HW2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "ccbd1a30",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.datasets import copper\n",
    "from statsmodels.formula.api import ols\n",
    "from statsmodels.stats.anova import anova_lm\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27aede39",
   "metadata": {},
   "source": [
    "#### Burada hangi regresyon modelinin en yüksek skoru verdiğini kontrol edebilmek adına bir fonksiyon yazdım. Bu sayede verilen data için en uygun regresyon modelini belirleyebiliyorum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "a8bc4b41",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getregressions(X,y):\n",
    "    modelfishLinear = LinearRegression()\n",
    "    modelfishLinear.fit(X,y)\n",
    "    LinearRsqr = modelfishLinear.score(X,y)\n",
    "    \n",
    "    modelfishRidge = Ridge()\n",
    "    modelfishRidge.fit(X,y)\n",
    "    RidgeRsqr = modelfishRidge.score(X,y)\n",
    "    \n",
    "    modelfishLasso = Lasso()\n",
    "    modelfishLasso.fit(X,y)\n",
    "    LassoRsqr = modelfishLasso.score(X,y)\n",
    "    \n",
    "    modelfishElastic = ElasticNet()\n",
    "    modelfishElastic.fit(X,y)\n",
    "    ElasticRsqr = modelfishElastic.score(X,y)\n",
    "    return LinearRsqr, RidgeRsqr, LassoRsqr, ElasticRsqr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bc1b8d6",
   "metadata": {},
   "source": [
    "## Question 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "473a0404",
   "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>WORLDCONSUMPTION</th>\n",
       "      <th>COPPERPRICE</th>\n",
       "      <th>INCOMEINDEX</th>\n",
       "      <th>ALUMPRICE</th>\n",
       "      <th>INVENTORYINDEX</th>\n",
       "      <th>TIME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3173.0</td>\n",
       "      <td>26.56</td>\n",
       "      <td>0.70</td>\n",
       "      <td>19.76</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3281.1</td>\n",
       "      <td>27.31</td>\n",
       "      <td>0.71</td>\n",
       "      <td>20.78</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3135.7</td>\n",
       "      <td>32.95</td>\n",
       "      <td>0.72</td>\n",
       "      <td>22.55</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3359.1</td>\n",
       "      <td>33.90</td>\n",
       "      <td>0.70</td>\n",
       "      <td>23.06</td>\n",
       "      <td>0.97</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3755.1</td>\n",
       "      <td>42.70</td>\n",
       "      <td>0.74</td>\n",
       "      <td>24.93</td>\n",
       "      <td>1.02</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3875.9</td>\n",
       "      <td>46.11</td>\n",
       "      <td>0.74</td>\n",
       "      <td>26.50</td>\n",
       "      <td>1.04</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3905.7</td>\n",
       "      <td>31.70</td>\n",
       "      <td>0.74</td>\n",
       "      <td>27.24</td>\n",
       "      <td>0.98</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3957.6</td>\n",
       "      <td>27.23</td>\n",
       "      <td>0.72</td>\n",
       "      <td>26.21</td>\n",
       "      <td>0.98</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4279.1</td>\n",
       "      <td>32.89</td>\n",
       "      <td>0.75</td>\n",
       "      <td>26.09</td>\n",
       "      <td>1.03</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4627.9</td>\n",
       "      <td>33.78</td>\n",
       "      <td>0.77</td>\n",
       "      <td>27.40</td>\n",
       "      <td>1.03</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4910.2</td>\n",
       "      <td>31.66</td>\n",
       "      <td>0.76</td>\n",
       "      <td>26.94</td>\n",
       "      <td>0.98</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4908.4</td>\n",
       "      <td>32.28</td>\n",
       "      <td>0.79</td>\n",
       "      <td>25.18</td>\n",
       "      <td>1.00</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5327.9</td>\n",
       "      <td>32.38</td>\n",
       "      <td>0.83</td>\n",
       "      <td>23.94</td>\n",
       "      <td>0.97</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5878.4</td>\n",
       "      <td>33.75</td>\n",
       "      <td>0.85</td>\n",
       "      <td>25.07</td>\n",
       "      <td>1.03</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>6075.2</td>\n",
       "      <td>36.25</td>\n",
       "      <td>0.89</td>\n",
       "      <td>25.37</td>\n",
       "      <td>1.08</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6312.7</td>\n",
       "      <td>36.24</td>\n",
       "      <td>0.93</td>\n",
       "      <td>24.55</td>\n",
       "      <td>1.05</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6056.8</td>\n",
       "      <td>38.23</td>\n",
       "      <td>0.95</td>\n",
       "      <td>24.98</td>\n",
       "      <td>1.03</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6375.9</td>\n",
       "      <td>40.83</td>\n",
       "      <td>0.99</td>\n",
       "      <td>24.96</td>\n",
       "      <td>1.03</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6974.3</td>\n",
       "      <td>44.62</td>\n",
       "      <td>1.00</td>\n",
       "      <td>25.52</td>\n",
       "      <td>0.99</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7101.6</td>\n",
       "      <td>52.27</td>\n",
       "      <td>1.00</td>\n",
       "      <td>26.01</td>\n",
       "      <td>1.00</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>7071.7</td>\n",
       "      <td>45.16</td>\n",
       "      <td>1.02</td>\n",
       "      <td>25.46</td>\n",
       "      <td>0.96</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7754.8</td>\n",
       "      <td>42.50</td>\n",
       "      <td>1.07</td>\n",
       "      <td>22.17</td>\n",
       "      <td>0.97</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>8480.3</td>\n",
       "      <td>43.70</td>\n",
       "      <td>1.12</td>\n",
       "      <td>18.56</td>\n",
       "      <td>0.98</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8105.2</td>\n",
       "      <td>47.88</td>\n",
       "      <td>1.10</td>\n",
       "      <td>21.32</td>\n",
       "      <td>1.01</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>7157.2</td>\n",
       "      <td>36.33</td>\n",
       "      <td>1.07</td>\n",
       "      <td>22.75</td>\n",
       "      <td>0.94</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    WORLDCONSUMPTION  COPPERPRICE  INCOMEINDEX  ALUMPRICE  INVENTORYINDEX  \\\n",
       "0             3173.0        26.56         0.70      19.76            0.98   \n",
       "1             3281.1        27.31         0.71      20.78            1.04   \n",
       "2             3135.7        32.95         0.72      22.55            1.05   \n",
       "3             3359.1        33.90         0.70      23.06            0.97   \n",
       "4             3755.1        42.70         0.74      24.93            1.02   \n",
       "5             3875.9        46.11         0.74      26.50            1.04   \n",
       "6             3905.7        31.70         0.74      27.24            0.98   \n",
       "7             3957.6        27.23         0.72      26.21            0.98   \n",
       "8             4279.1        32.89         0.75      26.09            1.03   \n",
       "9             4627.9        33.78         0.77      27.40            1.03   \n",
       "10            4910.2        31.66         0.76      26.94            0.98   \n",
       "11            4908.4        32.28         0.79      25.18            1.00   \n",
       "12            5327.9        32.38         0.83      23.94            0.97   \n",
       "13            5878.4        33.75         0.85      25.07            1.03   \n",
       "14            6075.2        36.25         0.89      25.37            1.08   \n",
       "15            6312.7        36.24         0.93      24.55            1.05   \n",
       "16            6056.8        38.23         0.95      24.98            1.03   \n",
       "17            6375.9        40.83         0.99      24.96            1.03   \n",
       "18            6974.3        44.62         1.00      25.52            0.99   \n",
       "19            7101.6        52.27         1.00      26.01            1.00   \n",
       "20            7071.7        45.16         1.02      25.46            0.96   \n",
       "21            7754.8        42.50         1.07      22.17            0.97   \n",
       "22            8480.3        43.70         1.12      18.56            0.98   \n",
       "23            8105.2        47.88         1.10      21.32            1.01   \n",
       "24            7157.2        36.33         1.07      22.75            0.94   \n",
       "\n",
       "    TIME  \n",
       "0    1.0  \n",
       "1    2.0  \n",
       "2    3.0  \n",
       "3    4.0  \n",
       "4    5.0  \n",
       "5    6.0  \n",
       "6    7.0  \n",
       "7    8.0  \n",
       "8    9.0  \n",
       "9   10.0  \n",
       "10  11.0  \n",
       "11  12.0  \n",
       "12  13.0  \n",
       "13  14.0  \n",
       "14  15.0  \n",
       "15  16.0  \n",
       "16  17.0  \n",
       "17  18.0  \n",
       "18  19.0  \n",
       "19  20.0  \n",
       "20  21.0  \n",
       "21  22.0  \n",
       "22  23.0  \n",
       "23  24.0  \n",
       "24  25.0  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.datasets import copper\n",
    "prices = copper.data.load_pandas()\n",
    "copper = prices['data']\n",
    "copper"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccd6550f",
   "metadata": {},
   "source": [
    "#### burada heatmap yardımı ile hangi değerlerin korelasyonlu oldugu bulundu\n",
    "#### burada 3 ve 4 numaralı değişkenler diğer bütün parametler ile oldukça düşük korelasyonlu iken, diğerleri arasında oldukça yüksek ilişki mevcut\n",
    "#### bu yüzden ols tablosundaki sıralamayı bu katsayılara bakarak ele alacağım. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "6168e124",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cor = np.corrcoef(copper.transpose())\n",
    "sns.heatmap(cor,cmap='PiYG', annot=True, vmin=-1, vmax=1) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a54e2c4",
   "metadata": {},
   "source": [
    "#### en yüksek ilişkili olan zaman gibi gözüküyor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "0458f0a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>TIME</td>       <th>  R-squared:         </th> <td>   0.988</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.985</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   307.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 01 Apr 2022</td> <th>  Prob (F-statistic):</th> <td>1.72e-17</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:47:14</td>     <th>  Log-Likelihood:    </th> <td> -29.785</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    25</td>      <th>  AIC:               </th> <td>   71.57</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    19</td>      <th>  BIC:               </th> <td>   78.88</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>  -23.1656</td> <td>    7.443</td> <td>   -3.112</td> <td> 0.006</td> <td>  -38.744</td> <td>   -7.587</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WORLDCONSUMPTION</th> <td>    0.0020</td> <td>    0.001</td> <td>    3.429</td> <td> 0.003</td> <td>    0.001</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>INCOMEINDEX</th>      <td>   32.2185</td> <td>    7.488</td> <td>    4.303</td> <td> 0.000</td> <td>   16.547</td> <td>   47.890</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>COPPERPRICE</th>      <td>   -0.1259</td> <td>    0.041</td> <td>   -3.080</td> <td> 0.006</td> <td>   -0.211</td> <td>   -0.040</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ALUMPRICE</th>        <td>    0.5748</td> <td>    0.097</td> <td>    5.950</td> <td> 0.000</td> <td>    0.373</td> <td>    0.777</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>INVENTORYINDEX</th>   <td>  -12.0591</td> <td>    5.675</td> <td>   -2.125</td> <td> 0.047</td> <td>  -23.938</td> <td>   -0.181</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.663</td> <th>  Durbin-Watson:     </th> <td>   1.322</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.718</td> <th>  Jarque-Bera (JB):  </th> <td>   0.271</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.255</td> <th>  Prob(JB):          </th> <td>   0.873</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.983</td> <th>  Cond. No.          </th> <td>3.12e+05</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 3.12e+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:                   TIME   R-squared:                       0.988\n",
       "Model:                            OLS   Adj. R-squared:                  0.985\n",
       "Method:                 Least Squares   F-statistic:                     307.7\n",
       "Date:                Fri, 01 Apr 2022   Prob (F-statistic):           1.72e-17\n",
       "Time:                        23:47:14   Log-Likelihood:                -29.785\n",
       "No. Observations:                  25   AIC:                             71.57\n",
