{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab 9: Overfitting, underfitting, fitting polynomials, k-fold cross validation\n",
    "\n",
    "We will look at [Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet), which are four constructed datasets that appear similar statistics, including regression lines, but look different when graphed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.formula.api as smf\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import datasets, linear_model\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load Anscombe's quartet in from Seaborn and look at it.  The four different datasets are indicated by the `dataset` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dataset</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
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       "      <td>I</td>\n",
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       "      <td>7.58</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I</td>\n",
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       "      <td>8.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "      <td>7.24</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>I</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>I</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>I</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>I</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>II</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>II</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>II</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>II</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>II</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>II</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>II</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>II</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>II</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>II</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>II</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>III</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>III</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>III</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>III</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>III</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>III</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>III</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>III</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>III</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>III</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>III</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>IV</td>\n",
       "      <td>19.0</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dataset     x      y\n",
       "0        I  10.0   8.04\n",
       "1        I   8.0   6.95\n",
       "2        I  13.0   7.58\n",
       "3        I   9.0   8.81\n",
       "4        I  11.0   8.33\n",
       "5        I  14.0   9.96\n",
       "6        I   6.0   7.24\n",
       "7        I   4.0   4.26\n",
       "8        I  12.0  10.84\n",
       "9        I   7.0   4.82\n",
