decisionstats/Anscombe Dataset

## Anscombe Dataset
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import statsmodels.formula.api as sm\n",
    "import numpy as np\n",
    "import ggplot as gg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "anscombe=pd.read_csv(\"https://vincentarelbundock.github.io/Rdatasets/csv/datasets/anscombe.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "      <th>x4</th>\n",
       "      <th>y1</th>\n",
       "      <th>y2</th>\n",
       "      <th>y3</th>\n",
       "      <th>y4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>8.04</td>\n",
       "      <td>9.14</td>\n",
       "      <td>7.46</td>\n",
       "      <td>6.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>6.95</td>\n",
       "      <td>8.14</td>\n",
       "      <td>6.77</td>\n",
       "      <td>5.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>7.58</td>\n",
       "      <td>8.74</td>\n",
       "      <td>12.74</td>\n",
       "      <td>7.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>8.81</td>\n",
       "      <td>8.77</td>\n",
       "      <td>7.11</td>\n",
       "      <td>8.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>8.33</td>\n",
       "      <td>9.26</td>\n",
       "      <td>7.81</td>\n",
       "      <td>8.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>9.96</td>\n",
       "      <td>8.10</td>\n",
       "      <td>8.84</td>\n",
       "      <td>7.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>7.24</td>\n",
       "      <td>6.13</td>\n",
       "      <td>6.08</td>\n",
       "      <td>5.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>4.26</td>\n",
       "      <td>3.10</td>\n",
       "      <td>5.39</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>10.84</td>\n",
       "      <td>9.13</td>\n",
       "      <td>8.15</td>\n",
       "      <td>5.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>4.82</td>\n",
       "      <td>7.26</td>\n",
       "      <td>6.42</td>\n",
       "      <td>7.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>5.68</td>\n",
       "      <td>4.74</td>\n",
       "      <td>5.73</td>\n",
       "      <td>6.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0  x1  x2  x3  x4     y1    y2     y3     y4\n",
       "0            1  10  10  10   8   8.04  9.14   7.46   6.58\n",
       "1            2   8   8   8   8   6.95  8.14   6.77   5.76\n",
       "2            3  13  13  13   8   7.58  8.74  12.74   7.71\n",
       "3            4   9   9   9   8   8.81  8.77   7.11   8.84\n",
       "4            5  11  11  11   8   8.33  9.26   7.81   8.47\n",
       "5            6  14  14  14   8   9.96  8.10   8.84   7.04\n",
       "6            7   6   6   6   8   7.24  6.13   6.08   5.25\n",
       "7            8   4   4   4  19   4.26  3.10   5.39  12.50\n",
       "8            9  12  12  12   8  10.84  9.13   8.15   5.56\n",
       "9           10   7   7   7   8   4.82  7.26   6.42   7.91\n",
       "10          11   5   5   5   8   5.68  4.74   5.73   6.89"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anscombe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping the column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "anscombe=anscombe.drop('Unnamed: 0', 1) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Anscombe Quartet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "      <th>x4</th>\n",
       "      <th>y1</th>\n",
       "      <th>y2</th>\n",
       "      <th>y3</th>\n",
       "      <th>y4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>8.04</td>\n",
       "      <td>9.14</td>\n",
       "      <td>7.46</td>\n",
       "      <td>6.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>6.95</td>\n",
       "      <td>8.14</td>\n",
       "      <td>6.77</td>\n",
       "      <td>5.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>7.58</td>\n",
       "      <td>8.74</td>\n",
       "      <td>12.74</td>\n",
       "      <td>7.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>8.81</td>\n",
       "      <td>8.77</td>\n",
       "      <td>7.11</td>\n",
       "      <td>8.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>8.33</td>\n",
       "      <td>9.26</td>\n",
       "      <td>7.81</td>\n",
       "      <td>8.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>9.96</td>\n",
       "      <td>8.10</td>\n",
       "      <td>8.84</td>\n",
       "      <td>7.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>7.24</td>\n",
       "      <td>6.13</td>\n",
       "      <td>6.08</td>\n",
       "      <td>5.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>4.26</td>\n",
       "      <td>3.10</td>\n",
       "      <td>5.39</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>10.84</td>\n",
       "      <td>9.13</td>\n",
       "      <td>8.15</td>\n",
       "      <td>5.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>4.82</td>\n",
       "      <td>7.26</td>\n",
       "      <td>6.42</td>\n",
       "      <td>7.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>5.68</td>\n",
       "      <td>4.74</td>\n",
       "      <td>5.73</td>\n",
       "      <td>6.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "    x1  x2  x3  x4     y1    y2     y3     y4\n",
       "0   10  10  10   8   8.04  9.14   7.46   6.58\n",
       "1    8   8   8   8   6.95  8.14   6.77   5.76\n",
       "2   13  13  13   8   7.58  8.74  12.74   7.71\n",
       "3    9   9   9   8   8.81  8.77   7.11   8.84\n",
       "4   11  11  11   8   8.33  9.26   7.81   8.47\n",
       "5   14  14  14   8   9.96  8.10   8.84   7.04\n",
       "6    6   6   6   8   7.24  6.13   6.08   5.25\n",
       "7    4   4   4  19   4.26  3.10   5.39  12.50\n",