       "Df Residuals:                      19   BIC:                             78.88\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "====================================================================================\n",
       "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------\n",
       "Intercept          -23.1656      7.443     -3.112      0.006     -38.744      -7.587\n",
       "WORLDCONSUMPTION     0.0020      0.001      3.429      0.003       0.001       0.003\n",
       "INCOMEINDEX         32.2185      7.488      4.303      0.000      16.547      47.890\n",
       "COPPERPRICE         -0.1259      0.041     -3.080      0.006      -0.211      -0.040\n",
       "ALUMPRICE            0.5748      0.097      5.950      0.000       0.373       0.777\n",
       "INVENTORYINDEX     -12.0591      5.675     -2.125      0.047     -23.938      -0.181\n",
       "==============================================================================\n",
       "Omnibus:                        0.663   Durbin-Watson:                   1.322\n",
       "Prob(Omnibus):                  0.718   Jarque-Bera (JB):                0.271\n",
       "Skew:                           0.255   Prob(JB):                        0.873\n",
       "Kurtosis:                       2.983   Cond. No.                     3.12e+05\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 3.12e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1 = copper.iloc[:,0:5]\n",
    "y1 = copper.iloc[:,5:6]\n",
    "\n",
    "modelcopper1 = ols(\"TIME ~  WORLDCONSUMPTION + INCOMEINDEX + COPPERPRICE + ALUMPRICE + INVENTORYINDEX\", data=copper).fit()\n",
    "modelcopper1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23499a7a",
   "metadata": {},
   "source": [
    "#### Ancak diğerlerine de bakmak gerekiyor. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "038652e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>WORLDCONSUMPTION</td> <th>  R-squared:         </th> <td>   0.978</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.973</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   171.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 01 Apr 2022</td> <th>  Prob (F-statistic):</th> <td>4.11e-15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:59:23</td>     <th>  Log-Likelihood:    </th> <td> -172.61</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    25</td>      <th>  AIC:               </th> <td>   357.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    19</td>      <th>  BIC:               </th> <td>   364.5</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>  283.9552</td> <td> 2768.572</td> <td>    0.103</td> <td> 0.919</td> <td>-5510.733</td> <td> 6078.644</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TIME</th>           <td>  187.2307</td> <td>   54.595</td> <td>    3.429</td> <td> 0.003</td> <td>   72.961</td> <td>  301.500</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>INCOMEINDEX</th>    <td> 1442.8858</td> <td> 3168.592</td> <td>    0.455</td> <td> 0.654</td> <td>-5189.054</td> <td> 8074.826</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>COPPERPRICE</th>    <td>   15.0432</td> <td>   14.761</td> <td>    1.019</td> <td> 0.321</td> <td>  -15.852</td> <td>   45.938</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ALUMPRICE</th>      <td>  -64.1230</td> <td>   47.258</td> <td>   -1.357</td> <td> 0.191</td> <td> -163.035</td> <td>   34.789</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>INVENTORYINDEX</th> <td> 2450.3845</td> <td> 1827.270</td> <td>    1.341</td> <td> 0.196</td> <td>-1374.136</td> <td> 6274.905</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 8.395</td> <th>  Durbin-Watson:     </th> <td>   1.312</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.015</td> <th>  Jarque-Bera (JB):  </th> <td>   6.465</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.959</td> <th>  Prob(JB):          </th> <td>  0.0395</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.589</td> <th>  Cond. No.          </th> <td>3.36e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 3.36e+03. 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:       WORLDCONSUMPTION   R-squared:                       0.978\n",
       "Model:                            OLS   Adj. R-squared:                  0.973\n",
       "Method:                 Least Squares   F-statistic:                     171.0\n",
       "Date:                Fri, 01 Apr 2022   Prob (F-statistic):           4.11e-15\n",
       "Time:                        23:59:23   Log-Likelihood:                -172.61\n",
       "No. Observations:                  25   AIC:                             357.2\n",
       "Df Residuals:                      19   BIC:                             364.5\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==================================================================================\n",
       "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "Intercept        283.9552   2768.572      0.103      0.919   -5510.733    6078.644\n",
       "TIME             187.2307     54.595      3.429      0.003      72.961     301.500\n",
       "INCOMEINDEX     1442.8858   3168.592      0.455      0.654   -5189.054    8074.826\n",
       "COPPERPRICE       15.0432     14.761      1.019      0.321     -15.852      45.938\n",
       "ALUMPRICE        -64.1230     47.258     -1.357      0.191    -163.035      34.789\n",
       "INVENTORYINDEX  2450.3845   1827.270      1.341      0.196   -1374.136    6274.905\n",
       "==============================================================================\n",
       "Omnibus:                        8.395   Durbin-Watson:                   1.312\n",
       "Prob(Omnibus):                  0.015   Jarque-Bera (JB):                6.465\n",
       "Skew:                          -0.959   Prob(JB):                       0.0395\n",
       "Kurtosis:                       4.589   Cond. No.                     3.36e+03\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 3.36e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelcopper2 = ols(\" WORLDCONSUMPTION ~  TIME + INCOMEINDEX + COPPERPRICE + ALUMPRICE + INVENTORYINDEX\", data=copper).fit()\n",
    "modelcopper2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "bba8b609",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>COPPERPRICE</td>   <th>  R-squared:         </th> <td>   0.709</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.633</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   9.278</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 01 Apr 2022</td> <th>  Prob (F-statistic):</th> <td>0.000133</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:59:51</td>     <th>  Log-Likelihood:    </th> <td> -67.842</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    25</td>      <th>  AIC:               </th> <td>   147.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    19</td>      <th>  BIC:               </th> <td>   155.0</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>  -97.4355</td> <td>   35.453</td> <td>   -2.748</td> <td> 0.013</td> <td> -171.639</td> <td>  -23.232</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TIME</th>             <td>   -2.6442</td> <td>    0.859</td> <td>   -3.080</td> <td> 0.006</td> <td>   -4.441</td> <td>   -0.847</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WORLDCONSUMPTION</th> <td>    0.0034</td> <td>    0.003</td> <td>    1.019</td> <td> 0.321</td> <td>   -0.004</td> <td>    0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>INCOMEINDEX</th>      <td>  132.8351</td> <td>   37.362</td> <td>    3.555</td> <td> 0.002</td> <td>   54.635</td> <td>  211.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ALUMPRICE</th>        <td>    2.0732</td> <td>    0.579</td> <td>    3.583</td> <td> 0.002</td> <td>    0.862</td> <td>    3.284</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>INVENTORYINDEX</th>   <td>  -15.1081</td> <td>   28.725</td> <td>   -0.526</td> <td> 0.605</td> <td>  -75.230</td> <td>   45.013</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.887</td> <th>  Durbin-Watson:     </th> <td>   1.504</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.236</td> <th>  Jarque-Bera (JB):  </th> <td>   2.376</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.736</td> <th>  Prob(JB):          </th> <td>   0.305</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.662</td> <th>  Cond. No.          </th> <td>3.18e+05</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 3.18e+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:            COPPERPRICE   R-squared:                       0.709\n",
       "Model:                            OLS   Adj. R-squared:                  0.633\n",
       "Method:                 Least Squares   F-statistic:                     9.278\n",
       "Date:                Fri, 01 Apr 2022   Prob (F-statistic):           0.000133\n",
       "Time:                        23:59:51   Log-Likelihood:                -67.842\n",
       "No. Observations:                  25   AIC:                             147.7\n",
       "Df Residuals:                      19   BIC:                             155.0\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "====================================================================================\n",
       "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------\n",
       "Intercept          -97.4355     35.453     -2.748      0.013    -171.639     -23.232\n",
       "TIME                -2.6442      0.859     -3.080      0.006      -4.441      -0.847\n",
       "WORLDCONSUMPTION     0.0034      0.003      1.019      0.321      -0.004       0.011\n",
       "INCOMEINDEX        132.8351     37.362      3.555      0.002      54.635     211.036\n",
       "ALUMPRICE            2.0732      0.579      3.583      0.002       0.862       3.284\n",
       "INVENTORYINDEX     -15.1081     28.725     -0.526      0.605     -75.230      45.013\n",
       "==============================================================================\n",
       "Omnibus:                        2.887   Durbin-Watson:                   1.504\n",
       "Prob(Omnibus):                  0.236   Jarque-Bera (JB):                2.376\n",
       "Skew:                           0.736   Prob(JB):                        0.305\n",
       "Kurtosis:                       2.662   Cond. No.                     3.18e+05\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 3.18e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelcopper3 = ols(\" COPPERPRICE ~  TIME + WORLDCONSUMPTION + INCOMEINDEX + ALUMPRICE + INVENTORYINDEX\", data=copper).fit()\n",
    "modelcopper3.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "9987b0cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>INCOMEINDEX</td>   <th>  R-squared:         </th> <td>   0.985</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.981</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   248.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 02 Apr 2022</td> <th>  Prob (F-statistic):</th> <td>1.28e-16</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>00:00:08</td>     <th>  Log-Likelihood:    </th> <td>  65.855</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    25</td>      <th>  AIC:               </th> <td>  -119.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    19</td>      <th>  BIC:               </th> <td>  -112.4</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>    0.6635</td> <td>    0.129</td> <td>    5.150</td> <td> 0.000</td> <td>    0.394</td> <td>    0.933</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TIME</th>             <td>    0.0153</td> <td>    0.004</td> <td>    4.303</td> <td> 0.000</td> <td>    0.008</td> <td>    0.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WORLDCONSUMPTION</th> <td> 7.482e-06</td> <td> 1.64e-05</td> <td>    0.455</td> <td> 0.654</td> <td>-2.69e-05</td> <td> 4.19e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>COPPERPRICE</th>      <td>    0.0030</td> <td>    0.001</td> <td>    3.555</td> <td> 0.002</td> <td>    0.001</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ALUMPRICE</th>        <td>   -0.0121</td> <td>    0.002</td> <td>   -5.456</td> <td> 0.000</td> <td>   -0.017</td> <td>   -0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>INVENTORYINDEX</th>   <td>    0.1453</td> <td>    0.134</td> <td>    1.088</td> <td> 0.290</td> <td>   -0.134</td> <td>    0.425</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.239</td> <th>  Durbin-Watson:     </th> <td>   1.184</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.538</td> <th>  Jarque-Bera (JB):  </th> <td>   1.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.250</td> <th>  Prob(JB):          </th> <td>   0.606</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.156</td> <th>  Cond. No.          </th> <td>2.52e+05</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 2.52e+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:            INCOMEINDEX   R-squared:                       0.985\n",
       "Model:                            OLS   Adj. R-squared:                  0.981\n",
       "Method:                 Least Squares   F-statistic:                     248.2\n",
