13_Recap.ipynb

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      "text/plain": [
       "'0.5.19'"
      ]
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   "source": [
    "#Hidden\n",
    "from datascience import Table\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plots\n",
    "import numpy as np\n",
    "plots.style.use('fivethirtyeight')\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "## Just for reference, since datasience is evolving rapidly\n",
    "import datascience as ds\n",
    "ds.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recap of some themes covered\n",
    "\n",
    "\n",
    "We start with a recap of hypothesis testing, and move to some observations that matter for historical thinking -- bunching in years recorded and unusual outliers.\n",
    "\n",
    "/I/ AB Testing\n",
    "\n",
    "/II/ Themes Covered"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## /I/ AB Testing, recap\n",
    "\n",
    "We will return to the slavery data from the start of the semester, and how much we have learned. The codebook is at: http://www.icpsr.umich.edu/icpsrweb/RCMD/studies/7423 , and you also have a copy in Bcourses. \n"
   ]
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   "source": [
    "data = Table.read_table(\"https://github.com/data-8/history-connector/raw/gh-pages/Data1.csv\")"
   ]
  },
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    "Recall that some records contain multiple entries and, to simplify, we examined only a subgroup, where prices are known and refer to individual records: "
   ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>V1</th> <th>V2</th> <th>V3</th> <th>V4</th> <th>V5</th> <th>V6</th> <th>V7</th> <th>V8</th> <th>V9</th> <th>V10</th> <th>V11</th> <th>V12</th> <th>V13</th> <th>V14</th> <th>V15</th> <th>V16</th> <th>V17</th> <th>V18</th> <th>V19</th> <th>V20</th> <th>V21</th> <th>V22</th> <th>V23</th> <th>V24</th> <th>V25</th> <th>V26</th> <th>V27</th> <th>V28</th> <th>V29</th> <th>V30</th> <th>V31</th> <th>V32</th> <th>V33</th> <th>V34</th> <th>V35</th> <th>V36</th> <th>V37</th> <th>V38</th> <th>V39</th> <th>V40</th> <th>V41</th> <th>V42</th> <th>V43</th> <th>V44</th> <th>V45</th> <th>V46</th> <th>V47</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>NO2 </td> <td>NB  </td> <td>2   </td> <td>1804</td> <td> PG </td> <td>VI  </td> <td>1   </td> <td>999 </td> <td>GA  </td> <td>9   </td> <td>999 </td> <td>1   </td> <td>1   </td> <td>350 </td> <td>1   </td> <td>11  </td> <td>4   </td> <td>0   </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>0   </td> <td>1   </td> <td>9999</td> <td>99  </td> <td>99  </td> <td>1   </td> <td>99  </td> <td>0   </td> <td>0   </td> <td>0   </td> <td>201 </td> <td>1   </td> <td>1   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>NO2 </td> <td>NB  </td> <td>3   </td> <td>1804</td> <td> PG </td> <td>BI  </td> <td>1   </td> <td>999 </td> <td>CO  </td> <td>1   </td> <td>999 </td> <td>1   </td> <td>1   </td> <td>1000</td> <td>1   </td> <td>24  </td> <td>4   </td> <td>0   </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>0   </td> <td>2   </td> <td>9999</td> <td>2   </td> <td>99  </td> <td>1   </td> <td>99  </td> <td>0   </td> <td>0   </td> <td>0   </td> <td>503 </td> <td>1   </td> <td>2   </td>\n",
       "        </tr>\n",
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       "            <td>NO2 </td> <td>NB  </td> <td>3   </td> <td>1804</td> <td> P  </td> <td>LA  </td> <td>1   </td> <td>999 </td> <td>DC  </td> <td>9   </td> <td>999 </td> <td>1   </td> <td>1   </td> <td>450 </td> <td>2   </td> <td>25  </td> <td>1   </td> <td>0   </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>0   </td> <td>2   </td> <td>210 </td> <td>10  </td> <td>99  </td> <td>2   </td> <td>42  </td> <td>0   </td> <td>0   </td> <td>0   </td> <td>503 </td> <td>1   </td> <td>3   </td>\n",