       "8   12  12  12   8  10.84  9.13   8.15   5.56\n",
       "9    7   7   7   8   4.82  7.26   6.42   7.91\n",
       "10   5   5   5   8   5.68  4.74   5.73   6.89"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anscombe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Taking means and standard deviations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x1    9.000000\n",
       "x2    9.000000\n",
       "x3    9.000000\n",
       "x4    9.000000\n",
       "y1    7.500909\n",
       "y2    7.500909\n",
       "y3    7.500000\n",
       "y4    7.500909\n",
       "dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(anscombe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x1    3.162278\n",
       "x2    3.162278\n",
       "x3    3.162278\n",
       "x4    3.162278\n",
       "y1    1.937024\n",
       "y2    1.937109\n",
       "y3    1.935933\n",
       "y4    1.936081\n",
       "dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(anscombe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting Regression Line Between Respective X and Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ajay/anaconda3/lib/python3.4/site-packages/scipy/stats/stats.py:1285: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=11\n",
      "  \"anyway, n=%i\" % int(n))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y1</td>        <th>  R-squared:         </th> <td>   0.667</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.629</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   17.99</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 07 Jul 2016</td> <th>  Prob (F-statistic):</th>  <td>0.00217</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>04:32:15</td>     <th>  Log-Likelihood:    </th> <td> -16.841</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    11</td>      <th>  AIC:               </th> <td>   37.68</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>     9</td>      <th>  BIC:               </th> <td>   38.48</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    3.0001</td> <td>    1.125</td> <td>    2.667</td> <td> 0.026</td> <td>    0.456     5.544</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>        <td>    0.5001</td> <td>    0.118</td> <td>    4.241</td> <td> 0.002</td> <td>    0.233     0.767</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.082</td> <th>  Durbin-Watson:     </th> <td>   3.212</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.960</td> <th>  Jarque-Bera (JB):  </th> <td>   0.289</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.122</td> <th>  Prob(JB):          </th> <td>   0.865</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.244</td> <th>  Cond. No.          </th> <td>    29.1</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                     y1   R-squared:                       0.667\n",
       "Model:                            OLS   Adj. R-squared:                  0.629\n",
       "Method:                 Least Squares   F-statistic:                     17.99\n",
       "Date:                Thu, 07 Jul 2016   Prob (F-statistic):            0.00217\n",
       "Time:                        04:32:15   Log-Likelihood:                -16.841\n",
       "No. Observations:                  11   AIC:                             37.68\n",
       "Df Residuals:                       9   BIC:                             38.48\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      3.0001      1.125      2.667      0.026         0.456     5.544\n",
       "x1             0.5001      0.118      4.241      0.002         0.233     0.767\n",
       "==============================================================================\n",
       "Omnibus:                        0.082   Durbin-Watson:                   3.212\n",
       "Prob(Omnibus):                  0.960   Jarque-Bera (JB):                0.289\n",
       "Skew:                          -0.122   Prob(JB):                        0.865\n",
       "Kurtosis:                       2.244   Cond. No.                         29.1\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result1 = sm.ols(formula=\"y1 ~ x1 \", data=anscombe).fit()\n",
    "result1.summary()\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['HC0_se',\n",
       " 'HC1_se',\n",
       " 'HC2_se',\n",
       " 'HC3_se',\n",
       " '_HCCM',\n",
       " '__class__',\n",
       " '__delattr__',\n",
       " '__dict__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__le__',\n",
       " '__lt__',\n",
       " '__module__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " '__weakref__',\n",
       " '_cache',\n",
       " '_data_attr',\n",
       " '_get_robustcov_results',\n",
       " '_is_nested',\n",
       " '_wexog_singular_values',\n",
       " 'aic',\n",
       " 'bic',\n",
       " 'bse',\n",
       " 'centered_tss',\n",
       " 'compare_f_test',\n",
       " 'compare_lm_test',\n",
       " 'compare_lr_test',\n",
       " 'condition_number',\n",
       " 'conf_int',\n",
       " 'conf_int_el',\n",
       " 'cov_HC0',\n",
       " 'cov_HC1',\n",
       " 'cov_HC2',\n",
       " 'cov_HC3',\n",
       " 'cov_kwds',\n",
       " 'cov_params',\n",
       " 'cov_type',\n",
       " 'df_model',\n",
       " 'df_resid',\n",
       " 'diagn',\n",
       " 'eigenvals',\n",
       " 'el_test',\n",
       " 'ess',\n",
       " 'f_pvalue',\n",
       " 'f_test',\n",
       " 'fittedvalues',\n",
       " 'fvalue',\n",
       " 'get_influence',\n",
       " 'get_robustcov_results',\n",
       " 'initialize',\n",
       " 'k_constant',\n",
       " 'llf',\n",
       " 'load',\n",
       " 'model',\n",
       " 'mse_model',\n",
       " 'mse_resid',\n",