       "Date:                Sat, 02 Apr 2022   Prob (F-statistic):           1.28e-16\n",
       "Time:                        00:00:08   Log-Likelihood:                 65.855\n",
       "No. Observations:                  25   AIC:                            -119.7\n",
       "Df Residuals:                      19   BIC:                            -112.4\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "====================================================================================\n",
       "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------\n",
       "Intercept            0.6635      0.129      5.150      0.000       0.394       0.933\n",
       "TIME                 0.0153      0.004      4.303      0.000       0.008       0.023\n",
       "WORLDCONSUMPTION  7.482e-06   1.64e-05      0.455      0.654   -2.69e-05    4.19e-05\n",
       "COPPERPRICE          0.0030      0.001      3.555      0.002       0.001       0.005\n",
       "ALUMPRICE           -0.0121      0.002     -5.456      0.000      -0.017      -0.007\n",
       "INVENTORYINDEX       0.1453      0.134      1.088      0.290      -0.134       0.425\n",
       "==============================================================================\n",
       "Omnibus:                        1.239   Durbin-Watson:                   1.184\n",
       "Prob(Omnibus):                  0.538   Jarque-Bera (JB):                1.001\n",
       "Skew:                           0.250   Prob(JB):                        0.606\n",
       "Kurtosis:                       2.156   Cond. No.                     2.52e+05\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 2.52e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelcopper4 = ols(\"INCOMEINDEX ~  TIME + WORLDCONSUMPTION +  COPPERPRICE+ ALUMPRICE + INVENTORYINDEX\", data=copper).fit()\n",
    "modelcopper4.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9b4c189",
   "metadata": {},
   "source": [
    "### OLS tablolarından da görüldüğü üzere R^2 değeri en yüksek ve p-değeri en düşük olan ilişki model1 olarak karşımıza çıkıyor. Ancak model1'de zaman bağımlı değişken olduğu için, bunun çok bir anlamı olmayabilir. Bu durumda 3. model INCOMEINDEX bağlı değişkenli olan model, en uygun çıktıyı bize sunmakta."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "f448c7b7",
   "metadata": {
    "scrolled": false
   },
   "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>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TIME</th>\n",
       "      <td>1.0</td>\n",
       "      <td>463.355580</td>\n",
       "      <td>463.355580</td>\n",
       "      <td>26.432303</td>\n",
       "      <td>0.000058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WORLDCONSUMPTION</th>\n",
       "      <td>1.0</td>\n",
       "      <td>63.141053</td>\n",
       "      <td>63.141053</td>\n",
       "      <td>3.601906</td>\n",
       "      <td>0.073014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INCOMEINDEX</th>\n",
       "      <td>1.0</td>\n",
       "      <td>45.859698</td>\n",
       "      <td>45.859698</td>\n",
       "      <td>2.616085</td>\n",
       "      <td>0.122267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALUMPRICE</th>\n",
       "      <td>1.0</td>\n",
       "      <td>236.013951</td>\n",
       "      <td>236.013951</td>\n",
       "      <td>13.463509</td>\n",
       "      <td>0.001630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INVENTORYINDEX</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.849386</td>\n",
       "      <td>4.849386</td>\n",
       "      <td>0.276635</td>\n",
       "      <td>0.605001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>19.0</td>\n",
       "      <td>333.068067</td>\n",
       "      <td>17.529898</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    df      sum_sq     mean_sq          F    PR(>F)\n",
       "TIME               1.0  463.355580  463.355580  26.432303  0.000058\n",
       "WORLDCONSUMPTION   1.0   63.141053   63.141053   3.601906  0.073014\n",
       "INCOMEINDEX        1.0   45.859698   45.859698   2.616085  0.122267\n",
       "ALUMPRICE          1.0  236.013951  236.013951  13.463509  0.001630\n",
       "INVENTORYINDEX     1.0    4.849386    4.849386   0.276635  0.605001\n",
       "Residual          19.0  333.068067   17.529898        NaN       NaN"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anova_lm(modelcopper3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d28eb449",
   "metadata": {},
   "source": [
    "#### Burada ise açık bir şekilde modelimiz açıklayan en iyi regresyon modelinin lineer regresyon olduğu gözükmekte. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "aa061257",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9877997611854856,\n",
       " 0.9722374878223046,\n",
       " 0.9661498206080497,\n",
       " 0.9697557535038337)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "getregressions(X1,y1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96d7f3fd",
   "metadata": {},
   "source": [
    "## Question 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "0162c65d",
   "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>CIC0</th>\n",
       "      <th>SM1_Dz</th>\n",
       "      <th>GATS1i</th>\n",
       "      <th>NdsCH</th>\n",
       "      <th>NdssC</th>\n",
       "      <th>MLOGP</th>\n",
       "      <th>LC50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.260</td>\n",
       "      <td>0.829</td>\n",
       "      <td>1.676</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.453</td>\n",
       "      <td>3.770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.189</td>\n",
       "      <td>0.580</td>\n",
       "      <td>0.863</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.348</td>\n",
       "      <td>3.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.125</td>\n",
       "      <td>0.638</td>\n",
       "      <td>0.831</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.348</td>\n",
       "      <td>3.531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.027</td>\n",
       "      <td>0.331</td>\n",
       "      <td>1.472</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.807</td>\n",
       "      <td>3.510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.094</td>\n",
       "      <td>0.827</td>\n",
       "      <td>0.860</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.886</td>\n",
       "      <td>5.390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>903</th>\n",
       "      <td>2.801</td>\n",
       "      <td>0.728</td>\n",
       "      <td>2.226</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.736</td>\n",
       "      <td>3.109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>904</th>\n",
       "      <td>3.652</td>\n",
       "      <td>0.872</td>\n",
       "      <td>0.867</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3.983</td>\n",
       "      <td>4.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>905</th>\n",
       "      <td>3.763</td>\n",
       "      <td>0.916</td>\n",
       "      <td>0.878</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2.918</td>\n",
       "      <td>4.818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>906</th>\n",
       "      <td>2.831</td>\n",
       "      <td>1.393</td>\n",
       "      <td>1.077</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.906</td>\n",
       "      <td>5.317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>907</th>\n",
       "      <td>4.057</td>\n",
       "      <td>1.032</td>\n",
       "      <td>1.183</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.754</td>\n",
       "      <td>8.201</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>908 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CIC0  SM1_Dz  GATS1i  NdsCH  NdssC  MLOGP   LC50\n",
       "0    3.260   0.829   1.676      0      1  1.453  3.770\n",
       "1    2.189   0.580   0.863      0      0  1.348  3.115\n",
       "2    2.125   0.638   0.831      0      0  1.348  3.531\n",
       "3    3.027   0.331   1.472      1      0  1.807  3.510\n",
       "4    2.094   0.827   0.860      0      0  1.886  5.390\n",
       "..     ...     ...     ...    ...    ...    ...    ...\n",
       "903  2.801   0.728   2.226      0      2  0.736  3.109\n",
       "904  3.652   0.872   0.867      2      3  3.983  4.040\n",
       "905  3.763   0.916   0.878      0      6  2.918  4.818\n",
       "906  2.831   1.393   1.077      0      1  0.906  5.317\n",
       "907  4.057   1.032   1.183      1      3  4.754  8.201\n",
       "\n",
       "[908 rows x 7 columns]"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fish = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/00504/qsar_fish_toxicity.csv',\n",
    "                   sep=';', header = None, names = [\"CIC0\", \"SM1_Dz\", \"GATS1i\", \"NdsCH\", \"NdssC\", \"MLOGP\", \"LC50\"])\n",
    "fish"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "7faa52b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 908 entries, 0 to 907\n",
      "Data columns (total 7 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   CIC0    908 non-null    float64\n",
      " 1   SM1_Dz  908 non-null    float64\n",
      " 2   GATS1i  908 non-null    float64\n",
      " 3   NdsCH   908 non-null    int64  \n",
      " 4   NdssC   908 non-null    int64  \n",
      " 5   MLOGP   908 non-null    float64\n",
      " 6   LC50    908 non-null    float64\n",
      "dtypes: float64(5), int64(2)\n",
      "memory usage: 49.8 KB\n"
     ]
    }
   ],
   "source": [
    "fish.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "d32ef52a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corfish = np.corrcoef(fish.transpose())\n",
    "sns.heatmap(corfish,cmap='PiYG', annot=True, vmin=-1, vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "ca9bfb5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5771635677620811, 0.577150787522513, 0.1944092781207699, 0.3218795769601678)"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X2 = fish.iloc[:,0:6]\n",
    "y2 = fish.iloc[:,6:7]\n",
    "getregressions(X2, y2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0ed0bb3",
   "metadata": {},
   "source": [
    "#### Bu model çok anlamsız."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "c3387833",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>LC50</td>       <th>  R-squared:         </th> <td>   0.577</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.574</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   205.0</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 02 Apr 2022</td> <th>  Prob (F-statistic):</th> <td>1.33e-164</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>03:28:56</td>     <th>  Log-Likelihood:    </th> <td> -1238.0</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   908</td>      <th>  AIC:               </th> <td>   2490.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   901</td>      <th>  BIC:               </th> <td>   2524.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     6</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    2.1746</td> <td>    0.181</td> <td>   12.000</td> <td> 0.000</td> <td>    1.819</td> <td>    2.530</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CIC0</th>      <td>    0.3856</td> <td>    0.061</td> <td>    6.333</td> <td> 0.000</td> <td>    0.266</td> <td>    0.505</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SM1_Dz</th>    <td>    1.2556</td> <td>    0.087</td> <td>   14.430</td> <td> 0.000</td> <td>    1.085</td> <td>    1.426</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GATS1i</th>    <td>   -0.7464</td> <td>    0.101</td> <td>   -7.365</td> <td> 0.000</td> <td>   -0.945</td> <td>   -0.548</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>NdsCH</th>     <td>    0.4136</td> <td>    0.054</td> <td>    7.644</td> <td> 0.000</td> <td>    0.307</td> <td>    0.520</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>NdssC</th>     <td>    0.0643</td> <td>    0.041</td> <td>    1.583</td> <td> 0.114</td> <td>   -0.015</td> <td>    0.144</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MLOGP</th>     <td>    0.3901</td> <td>    0.034</td> <td>   11.555</td> <td> 0.000</td> <td>    0.324</td> <td>    0.456</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>100.278</td> <th>  Durbin-Watson:     </th> <td>   1.979</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 379.239</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.468</td>  <th>  Prob(JB):          </th> <td>4.46e-83</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 6.024</td>  <th>  Cond. No.          </th> <td>    25.9</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                   LC50   R-squared:                       0.577\n",
       "Model:                            OLS   Adj. R-squared:                  0.574\n",
       "Method:                 Least Squares   F-statistic:                     205.0\n",
       "Date:                Sat, 02 Apr 2022   Prob (F-statistic):          1.33e-164\n",
       "Time:                        03:28:56   Log-Likelihood:                -1238.0\n",
       "No. Observations:                 908   AIC:                             2490.\n",