       "        </tr>\n",
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       "            <td>NO2 </td> <td>NB  </td> <td>10  </td> <td>1804</td> <td> PG </td> <td>PE  </td> <td>1   </td> <td>999 </td> <td>LE  </td> <td>9   </td> <td>999 </td> <td>1   </td> <td>1   </td> <td>400 </td> <td>2   </td> <td>20  </td> <td>1   </td> <td>0   </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>0   </td> <td>1   </td> <td>9999</td> <td>99  </td> <td>99  </td> <td>1   </td> <td>99  </td> <td>0   </td> <td>0   </td> <td>0   </td> <td>503 </td> <td>1   </td> <td>6   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
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       "            <td>NO2 </td> <td>PP  </td> <td>1   </td> <td>1804</td> <td> PG </td> <td>GA  </td> <td>1   </td> <td>999 </td> <td>MO  </td> <td>9   </td> <td>999 </td> <td>1   </td> <td>1   </td> <td>750 </td> <td>1   </td> <td>22  </td> <td>1   </td> <td>0   </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>0   </td> <td>2   </td> <td>9999</td> <td>99  </td> <td>99  </td> <td>1   </td> <td>99  </td> <td>2   </td> <td>0   </td> <td>0   </td> <td>267 </td> <td>1   </td> <td>7   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>NO2 </td> <td>PP  </td> <td>1   </td> <td>1804</td> <td> PG </td> <td>PO  </td> <td>1   </td> <td>999 </td> <td>BO  </td> <td>1   </td> <td>999 </td> <td>1   </td> <td>1   </td> <td>500 </td> <td>2   </td> <td>21  </td> <td>1   </td> <td>0   </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>0   </td> <td>1   </td> <td>9999</td> <td>99  </td> <td>99  </td> <td>2   </td> <td>30  </td> <td>0   </td> <td>0   </td> <td>0   </td> <td>267 </td> <td>1   </td> <td>8   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>NO2 </td> <td>PP  </td> <td>2   </td> <td>1804</td> <td> PG </td> <td>CR  </td> <td>1   </td> <td>999 </td> <td>LA  </td> <td>2   </td> <td>SLO </td> <td>1   </td> <td>1   </td> <td>200 </td> <td>2   </td> <td>2.5 </td> <td>1   </td> <td>0   </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>0   </td> <td>1   </td> <td>9999</td> <td>99  </td> <td>99  </td> <td>2   </td> <td>99  </td> <td>0   </td> <td>0   </td> <td>0   </td> <td>999 </td> <td>1   </td> <td>11  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>NO2 </td> <td>PP  </td> <td>3   </td> <td>1804</td> <td> PG </td> <td>ME  </td> <td>1   </td> <td>999 </td> <td>MA  </td> <td>9   </td> <td>999 </td> <td>1   </td> <td>1   </td> <td>450 </td> <td>2   </td> <td>14  </td> <td>1   </td> <td>0   </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>0   </td> <td>1   </td> <td>9999</td> <td>99  </td> <td>99  </td> <td>2   </td> <td>99  </td> <td>0   </td> <td>7   </td> <td>8   </td> <td>117 </td> <td>1   </td> <td>12  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>NO2 </td> <td>PP  </td> <td>3   </td> <td>1804</td> <td> PG </td> <td>AR  </td> <td>1   </td> <td>999 </td> <td>CH  </td> <td>1   </td> <td>999 </td> <td>1   </td> <td>1   </td> <td>360 </td> <td>2   </td> <td>11  </td> <td>1   </td> <td>0   </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>0   </td> <td>1   </td> <td>9999</td> <td>99  </td> <td>99  </td> <td>2   </td> <td>42  </td> <td>0   </td> <td>0   </td> <td>0   </td> <td>503 </td> <td>1   </td> <td>13  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>NO2 </td> <td>PP  </td> <td>3   </td> <td>1804</td> <td> PG </td> <td>CH  </td> <td>2   </td> <td>SLO </td> <td>GI  </td> <td>2   </td> <td>SLO </td> <td>1   </td> <td>1   </td> <td>350 </td> <td>1   </td> <td>18  </td> <td>4   </td> <td>0   </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>0   </td> <td>1   </td> <td>9999</td> <td>99  </td> <td>99  </td> <td>2   </td> <td>99  </td> <td>0   </td> <td>7   </td> <td>8   </td> <td>117 </td> <td>1   </td> <td>14  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
