",
       "19    116793.710748\n",
       "20    117108.281313\n",
       "21    118793.642939\n",
       "22    115484.209637\n",
       "23    110513.437292\n",
       "24    113382.099441\n",
       "25    103711.697086\n",
       "26    111873.132740\n",
       "27    115389.649335\n",
       "28    104243.190309\n",
       "29    103595.167960\n",
       "30    100355.932697\n",
       "31     99610.626757\n",
       "32    100216.309933\n",
       "33     98705.145163\n",
       "34     91568.745065\n",
       "35     91104.335025\n",
       "36     77530.715090\n",
       "37     89363.970985\n",
       "38     70555.662763\n",
       "39     84485.861451\n",
       "40     76823.378730\n",
       "41     75926.972539\n",
       "42     72178.031523\n",
       "43     62783.416125\n",
       "44     67759.192437\n",
       "45     50613.086380\n",
       "46     58959.714353\n",
       "47     49785.269847\n",
       "48     50205.566671\n",
       "dtype: float64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CHECKING WHETHER RESUDUALS ARE NORMALLY DISTRIBUTED OR NOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 3.,  0.,  2.,  8., 11.,  8.,  3.,  5.,  6.,  3.]),\n",
       " array([-2.23934723, -1.82933336, -1.41931948, -1.00930561, -0.59929174,\n",
       "        -0.18927787,  0.220736  ,  0.63074987,  1.04076374,  1.45077761,\n",
       "         1.86079148]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(model2.resid_pearson)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Q-Q Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stats.probplot(model2.resid_pearson,dist=\"norm\",plot=pylab)\n",
    "plt.plot()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1) The Q-Q plot line almost passes through the maximum number of residual Values.\n",
    "# 2) But still we can see some s values slightly far from the regression Line,\n",
    "#This is due to The presence of less number of datapoints."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CHECKING HOMOSEDASTICITY"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"\"\"\n",
    "What is the Assumption of Equal Variance?\n",
    "The assumption of equal variances (i.e. assumption of homoscedasticity) assumes that different samples have the same variance, even if they came from different populations\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### RESIDUALS VS FITTED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Residual')"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(model2_pred,model2.resid_pearson)\n",
    "plt.axhline(y=0,color='Blue')\n",
    "plt.xlabel(\"Fitted\")\n",
    "plt.ylabel(\"Residual\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### RESIDUALS VS FITTED PLOT IS NOT IN CONE SHAPE .. THEREFORE IT IS HOMOSCEDASTIC.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Heteroscedasticity can also arise as a result of the presence of outliers.\n",
    "The inclusion or exclusion of such observations, especially when the sample size is small, can substantially alter the results of regression analysis.\n",
    "Heteroscedasticity arises from violating the assumption of CLRM (classical linear regression model), that the regression model is not correctly specified.\n",
    "Skewness in the distribution of one or more regressors included in the model is another source of heteroscedasticity.\n",
    "Incorrect data transformation, incorrect functional form (linear or log-linear model) is also the source of heteroscedasticity\n",
    "\n",
    "\n",
    "Consequences of Heteroscedasticity\n",
    "The OLS estimators and regression predictions based on them remains unbiased and consistent.\n",
    "The OLS estimators are no longer the BLUE (Best Linear Unbiased Estimators) because they are no longer efficient, so the regression predictions will be inefficient too.\n",
    "Because of the inconsistency of the covariance matrix of the estimated regression coefficients, the tests of hypotheses, (t-test, F-test) are no longer valid.\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "##-->Observed Values vs Fitted(Predicted) values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Fitted')"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Profit_new,model2_pred)\n",
    "plt.xlabel(\"Observed\")\n",
    "plt.ylabel(\"Fitted\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) The predicted values are uniformly distributed across the given data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kumar\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py:445: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  % get_backend())\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.tsa.api as smt\n",
    "\n",
    "acf = smt.graphics.plot_acf(model2.resid_pearson, lags=40 , alpha=0.05)\n",
    "acf.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.546074105671031"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.stats.stattools import durbin_watson\n",
    "durbin_watson(model2.resid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [],
   "source": [