       "Df Residuals:                     901   BIC:                             2524.\n",
       "Df Model:                           6                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      2.1746      0.181     12.000      0.000       1.819       2.530\n",
       "CIC0           0.3856      0.061      6.333      0.000       0.266       0.505\n",
       "SM1_Dz         1.2556      0.087     14.430      0.000       1.085       1.426\n",
       "GATS1i        -0.7464      0.101     -7.365      0.000      -0.945      -0.548\n",
       "NdsCH          0.4136      0.054      7.644      0.000       0.307       0.520\n",
       "NdssC          0.0643      0.041      1.583      0.114      -0.015       0.144\n",
       "MLOGP          0.3901      0.034     11.555      0.000       0.324       0.456\n",
       "==============================================================================\n",
       "Omnibus:                      100.278   Durbin-Watson:                   1.979\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              379.239\n",
       "Skew:                           0.468   Prob(JB):                     4.46e-83\n",
       "Kurtosis:                       6.024   Cond. No.                         25.9\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelfish = ols(\"LC50 ~  CIC0 + SM1_Dz + GATS1i + NdsCH + NdssC + MLOGP\", data=fish).fit()\n",
    "modelfish.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3a1924b",
   "metadata": {},
   "source": [
    "#### integer değerleri çıkarıp baktığımızda yine anlamsız bir sonuç geldi. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "4593742b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>LC50</td>       <th>  R-squared:         </th> <td>   0.516</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.515</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   321.5</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 02 Apr 2022</td> <th>  Prob (F-statistic):</th> <td>5.11e-142</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>03:28:43</td>     <th>  Log-Likelihood:    </th> <td> -1299.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   908</td>      <th>  AIC:               </th> <td>   2606.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   904</td>      <th>  BIC:               </th> <td>   2626.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    2.8183</td> <td>    0.168</td> <td>   16.780</td> <td> 0.000</td> <td>    2.489</td> <td>    3.148</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SM1_Dz</th>    <td>    0.9699</td> <td>    0.080</td> <td>   12.064</td> <td> 0.000</td> <td>    0.812</td> <td>    1.128</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MLOGP</th>     <td>    0.5529</td> <td>    0.027</td> <td>   20.759</td> <td> 0.000</td> <td>    0.501</td> <td>    0.605</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GATS1i</th>    <td>   -0.4095</td> <td>    0.096</td> <td>   -4.271</td> <td> 0.000</td> <td>   -0.598</td> <td>   -0.221</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>124.173</td> <th>  Durbin-Watson:     </th> <td>   2.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 350.462</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.697</td>  <th>  Prob(JB):          </th> <td>7.91e-77</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.706</td>  <th>  Cond. No.          </th> <td>    16.9</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                   LC50   R-squared:                       0.516\n",
       "Model:                            OLS   Adj. R-squared:                  0.515\n",
       "Method:                 Least Squares   F-statistic:                     321.5\n",
       "Date:                Sat, 02 Apr 2022   Prob (F-statistic):          5.11e-142\n",
       "Time:                        03:28:43   Log-Likelihood:                -1299.2\n",
       "No. Observations:                 908   AIC:                             2606.\n",
       "Df Residuals:                     904   BIC:                             2626.\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      2.8183      0.168     16.780      0.000       2.489       3.148\n",
       "SM1_Dz         0.9699      0.080     12.064      0.000       0.812       1.128\n",
       "MLOGP          0.5529      0.027     20.759      0.000       0.501       0.605\n",
       "GATS1i        -0.4095      0.096     -4.271      0.000      -0.598      -0.221\n",
       "==============================================================================\n",
       "Omnibus:                      124.173   Durbin-Watson:                   2.038\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              350.462\n",
       "Skew:                           0.697   Prob(JB):                     7.91e-77\n",
       "Kurtosis:                       5.706   Cond. No.                         16.9\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelfish = ols(\"LC50 ~  SM1_Dz + MLOGP + GATS1i\", data=fish).fit()\n",
    "modelfish.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "e7e5c860",
   "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>CIC0</th>\n",
       "      <th>SM1_Dz</th>\n",
       "      <th>GATS1i</th>\n",
       "      <th>NdssC</th>\n",
       "      <th>MLOGP</th>\n",
       "      <th>LC50</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NdsCH</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.875372</td>\n",
       "      <td>0.654286</td>\n",
       "      <td>1.297339</td>\n",
       "      <td>0.435526</td>\n",
       "      <td>2.100351</td>\n",
       "      <td>3.957566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.880626</td>\n",
       "      <td>0.509467</td>\n",
       "      <td>1.256019</td>\n",
       "      <td>0.523364</td>\n",
       "      <td>2.010439</td>\n",
       "      <td>4.500028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.288517</td>\n",
       "      <td>0.540483</td>\n",
       "      <td>1.345793</td>\n",
       "      <td>1.344828</td>\n",
       "      <td>2.455172</td>\n",
       "      <td>4.830862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.255000</td>\n",
       "      <td>0.244600</td>\n",
       "      <td>1.282800</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>2.223200</td>\n",
       "      <td>4.688000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.764143</td>\n",
       "      <td>0.283143</td>\n",
       "      <td>1.252429</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>3.075857</td>\n",
       "      <td>5.387857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           CIC0    SM1_Dz    GATS1i     NdssC     MLOGP      LC50\n",
       "NdsCH                                                            \n",
       "0      2.875372  0.654286  1.297339  0.435526  2.100351  3.957566\n",
       "1      2.880626  0.509467  1.256019  0.523364  2.010439  4.500028\n",
       "2      3.288517  0.540483  1.345793  1.344828  2.455172  4.830862\n",
       "3      3.255000  0.244600  1.282800  0.800000  2.223200  4.688000\n",
       "4      3.764143  0.283143  1.252429  1.571429  3.075857  5.387857"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fishdata = fish.groupby(\"NdsCH\").mean()\n",
    "fishdata "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc1cd82e",
   "metadata": {},
   "source": [
    "#### Hala anlamlı bir şey bulamadım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "7947165e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>LC50</td>       <th>  R-squared:         </th> <td>   0.550</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.547</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   220.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 02 Apr 2022</td> <th>  Prob (F-statistic):</th> <td>1.51e-153</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>03:31:56</td>     <th>  Log-Likelihood:    </th> <td> -1266.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   908</td>      <th>  AIC:               </th> <td>   2545.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   902</td>      <th>  BIC:               </th> <td>   2574.</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>    2.3149</td> <td>    0.186</td> <td>   12.450</td> <td> 0.000</td> <td>    1.950</td> <td>    2.680</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CIC0</th>      <td>    0.3809</td> <td>    0.063</td> <td>    6.066</td> <td> 0.000</td> <td>    0.258</td> <td>    0.504</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SM1_Dz</th>    <td>    1.1385</td> <td>    0.088</td> <td>   12.887</td> <td> 0.000</td> <td>    0.965</td> <td>    1.312</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MLOGP</th>     <td>    0.4038</td> <td>    0.035</td> <td>   11.615</td> <td> 0.000</td> <td>    0.336</td> <td>    0.472</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GATS1i</th>    <td>   -0.7611</td> <td>    0.105</td> <td>   -7.283</td> <td> 0.000</td> <td>   -0.966</td> <td>   -0.556</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>NdssC</th>     <td>    0.1295</td> <td>    0.041</td> <td>    3.161</td> <td> 0.002</td> <td>    0.049</td> <td>    0.210</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>99.276</td> <th>  Durbin-Watson:     </th> <td>   2.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 317.966</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.518</td> <th>  Prob(JB):          </th> <td>9.01e-70</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.708</td> <th>  Cond. No.          </th> <td>    25.7</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                   LC50   R-squared:                       0.550\n",
       "Model:                            OLS   Adj. R-squared:                  0.547\n",
       "Method:                 Least Squares   F-statistic:                     220.3\n",
       "Date:                Sat, 02 Apr 2022   Prob (F-statistic):          1.51e-153\n",
       "Time:                        03:31:56   Log-Likelihood:                -1266.6\n",
       "No. Observations:                 908   AIC:                             2545.\n",
       "Df Residuals:                     902   BIC:                             2574.\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      2.3149      0.186     12.450      0.000       1.950       2.680\n",
       "CIC0           0.3809      0.063      6.066      0.000       0.258       0.504\n",
       "SM1_Dz         1.1385      0.088     12.887      0.000       0.965       1.312\n",
       "MLOGP          0.4038      0.035     11.615      0.000       0.336       0.472\n",
       "GATS1i        -0.7611      0.105     -7.283      0.000      -0.966      -0.556\n",
       "NdssC          0.1295      0.041      3.161      0.002       0.049       0.210\n",
       "==============================================================================\n",
       "Omnibus:                       99.276   Durbin-Watson:                   2.009\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              317.966\n",
       "Skew:                           0.518   Prob(JB):                     9.01e-70\n",
       "Kurtosis:                       5.708   Cond. No.                         25.7\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelfishdata = ols(\"LC50 ~  CIC0 + SM1_Dz + MLOGP + GATS1i\", data=fish).fit()\n",
    "modelfishdata.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "e88801a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>LC50</td>       <th>  R-squared:         </th> <td>   0.545</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.543</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   270.1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 02 Apr 2022</td> <th>  Prob (F-statistic):</th> <td>1.23e-152</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>03:32:55</td>     <th>  Log-Likelihood:    </th> <td> -1271.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   908</td>      <th>  AIC:               </th> <td>   2553.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   903</td>      <th>  BIC:               </th> <td>   2577.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    2.1944</td> <td>    0.183</td> <td>   11.999</td> <td> 0.000</td> <td>    1.835</td> <td>    2.553</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CIC0</th>      <td>    0.4475</td> <td>    0.059</td> <td>    7.527</td> <td> 0.000</td> <td>    0.331</td> <td>    0.564</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SM1_Dz</th>    <td>    1.2207</td> <td>    0.085</td> <td>   14.388</td> <td> 0.000</td> <td>    1.054</td> <td>    1.387</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MLOGP</th>     <td>    0.3831</td> <td>    0.034</td> <td>   11.165</td> <td> 0.000</td> <td>    0.316</td> <td>    0.450</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GATS1i</th>    <td>   -0.7746</td> <td>    0.105</td> <td>   -7.382</td> <td> 0.000</td> <td>   -0.981</td> <td>   -0.569</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>106.679</td> <th>  Durbin-Watson:     </th> <td>   2.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 332.948</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.568</td>  <th>  Prob(JB):          </th> <td>5.03e-73</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.740</td>  <th>  Cond. No.          </th> <td>    24.9</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                   LC50   R-squared:                       0.545\n",