       "10       I   5.0   5.68\n",
       "11      II  10.0   9.14\n",
       "12      II   8.0   8.14\n",
       "13      II  13.0   8.74\n",
       "14      II   9.0   8.77\n",
       "15      II  11.0   9.26\n",
       "16      II  14.0   8.10\n",
       "17      II   6.0   6.13\n",
       "18      II   4.0   3.10\n",
       "19      II  12.0   9.13\n",
       "20      II   7.0   7.26\n",
       "21      II   5.0   4.74\n",
       "22     III  10.0   7.46\n",
       "23     III   8.0   6.77\n",
       "24     III  13.0  12.74\n",
       "25     III   9.0   7.11\n",
       "26     III  11.0   7.81\n",
       "27     III  14.0   8.84\n",
       "28     III   6.0   6.08\n",
       "29     III   4.0   5.39\n",
       "30     III  12.0   8.15\n",
       "31     III   7.0   6.42\n",
       "32     III   5.0   5.73\n",
       "33      IV   8.0   6.58\n",
       "34      IV   8.0   5.76\n",
       "35      IV   8.0   7.71\n",
       "36      IV   8.0   8.84\n",
       "37      IV   8.0   8.47\n",
       "38      IV   8.0   7.04\n",
       "39      IV   8.0   5.25\n",
       "40      IV  19.0  12.50\n",
       "41      IV   8.0   5.56\n",
       "42      IV   8.0   7.91\n",
       "43      IV   8.0   6.89"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anscombe = sns.load_dataset(\"anscombe\")\n",
    "anscombe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make things easier, create a new dataframe for each of the four datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>9.96</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>I</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.24</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "      <td>10.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>I</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>I</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.68</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   dataset     x      y\n",
       "0        I  10.0   8.04\n",
       "1        I   8.0   6.95\n",
       "2        I  13.0   7.58\n",
       "3        I   9.0   8.81\n",
       "4        I  11.0   8.33\n",
       "5        I  14.0   9.96\n",
       "6        I   6.0   7.24\n",
       "7        I   4.0   4.26\n",
       "8        I  12.0  10.84\n",
       "9        I   7.0   4.82\n",
       "10       I   5.0   5.68"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filter_1 = anscombe[\"dataset\"] == \"I\"\n",
    "anscombe_1  = anscombe[filter_1]\n",
    "anscombe_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>dataset</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
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       "  <tbody>\n",
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       "      <th>15</th>\n",
       "      <td>II</td>\n",
       "      <td>11.0</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>II</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>II</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>II</td>\n",
       "      <td>4.0</td>\n",
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       "      <th>19</th>\n",