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       "<p>... (2668 rows omitted)</p"
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       "V1   | V2   | V3   | V4   | V5   | V6   | V7   | V8   | V9   | V10  | V11  | V12  | V13  | V14  | V15  | V16  | V17  | V18  | V19  | V20  | V21  | V22  | V23  | V24  | V25  | V26  | V27  | V28  | V29  | V30  | V31  | V32  | V33  | V34  | V35  | V36  | V37  | V38  | V39  | V40  | V41  | V42  | V43  | V44  | V45  | V46  | V47\n",
       "NO2  | NB   | 2    | 1804 |  PG  | VI   | 1    | 999  | GA   | 9    | 999  | 1    | 1    | 350  | 1    | 11   | 4    | 0    | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 0    | 1    | 9999 | 99   | 99   | 1    | 99   | 0    | 0    | 0    | 201  | 1    | 1\n",
       "NO2  | NB   | 3    | 1804 |  PG  | BI   | 1    | 999  | CO   | 1    | 999  | 1    | 1    | 1000 | 1    | 24   | 4    | 0    | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 0    | 2    | 9999 | 2    | 99   | 1    | 99   | 0    | 0    | 0    | 503  | 1    | 2\n",
       "NO2  | NB   | 3    | 1804 |  P   | LA   | 1    | 999  | DC   | 9    | 999  | 1    | 1    | 450  | 2    | 25   | 1    | 0    | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 0    | 2    | 210  | 10   | 99   | 2    | 42   | 0    | 0    | 0    | 503  | 1    | 3\n",
       "NO2  | NB   | 10   | 1804 |  PG  | PE   | 1    | 999  | LE   | 9    | 999  | 1    | 1    | 400  | 2    | 20   | 1    | 0    | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 0    | 1    | 9999 | 99   | 99   | 1    | 99   | 0    | 0    | 0    | 503  | 1    | 6\n",
       "NO2  | PP   | 1    | 1804 |  PG  | GA   | 1    | 999  | MO   | 9    | 999  | 1    | 1    | 750  | 1    | 22   | 1    | 0    | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 0    | 2    | 9999 | 99   | 99   | 1    | 99   | 2    | 0    | 0    | 267  | 1    | 7\n",
       "NO2  | PP   | 1    | 1804 |  PG  | PO   | 1    | 999  | BO   | 1    | 999  | 1    | 1    | 500  | 2    | 21   | 1    | 0    | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 0    | 1    | 9999 | 99   | 99   | 2    | 30   | 0    | 0    | 0    | 267  | 1    | 8\n",
       "NO2  | PP   | 2    | 1804 |  PG  | CR   | 1    | 999  | LA   | 2    | SLO  | 1    | 1    | 200  | 2    | 2.5  | 1    | 0    | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 0    | 1    | 9999 | 99   | 99   | 2    | 99   | 0    | 0    | 0    | 999  | 1    | 11\n",
       "NO2  | PP   | 3    | 1804 |  PG  | ME   | 1    | 999  | MA   | 9    | 999  | 1    | 1    | 450  | 2    | 14   | 1    | 0    | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 0    | 1    | 9999 | 99   | 99   | 2    | 99   | 0    | 7    | 8    | 117  | 1    | 12\n",
       "NO2  | PP   | 3    | 1804 |  PG  | AR   | 1    | 999  | CH   | 1    | 999  | 1    | 1    | 360  | 2    | 11   | 1    | 0    | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 0    | 1    | 9999 | 99   | 99   | 2    | 42   | 0    | 0    | 0    | 503  | 1    | 13\n",
       "NO2  | PP   | 3    | 1804 |  PG  | CH   | 2    | SLO  | GI   | 2    | SLO  | 1    | 1    | 350  | 1    | 18   | 4    | 0    | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 0    | 1    | 9999 | 99   | 99   | 2    | 99   | 0    | 7    | 8    | 117  | 1    | 14\n",
       "... (2668 rows omitted)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.where(data[\"V14\"] != 99999)\n",
    "data = data.where(data[\"V12\"] == 1)\n",
    "data = data.where(data[\"V40\"]!=9)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, we will restrict data to a single year so that we can easily check the real value (or labor value, labor value, or income value; here is the web-site where we can check these figures:  https://www.measuringworth.com/uscompare/) \n",
    "\n",