    "# minor values (1-2) so there may be chance of better tuning the model and best suits  for time series models.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Splitting the data into train and test data \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Profit', 'Marketing Spend', 'R&D Spend'], dtype='object')"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_train,dataset_test  = train_test_split(new_dataset.iloc[:,::-2],test_size = 0.2)\n",
    "dataset_train.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "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>Profit</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>R&amp;D Spend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>155752.60</td>\n",
       "      <td>323876.68</td>\n",
       "      <td>130298.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>125370.37</td>\n",
       "      <td>282574.31</td>\n",
       "      <td>94657.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>35673.41</td>\n",
       "      <td>0.00</td>\n",
       "      <td>542.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>110352.25</td>\n",
       "      <td>303319.26</td>\n",
       "      <td>73994.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>64926.08</td>\n",
       "      <td>1903.93</td>\n",
       "      <td>1000.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>146121.95</td>\n",
       "      <td>229160.95</td>\n",
       "      <td>101913.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>132602.65</td>\n",
       "      <td>256512.92</td>\n",
       "      <td>119943.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>111313.02</td>\n",
       "      <td>299737.29</td>\n",
       "      <td>78389.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>105008.31</td>\n",
       "      <td>353183.81</td>\n",
       "      <td>72107.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>101004.64</td>\n",
       "      <td>107138.38</td>\n",
       "      <td>65605.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>81229.06</td>\n",
       "      <td>185265.10</td>\n",
       "      <td>20229.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>156991.12</td>\n",
       "      <td>362861.36</td>\n",
       "      <td>131876.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>107404.34</td>\n",
       "      <td>137962.62</td>\n",
       "      <td>64664.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>96479.51</td>\n",
       "      <td>205517.64</td>\n",
       "      <td>46014.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>78239.91</td>\n",
       "      <td>172795.67</td>\n",
       "      <td>28754.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>166187.94</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>142107.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>49490.75</td>\n",
       "      <td>297114.46</td>\n",
       "      <td>1315.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>182901.99</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>144372.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>191792.06</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>162597.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>192261.83</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>165349.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>96778.92</td>\n",
       "      <td>214634.81</td>\n",
       "      <td>55493.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>97483.56</td>\n",
       "      <td>88218.23</td>\n",
       "      <td>61136.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>90708.19</td>\n",
       "      <td>201126.82</td>\n",
       "      <td>28663.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>105733.54</td>\n",
       "      <td>134050.07</td>\n",
       "      <td>75328.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>126992.93</td>\n",
       "      <td>264346.06</td>\n",
       "      <td>78013.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>71498.49</td>\n",
       "      <td>148001.11</td>\n",
       "      <td>23640.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>118474.03</td>\n",
       "      <td>298664.47</td>\n",
       "      <td>76253.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>141585.52</td>\n",
       "      <td>249839.44</td>\n",
       "      <td>93863.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>81005.76</td>\n",
       "      <td>174999.30</td>\n",
       "      <td>38558.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>65200.33</td>\n",
       "      <td>28334.72</td>\n",
       "      <td>22177.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>103282.38</td>\n",
       "      <td>118148.20</td>\n",
       "      <td>66051.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>152211.77</td>\n",
       "      <td>311613.29</td>\n",
       "      <td>120542.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>42559.73</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>99937.59</td>\n",
       "      <td>91131.24</td>\n",
       "      <td>61994.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>89949.14</td>\n",
       "      <td>197029.42</td>\n",
       "      <td>44069.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>69758.98</td>\n",
       "      <td>35534.17</td>\n",
       "      <td>15505.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>156122.51</td>\n",
       "      <td>127716.82</td>\n",