       "      <td>II</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>II</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>II</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dataset     x     y\n",
       "11      II  10.0  9.14\n",
       "12      II   8.0  8.14\n",
       "13      II  13.0  8.74\n",
       "14      II   9.0  8.77\n",
       "15      II  11.0  9.26\n",
       "16      II  14.0  8.10\n",
       "17      II   6.0  6.13\n",
       "18      II   4.0  3.10\n",
       "19      II  12.0  9.13\n",
       "20      II   7.0  7.26\n",
       "21      II   5.0  4.74"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filter_2 = anscombe[\"dataset\"] == \"II\"\n",
    "anscombe_2  = anscombe[filter_2]\n",
    "anscombe_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>dataset</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>III</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>III</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>III</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>III</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>III</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>III</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>III</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>III</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>III</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>III</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>III</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dataset     x      y\n",
       "22     III  10.0   7.46\n",
       "23     III   8.0   6.77\n",
       "24     III  13.0  12.74\n",
       "25     III   9.0   7.11\n",
       "26     III  11.0   7.81\n",
       "27     III  14.0   8.84\n",
       "28     III   6.0   6.08\n",
       "29     III   4.0   5.39\n",
       "30     III  12.0   8.15\n",
       "31     III   7.0   6.42\n",
       "32     III   5.0   5.73"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filter_3 = anscombe[\"dataset\"] == \"III\"\n",
    "anscombe_3  = anscombe[filter_3]\n",
    "anscombe_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>dataset</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>IV</td>\n",
       "      <td>19.0</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>IV</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dataset     x      y\n",
       "33      IV   8.0   6.58\n",
       "34      IV   8.0   5.76\n",
       "35      IV   8.0   7.71\n",
       "36      IV   8.0   8.84\n",
       "37      IV   8.0   8.47\n",
       "38      IV   8.0   7.04\n",
       "39      IV   8.0   5.25\n",
       "40      IV  19.0  12.50\n",
       "41      IV   8.0   5.56\n",
       "42      IV   8.0   7.91\n",
       "43      IV   8.0   6.89"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filter_4 = anscombe[\"dataset\"] == \"IV\"\n",
    "anscombe_4  = anscombe[filter_4]\n",
    "anscombe_4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot of data\n",