       "Model:                            OLS   Adj. R-squared:                  0.543\n",
       "Method:                 Least Squares   F-statistic:                     270.1\n",
       "Date:                Sat, 02 Apr 2022   Prob (F-statistic):          1.23e-152\n",
       "Time:                        03:32:55   Log-Likelihood:                -1271.6\n",
       "No. Observations:                 908   AIC:                             2553.\n",
       "Df Residuals:                     903   BIC:                             2577.\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      2.1944      0.183     11.999      0.000       1.835       2.553\n",
       "CIC0           0.4475      0.059      7.527      0.000       0.331       0.564\n",
       "SM1_Dz         1.2207      0.085     14.388      0.000       1.054       1.387\n",
       "MLOGP          0.3831      0.034     11.165      0.000       0.316       0.450\n",
       "GATS1i        -0.7746      0.105     -7.382      0.000      -0.981      -0.569\n",
       "==============================================================================\n",
       "Omnibus:                      106.679   Durbin-Watson:                   2.007\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              332.948\n",
       "Skew:                           0.568   Prob(JB):                     5.03e-73\n",
       "Kurtosis:                       5.740   Cond. No.                         24.9\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fishdata2 = fish.groupby(\"NdssC\").mean()\n",
    "modelfishdata2 = ols(\"LC50 ~  CIC0 + SM1_Dz + MLOGP + GATS1i\", data=fish).fit()\n",
    "modelfishdata2.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e8c2c45",
   "metadata": {},
   "source": [
    "## Question3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0407652a",
   "metadata": {},
   "source": [
    "#### Datanın alındığı siteyi inceleyerek ilgili sutünların isimlerinin açıklamalarını buldum\n",
    "#### Ardından bunları kopyalayarak bir excel dosyası içerisine attım ve headerları bu şekilde yerleştirdim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "05be76fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "n = pd.read_excel(\"names.xlsx\") \n",
    "name = n.iloc[:,0].tolist()\n",
    "names = []\n",
    "for i in name:\n",
    "    names.append(i.replace(\":\", \"\"))\n",
    "interconnections = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/00211/CommViolPredUnnormalizedData.txt',\n",
    "                               sep=',',\n",
    "                               header=None, names = names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8ebb1b4",
   "metadata": {},
   "source": [
    "#### Burada ise, ? işareti olan verileri 0 ile değiştirdim. Doğrudan NA olan dataları ise dropladım. Ardından prediction için anlam ifade etmeyen sutünları bıraktım. Sonrasında hem State'e hem de Country'ye göre gruplayarak bu iki gruba göre ayrı ayrı incelemek için iki farklı data set kaydettim.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "01551779",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(names)-2):\n",
    "    interconnections.loc[:,names[i+2]] = interconnections.loc[:,names[i+2]].replace(\"?\",0).astype(float)\n",
    "\n",
    "interconnections.dropna(inplace=True)\n",
    "community = interconnections.drop(columns=[\"state\", \"countyCode\", \"communityCode\",\"fold\"])\n",
    "communitydata = community.groupby(\"communityname\").mean()\n",
    "\n",
    "state = interconnections.drop(columns=[\"communityname\", \"countyCode\", \"communityCode\",\"fold\"])\n",
    "statedata = state.groupby(\"state\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "4ee82cb8",
   "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>population</th>\n",
       "      <th>householdsize</th>\n",
       "      <th>racepctblack</th>\n",
       "      <th>racePctWhite</th>\n",
       "      <th>racePctAsian</th>\n",
       "      <th>racePctHisp</th>\n",
       "      <th>agePct12t21</th>\n",
       "      <th>agePct12t29</th>\n",
       "      <th>agePct16t24</th>\n",
       "      <th>agePct65up</th>\n",
       "      <th>...</th>\n",
       "      <th>burglaries</th>\n",
       "      <th>burglPerPop</th>\n",
       "      <th>larcenies</th>\n",
       "      <th>larcPerPop</th>\n",
       "      <th>autoTheft</th>\n",
       "      <th>autoTheftPerPop</th>\n",
       "      <th>arsons</th>\n",
       "      <th>arsonsPerPop</th>\n",
       "      <th>ViolentCrimesPerPop</th>\n",
       "      <th>nonViolPerPop</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CT</th>\n",
       "      <td>76882.176471</td>\n",
       "      <td>2.592353</td>\n",
       "      <td>12.388235</td>\n",
       "      <td>80.965882</td>\n",
       "      <td>1.965294</td>\n",
       "      <td>9.925882</td>\n",
       "      <td>12.525882</td>\n",
       "      <td>26.712941</td>\n",
       "      <td>12.998824</td>\n",
       "      <td>13.481765</td>\n",
       "      <td>...</td>\n",
       "      <td>1153.117647</td>\n",
       "      <td>1274.794118</td>\n",
       "      <td>2907.117647</td>\n",
       "      <td>3568.864706</td>\n",
       "      <td>848.411765</td>\n",
       "      <td>927.178824</td>\n",
       "      <td>41.176471</td>\n",
       "      <td>39.391765</td>\n",
       "      <td>630.151765</td>\n",
       "      <td>5810.228235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DC</th>\n",
       "      <td>606900.000000</td>\n",
       "      <td>2.430000</td>\n",
       "      <td>65.840000</td>\n",
       "      <td>29.600000</td>\n",
       "      <td>1.850000</td>\n",
       "      <td>5.390000</td>\n",
       "      <td>13.540000</td>\n",
       "      <td>29.830000</td>\n",
       "      <td>15.690000</td>\n",
       "      <td>11.530000</td>\n",
       "      <td>...</td>\n",
       "      <td>11532.000000</td>\n",
       "      <td>2081.590000</td>\n",
       "      <td>31466.000000</td>\n",
       "      <td>5679.780000</td>\n",
       "      <td>8060.000000</td>\n",
       "      <td>1454.870000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>36.100000</td>\n",
       "      <td>3048.380000</td>\n",
       "      <td>9252.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MA</th>\n",
       "      <td>71790.272727</td>\n",
       "      <td>2.558182</td>\n",
       "      <td>4.610000</td>\n",
       "      <td>88.352727</td>\n",
       "      <td>4.656364</td>\n",
       "      <td>5.487273</td>\n",
       "      <td>13.421818</td>\n",
       "      <td>31.127273</td>\n",
       "      <td>15.872727</td>\n",
       "      <td>12.375455</td>\n",
       "      <td>...</td>\n",
       "      <td>797.818182</td>\n",
       "      <td>1085.870000</td>\n",
       "      <td>1590.181818</td>\n",
       "      <td>2194.740909</td>\n",
       "      <td>698.000000</td>\n",
       "      <td>928.831818</td>\n",
       "      <td>21.545455</td>\n",
       "      <td>25.666364</td>\n",
       "      <td>823.855455</td>\n",
       "      <td>4235.105455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MD</th>\n",
       "      <td>736014.000000</td>\n",
       "      <td>2.660000</td>\n",
       "      <td>59.210000</td>\n",
       "      <td>39.100000</td>\n",
       "      <td>1.080000</td>\n",
       "      <td>1.030000</td>\n",
       "      <td>13.230000</td>\n",
       "      <td>27.660000</td>\n",
       "      <td>13.550000</td>\n",
       "      <td>12.390000</td>\n",
       "      <td>...</td>\n",
       "      <td>17901.000000</td>\n",
       "      <td>2513.450000</td>\n",
       "      <td>41451.000000</td>\n",
       "      <td>5820.060000</td>\n",
       "      <td>10623.000000</td>\n",
       "      <td>1491.560000</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>76.520000</td>\n",
       "      <td>3081.260000</td>\n",
       "      <td>9901.590000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ME</th>\n",
       "      <td>64358.000000</td>\n",
       "      <td>2.280000</td>\n",
       "      <td>1.120000</td>\n",
       "      <td>96.590000</td>\n",
       "      <td>1.660000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>11.840000</td>\n",
       "      <td>30.020000</td>\n",
       "      <td>14.820000</td>\n",
       "      <td>12.710000</td>\n",
       "      <td>...</td>\n",
       "      <td>1142.000000</td>\n",
       "      <td>1847.810000</td>\n",
       "      <td>2978.000000</td>\n",
       "      <td>4818.540000</td>\n",
       "      <td>272.000000</td>\n",
       "      <td>440.110000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>182.840000</td>\n",
       "      <td>673.110000</td>\n",
       "      <td>7289.290000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NH</th>\n",
       "      <td>89614.500000</td>\n",
       "      <td>2.520000</td>\n",
       "      <td>1.295000</td>\n",
       "      <td>96.060000</td>\n",
       "      <td>1.515000</td>\n",
       "      <td>2.575000</td>\n",
       "      <td>12.405000</td>\n",
       "      <td>29.100000</td>\n",
       "      <td>13.440000</td>\n",
       "      <td>10.655000</td>\n",
       "      <td>...</td>\n",
       "      <td>828.500000</td>\n",
       "      <td>886.785000</td>\n",
       "      <td>2735.500000</td>\n",
       "      <td>3004.250000</td>\n",
       "      <td>357.000000</td>\n",
       "      <td>393.585000</td>\n",
       "      <td>31.500000</td>\n",
       "      <td>36.020000</td>\n",
       "      <td>153.840000</td>\n",
       "      <td>4320.645000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NJ</th>\n",
       "      <td>71716.406250</td>\n",
       "      <td>2.785000</td>\n",
       "      <td>21.936563</td>\n",
       "      <td>66.266250</td>\n",
       "      <td>3.780313</td>\n",
       "      <td>19.971250</td>\n",
       "      <td>13.589375</td>\n",
       "      <td>28.148750</td>\n",
       "      <td>13.848750</td>\n",
       "      <td>12.010937</td>\n",
       "      <td>...</td>\n",
       "      <td>1193.562500</td>\n",
       "      <td>1463.495000</td>\n",
       "      <td>2239.718750</td>\n",
       "      <td>3112.069375</td>\n",
       "      <td>1136.812500</td>\n",
       "      <td>1260.657500</td>\n",
       "      <td>35.437500</td>\n",
       "      <td>43.653750</td>\n",
       "      <td>1053.811250</td>\n",
       "      <td>5879.875625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>142919.125000</td>\n",
       "      <td>2.460000</td>\n",
       "      <td>21.582500</td>\n",
       "      <td>72.938750</td>\n",
       "      <td>2.107500</td>\n",
       "      <td>7.015000</td>\n",
       "      <td>13.986250</td>\n",
       "      <td>29.560000</td>\n",
       "      <td>15.575000</td>\n",
       "      <td>13.548750</td>\n",
       "      <td>...</td>\n",
       "      <td>2695.000000</td>\n",
       "      <td>1583.182500</td>\n",
       "      <td>5909.375000</td>\n",
       "      <td>4249.648750</td>\n",
       "      <td>1474.500000</td>\n",
       "      <td>735.275000</td>\n",
       "      <td>131.750000</td>\n",
       "      <td>63.791250</td>\n",
       "      <td>954.011250</td>\n",
       "      <td>6631.893750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OH</th>\n",
       "      <td>279116.125000</td>\n",
       "      <td>2.508750</td>\n",
       "      <td>26.263750</td>\n",
       "      <td>71.742500</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>1.655000</td>\n",
       "      <td>14.476250</td>\n",
       "      <td>28.820000</td>\n",
       "      <td>14.612500</td>\n",
       "      <td>12.268750</td>\n",
       "      <td>...</td>\n",
       "      <td>5107.500000</td>\n",
       "      <td>1826.753750</td>\n",
       "      <td>11900.125000</td>\n",
       "      <td>4599.243750</td>\n",
       "      <td>3531.125000</td>\n",
       "      <td>1096.946250</td>\n",
       "      <td>376.750000</td>\n",
       "      <td>110.477500</td>\n",
       "      <td>1391.083750</td>\n",
       "      <td>7633.418750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PA</th>\n",
       "      <td>304672.000000</td>\n",
       "      <td>2.517500</td>\n",
       "      <td>19.598750</td>\n",
       "      <td>73.393750</td>\n",
       "      <td>1.527500</td>\n",
       "      <td>8.505000</td>\n",
       "      <td>14.303750</td>\n",
       "      <td>28.215000</td>\n",
       "      <td>14.635000</td>\n",
       "      <td>14.593750</td>\n",
       "      <td>...</td>\n",
       "      <td>3292.750000</td>\n",
       "      <td>1349.506250</td>\n",
       "      <td>8814.500000</td>\n",
       "      <td>3684.141250</td>\n",
       "      <td>4082.750000</td>\n",
       "      <td>812.270000</td>\n",
       "      <td>356.375000</td>\n",
       "      <td>66.420000</td>\n",
       "      <td>1057.403750</td>\n",
       "      <td>5912.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RI</th>\n",
       "      <td>84617.200000</td>\n",