       "      <td>134615.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>122776.86</td>\n",
       "      <td>0.00</td>\n",
       "      <td>86419.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>77798.83</td>\n",
       "      <td>164470.71</td>\n",
       "      <td>27892.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Profit  Marketing Spend  R&D Spend\n",
       "7   155752.60        323876.68  130298.13\n",
       "17  125370.37        282574.31   94657.16\n",
       "48   35673.41             0.00     542.05\n",
       "22  110352.25        303319.26   73994.56\n",
       "45   64926.08          1903.93    1000.23\n",
       "10  146121.95        229160.95  101913.08\n",
       "14  132602.65        256512.92  119943.24\n",
       "21  111313.02        299737.29   78389.47\n",
       "27  105008.31        353183.81   72107.60\n",
       "29  101004.64        107138.38   65605.48\n",
       "38   81229.06        185265.10   20229.59\n",
       "5   156991.12        362861.36  131876.90\n",
       "25  107404.34        137962.62   64664.71\n",
       "35   96479.51        205517.64   46014.02\n",
       "40   78239.91        172795.67   28754.33\n",
       "4   166187.94        366168.42  142107.34\n",
       "46   49490.75        297114.46    1315.46\n",
       "3   182901.99        383199.62  144372.41\n",
       "1   191792.06        443898.53  162597.70\n",
       "0   192261.83        471784.10  165349.20\n",
       "33   96778.92        214634.81   55493.95\n",
       "31   97483.56         88218.23   61136.38\n",
       "36   90708.19        201126.82   28663.76\n",
       "26  105733.54        134050.07   75328.87\n",
       "16  126992.93        264346.06   78013.11\n",
       "42   71498.49        148001.11   23640.93\n",
       "20  118474.03        298664.47   76253.86\n",
       "12  141585.52        249839.44   93863.75\n",
       "39   81005.76        174999.30   38558.51\n",
       "44   65200.33         28334.72   22177.74\n",
       "28  103282.38        118148.20   66051.52\n",
       "8   152211.77        311613.29  120542.52\n",
       "47   42559.73             0.00       0.00\n",
       "30   99937.59         91131.24   61994.48\n",
       "37   89949.14        197029.42   44069.95\n",
       "43   69758.98         35534.17   15505.73\n",
       "6   156122.51        127716.82  134615.46\n",
       "19  122776.86             0.00   86419.70\n",
       "41   77798.83        164470.71   27892.92"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "tProfit= dataset_train['Profit']\n",
    "tRDSpend= dataset_train['R&D Spend']\n",
    "tMarketingSpend=dataset_train['Marketing Spend']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7354.25630789658"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_train = smf.ols('tProfit~tRDSpend+tMarketingSpend',data=dataset_train).fit()\n",
    "train_pred = model_train.predict(dataset_train)\n",
    "train_resid = train_pred - dataset_train['Profit']\n",
    "train_rmse = np.sqrt(np.mean(train_resid*train_resid))\n",
    "train_rmse\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "teProfit=dataset_test['Profit']\n",
    "teRDSpend= dataset_test['R&D Spend']\n",
    "teMarketingSpend=dataset_test['Marketing Spend']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7443.99298133912"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_test = smf.ols('teProfit~teRDSpend+teMarketingSpend',data=dataset_test).fit()\n",
    "test_pred = model_test.predict(dataset_test)\n",
    "test_resid = test_pred - dataset_test['Profit']\n",
    "test_rmse = np.sqrt(np.mean(test_resid*test_resid))\n",
    "test_rmse\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "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>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>California</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Florida</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>New York</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Florida</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>131876.90</td>\n",
       "      <td>99814.71</td>\n",
       "      <td>362861.36</td>\n",
       "      <td>New York</td>\n",
       "      <td>156991.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>134615.46</td>\n",
       "      <td>147198.87</td>\n",
       "      <td>127716.82</td>\n",
       "      <td>California</td>\n",
       "      <td>156122.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>130298.13</td>\n",
       "      <td>145530.06</td>\n",
       "      <td>323876.68</td>\n",
       "      <td>Florida</td>\n",
       "      <td>155752.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>120542.52</td>\n",
       "      <td>148718.95</td>\n",
       "      <td>311613.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>152211.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>123334.88</td>\n",
       "      <td>108679.17</td>\n",
       "      <td>304981.62</td>\n",
       "      <td>California</td>\n",
       "      <td>149759.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>101913.08</td>\n",
       "      <td>110594.11</td>\n",
       "      <td>229160.95</td>\n",
       "      <td>Florida</td>\n",