    "Plot each of the data sets with a regression line using Seaborn.  Which regression lines fit the data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c83395240>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x = \"x\", y = \"y\", data = anscombe_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c6980a240>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x = \"x\", y = \"y\", data = anscombe_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c697cac50>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x = \"x\", y = \"y\", data = anscombe_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c69781828>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x = \"x\", y = \"y\", data = anscombe_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c696c2400>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x = \"x\", y = \"y\", data = anscombe_1)\n",
    "sns.regplot(x = \"x\", y = \"y\", data = anscombe_2)\n",
    "sns.regplot(x = \"x\", y = \"y\", data = anscombe_3)\n",
    "sns.regplot(x = \"x\", y = \"y\", data = anscombe_4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## R-Squared\n",
    "\n",
    "Compute R-Squared for each regression (you will have to compute the linear model using statsmodel).  What do you notice about the R-Squared values?  How do they compare to your visual assessment of the fit in the previous section?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.666542459508775"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm = smf.ols(\"y ~ x\",data = anscombe_1).fit()\n",
    "lm.rsquared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6662420337274843"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm2 = smf.ols(\"y ~ x\",data = anscombe_2).fit()\n",
    "lm2.rsquared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6663240410665592"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm3 = smf.ols(\"y ~ x\",data = anscombe_3).fit()\n",
    "lm3.rsquared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6667072568984653"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm4 = smf.ols(\"y ~ x\",data = anscombe_4).fit()\n",
    "lm4.rsquared"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overfitting and Underfitting models\n",
    "\n",
    "Overfitting and underfitting refer to the complexity of the model relative to the data.  \n",
    "\n",
    "First plot Anscombe's second quartet and the regression line again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c69689198>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x = \"x\", y = \"y\", data = anscombe_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above data set and linear model is an example of *underfitting* because the model is too simple compared to the data.  The linear model is not capturing the curve of the data.\n",
    "\n",
    "We can increase the order of a model to increase it's complexity.  Add the parameter `order = 2` to your plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c69635c18>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x = \"x\", y = \"y\", data = anscombe_2, order = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What happened?\n",
    "\n",
    "What happens if we increase the order to 3?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c695f9c88>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x = \"x\", y = \"y\", data = anscombe_2, order = 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overfitting\n",