       "      <td>2.568000</td>\n",
       "      <td>5.942000</td>\n",
       "      <td>88.176000</td>\n",
       "      <td>2.120000</td>\n",
       "      <td>5.702000</td>\n",
       "      <td>14.066000</td>\n",
       "      <td>28.636000</td>\n",
       "      <td>14.996000</td>\n",
       "      <td>14.026000</td>\n",
       "      <td>...</td>\n",
       "      <td>1419.800000</td>\n",
       "      <td>1620.398000</td>\n",
       "      <td>2780.800000</td>\n",
       "      <td>3643.206000</td>\n",
       "      <td>988.600000</td>\n",
       "      <td>946.768000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>106.488000</td>\n",
       "      <td>630.072000</td>\n",
       "      <td>6316.858000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>99405.000000</td>\n",
       "      <td>2.530000</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>96.820000</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>0.570000</td>\n",
       "      <td>13.700000</td>\n",
       "      <td>29.200000</td>\n",
       "      <td>13.920000</td>\n",
       "      <td>10.290000</td>\n",
       "      <td>...</td>\n",
       "      <td>735.000000</td>\n",
       "      <td>665.850000</td>\n",
       "      <td>3470.000000</td>\n",
       "      <td>3143.540000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>176.650000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>26.270000</td>\n",
       "      <td>430.310000</td>\n",
       "      <td>4012.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VA</th>\n",
       "      <td>169070.400000</td>\n",
       "      <td>2.622000</td>\n",
       "      <td>32.791000</td>\n",
       "      <td>63.957000</td>\n",
       "      <td>1.962000</td>\n",
       "      <td>2.534000</td>\n",
       "      <td>14.238000</td>\n",
       "      <td>30.384000</td>\n",
       "      <td>15.422000</td>\n",
       "      <td>10.519000</td>\n",
       "      <td>...</td>\n",
       "      <td>2006.800000</td>\n",
       "      <td>1091.522000</td>\n",
       "      <td>7507.900000</td>\n",
       "      <td>4303.099000</td>\n",
       "      <td>1003.800000</td>\n",
       "      <td>594.469000</td>\n",
       "      <td>100.100000</td>\n",
       "      <td>60.515000</td>\n",
       "      <td>807.127000</td>\n",
       "      <td>6049.601000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WI</th>\n",
       "      <td>190614.500000</td>\n",
       "      <td>2.548333</td>\n",
       "      <td>10.050000</td>\n",
       "      <td>85.448333</td>\n",
       "      <td>1.640000</td>\n",
       "      <td>4.145000</td>\n",
       "      <td>14.278333</td>\n",
       "      <td>29.521667</td>\n",
       "      <td>14.771667</td>\n",
       "      <td>11.648333</td>\n",
       "      <td>...</td>\n",
       "      <td>2178.333333</td>\n",
       "      <td>989.605000</td>\n",
       "      <td>7600.166667</td>\n",
       "      <td>3798.053333</td>\n",
       "      <td>2136.333333</td>\n",
       "      <td>623.498333</td>\n",
       "      <td>115.666667</td>\n",
       "      <td>45.733333</td>\n",
       "      <td>551.311667</td>\n",
       "      <td>5456.888333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14 rows × 142 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          population  householdsize  racepctblack  racePctWhite  racePctAsian  \\\n",
       "state                                                                           \n",
       "CT      76882.176471       2.592353     12.388235     80.965882      1.965294   \n",
       "DC     606900.000000       2.430000     65.840000     29.600000      1.850000   \n",
       "MA      71790.272727       2.558182      4.610000     88.352727      4.656364   \n",
       "MD     736014.000000       2.660000     59.210000     39.100000      1.080000   \n",
       "ME      64358.000000       2.280000      1.120000     96.590000      1.660000   \n",
       "NH      89614.500000       2.520000      1.295000     96.060000      1.515000   \n",
       "NJ      71716.406250       2.785000     21.936563     66.266250      3.780313   \n",
       "NY     142919.125000       2.460000     21.582500     72.938750      2.107500   \n",
       "OH     279116.125000       2.508750     26.263750     71.742500      0.925000   \n",
       "PA     304672.000000       2.517500     19.598750     73.393750      1.527500   \n",
       "RI      84617.200000       2.568000      5.942000     88.176000      2.120000   \n",
       "SD      99405.000000       2.530000      0.730000     96.820000      0.680000   \n",
       "VA     169070.400000       2.622000     32.791000     63.957000      1.962000   \n",
       "WI     190614.500000       2.548333     10.050000     85.448333      1.640000   \n",
       "\n",
       "       racePctHisp  agePct12t21  agePct12t29  agePct16t24  agePct65up  ...  \\\n",
       "state                                                                  ...   \n",
       "CT        9.925882    12.525882    26.712941    12.998824   13.481765  ...   \n",
       "DC        5.390000    13.540000    29.830000    15.690000   11.530000  ...   \n",
       "MA        5.487273    13.421818    31.127273    15.872727   12.375455  ...   \n",
       "MD        1.030000    13.230000    27.660000    13.550000   12.390000  ...   \n",
       "ME        0.800000    11.840000    30.020000    14.820000   12.710000  ...   \n",
       "NH        2.575000    12.405000    29.100000    13.440000   10.655000  ...   \n",
       "NJ       19.971250    13.589375    28.148750    13.848750   12.010937  ...   \n",
       "NY        7.015000    13.986250    29.560000    15.575000   13.548750  ...   \n",
       "OH        1.655000    14.476250    28.820000    14.612500   12.268750  ...   \n",
       "PA        8.505000    14.303750    28.215000    14.635000   14.593750  ...   \n",
       "RI        5.702000    14.066000    28.636000    14.996000   14.026000  ...   \n",
       "SD        0.570000    13.700000    29.200000    13.920000   10.290000  ...   \n",
       "VA        2.534000    14.238000    30.384000    15.422000   10.519000  ...   \n",
       "WI        4.145000    14.278333    29.521667    14.771667   11.648333  ...   \n",
       "\n",
       "         burglaries  burglPerPop     larcenies   larcPerPop     autoTheft  \\\n",
       "state                                                                       \n",
       "CT      1153.117647  1274.794118   2907.117647  3568.864706    848.411765   \n",
       "DC     11532.000000  2081.590000  31466.000000  5679.780000   8060.000000   \n",
       "MA       797.818182  1085.870000   1590.181818  2194.740909    698.000000   \n",
       "MD     17901.000000  2513.450000  41451.000000  5820.060000  10623.000000   \n",
       "ME      1142.000000  1847.810000   2978.000000  4818.540000    272.000000   \n",
       "NH       828.500000   886.785000   2735.500000  3004.250000    357.000000   \n",
       "NJ      1193.562500  1463.495000   2239.718750  3112.069375   1136.812500   \n",
       "NY      2695.000000  1583.182500   5909.375000  4249.648750   1474.500000   \n",
       "OH      5107.500000  1826.753750  11900.125000  4599.243750   3531.125000   \n",
       "PA      3292.750000  1349.506250   8814.500000  3684.141250   4082.750000   \n",
       "RI      1419.800000  1620.398000   2780.800000  3643.206000    988.600000   \n",
       "SD       735.000000   665.850000   3470.000000  3143.540000    195.000000   \n",
       "VA      2006.800000  1091.522000   7507.900000  4303.099000   1003.800000   \n",
       "WI      2178.333333   989.605000   7600.166667  3798.053333   2136.333333   \n",
       "\n",
       "       autoTheftPerPop      arsons  arsonsPerPop  ViolentCrimesPerPop  \\\n",
       "state                                                                   \n",
       "CT          927.178824   41.176471     39.391765           630.151765   \n",
       "DC         1454.870000  200.000000     36.100000          3048.380000   \n",
       "MA          928.831818   21.545455     25.666364           823.855455   \n",
       "MD         1491.560000  545.000000     76.520000          3081.260000   \n",
       "ME          440.110000  113.000000    182.840000           673.110000   \n",
       "NH          393.585000   31.500000     36.020000           153.840000   \n",
       "NJ         1260.657500   35.437500     43.653750          1053.811250   \n",
       "NY          735.275000  131.750000     63.791250           954.011250   \n",
       "OH         1096.946250  376.750000    110.477500          1391.083750   \n",
       "PA          812.270000  356.375000     66.420000          1057.403750   \n",
       "RI          946.768000  101.000000    106.488000           630.072000   \n",
       "SD          176.650000   29.000000     26.270000           430.310000   \n",
       "VA          594.469000  100.100000     60.515000           807.127000   \n",
       "WI          623.498333  115.666667     45.733333           551.311667   \n",
       "\n",
       "       nonViolPerPop  \n",
       "state                 \n",
       "CT       5810.228235  \n",
       "DC       9252.350000  \n",
       "MA       4235.105455  \n",
       "MD       9901.590000  \n",
       "ME       7289.290000  \n",
       "NH       4320.645000  \n",
       "NJ       5879.875625  \n",
       "NY       6631.893750  \n",
       "OH       7633.418750  \n",
       "PA       5912.340000  \n",
       "RI       6316.858000  \n",
       "SD       4012.320000  \n",
       "VA       6049.601000  \n",
       "WI       5456.888333  \n",
       "\n",
       "[14 rows x 142 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statedata\n",
    "#124"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "2c230ad8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>householdsize</th>\n",
       "      <th>racepctblack</th>\n",
       "      <th>racePctWhite</th>\n",
       "      <th>racePctAsian</th>\n",
       "      <th>racePctHisp</th>\n",
       "      <th>agePct12t21</th>\n",
       "      <th>agePct12t29</th>\n",
       "      <th>agePct16t24</th>\n",
       "      <th>agePct65up</th>\n",
       "      <th>...</th>\n",
       "      <th>burglaries</th>\n",
       "      <th>burglPerPop</th>\n",
       "      <th>larcenies</th>\n",
       "      <th>larcPerPop</th>\n",
       "      <th>autoTheft</th>\n",
       "      <th>autoTheftPerPop</th>\n",
       "      <th>arsons</th>\n",
       "      <th>arsonsPerPop</th>\n",
       "      <th>ViolentCrimesPerPop</th>\n",
       "      <th>nonViolPerPop</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>communityname</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Albanycity</th>\n",
       "      <td>101082.0</td>\n",
       "      <td>2.40</td>\n",
       "      <td>20.65</td>\n",
       "      <td>75.51</td>\n",
       "      <td>2.30</td>\n",
       "      <td>3.15</td>\n",
       "      <td>17.82</td>\n",
       "      <td>35.77</td>\n",
       "      <td>22.08</td>\n",
       "      <td>13.02</td>\n",
       "      <td>...</td>\n",
       "      <td>2063.0</td>\n",
       "      <td>1971.58</td>\n",
       "      <td>4030.0</td>\n",
       "      <td>3851.41</td>\n",
       "      <td>471.0</td>\n",
       "      <td>450.13</td>\n",
       "      <td>45.0</td>\n",
       "      <td>43.01</td>\n",
       "      <td>1183.14</td>\n",
       "      <td>6316.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alexandriacity</th>\n",
       "      <td>111183.0</td>\n",
       "      <td>2.09</td>\n",
       "      <td>21.89</td>\n",
       "      <td>69.07</td>\n",
       "      <td>4.17</td>\n",
       "      <td>9.69</td>\n",
       "      <td>8.30</td>\n",
       "      <td>30.47</td>\n",
       "      <td>12.81</td>\n",
       "      <td>9.02</td>\n",
       "      <td>...</td>\n",
       "      <td>921.0</td>\n",
       "      <td>807.79</td>\n",
       "      <td>5682.0</td>\n",
       "      <td>4983.55</td>\n",
       "      <td>969.0</td>\n",
       "      <td>849.89</td>\n",
       "      <td>31.0</td>\n",
       "      <td>27.19</td>\n",
       "      <td>659.56</td>\n",
       "      <td>6668.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Allentowncity</th>\n",
       "      <td>105090.0</td>\n",
       "      <td>2.46</td>\n",
       "      <td>4.98</td>\n",
       "      <td>86.17</td>\n",
       "      <td>1.35</td>\n",
       "      <td>11.68</td>\n",
       "      <td>12.67</td>\n",
       "      <td>27.39</td>\n",
       "      <td>13.52</td>\n",
       "      <td>15.05</td>\n",
       "      <td>...</td>\n",
       "      <td>1682.0</td>\n",
       "      <td>1594.12</td>\n",
       "      <td>4659.0</td>\n",
       "      <td>4415.57</td>\n",
       "      <td>448.0</td>\n",
       "      <td>424.59</td>\n",
       "      <td>42.0</td>\n",
       "      <td>39.81</td>\n",
       "      <td>681.43</td>\n",
       "      <td>6474.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baltimorecity</th>\n",
       "      <td>736014.0</td>\n",
       "      <td>2.66</td>\n",
       "      <td>59.21</td>\n",
       "      <td>39.10</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.03</td>\n",
       "      <td>13.23</td>\n",
       "      <td>27.66</td>\n",