       "      <td>146121.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100671.96</td>\n",
       "      <td>91790.61</td>\n",
       "      <td>249744.55</td>\n",
       "      <td>California</td>\n",
       "      <td>144259.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>93863.75</td>\n",
       "      <td>127320.38</td>\n",
       "      <td>249839.44</td>\n",
       "      <td>Florida</td>\n",
       "      <td>141585.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>91992.39</td>\n",
       "      <td>135495.07</td>\n",
       "      <td>252664.93</td>\n",
       "      <td>California</td>\n",
       "      <td>134307.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>119943.24</td>\n",
       "      <td>156547.42</td>\n",
       "      <td>256512.92</td>\n",
       "      <td>Florida</td>\n",
       "      <td>132602.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>114523.61</td>\n",
       "      <td>122616.84</td>\n",
       "      <td>261776.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>129917.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>78013.11</td>\n",
       "      <td>121597.55</td>\n",
       "      <td>264346.06</td>\n",
       "      <td>California</td>\n",
       "      <td>126992.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>94657.16</td>\n",
       "      <td>145077.58</td>\n",
       "      <td>282574.31</td>\n",
       "      <td>New York</td>\n",
       "      <td>125370.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>91749.16</td>\n",
       "      <td>114175.79</td>\n",
       "      <td>294919.57</td>\n",
       "      <td>Florida</td>\n",
       "      <td>124266.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>86419.70</td>\n",
       "      <td>153514.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>122776.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>76253.86</td>\n",
       "      <td>113867.30</td>\n",
       "      <td>298664.47</td>\n",
       "      <td>California</td>\n",
       "      <td>118474.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>78389.47</td>\n",
       "      <td>153773.43</td>\n",
       "      <td>299737.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>111313.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>73994.56</td>\n",
       "      <td>122782.75</td>\n",
       "      <td>303319.26</td>\n",
       "      <td>Florida</td>\n",
       "      <td>110352.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>67532.53</td>\n",
       "      <td>105751.03</td>\n",
       "      <td>304768.73</td>\n",
       "      <td>Florida</td>\n",
       "      <td>108733.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>77044.01</td>\n",
       "      <td>99281.34</td>\n",
       "      <td>140574.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>108552.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>64664.71</td>\n",
       "      <td>139553.16</td>\n",
       "      <td>137962.62</td>\n",
       "      <td>California</td>\n",
       "      <td>107404.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>75328.87</td>\n",
       "      <td>144135.98</td>\n",
       "      <td>134050.07</td>\n",
       "      <td>Florida</td>\n",
       "      <td>105733.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>72107.60</td>\n",
       "      <td>127864.55</td>\n",
       "      <td>353183.81</td>\n",
       "      <td>New York</td>\n",
       "      <td>105008.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>66051.52</td>\n",
       "      <td>182645.56</td>\n",
       "      <td>118148.20</td>\n",
       "      <td>Florida</td>\n",
       "      <td>103282.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>65605.48</td>\n",
       "      <td>153032.06</td>\n",
       "      <td>107138.38</td>\n",
       "      <td>New York</td>\n",
       "      <td>101004.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>61994.48</td>\n",
       "      <td>115641.28</td>\n",
       "      <td>91131.24</td>\n",
       "      <td>Florida</td>\n",
       "      <td>99937.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>61136.38</td>\n",
       "      <td>152701.92</td>\n",
       "      <td>88218.23</td>\n",
       "      <td>New York</td>\n",
       "      <td>97483.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>63408.86</td>\n",
       "      <td>129219.61</td>\n",
       "      <td>46085.25</td>\n",
       "      <td>California</td>\n",
       "      <td>97427.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>55493.95</td>\n",
       "      <td>103057.49</td>\n",
       "      <td>214634.81</td>\n",
       "      <td>Florida</td>\n",
       "      <td>96778.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>46426.07</td>\n",
       "      <td>157693.92</td>\n",
       "      <td>210797.67</td>\n",
       "      <td>California</td>\n",
       "      <td>96712.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>46014.02</td>\n",
       "      <td>85047.44</td>\n",
       "      <td>205517.64</td>\n",
       "      <td>New York</td>\n",
       "      <td>96479.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>28663.76</td>\n",
       "      <td>127056.21</td>\n",
       "      <td>201126.82</td>\n",
       "      <td>Florida</td>\n",