    "\n",
    "Use Seaborn to plot Anscombe's first dataset with the regression line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c695b8ba8>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0U1LobW9nkOFImMePhOltV43/W/G4XAS8LvzFQd1AjYSlgkCkyqy1rKRzLCYyNT0N1FrLhcmltUJvSxtq/Lf6PTx8uIfhwT6O7GlTobeb8LpdxYFd19oAb63OjlIQiFRJJpdnMZFhOZWt6Wqg00uptdW+V2bX10YYoC3gwRi4p6uFhwa6Gbyr3bmG1pCNF31/cSZPrV70b0ZBIFJhyUxuLQBqVTKT47UPrjMSjfH25bmSQm8D3S0c7mvjzctzxe4MF3PxNM+dvsQzHOT4QJdj7XaCyxQHdL31edG/GQWBSAXk85bldJalZJZUjXb/WGt595MFRqMxzlycJr5hsdquYLHQ22CY+3pb+Sd/8nP8HtfaAPDq+okX35hoiiDwul20+D2EfDtbsFXrFAQiZbR697+SztXsbmCTC0lGxyYZica4trBe6M3jMjw4sJvhSJhfPNBVUujt2mKC9htKFgS8LiYXEzQiYwwBbyH4Qj5Pw+93oCAQuUP5vGUplWUxUbvTP+PpLK9eLNT4/9nVhZLXDoXbGIqEeeRQL7tCNy/0tqc9yMxKqmRKaDKTp6+9/heIGWPwugsLt3xu11q5hka7678VBYHIDiUzORaTGVZStXn3n7eWd67M8/3oJK9duk5ywxTV3S0+HjvSy1CkjwPdLbd9ryeP9fPc6UskMjkCXhfJTJ5s3vLksf5KfoSyWy3Itjpnf3WQt9kpCES2wVrLcirLYg33/V+ZjXNqLMapsRhTS6m141634Sv3dTMc6eMX7tleobfjA108w0FefGOCycUEfe1BnjzWXxfjA77ifP1g8T9XnQ/sVoIjQWCM+UfA3wUs8C7wa9ba5K1/SsQ5ubxlMZFhMZlxfOrnufFZXnxjgmuLCfYUL8hH7mrjB+9PMxqdZOxaaaG3yF3tDEfCnLy/d1uliW90fKCr5i/8q7V5gsVZPQGPLvxbUfUgMMbcDfxDYNBamzDG/AnwJPCdardF5HZyectCIsNiIkO+Brp/zo3P8tzpS3hchja/m6vzcf7Vn0XJ5izZDQHV2+ZfK/RWyfLYTlvt6lm94/d7XLrw74BTXUMeIGiMyQAh4FOH2iFyU8lMjqVklpVUtiYCYNWLb0yQt5blZI5Pb1iYFvC6OHGwUOjt8/0dDVvozesuXPhDPnX1lEvVg8Ba+4kx5veAK0ACGLXWjla7HSI3yubyLKcKc/9rbfbPfDzNKxemeO/ThZI7f4CgtzDb5X/+vQcJ+mqjdk05rU7lDHk9BH3OFWZrZE50DXUC3wAOAPPA/zbG/LK19o9u+L6ngKcA9u3bV+1mShOp1bn/mVye18dnGY1O8vpHsyV3/163oT3gpT3gIZu37G7xN0wIeFyutU1XqlmKuZndNgiMMb8B/JG1dq5M53wM+MhaO118//8DfBkoCQJr7QvACwBHjx6tnX+d0hBS2RwrqRwrqdq6+7fWcmlqmZFojFfOx1jcUOitxecmclc7H06vEPS6CPrcdTuNc5XLmOLF3l2s0+MqWcgm1bGVJ4Iw8IYx5m3gD4ARe2e3TVeAB40xIQpdQ48Cb97B+4lsSS5vWU5mWUplaq7s88xyilPFGv8fz6wXenMZ+IV7Ohka7OMr9+3G73WvzRqqt2mcsD6Vc2NxNnGe2co13RSey4aAXwOOAn8C/L619sMdndSYfw38dSAL/BT4u9ba1Gbff/ToUfvmm8oK2T5rLfF0juVUlniNdf2ks3l+9MF1RsZivPnxbEmN/3u6QgxHwjw2GKa71e9cI++Q113o5gn5PAQrtM2ibM4Y85a19ujtvm9LYwTWWmuMmQQmKVy8O4HvGWNOWWt/c7uNs9Z+E/jmdn9OZCustSQzeVbShVk/Ts/738hay9i1RUaiMX7w/hQrqfVFae0BDw8f7uWJSB/3h1vrsl/c73UT8rrXunt04a8PWxkjeAb428B14NvAP7XWZowxLuASsO0gECm3dDZPPF24609l8zV15w8QW0yu1fi/OrdeqM3tMvzigS6GImEePLC77rpKPK7CWIWmcta3rTwRdAF/xVp7eeNBa23eGPP1yjRL5PZS2RzJdJ7ldG2We0hkcvzw4jQjYzHeuTJfUuP/vp5WhiJhHj3SS2fI51gbt2vjVM6Ar3DX38jOXJji+bPjTMzF6e8M8fSJAU4e7nW6WWV32yAoduNs9tr58jZHZHOr8/wTmRypTL6mFnqtylvLz68uMBKd5NWL0yQz64PSnSEvjx0JMxQJc29Pq4Ot3B63yxDyeWjxF+7667HLaifOXJji2ZeieN2GjqCXqaUkz74U5VvQcGGgonNS01YHe5eSWeLp2t3h65O5BKNjk5wam2Jycb1sltdt+NK9u3ki0sex/V1102fucblo8btp8XtqZoP1anv+7DhedyEEAUI+D/F0lufPjisIRKphtcRDPF1bg70bLaeynCkWenvv08WS147saWNosI+HD/XQHrx5jf9as3EXrma9+G80MRen44a/u6DXzdW5+CY/Ub8UBFIzMrk8y8ksyzW2yGujXN7y9pU5RqIxXvvgesl6hJ5WP48P9jI02Me+3fVR6M3nca11+zR6f/929XeGmFpKrj0RQGHcpxGL+CkIxDHJTI50Lk8qky9+rb0B31WXZ1YYicY4dT7GzHJ67bjf4+KrB7sZGgzzxX3bq/HvlKDPTcjrIeR349Uq3k09fWKAZ1+KEk9n1/ZozuQsT58YcLppZacgkKrK5y1LySyLydrd1nHVQiLDDy5MMTIW4/3J0hr/n9u7i+HBMCfu76HFX9v/jIwxhb13/W5afJ66CKtacPJwL9+iMFZwdS7O3maeNSRyp1aneSazuZpb3XujbC7PuY9nGYnG+PGHMyWVPvfsCvD4YKHG/10dtb1Xr8sYQj43Ib+HkOb379jJw70NeeG/kYKggdTCnGdrLalsobsnkcmRzORqcprnjT6YWmYkOsnpC1PMxTNrx4NeNycP9TAUCfPZu3fVdI1/r7uwuKvF52m6zdflzigIGoQTc55zeUs6myedzZPK5Uhn82Rytqbv+DeaXSnU+B+NTvLh9MracQM8sK+DoUgfXznYTbBGZ9CoTr+Ui4KgQVRjzvPqnP54OkcinSObr+0+/ptJZ/P8eHyGkegk5z4qLfTW3xlkKBLm8SNhetsDzjXyFrxuV6Gcg8+t/XilbBQEDaLcc55Xu3jSufz6XX8N1vDZCmstFyaX1gq9LW2o8d8W8PDwoV6GI2EO97XVZHeKFndJpSkIGsSdzHm+sYsnVdzspB4v+htNL6XWCr1dmS2t8X/8QBfDkT6+NFCbhd60uEuqSUHQILY65zmby5PMrt/lp7P5uuzi2Uwyk+O1D64zEo3x9uW5kkJvA90tDEfCPHokTFdLbRV6W92wJeB1E9AuXVJlCoIGsdmc5xP397BSLNSWSOdqfu7+dp0bn+W7565wZS6OwbCSzpLasNp3V9DLo0d6GR4Mc19v7dT4X+3uUV+/1AIFQQM5ebiXr97fU5i3n8mTzOS4PBuv+y6ezXz/3Un+86sfksjkSuoRuU2h0NtwJMwvHuiqmbtr9fVLrVIQ1LF8vjhnP5tbG8xttDv+G8XTWV69eJ3R6CQ/u7pQ8prf4yLodbO3I8i3vhFxqIWldPGXeqAgqCPZ3OoircLdfqNf9Ffl8pZ3JuYZiU7y2qXrJDd0/bhdhvaAh/aAB7/HjcVyfWXT7a+rYnW7xpAKuUmdUBDUsNW9dxOZHPF0tqTSZTOYmI0zOhbj1FiMqaX1i7vP4+Khe3dzeSZOOpsrmSmVzOTpa69++YfVWT6tfk9NzkISuRUFQY3J5vLEiwO7iXR9lGcop6VkhtMXpjk1NsnYtdJCb5G72hmO9HHyUA+tfg/nxmd57vQlEpkcAa+LZHHa65PH+qvSVo/LRcjvplXdPlLnFAQOW53OmcoUVuw2S3fPRrm85Y1iobc///A6mdx6+IXb/QwNhhka7OPuztI7/eMDXTzDQV58Y4LJxQR97UGePNbP8YGuirVVF39pRAqCKsrnLelcoX9/tTBbI83h367x6WVGojFeuTDF7Mp6jf+A18XX7u9haDDM5/s7blno7fhAV0Uv/KCLvzQ+BUEFpYszepKZ9Zk9zW4+Xij0NhKN8cHUcslrX+jv4IlImK8e7CHoc/aCq4u/NBMFQZmsTuVcu9vP5mp2r91qy+TyvD4+y2h0ktc/mi35c7m7o1jobTBMn8OF3nTxl2alINgh3e3fmrWWS1PLfP+9Qo3/xQ2F3lp8bk4WC71F7mp3dLWv22XWZvvo4i/NSkGwBbrb37qZ5RSnzhdq/H88U1ro7eg9nQxF+njo3t34Hbzoul2Fct2tfo/jXVAitUBBcBO629+edDbPjz64zshYjDc/Lq3xf8/uEMODYR4bDNPd6nesjaa4dWNrsaJnrdQcEqkFTR8Eq3X3k5n1C7/u9m/PWkv000VGxwo1/ldSubXX2gMeHjncy3Ckj/vDzhV6c7sKm7YHfW5C2rRdZFNNFQTW2rWNVlLF/9J1utmKUyYXk5wqrva9OpdYO+52GR480MVQpI8HB7rwOlTozRhDi89Na8BD0Ks7f5GtaNggyOY27K5V/FpP++nWkkQ6x9lL04xEY7wzMV/y2n29rYUa/4d76Qg5V+Pf43KxK+ilNaA7f5HtqvsgyOUtmeJFP7Phoq/unTuTt5afTcwzOhbj1YvTJDPr4ySdIS+PHQkzHAkz0NPqYCsLNX52hby0+T26+xfZoboKgtX+/FQmTzKba/qVuZXwyVyCkbFJTo3FiC2uF3rzug1fvreb4UiYY/u7HL/r9nvddAS9tPjr6n9hkZpUF/+KsnnLxGy8KevwVMNyKsuZ96cZjU7y3qeLJa8N7mljKNLHw4d6aAt4HWrhupDPw66gV9M+RcqoLoJgtftHyieXt7x9ZY6RaIzXPrheMkW2p9XP44O9DEX62NcVcrCVBV63i1a/h7aAp2Z2GxNpJHURBFI+H8+sMBqNcep8jJnl9UJvfo+Lrx7sZmgwzBf3dTre9WOMocXvps2vu3+RSnMkCIwxHcC3gc8AFvh1a+2PnWhLM1hIZDh9YYrRaIz3Y6U1/j+3dxfDg2FO3N/jeH+722XW5vyHvNrQXaRanPqX/xzwfWvtXzXG+ADn+x8aTDaX5ycfzTI6FuPHH86Q3TCLas+uQKHGfyTMnl3V383rRur3F3FW1YPAGLMLOAH8KoC1Ng2kb/UzsnUfTC3z/egkp89PMZ/IrB0P+dx87f4ehiNhPnv3LsenWq4u/NoV8mpfXxGHOfFEcACYBv67MebzwFvAM9baFQfa0hBmV9K8cj7GyFiM8en1P0YDPHBPJ8ORMF+5r7smqmt63S7aA1r4JVJLnAgCD/AA8BvW2p8YY54Dfgv4lxu/yRjzFPAUwF17q7MHbT1JZ/P8+YczjI5Ncu6j0kJv/Z1BhiN9PD4YpqfNuUJvq1ymUOq5LaBSzyK1yIkguApctdb+pPj771EIghLW2heAFwA++4UHtEyYwoK6C5NLjEQLhd6WNtT4b/V7ePhwD8ODfRzZ0+Z41w+Az+OiPeil1efRwK9IDat6EFhrJ40xE8aYQ9ba94FHgbFqt6OeTC+lODUWYyQ6ycSGQm8uA8cPdDE02MeX792Nz+P8HPvVaZ/tAa/u/kXqhFOzhn4D+OPijKFx4NccakfNSmZyvPbBdUaiMd6+PMfGR6KBnhaGB8M8eiRMV4tzhd5WGVMo99zid9Oiu3+RuuNIEFhr3wGOOnHuajg3PsuLb0xwbTHBnvYgTx7r5/hA121/zlrLzz9ZYDRaKPQWT6/X+O8Ienn0SKHG/329zhZ6W+VxuWgPFnb60opfkfqllcVldm58ludOX8LjMrQHPMyspHju9CWe4eCmYXBtIcFoNMboWIxrC8m14x6X4Uv37mZoMMwvHuiqmYut1+2iq8Xn+AI0ESkP/UsusxffmMBT3BkLIOh1k8jkePGNiZIgiKezvHrxOqPRSX52daHkPQ6F2xiKhHnkcC+7gs4XelvlMoaOkJddQW9NDEaLSHkoCMrs2mKC9kDpH2vA62JyMUEub3lnYp6R6CSvXbpOckOht92tPh4/EubxwTAHuluq3exbMsbQFvDQEfR3Tfb+AAALrklEQVTWzFOJiJSPgqDM9rQHmVlJrT0RACwls+