    "Prices of cotton tended to rise after the 1840s, so we can look at 1850. You can look at some other year, and recall that prices were very high after the War of 1812 and in the 1830s due to a boom in land prices (“internal improvements”); in contrast, the “Panic of 1837” had the opposite effect on prices.\n",
    "Keep in mind the following values:\n",
    "\n",
    "“If you want to compare the value of a $1.00 Commodity in 1850 there are three choices. \n",
    "In 2014 the relative:\n",
    "\n",
    "/1/ real price of that commodity is \\$31.30\n",
    "\n",
    "/2/ labor value of that commodity is \\$223.30 (using the unskilled wage) or \\$482.00 (using production worker compensation)\n",
    "\n",
    "/3/ income value of that commodity is \\$490.00”"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Your turn\n",
    "\n",
    "Now Calculate the mean price for 1850, and express it in 2014 dollars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#sort gender where now male = 0, female = 1 for future AB test\n",
    "new_gender = []\n",
    "for i in data[\"V15\"]:\n",
    "    new_gender.append(i - 1)\n",
    "data.append_column(\"V15_new\", new_gender)\n",
    "\n",
    "#sort status where now 0=Guarantee; 1=Unguaranteed and drop all 9 value for future AB test\n",
    "data = data.where(data[\"V40\"]!=99)\n",
    "new_status = []\n",
    "for a in data[\"V40\"]:\n",
    "    new_status.append(a - 1)\n",
    "data.append_column(\"V40_new\", new_status)\n",
    "\n",
    "#Do not define data1850 before modifying the original data, otherwise data1850 would not have column \"V15_new\"\n",
    "data1850 = data.where(data[\"V4\"] == 1850)\n",
    "data1851 = data.where(data[\"V4\"] == 1851)\n",
    "data1852 = data.where(data[\"V4\"] == 1852)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "    <thead>\n",
       "        <tr>\n",
       "            <th>V1</th> <th>V2</th> <th>V3</th> <th>V4</th> <th>V5</th> <th>V6</th> <th>V7</th> <th>V8</th> <th>V9</th> <th>V10</th> <th>V11</th> <th>V12</th> <th>V13</th> <th>V14</th> <th>V15</th> <th>V16</th> <th>V17</th> <th>V18</th> <th>V19</th> <th>V20</th> <th>V21</th> <th>V22</th> <th>V23</th> <th>V24</th> <th>V25</th> <th>V26</th> <th>V27</th> <th>V28</th> <th>V29</th> <th>V30</th> <th>V31</th> <th>V32</th> <th>V33</th> <th>V34</th> <th>V35</th> <th>V36</th> <th>V37</th> <th>V38</th> <th>V39</th> <th>V40</th> <th>V41</th> <th>V42</th> <th>V43</th> <th>V44</th> <th>V45</th> <th>V46</th> <th>V47</th> <th>V15_new</th> <th>V40_new</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>NO2 </td> <td>AC  </td> <td>1   </td> <td>1850</td> <td>999 </td> <td>TO  </td> <td>3   </td> <td>KTY </td> <td>GI  </td> <td>1   </td> <td>999 </td> <td>1   </td> <td>1   </td> <td>375 </td> <td>2   </td> <td>8   </td> <td>2   </td> <td>0   </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>0   </td> <td>1   </td> <td>9999</td> <td>99  </td> <td>99  </td> <td>1   </td> <td>99  </td> <td>99  </td> <td>1   </td> <td>3   </td> <td>115 </td> <td>1   </td> <td>3628</td> <td>1      </td> <td>0      </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>NO2 </td> <td>AC  </td> <td>4   </td> <td>1850</td> <td>999 </td> <td>PA  </td> <td>1   </td> <td>999 </td> <td>GE  </td> <td>1   </td> <td>999 </td> <td>1   </td> <td>1   </td> <td>500 </td> <td>2   </td> <td>16  </td> <td>1   </td> <td>0   </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>0   </td> <td>2   </td> <td>9999</td> <td>36  </td> <td>99  </td> <td>1   </td> <td>99  </td> <td>0   </td> <td>0   </td> <td>0   </td> <td>267 </td> <td>1   </td> <td>3629</td> <td>1      </td> <td>0      </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>NO2 </td> <td>AC  </td> <td>4   </td> <td>1850</td> <td>999 </td> <td>WT  </td> <td>1   </td> <td>999 </td> <td>PA  </td> <td>1   </td> <td>999 </td> <td>1   </td> <td>1   </td> <td>800 </td> <td>2   </td> <td>27  </td> <td>1   </td> <td>0   </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>0   </td> <td>1   </td> <td>9999</td> <td>99  </td> <td>99  </td> <td>2   </td> <td>22  </td> <td>0   </td> <td>0   </td> <td>0   </td> <td>267 </td> <td>1   </td> <td>3630</td> <td>1      </td> <td>1      </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>NO2 </td> <td>AC  </td> <td>4   </td> <td>1850</td> <td>999 </td> <td>DE  </td> <td>1   </td> <td>999 </td> <td>LA  </td> <td>1   </td> <td>999 </td> <td>1   </td> <td>1   </td> <td>600 </td> <td>2   </td> <td>19  </td> <td>2   </td> <td>0   </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>0   </td> <td>1   </td> <td>9999</td> <td>99  </td> <td>99  </td> <td>2   </td> <td>4   </td> <td>0   </td> <td>0   </td> <td>0   </td> <td>267 </td> <td>1   </td> <td>3631</td> <td>1      </td> <td>1      </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>NO2 </td> <td>AC  </td> <td>5   </td> <td>1850</td> <td>999 </td> <td>TR  </td> <td>1   </td> <td>999 </td> <td>LA  </td> <td>1   </td> <td>999 </td> <td>1   </td> <td>1   </td> <td>375 </td> <td>2   </td> <td>13.5</td> <td>1   </td> <td>0   </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>0   </td> <td>1   </td> <td>9999</td> <td>99  </td> <td>99  </td> <td>1   </td> <td>99  </td> <td>0   </td> <td>0   </td> <td>0   </td> <td>267 </td> <td>1   </td> <td>3633</td> <td>1      </td> <td>0      </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>NO2 </td> <td>AC  </td> <td>6   </td> <td>1850</td> <td>999 </td> <td>PE  </td> <td>1   </td> <td>999 </td> <td>LA  </td> <td>1   </td> <td>999 </td> <td>1   </td> <td>1   </td> <td>500 </td> <td>1   </td> <td>15  </td> <td>1   </td> <td>0   </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>0   </td> <td>1   </td> <td>9999</td> <td>99  </td> <td>99  </td> <td>2   </td> <td>99  </td> <td>0   </td> <td>0   </td> <td>0   </td> <td>932 </td> <td>1   </td> <td>3634</td> <td>0      </td> <td>1      </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>NO2 </td> <td>AC  </td> <td>6   </td> <td>1850</td> <td>999 </td> <td>CO  </td> <td>1   </td> <td>999 </td> <td>CR  </td> <td>1   </td> <td>999 </td> <td>1   </td> <td>1   </td> <td>465 </td> <td>2   </td> <td>16  </td> <td>1   </td> <td>0   </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>0   </td> <td>1   </td> <td>9999</td> <td>99  </td> <td>99  </td> <td>1   </td> <td>99  </td> <td>7   </td> <td>0   </td> <td>0   </td> <td>267 </td> <td>1   </td> <td>3635</td> <td>1      </td> <td>0      </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>NO2 </td> <td>AC  </td> <td>11  </td> <td>1850</td> <td>999 </td> <td>WG  </td> <td>1   </td> <td>999 </td> <td>DE  </td> <td>1   </td> <td>999 </td> <td>1   </td> <td>1   </td> <td>400 </td> <td>2   </td> <td>16  </td> <td>4   </td> <td>0   </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>0   </td> <td>1   </td> <td>9999</td> <td>99  </td> <td>99  </td> <td>1   </td> <td>99  </td> <td>0   </td> <td>0   </td> <td>0   </td> <td>932 </td> <td>1   </td> <td>3637</td> <td>1      </td> <td>0      </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>NO2 </td> <td>AC  </td> <td>12  </td> <td>1850</td> <td>999 </td> <td>CH  </td> <td>1   </td> <td>999 </td> <td>DM  </td> <td>1   </td> <td>999 </td> <td>1   </td> <td>1   </td> <td>500 </td> <td>2   </td> <td>37  </td> <td>1   </td> <td>0   </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>0   </td> <td>1   </td> <td>9999</td> <td>99  </td> <td>99  </td> <td>2   </td> <td>99  </td> <td>0   </td> <td>0   </td> <td>0   </td> <td>267 </td> <td>1   </td> <td>3638</td> <td>1      </td> <td>1      </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>NO2 </td> <td>AB  </td> <td>1   </td> <td>1850</td> <td>999 </td> <td>MO  </td> <td>2   </td> <td>WBR </td> <td>BB  </td> <td>2   </td> <td>WBR </td> <td>1   </td> <td>1   </td> <td>350 </td> <td>1   </td> <td>26  </td> <td>1   </td> <td>0   </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>0   </td> <td>1   </td> <td>9999</td> <td>99  </td> <td>99  </td> <td>2   </td> <td>99  </td> <td>0   </td> <td>0   </td> <td>0   </td> <td>267 </td> <td>1   </td> <td>3639</td> <td>0      </td> <td>1      </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (97 rows omitted)</p"