       "      <td>90708.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>44069.95</td>\n",
       "      <td>51283.14</td>\n",
       "      <td>197029.42</td>\n",
       "      <td>California</td>\n",
       "      <td>89949.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>20229.59</td>\n",
       "      <td>65947.93</td>\n",
       "      <td>185265.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>81229.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>38558.51</td>\n",
       "      <td>82982.09</td>\n",
       "      <td>174999.30</td>\n",
       "      <td>California</td>\n",
       "      <td>81005.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>28754.33</td>\n",
       "      <td>118546.05</td>\n",
       "      <td>172795.67</td>\n",
       "      <td>California</td>\n",
       "      <td>78239.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>27892.92</td>\n",
       "      <td>84710.77</td>\n",
       "      <td>164470.71</td>\n",
       "      <td>Florida</td>\n",
       "      <td>77798.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>23640.93</td>\n",
       "      <td>96189.63</td>\n",
       "      <td>148001.11</td>\n",
       "      <td>California</td>\n",
       "      <td>71498.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>15505.73</td>\n",
       "      <td>127382.30</td>\n",
       "      <td>35534.17</td>\n",
       "      <td>New York</td>\n",
       "      <td>69758.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>22177.74</td>\n",
       "      <td>154806.14</td>\n",
       "      <td>28334.72</td>\n",
       "      <td>California</td>\n",
       "      <td>65200.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1000.23</td>\n",
       "      <td>124153.04</td>\n",
       "      <td>1903.93</td>\n",
       "      <td>New York</td>\n",
       "      <td>64926.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1315.46</td>\n",
       "      <td>115816.21</td>\n",
       "      <td>297114.46</td>\n",
       "      <td>Florida</td>\n",
       "      <td>49490.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.00</td>\n",
       "      <td>135426.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>California</td>\n",
       "      <td>42559.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>542.05</td>\n",
       "      <td>51743.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>New York</td>\n",
       "      <td>35673.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    R&D Spend  Administration  Marketing Spend       State     Profit\n",
       "0   165349.20       136897.80        471784.10    New York  192261.83\n",
       "1   162597.70       151377.59        443898.53  California  191792.06\n",
       "2   153441.51       101145.55        407934.54     Florida  191050.39\n",
       "3   144372.41       118671.85        383199.62    New York  182901.99\n",
       "4   142107.34        91391.77        366168.42     Florida  166187.94\n",
       "5   131876.90        99814.71        362861.36    New York  156991.12\n",
       "6   134615.46       147198.87        127716.82  California  156122.51\n",
       "7   130298.13       145530.06        323876.68     Florida  155752.60\n",
       "8   120542.52       148718.95        311613.29    New York  152211.77\n",
       "9   123334.88       108679.17        304981.62  California  149759.96\n",
       "10  101913.08       110594.11        229160.95     Florida  146121.95\n",
       "11  100671.96        91790.61        249744.55  California  144259.40\n",
       "12   93863.75       127320.38        249839.44     Florida  141585.52\n",
       "13   91992.39       135495.07        252664.93  California  134307.35\n",
       "14  119943.24       156547.42        256512.92     Florida  132602.65\n",
       "15  114523.61       122616.84        261776.23    New York  129917.04\n",
       "16   78013.11       121597.55        264346.06  California  126992.93\n",
       "17   94657.16       145077.58        282574.31    New York  125370.37\n",
       "18   91749.16       114175.79        294919.57     Florida  124266.90\n",
       "19   86419.70       153514.11             0.00    New York  122776.86\n",
       "20   76253.86       113867.30        298664.47  California  118474.03\n",
       "21   78389.47       153773.43        299737.29    New York  111313.02\n",
       "22   73994.56       122782.75        303319.26     Florida  110352.25\n",
       "23   67532.53       105751.03        304768.73     Florida  108733.99\n",
       "24   77044.01        99281.34        140574.81    New York  108552.04\n",
       "25   64664.71       139553.16        137962.62  California  107404.34\n",
       "26   75328.87       144135.98        134050.07     Florida  105733.54\n",
       "27   72107.60       127864.55        353183.81    New York  105008.31\n",
       "28   66051.52       182645.56        118148.20     Florida  103282.38\n",
       "29   65605.48       153032.06        107138.38    New York  101004.64\n",
       "30   61994.48       115641.28         91131.24     Florida   99937.59\n",
       "31   61136.38       152701.92         88218.23    New York   97483.56\n",
       "32   63408.86       129219.61         46085.25  California   97427.84\n",