Qt/M3/9hOml9dr/Ps8Lr5yX6HG/wM1UOjtRqtjAG0Bb821TUTKR0FQZk8e6+e505dYSWVJ5/IsJDKkc6XLID5zVzvDkT6+dqiH1hrsZw/63LQFvLT4tOevSDOovatQHcvlLXks3a1+3vtkoWTKZ2+bn6FImKHBMHs7a6/GnjGGVr+HjpDXsY3nRcQZCoIyGJ9eZiQa4+XzMebi64XeAl4XX7u/h6HBMJ/v78BVg3fXqwvAOkM+BYBIk1IQ7NB8PM0rF6YYicb4YGq55LUv9HfwRCTMVw/21GxpZVdxAHiXBoBFmp6CYBsyuTw/Hp9hNBrjJx/NkttQ6e3ujiBDkcKsn772gIOtvLXVAeD2gFcrgEUEUBDclrWWi7FlRqKTnL4wxeKGQm8tfjcPH+plaDBM5K72mh5Y9boLBeDaA56abqeIVJ+CYBPXl1O8PFao8X95Jr523GXg6D2dDEX6eOje3fhrvLCa1+2iI+Sl1a8AEJGbUxBskMrk+NGHM4xEJ3nr8lxJjf97docYHgzz2GCY7lbna/zfjt/rpiPoVRkIEbmtpr9KWGuJfrrISDTGmYtTrKTWC721Bzw8crhQ6O3+cGvN31GrBLSI7ETTBsHkYpJTYzFOjcW4uqHGv9tlePBAF0ORPh4c6KqLKZUuY2gPFmoAaQWwiGxXUwVBIp3j7KVpRqIx3pmYL3ntvt5WhiNhHj3cS0fI+Rr/W+Eyhl1BL+0KABG5Aw0fBHlr+dnEPCPRGGcvTZPMrBd66wx5eexImOFImIGe2qjxvxVul6E9UHgC0BRQEblTDRsEV+fijI7FGI3GmFpaL/TmdRseureboUiYY/u76upOerUMtNYAiEg5NVQQLCeznLlYWO0b/XSx5LXBPW0MRfp4+FAPbYHaqfG/FabYBaQxABGphLoPglze8tblOUaik/zowxnSG2r897T611b77uuqvUJvW9Hq99DV4qv5MhBnLkzx/NlxJubi9HeGePrEACcP9zrdLBHZgroNgo+urzAaneTl81PMrKTXjvs9Lr56sJvhSB9f6O+o2ztor9vF7lYfIV/t/xWduTDFsy9F8boNHUEvU0tJnn0pyrdAYSBSB2r/KrPBQiLD6QtTjEQnuRgrLfT2ub27GB4M87VDPXVx8byVXUEvnSFf3YwDPH92HK/brP25h3we4uksz58dVxCI1IG6uGIup7I8+6dRXh+fIbthue+eXQGGBgtdP3d1BB1sYXl43S562vx1txhsYi5Oxw17Kwe9bq7OxTf5CRGpJXURBJ/OJ3jtg+sAhHxuTt7fw1AkzGfv3lXzq323YnU9QEeoPjeF7+8MMbWULHkSS2RyNbkBj4j8RXURBAC/cE8nT0TCPHRfd93dMW9mdVP4zpCvbscyAJ4+McCzL0WJp7MEvW4SmRyZnOXpEwNON01EtqAugmCgu4Xf/aufc7oZZdNo20KePNzLtyiMFVydi7NXs4ZE6kpdBEGtT53cqtVNYdoCjbce4OThXl34RepUXQRBvdOmMCJSyxQEFeRxueho8dKmTWFEpIYpCCrA7TJ0BH20BxUAIlL7FARlpI3hRaQeKQjKwO910x7waF9gEalLDRsE58ZnefGNCa4tJtjTHuTJY/0cH+gq6zla/R7ag9oWUkTqW2PMy7zBufFZnjt9iZmVFO0BDzMrKZ47fYlz47N3/N6rq4D7u0L0tgcUAiJS9xoyCF58YwKPyxD0ujEUvnpchhffmNjxe7pdhs6Qj/6uELtb/Q2xEExEBBq0a+jaYoL2QOlHC3hdTC4mNvmJzbk2bAqjAWARaUQNGQR72oPMrKQIbui2SWby9LVvvUKpdgUTkWbhWP+GMcZtjPmpMebPyv3eTx7rJ5u3JDI5LIWv2bzlyWP9W/r5kM/D3s4gXS31XQxORGQrnOzofgY4X4k3Pj7QxTOPHGR3i5+lZJbdLX6eeeTgbWcNeVwuwu0B+nYFNAYgIk3Dka4hY8xe4C8B/xb4x5U4x/GBri1PFzWmsMVive4HICJyJ5waI/iPwG8CbQ6df01rwENXqPY3hxcRqZSqX/2MMV8Hpqy1b93m+54yxrxpjHlzduZ6udtAW6C4FqAtoBAQkabmxBXwIeCXjDEfAy8Cjxhj/ujGb7LWvmCtPWqtPdq1u7ssJ15bDNYZpKdNawFERMCBILDW/ra1dq+1dj/wJHDaWvvLlTznjYvB9AQgIrKuIdcRrHK7Ck8AqgYqIrI5R4PAWnsGOFPu93UZQ0eosBhMs4BERG6t4Z4IWv0eulo0C0hEZKsaJgi8bhfdrX6CPlUDFRHZjroPApcpDARrW0gRkZ2p6yDQYjARkTtXl0EQ9LnpavHh96gbSETkTtVVEAR9bjqCPo0DiIiUUV0EgdsY7uoIaltIEZEKqIvOdY/bKARERCqkLoJAREQqR0EgItLkFAQiIk1OQSAi0uQUBCIiTU5BICLS5BQEIiJNTkEgItLkFAQiIk3OWGudbsNtGWOmgcs7/PFu4HoZm1MP9JmbQ7N95mb7vHDnn/kea23P7b6pLoLgThhj3rTWHnW6HdWkz9wcmu0zN9vnhep9ZnUNiYg0OQWBiEiTa4YgeMHpBjhAn7k5NNtnbrbPC1X6zA0/RiAiIrfWDE8EIiJyCw0fBMYYtzHmp8aYP3O6LdVgjOkwxnzPGHPBGHPeGPMlp9tUScaYf2SMiRpj3jPGfNcYE3C6TZVgjPkDY8yUMea9Dce6jDGnjDGXil87nWxjOW3yeX+3+P/1z40x/9cY0+FkG8vtZp95w2v/xBhjjTHdlTh3wwcB8Axw3ulGVNFzwPettYeBz9PAn90YczfwD4Gj1trPAG7gSWdbVTHfAZ644dhvAa9Yaw8CrxR/3yi+w1/8vKeAz1hrPwdcBH672o2qsO/wFz8zxph+YAi4UqkTN3QQGGP2An8J+LbTbakGY8wu4ATw+wDW2rS1dt7ZVlWcBwgaYzxACPjU4fZUhLX2LDB7w+FvAH9Y/PUfAn+5qo2qoJt9XmvtqLU2W/zt68Deqjesgjb5Owb4D8BvAhUb0G3oIAD+I4U/wLzTDamSA8A08N+L3WHfNsa0ON2oSrHWfgL8HoU7pWvAgrV21NlWVVXYWnut+OtJIOxkY6rs14H/53QjKs0Y8w3gE2vtzyp5noYNAmPM14Epa+1bTrelijzAA8B/sdZ+EVihsboLShT7xL9BIQDvAlqMMb/sbKucYQvT/5piCqAx5l8AWeCPnW5LJRljQsA/B56t9LkaNgiAh4BfMsZ8DLwIPGKM+SNnm1RxV4Gr1tqfFH//PQrB0KgeAz6y1k5bazPA/wG+7HCbqilmjNkDUPw65XB7Ks4Y86vA14G/ZRt/7vu9FG5yfla8ju0F3jbG9JX7RA0bBNba37bW7rXW7qcwgHjaWtvQd4vW2klgwhhzqHjoUWDMwSZV2hXgQWNMyBhjKHzehh0cv4mXgF8p/vpXgD91sC0VZ4x5gkJX7y9Za+NOt6fSrLXvWmt7rbX7i9exq8ADxX/nZdWwQdDEfgP4Y2PMz4EvAP/O4fZUTPHJ53vA28C7FP5/bsjVp8aY7wI/Bg4ZY64aY/4O8DvA48aYSxSejn7HyTaW0yaf9z8BbcApY8w7xpj/6mgjy2yTz1ydczf+05WIiNyKnghERJqcgkBEpMkpCEREmpyCQESkySkIRESanIJARKTJKQhERJqcgkBkB4wxx4p18QPGmJbingifcbpdIjuhBWUiO2SM+TdAAAhSqPH07x1uksiOKAhEdsgY4wPeAJLAl621OYebJLIj6hoS2bndQCuF+jcNuUWmNAc9EYjskDHmJQolzg8Ae6y1/8DhJonsiMfpBojUI2PM3wYy1tr/aYxxA39ujHnEWnva6baJbJeeCEREmpzGCEREmpyCQESkySkIRESanIJARKTJKQhERJqcgkBEpMkpCEREmpyCQESkyf1/f492Jjhv01EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x = \"x\", y = \"y\", data = anscombe_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What happens if you add the parameter `order = 2`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c6956fb38>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x = \"x\", y = \"y\", data = anscombe_1, order = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What happens if you use `order = 3`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/megan.owen/.local/lib/python3.4/site-packages/seaborn/regression.py:237: RankWarning: Polyfit may be poorly conditioned\n",
      "  return np.polyval(np.polyfit(_x, _y, order), grid)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c69594c18>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x = \"x\", y = \"y\", data = anscombe_1, order = 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is an example of overfitting, because the equations for the lines are more complex than they need to be.\n",
    "\n",
    "## k-fold cross validation\n",
    "\n",
    "We will use k = 2, because our datasets are so small.  We will also do the computations manually.\n",
    "\n",
    "Use `test_train_split()` from the last lab to split the third Anscombe data in half."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(anscombe_3[[\"x\"]], \\\n",
    "                                                    anscombe_3[\"y\"], test_size=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       x\n",
       "31   7.0\n",
       "23   8.0\n",
       "29   4.0\n",
       "22  10.0\n",
       "26  11.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31    6.42\n",
       "23    6.77\n",
       "29    5.39\n",
       "22    7.46\n",
       "26    7.81\n",
       "Name: y, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the linear model for the training data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Compute the mean squared error for the training data predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Now compute the linear model for the test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the mean squared error for the test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do the two mean squared errors differ?  Does this make sense?  (You may need to plot the test and training data to answer this question.)"
   ]
  }
 ],
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