      ],
      "text/plain": [
       "V1   | V2   | V3   | V4   | V5   | V6   | V7   | V8   | V9   | V10  | V11  | V12  | V13  | V14  | V15  | V16  | V17  | V18  | V19  | V20  | V21  | V22  | V23  | V24  | V25  | V26  | V27  | V28  | V29  | V30  | V31  | V32  | V33  | V34  | V35  | V36  | V37  | V38  | V39  | V40  | V41  | V42  | V43  | V44  | V45  | V46  | V47  | V15_new | V40_new\n",
       "NO2  | AC   | 1    | 1850 | 999  | TO   | 3    | KTY  | GI   | 1    | 999  | 1    | 1    | 375  | 2    | 8    | 2    | 0    | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 0    | 1    | 9999 | 99   | 99   | 1    | 99   | 99   | 1    | 3    | 115  | 1    | 3628 | 1       | 0\n",
       "NO2  | AC   | 4    | 1850 | 999  | PA   | 1    | 999  | GE   | 1    | 999  | 1    | 1    | 500  | 2    | 16   | 1    | 0    | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 0    | 2    | 9999 | 36   | 99   | 1    | 99   | 0    | 0    | 0    | 267  | 1    | 3629 | 1       | 0\n",
       "NO2  | AC   | 4    | 1850 | 999  | WT   | 1    | 999  | PA   | 1    | 999  | 1    | 1    | 800  | 2    | 27   | 1    | 0    | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 0    | 1    | 9999 | 99   | 99   | 2    | 22   | 0    | 0    | 0    | 267  | 1    | 3630 | 1       | 1\n",
       "NO2  | AC   | 4    | 1850 | 999  | DE   | 1    | 999  | LA   | 1    | 999  | 1    | 1    | 600  | 2    | 19   | 2    | 0    | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 0    | 1    | 9999 | 99   | 99   | 2    | 4    | 0    | 0    | 0    | 267  | 1    | 3631 | 1       | 1\n",
       "NO2  | AC   | 5    | 1850 | 999  | TR   | 1    | 999  | LA   | 1    | 999  | 1    | 1    | 375  | 2    | 13.5 | 1    | 0    | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 0    | 1    | 9999 | 99   | 99   | 1    | 99   | 0    | 0    | 0    | 267  | 1    | 3633 | 1       | 0\n",
       "NO2  | AC   | 6    | 1850 | 999  | PE   | 1    | 999  | LA   | 1    | 999  | 1    | 1    | 500  | 1    | 15   | 1    | 0    | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 0    | 1    | 9999 | 99   | 99   | 2    | 99   | 0    | 0    | 0    | 932  | 1    | 3634 | 0       | 1\n",
       "NO2  | AC   | 6    | 1850 | 999  | CO   | 1    | 999  | CR   | 1    | 999  | 1    | 1    | 465  | 2    | 16   | 1    | 0    | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 0    | 1    | 9999 | 99   | 99   | 1    | 99   | 7    | 0    | 0    | 267  | 1    | 3635 | 1       | 0\n",
       "NO2  | AC   | 11   | 1850 | 999  | WG   | 1    | 999  | DE   | 1    | 999  | 1    | 1    | 400  | 2    | 16   | 4    | 0    | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 0    | 1    | 9999 | 99   | 99   | 1    | 99   | 0    | 0    | 0    | 932  | 1    | 3637 | 1       | 0\n",
       "NO2  | AC   | 12   | 1850 | 999  | CH   | 1    | 999  | DM   | 1    | 999  | 1    | 1    | 500  | 2    | 37   | 1    | 0    | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 0    | 1    | 9999 | 99   | 99   | 2    | 99   | 0    | 0    | 0    | 267  | 1    | 3638 | 1       | 1\n",
       "NO2  | AB   | 1    | 1850 | 999  | MO   | 2    | WBR  | BB   | 2    | WBR  | 1    | 1    | 350  | 1    | 26   | 1    | 0    | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 0    | 1    | 9999 | 99   | 99   | 2    | 99   | 0    | 0    | 0    | 267  | 1    | 3639 | 0       | 1\n",