       "      <td>13.55</td>\n",
       "      <td>12.39</td>\n",
       "      <td>...</td>\n",
       "      <td>17901.0</td>\n",
       "      <td>2513.45</td>\n",
       "      <td>41451.0</td>\n",
       "      <td>5820.06</td>\n",
       "      <td>10623.0</td>\n",
       "      <td>1491.56</td>\n",
       "      <td>545.0</td>\n",
       "      <td>76.52</td>\n",
       "      <td>3081.26</td>\n",
       "      <td>9901.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bayonnecity</th>\n",
       "      <td>61444.0</td>\n",
       "      <td>2.43</td>\n",
       "      <td>4.71</td>\n",
       "      <td>90.44</td>\n",
       "      <td>1.84</td>\n",
       "      <td>9.50</td>\n",
       "      <td>11.34</td>\n",
       "      <td>24.30</td>\n",
       "      <td>11.40</td>\n",
       "      <td>17.37</td>\n",
       "      <td>...</td>\n",
       "      <td>335.0</td>\n",
       "      <td>535.20</td>\n",
       "      <td>1157.0</td>\n",
       "      <td>1848.45</td>\n",
       "      <td>249.0</td>\n",
       "      <td>397.81</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.99</td>\n",
       "      <td>487.27</td>\n",
       "      <td>2789.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WestOrangetownship</th>\n",
       "      <td>39103.0</td>\n",
       "      <td>2.64</td>\n",
       "      <td>5.73</td>\n",
       "      <td>87.64</td>\n",
       "      <td>5.58</td>\n",
       "      <td>4.42</td>\n",
       "      <td>10.33</td>\n",
       "      <td>22.36</td>\n",
       "      <td>10.39</td>\n",
       "      <td>16.38</td>\n",
       "      <td>...</td>\n",
       "      <td>301.0</td>\n",
       "      <td>758.42</td>\n",
       "      <td>795.0</td>\n",
       "      <td>2003.12</td>\n",
       "      <td>450.0</td>\n",
       "      <td>1133.84</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.52</td>\n",
       "      <td>297.32</td>\n",
       "      <td>3897.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Westporttown</th>\n",
       "      <td>24410.0</td>\n",
       "      <td>2.63</td>\n",
       "      <td>1.07</td>\n",
       "      <td>96.97</td>\n",
       "      <td>1.63</td>\n",
       "      <td>2.24</td>\n",
       "      <td>11.25</td>\n",
       "      <td>19.84</td>\n",
       "      <td>9.77</td>\n",
       "      <td>12.35</td>\n",
       "      <td>...</td>\n",
       "      <td>173.0</td>\n",
       "      <td>698.17</td>\n",
       "      <td>559.0</td>\n",
       "      <td>2255.94</td>\n",
       "      <td>79.0</td>\n",
       "      <td>318.82</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.11</td>\n",
       "      <td>121.07</td>\n",
       "      <td>3285.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WhitePlainscity</th>\n",
       "      <td>48718.0</td>\n",
       "      <td>2.51</td>\n",
       "      <td>19.03</td>\n",
       "      <td>73.65</td>\n",
       "      <td>3.07</td>\n",
       "      <td>14.16</td>\n",
       "      <td>10.76</td>\n",
       "      <td>24.85</td>\n",
       "      <td>11.54</td>\n",
       "      <td>13.51</td>\n",
       "      <td>...</td>\n",
       "      <td>205.0</td>\n",
       "      <td>412.64</td>\n",
       "      <td>2201.0</td>\n",
       "      <td>4430.35</td>\n",
       "      <td>278.0</td>\n",
       "      <td>559.58</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.05</td>\n",
       "      <td>366.34</td>\n",
       "      <td>5410.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Woodbridgetownship</th>\n",
       "      <td>93086.0</td>\n",
       "      <td>2.78</td>\n",
       "      <td>6.46</td>\n",
       "      <td>86.61</td>\n",
       "      <td>5.47</td>\n",
       "      <td>5.56</td>\n",
       "      <td>10.81</td>\n",
       "      <td>25.74</td>\n",
       "      <td>11.46</td>\n",
       "      <td>12.38</td>\n",
       "      <td>...</td>\n",
       "      <td>665.0</td>\n",
       "      <td>685.65</td>\n",
       "      <td>2444.0</td>\n",
       "      <td>2519.90</td>\n",
       "      <td>718.0</td>\n",
       "      <td>740.30</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.65</td>\n",
       "      <td>408.30</td>\n",
       "      <td>3967.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yonkerscity</th>\n",
       "      <td>188082.0</td>\n",
       "      <td>2.61</td>\n",
       "      <td>14.11</td>\n",
       "      <td>76.21</td>\n",
       "      <td>3.00</td>\n",
       "      <td>16.74</td>\n",
       "      <td>12.05</td>\n",
       "      <td>25.74</td>\n",
       "      <td>12.25</td>\n",
       "      <td>14.86</td>\n",
       "      <td>...</td>\n",
       "      <td>1682.0</td>\n",
       "      <td>918.34</td>\n",
       "      <td>4593.0</td>\n",
       "      <td>2507.70</td>\n",
       "      <td>1868.0</td>\n",
       "      <td>1019.90</td>\n",
       "      <td>89.0</td>\n",
       "      <td>48.59</td>\n",
       "      <td>737.62</td>\n",
       "      <td>4494.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>111 rows × 142 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    population  householdsize  racepctblack  racePctWhite  \\\n",
       "communityname                                                               \n",
       "Albanycity            101082.0           2.40         20.65         75.51   \n",
       "Alexandriacity        111183.0           2.09         21.89         69.07   \n",
       "Allentowncity         105090.0           2.46          4.98         86.17   \n",
       "Baltimorecity         736014.0           2.66         59.21         39.10   \n",
       "Bayonnecity            61444.0           2.43          4.71         90.44   \n",
       "...                        ...            ...           ...           ...   \n",
       "WestOrangetownship     39103.0           2.64          5.73         87.64   \n",
       "Westporttown           24410.0           2.63          1.07         96.97   \n",
       "WhitePlainscity        48718.0           2.51         19.03         73.65   \n",
       "Woodbridgetownship     93086.0           2.78          6.46         86.61   \n",
       "Yonkerscity           188082.0           2.61         14.11         76.21   \n",
       "\n",
       "                    racePctAsian  racePctHisp  agePct12t21  agePct12t29  \\\n",
       "communityname                                                             \n",
       "Albanycity                  2.30         3.15        17.82        35.77   \n",
       "Alexandriacity              4.17         9.69         8.30        30.47   \n",
       "Allentowncity               1.35        11.68        12.67        27.39   \n",
       "Baltimorecity               1.08         1.03        13.23        27.66   \n",
       "Bayonnecity                 1.84         9.50        11.34        24.30   \n",
       "...                          ...          ...          ...          ...   \n",
       "WestOrangetownship          5.58         4.42        10.33        22.36   \n",
       "Westporttown                1.63         2.24        11.25        19.84   \n",
       "WhitePlainscity             3.07        14.16        10.76        24.85   \n",
       "Woodbridgetownship          5.47         5.56        10.81        25.74   \n",
       "Yonkerscity                 3.00        16.74        12.05        25.74   \n",
       "\n",
       "                    agePct16t24  agePct65up  ...  burglaries  burglPerPop  \\\n",
       "communityname                                ...                            \n",
       "Albanycity                22.08       13.02  ...      2063.0      1971.58   \n",
       "Alexandriacity            12.81        9.02  ...       921.0       807.79   \n",
       "Allentowncity             13.52       15.05  ...      1682.0      1594.12   \n",
       "Baltimorecity             13.55       12.39  ...     17901.0      2513.45   \n",
       "Bayonnecity               11.40       17.37  ...       335.0       535.20   \n",
       "...                         ...         ...  ...         ...          ...   \n",
       "WestOrangetownship        10.39       16.38  ...       301.0       758.42   \n",
       "Westporttown               9.77       12.35  ...       173.0       698.17   \n",
       "WhitePlainscity           11.54       13.51  ...       205.0       412.64   \n",
       "Woodbridgetownship        11.46       12.38  ...       665.0       685.65   \n",
       "Yonkerscity               12.25       14.86  ...      1682.0       918.34   \n",
       "\n",
       "                    larcenies  larcPerPop  autoTheft  autoTheftPerPop  arsons  \\\n",
       "communityname                                                                   \n",
       "Albanycity             4030.0     3851.41      471.0           450.13    45.0   \n",
       "Alexandriacity         5682.0     4983.55      969.0           849.89    31.0   \n",
       "Allentowncity          4659.0     4415.57      448.0           424.59    42.0   \n",
       "Baltimorecity         41451.0     5820.06    10623.0          1491.56   545.0   \n",
       "Bayonnecity            1157.0     1848.45      249.0           397.81     5.0   \n",
       "...                       ...         ...        ...              ...     ...   \n",
       "WestOrangetownship      795.0     2003.12      450.0          1133.84     1.0   \n",
       "Westporttown            559.0     2255.94       79.0           318.82     3.0   \n",
       "WhitePlainscity        2201.0     4430.35      278.0           559.58     4.0   \n",
       "Woodbridgetownship     2444.0     2519.90      718.0           740.30    21.0   \n",
       "Yonkerscity            4593.0     2507.70     1868.0          1019.90    89.0   \n",
       "\n",
       "                    arsonsPerPop  ViolentCrimesPerPop  nonViolPerPop  \n",
       "communityname                                                         \n",
       "Albanycity                 43.01              1183.14        6316.12  \n",
       "Alexandriacity             27.19               659.56        6668.42  \n",
       "Allentowncity              39.81               681.43        6474.08  \n",
       "Baltimorecity              76.52              3081.26        9901.59  \n",
       "Bayonnecity                 7.99               487.27        2789.45  \n",
       "...                          ...                  ...            ...  \n",
       "WestOrangetownship          2.52               297.32        3897.90  \n",
       "Westporttown               12.11               121.07        3285.04  \n",
       "WhitePlainscity             8.05               366.34        5410.63  \n",
       "Woodbridgetownship         21.65               408.30        3967.50  \n",
       "Yonkerscity                48.59               737.62        4494.53  \n",
       "\n",
       "[111 rows x 142 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "communitydata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2278d1a0",
   "metadata": {},
   "source": [
    "#### Burada yaptığım ufak bir trick var. OLS fonksiyonu içerisine sadece String ifade girebiliyoruz. Bunu elle teker teker girmek oldukça zor. Bu sebeple dataya header atamak için kullandığım names listesi içerisinden, bunların tamamını OLS formatına gönderilebilecek şekilde manipüle ettim. Bunu yaparken son 18 sutünü almadım, çünkü onlar bağlı değişken olacak ve onlardan birisini seçip kullanacağım. Aynı zamanda ilk 5 sutünü da dropladığım için dahil etmedim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "8c22e406",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'population + householdsize + racepctblack + racePctWhite + racePctAsian + racePctHisp + agePct12t21 + agePct12t29 + agePct16t24 + agePct65up + numbUrban + pctUrban + medIncome + pctWWage + pctWFarmSelf + pctWInvInc + pctWSocSec + pctWPubAsst + pctWRetire + medFamInc + perCapInc + whitePerCap + blackPerCap + indianPerCap + AsianPerCap + OtherPerCap + HispPerCap + NumUnderPov + PctPopUnderPov + PctLess9thGrade + PctNotHSGrad + PctBSorMore + PctUnemployed + PctEmploy + PctEmplManu + PctEmplProfServ + PctOccupManu + PctOccupMgmtProf + MalePctDivorce + MalePctNevMarr + FemalePctDiv + TotalPctDiv + PersPerFam + PctFam2Par + PctKids2Par + PctYoungKids2Par + PctTeen2Par + PctWorkMomYoungKids + PctWorkMom + NumKidsBornNeverMar + PctKidsBornNeverMar + NumImmig + PctImmigRecent + PctImmigRec5 + PctImmigRec8 + PctImmigRec10 + PctRecentImmig + PctRecImmig5 + PctRecImmig8 + PctRecImmig10 + PctSpeakEnglOnly + PctNotSpeakEnglWell + PctLargHouseFam + PctLargHouseOccup + PersPerOccupHous + PersPerOwnOccHous + PersPerRentOccHous + PctPersOwnOccup + PctPersDenseHous + PctHousLess3BR + MedNumBR + HousVacant + PctHousOccup + PctHousOwnOcc + PctVacantBoarded + PctVacMore6Mos + MedYrHousBuilt + PctHousNoPhone + PctWOFullPlumb + OwnOccLowQuart + OwnOccMedVal + OwnOccHiQuart + OwnOccQrange + RentLowQ + RentMedian + RentHighQ + RentQrange + MedRent + MedRentPctHousInc + MedOwnCostPctInc + MedOwnCostPctIncNoMtg + NumInShelters + NumStreet + PctForeignBorn + PctBornSameState + PctSameHouse85 + PctSameCity85 + PctSameState85 + LemasSwornFT + LemasSwFTPerPop + LemasSwFTFieldOps + LemasSwFTFieldPerPop + LemasTotalReq + LemasTotReqPerPop + PolicReqPerOffic + PolicPerPop + RacialMatchCommPol + PctPolicWhite + PctPolicBlack + PctPolicHisp + PctPolicAsian + PctPolicMinor + OfficAssgnDrugUnits + NumKindsDrugsSeiz + PolicAveOTWorked + LandArea + PopDens + PctUsePubTrans + PolicCars + PolicOperBudg + LemasPctPolicOnPatr + LemasGangUnitDeploy + LemasPctOfficDrugUn + PolicBudgPerPop'"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn = names[5:129]\n",