       "33   55493.95       103057.49        214634.81     Florida   96778.92\n",
       "34   46426.07       157693.92        210797.67  California   96712.80\n",
       "35   46014.02        85047.44        205517.64    New York   96479.51\n",
       "36   28663.76       127056.21        201126.82     Florida   90708.19\n",
       "37   44069.95        51283.14        197029.42  California   89949.14\n",
       "38   20229.59        65947.93        185265.10    New York   81229.06\n",
       "39   38558.51        82982.09        174999.30  California   81005.76\n",
       "40   28754.33       118546.05        172795.67  California   78239.91\n",
       "41   27892.92        84710.77        164470.71     Florida   77798.83\n",
       "42   23640.93        96189.63        148001.11  California   71498.49\n",
       "43   15505.73       127382.30         35534.17    New York   69758.98\n",
       "44   22177.74       154806.14         28334.72  California   65200.33\n",
       "45    1000.23       124153.04          1903.93    New York   64926.08\n",
       "46    1315.46       115816.21        297114.46     Florida   49490.75\n",
       "47       0.00       135426.92             0.00  California   42559.73\n",
       "48     542.05        51743.15             0.00    New York   35673.41"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=new_dataset.iloc[:,:3].values\n",
    "y=new_dataset.iloc[:,4].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([192261.83, 191792.06, 191050.39, 182901.99, 166187.94, 156991.12,\n",
       "       156122.51, 155752.6 , 152211.77, 149759.96, 146121.95, 144259.4 ,\n",
       "       141585.52, 134307.35, 132602.65, 129917.04, 126992.93, 125370.37,\n",
       "       124266.9 , 122776.86, 118474.03, 111313.02, 110352.25, 108733.99,\n",
       "       108552.04, 107404.34, 105733.54, 105008.31, 103282.38, 101004.64,\n",
       "        99937.59,  97483.56,  97427.84,  96778.92,  96712.8 ,  96479.51,\n",
       "        90708.19,  89949.14,  81229.06,  81005.76,  78239.91,  77798.83,\n",
       "        71498.49,  69758.98,  65200.33,  64926.08,  49490.75,  42559.73,\n",
       "        35673.41])"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97.00730432596352\n"
     ]
    }
   ],
   "source": [
    "regressor=LinearRegression()\n",
    "regressor.fit(x_train,y_train)\n",
    "accuracy=regressor.score(x_test,y_test)\n",
    "print((accuracy*100))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Underfitting – Validation and training error high\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AS THE DATASET VALUES ARE LESS THAN 1OOO MODEL BECAME UNDERFITTING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### to overcome underfitting we divide the test size into more\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Profit', 'Marketing Spend', 'R&D Spend'], dtype='object')"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_train,dataset_test  = train_test_split(new_dataset.iloc[:,::-2],test_size = 0.30)\n",
    "dataset_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [],
   "source": [
    "tProfit= dataset_train['Profit']\n",
    "tRDSpend= dataset_train['R&D Spend']\n",
    "tMarketingSpend=dataset_train['Marketing Spend']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7185.815045500319"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_train = smf.ols('tProfit~tRDSpend+tMarketingSpend',data=dataset_train).fit()\n",
    "train_pred = model_train.predict(dataset_train)\n",
    "train_resid = train_pred - dataset_train['Profit']\n",
    "train_rmse = np.sqrt(np.mean(train_resid*train_resid))\n",
    "train_rmse\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "teProfit=dataset_test['Profit']\n",
    "teRDSpend= dataset_test['R&D Spend']\n",
    "teMarketingSpend=dataset_test['Marketing Spend']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7297.865498566233"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_test = smf.ols('teProfit~teRDSpend+teMarketingSpend',data=dataset_test).fit()\n",
    "test_pred = model_test.predict(dataset_test)\n",
    "test_resid = test_pred - dataset_test['Profit']\n",
    "test_rmse = np.sqrt(np.mean(test_resid*test_resid))\n",
    "test_rmse\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=new_dataset.iloc[:,:3].values\n",
    "y=new_dataset.iloc[:,4].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.30, random_state=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "94.23501956468417\n"
     ]
    }
   ],
   "source": [
    "regressor=LinearRegression()\n",
    "regressor.fit(x_train,y_train)\n",
    "accuracy=regressor.score(x_test,y_test)\n",
    "print((accuracy*100))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ACCURACY IS MORE FOR TEST SIZE 0.2  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# WE SATISFIED ASSUMPTIONS::\n",
    "# 1.LINEARITY 2.MULTIVARIATE NORMALITY\n",
    "# 3. NO MULTICOLLINEARITY\n",
    "# 4 .HOMOSEDASTICITY"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### OUR FINAL MODEL IS MODEL2 AND DATASET IS NEW_DATASET, GOOD FIT MODEL AS TEST ERROR IS SLIGHTKY GREATER THAN TRAIN ERROR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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