       " 'mse_total',\n",
       " 'nobs',\n",
       " 'normalized_cov_params',\n",
       " 'outlier_test',\n",
       " 'params',\n",
       " 'predict',\n",
       " 'pvalues',\n",
       " 'remove_data',\n",
       " 'resid',\n",
       " 'resid_pearson',\n",
       " 'rsquared',\n",
       " 'rsquared_adj',\n",
       " 'save',\n",
       " 'scale',\n",
       " 'ssr',\n",
       " 'summary',\n",
       " 'summary2',\n",
       " 't_test',\n",
       " 'tvalues',\n",
       " 'uncentered_tss',\n",
       " 'use_t',\n",
       " 'wald_test',\n",
       " 'wresid']"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(result1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept    3.000091\n",
       "x1           0.500091\n",
       "dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result1.params\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.66654245950877489"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result1.rsquared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "result2 = sm.ols(formula=\"y2 ~ x2 \", data=anscombe).fit()\n",
    "result3 = sm.ols(formula=\"y3 ~ x3 \", data=anscombe).fit()\n",
    "result4 = sm.ols(formula=\"y4 ~ x4 \", data=anscombe).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept    3.000091\n",
      "x1           0.500091\n",
      "dtype: float64\n",
      "Intercept    3.000909\n",
      "x2           0.500000\n",
      "dtype: float64\n",
      "Intercept    3.002455\n",
      "x3           0.499727\n",
      "dtype: float64\n",
      "Intercept    3.001727\n",
      "x4           0.499909\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(result1.params)\n",
    "print(result2.params)\n",
    "print(result3.params)\n",
    "print(result4.params)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.666542459509\n",
      "0.666242033727\n",
      "0.666324041067\n",
      "0.666707256898\n"
     ]
    }
   ],
   "source": [
    "print(result1.rsquared)\n",
    "print(result2.rsquared)\n",
    "print(result3.rsquared)\n",
    "print(result4.rsquared)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x1    9.000000\n",
      "x2    9.000000\n",
      "x3    9.000000\n",
      "x4    9.000000\n",
      "y1    7.500909\n",
      "y2    7.500909\n",
      "y3    7.500000\n",
      "y4    7.500909\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(anscombe))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x1    3.162278\n",
      "x2    3.162278\n",
      "x3    3.162278\n",
      "x4    3.162278\n",
      "y1    1.937024\n",
      "y2    1.937109\n",
      "y3    1.935933\n",
      "y4    1.936081\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(np.std(anscombe))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "It seems that X and Y have the same  means, same standard deviations, and same regression parameters, and same R squared value (upto two decimal places). So as per summary statistics the data between all four quartets ( X1 Y1, X2 Y2, X3 Y3, X4 Y4) is the same."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ajay/anaconda3/lib/python3.4/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    },
    {
     "data": {
      "image/png": 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l37SO9rvk8xJkWZLx8fH40pe+FJVKJebm5uJNb3pTvOENb4iIiO7u7uju7n7J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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa769904c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-901764730)>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = gg.ggplot(gg.aes(x='x1', y='y1'), data=anscombe)\n",
    "p + gg.geom_point()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ajay/anaconda3/lib/python3.4/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    },
    {
     "data": {
      "image/png": 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a2qJYnBOxZ6SxsTFKpVLeMWbkUK/10q9us6XfbOk3W/XSr26zpd9sHWmvhVqt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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa4a4fc2c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-901793152)>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "p2 = gg.ggplot(gg.aes(x='x2', y='y2'), data=anscombe)\n",
    "p2 + gg.geom_point()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ajay/anaconda3/lib/python3.4/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    },
    {
     "data": {
      "image/png": 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VqlU6efKkrTgAAADwgLXb7tPJ5/OKRCKSpHnz5imfz0/5/uDgoIaHh6cci0Qicl1PY89I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\nAgAAwJr/A4rtP3eR0pYCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa3f4c58c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-901915866)>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p3 = gg.ggplot(gg.aes(x='x3', y='y3'), data=anscombe)\n",
    "p3 + gg.geom_point()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ajay/anaconda3/lib/python3.4/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    },
    {
     "data": {
      "image/png": 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fAABk493uAABkY3wCAJCN8QkAQDbGJwAA2RifAABkY3wCAJCN8QkA\nQDbGJwAA2fw/5H43Ci1EJI8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa4130fec>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-901651556)>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p4= gg.ggplot(gg.aes(x='x4', y='y4'), data=anscombe)\n",
    "p4 + gg.geom_point()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "The graphs show that the four quartets are completely different even though summary statistics ( means, deviations, regression) was showing identical result"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}