    "newnames = \" + \".join(nn)\n",
    "newnames"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfd5b59b",
   "metadata": {},
   "source": [
    "#### Burada ise ufak bir for döngüsü ile, her ayrı bağlı değişken için (örneğin: murders, murderPop, rapes ...) ayrı ayrı R^2 ve P değerlerini hesaplattım. Dolayısıyla bir döngü ile bütün ilişkilere bakmayı hedefledim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "ec6c365c",
   "metadata": {},
   "outputs": [],
   "source": [
    "rvalues = np.array([])\n",
    "pvalues = np.array([])\n",
    "for i in range(18):\n",
    "    string = str(names[i+129]) + \" ~ \" + newnames\n",
    "    model = ols(string, data = statedata).fit()\n",
    "    rval = model.rsquared\n",
    "    pval = model.f_pvalue\n",
    "    rvalues = np.append(rvalues, rval)\n",
    "    pvalues = np.append(pvalues, pval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "fa6f278b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "586118fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1.])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rvalues"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "578d54cb",
   "metadata": {},
   "source": [
    "### Bu şekilde çalışmadı. p-değerleri nan dönerken, R^2 değerleri 1 döndü. İlgili değişken sayısını azaltmak gerekiyor. Bunun için korelasyon matrisinden yararlanacağım."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a69f5de7",
   "metadata": {},
   "source": [
    "#### *****"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8b31e74",
   "metadata": {},
   "source": [
    "#### Burada ise bir fonksiyon yazdım. Aldığı 3 parametre sırasıyla şöyle:\n",
    "##### data: hangi datasetin kullanılacağı(communitydata ya da statedata)\n",
    "##### val: korelasyon katsayımızın hangi değerden büyük ya da küçük olacağı. sadece pozitif değer girilmesi gerekiyor.\n",
    "##### which: bu ise 0 ile 17 arasında bir değer alıyor (0: murders, 1: murdersPop, 2: rapes etc...)\n",
    "\n",
    "### Fonksiyon şu işe yarıyor: Örneğin which = 0(murders), val = 0.85, data = statedata) parametreleri girilmiş olsun. State için gruplanmış datasette, murder bağımlı değişkeni ile, önceki 123 bağımsız değişkenin aralarındaki korelasyona bakıyor. 0.85'den büyük ya da -0.85'den küçük olanları seçiyor ve onların headerlarını tutuyor. Daha sonra Murder ile tutulan değişken isimleri OLS ile analiz ediliyor. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "93d63157",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getcorrvaluessummary(data, val, which):\n",
    "    corstate = np.corrcoef(data.transpose())\n",
    "    cfnames = []\n",
    "    for j in range(123):\n",
    "        if abs(corstate[124+int(which)][j]) > float(val):\n",
    "            cfnames.append(names[j+5])\n",
    "    newcfnames = \" + \".join(cfnames)\n",
    "    \n",
    "    cfrvalues = np.array([])\n",
    "    cfpvalues = np.array([])\n",
    "    cfstring = str(names[129+int(which)]) + \" ~ \" + newcfnames\n",
    "    model = ols(cfstring, data = statedata).fit()\n",
    "    model.summary()\n",
    "    return model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "95ef060e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\asuhe\\anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1603: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=14\n",
      "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>murders</td>     <th>  R-squared:         </th> <td>   0.998</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.995</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   369.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 02 Apr 2022</td> <th>  Prob (F-statistic):</th> <td>1.78e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>03:17:45</td>     <th>  Log-Likelihood:    </th> <td> -46.257</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    14</td>      <th>  AIC:               </th> <td>   108.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>     6</td>      <th>  BIC:               </th> <td>   113.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     7</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>  -12.5466</td> <td>    6.160</td> <td>   -2.037</td> <td> 0.088</td> <td>  -27.619</td> <td>    2.526</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>NumKidsBornNeverMar</th> <td>    0.0027</td> <td>    0.001</td> <td>    2.313</td> <td> 0.060</td> <td>   -0.000</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HousVacant</th>          <td>   -0.0040</td> <td>    0.003</td> <td>   -1.181</td> <td> 0.282</td> <td>   -0.012</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LemasSwornFT</th>        <td>    0.2093</td> <td>    0.175</td> <td>    1.196</td> <td> 0.277</td> <td>   -0.219</td> <td>    0.638</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LemasSwFTFieldOps</th>   <td>   -0.1203</td> <td>    0.208</td> <td>   -0.579</td> <td> 0.584</td> <td>   -0.629</td> <td>    0.388</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OfficAssgnDrugUnits</th> <td>    0.3044</td> <td>    1.129</td> <td>    0.269</td> <td> 0.797</td> <td>   -2.459</td> <td>    3.068</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PolicCars</th>           <td>    0.0464</td> <td>    0.116</td> <td>    0.400</td> <td> 0.703</td> <td>   -0.237</td> <td>    0.330</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PolicOperBudg</th>       <td>-7.403e-07</td> <td> 8.02e-07</td> <td>   -0.923</td> <td> 0.392</td> <td> -2.7e-06</td> <td> 1.22e-06</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 3.627</td> <th>  Durbin-Watson:     </th> <td>   1.577</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.163</td> <th>  Jarque-Bera (JB):  </th> <td>   1.465</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.738</td> <th>  Prob(JB):          </th> <td>   0.481</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.576</td> <th>  Cond. No.          </th> <td>1.71e+08</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.71e+08. 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:                murders   R-squared:                       0.998\n",
       "Model:                            OLS   Adj. R-squared:                  0.995\n",
       "Method:                 Least Squares   F-statistic:                     369.5\n",
       "Date:                Sat, 02 Apr 2022   Prob (F-statistic):           1.78e-07\n",
       "Time:                        03:17:45   Log-Likelihood:                -46.257\n",
       "No. Observations:                  14   AIC:                             108.5\n",
       "Df Residuals:                       6   BIC:                             113.6\n",
       "Df Model:                           7                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=======================================================================================\n",
       "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------\n",
       "Intercept             -12.5466      6.160     -2.037      0.088     -27.619       2.526\n",
       "NumKidsBornNeverMar     0.0027      0.001      2.313      0.060      -0.000       0.006\n",
       "HousVacant             -0.0040      0.003     -1.181      0.282      -0.012       0.004\n",
       "LemasSwornFT            0.2093      0.175      1.196      0.277      -0.219       0.638\n",
       "LemasSwFTFieldOps      -0.1203      0.208     -0.579      0.584      -0.629       0.388\n",
       "OfficAssgnDrugUnits     0.3044      1.129      0.269      0.797      -2.459       3.068\n",
       "PolicCars               0.0464      0.116      0.400      0.703      -0.237       0.330\n",
       "PolicOperBudg       -7.403e-07   8.02e-07     -0.923      0.392    -2.7e-06    1.22e-06\n",
       "==============================================================================\n",
       "Omnibus:                        3.627   Durbin-Watson:                   1.577\n",
       "Prob(Omnibus):                  0.163   Jarque-Bera (JB):                1.465\n",
       "Skew:                          -0.738   Prob(JB):                        0.481\n",
       "Kurtosis:                       3.576   Cond. No.                     1.71e+08\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.71e+08. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 0.95\n",
    "b = 0\n",
    "getcorrvaluessummary(statedata, a, b )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95d18ccd",
   "metadata": {},
   "source": [
    "#### Burada ise yukarıdaki fonksiyonun sadece R^2 ve p değerlerini çıktı olarak veren başka bir fonksiyon tanımladım. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "8b6766ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getcorrvaluesRP(data, val, which):\n",
    "    corstate = np.corrcoef(data.transpose())\n",
    "    cfnames = []\n",
    "    for j in range(123):\n",
    "        if abs(corstate[124+int(which)][j]) > float(val):\n",
    "            cfnames.append(names[j+5])\n",
    "    newcfnames = \" + \".join(cfnames)\n",
    "    \n",
    "    cfrvalues = np.array([])\n",
    "    cfpvalues = np.array([])\n",
    "    cfstring = str(names[129+int(which)]) + \" ~ \" + newcfnames\n",
    "    model = ols(cfstring, data = statedata).fit()\n",
    "    \n",
    "    return model.rsquared, model.f_pvalue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "65913b2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9403919708831866, 1.8372835808376858e-07)"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val =0.95; #0.85 ve üstü korelasyona sahip olanlar seçildi\n",
    "which = 5; #murder sutünü seçildi\n",
    "getcorrvaluesRP(statedata, val, which)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c36b7281",
   "metadata": {},
   "source": [
    "#### Son olarak burada ise bütün bağlı değişkenler için değerlere bakmak istedim. Ancak robberies sütunu oldukça düşük korelasyonlu geldiği için korelasyon katsayısını düşürmek zorunda kaldım. Bu durumda da başka R^2 ve P değerleri yanlış geldi. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "6b2511cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "val = 0.6\n",
    "allarr = np.array([])\n",
    "for i in range(18):\n",
    "    a = getcorrvaluesRP(statedata, val, i)\n",
    "    allarr = np.append(allarr, a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "4f2f7ced",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        ,        nan, 1.        ,        nan, 1.        ,\n",
       "              nan, 1.        ,        nan, 1.        ,        nan,\n",
       "       1.        ,        nan, 1.        ,        nan, 1.        ,\n",
       "              nan, 1.        ,        nan, 1.        ,        nan,\n",
       "       1.        ,        nan, 1.        ,        nan, 1.        ,\n",
       "              nan, 1.        ,        nan, 1.        ,        nan,\n",
       "       0.74108431, 0.00991265, 1.        ,        nan, 1.        ,\n",
       "              nan])"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allarr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52779a14",
   "metadata": {},
   "source": [
    "#### Burada ise başlangıçta yazdığım Lasso, Lineer vs gibi regresyonlardan hangisinin model için en uygun olduğunu tespit etmeye yarayan fonksiyonu, yukarıdaki fonksiyon ile iç içe bir şekilde kullandım. Bu bağlamda seçilen murder ve state data değerleri için, korelasyon katsayısı 0.9 ve daha büyük değerlerde lineer regresyon bunları en iyi açıklayan model olarak karşıma çıktı. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "f389bccf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def whichcorr(data, val, which):\n",
    "    corstate = np.corrcoef(data.transpose())\n",
    "    cfnames = []\n",
    "    for j in range(123):\n",
    "        if abs(corstate[124+int(which)][j]) > float(val):\n",
    "            cfnames.append(names[j+5])\n",
    "    X = statedata[cfnames]\n",
    "    y = statedata.iloc[:,124+int(which)]\n",
    "    return getregressions(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "c3d48bbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\asuhe\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 224.27767967449537, tolerance: 26.24915232269957\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\asuhe\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 222.21456983383354, tolerance: 26.24915232269957\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.9993889786874027,\n",
       " 0.9993878160263979,\n",
       " 0.9983169111809059,\n",
       " 0.9983207546097299)"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val2 = 0.93\n",
    "val = 0\n",
    "whichcorr(statedata, val2, val)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f6123e8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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