       "... (97 rows omitted)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1850"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"Bootstrap A/B test for the difference in the mean response\n",
    "Assumes A=0, B=1\"\"\"\n",
    "\n",
    "def bootstrap_AB_test(samp_table, response_label, ab_label, repetitions):\n",
    "    \n",
    "    # Sort the sample table according to the A/B column; \n",
    "    # then select only the column of effects.\n",
    "    response = samp_table.sort(ab_label).select(response_label)\n",
    "    \n",
    "    # Find the number of entries in Category A.\n",
    "    n_A = samp_table.where(samp_table[ab_label],0).num_rows\n",
    "      \n",
    "    # Calculate the observed value of the test statistic.\n",
    "    meanA = np.mean(response[response_label][:n_A])\n",
    "    meanB = np.mean(response[response_label][n_A:])\n",
    "    obs_diff = meanA - meanB\n",
    "    \n",
    "    # Run the bootstrap procedure and get a list of resampled differences in means\n",
    "    diffs = []\n",
    "    for i in range(repetitions):\n",
    "        resample = response.sample(with_replacement=True)\n",
    "        d = np.mean(resample[response_label][:n_A]) - np.mean(resample[response_label][n_A:])\n",
    "        diffs.append([d])\n",
    "    \n",
    "    # Compute the bootstrap empirical P-value\n",
    "    diff_array = np.array(diffs)\n",
    "    p_value = np.count_nonzero(abs(diff_array) >= abs(obs_diff))/repetitions\n",
    "    \n",
    "    # Display results\n",
    "    diffs = Table([diffs],['diff_in_means'])\n",
    "    diffs.hist(bins=20,normed=True)\n",
    "    plots.xlabel('Approx null distribution of difference in means')\n",
    "    plots.title('Bootstrap A-B Test')\n",
    "    print(\"Observed difference in means: \", obs_diff)\n",
    "    print(\"Bootstrap empirical P-value: \", p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### /1/ Does gender seem to matter?\n",
    "\n",
    "Let's use the bootstrap to see the difference between recorded prices for the recorded genders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Anon\\Anaconda3\\lib\\site-packages\\datascience\\tables.py:49: FutureWarning: Two-argument __init__ is deprecated. Use Table().with_columns(...)\n",
      "  warnings.warn(\"Two-argument __init__ is deprecated. Use Table().with_columns(...)\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observed difference in means:  17.2921146953\n",
      "Bootstrap empirical P-value:  0.6315\n"
     ]
    },
    {
     "data": {
      "image/png": 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tWrWCq6urWvpDpUfj9yFevnwZ69evx82bN/H8+XMEBwcXOp1TZGQkgoODcf36daSnp6Nu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\n1tbWMDY2xrFjxwAAu3btQuvWrWFmZgY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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d95b5a8d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bootstrap_AB_test(data1850, \"V14\",\"V15_new\", 10000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### /2/ Does the sale status seem to matter?\n",
    "\n",
    "Let's see if having a guarantee matters in terms of a recorded price. We will have to look at the codebook, and let's note that several versions of the codebook exist -- you might note that word choice, sex, and that we might see gender instead these days.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Anon\\Anaconda3\\lib\\site-packages\\datascience\\tables.py:49: FutureWarning: Two-argument __init__ is deprecated. Use Table().with_columns(...)\n",
      "  warnings.warn(\"Two-argument __init__ is deprecated. Use Table().with_columns(...)\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observed difference in means:  151.368845501\n",
      "Bootstrap empirical P-value:  0.0004\n"
     ]
    },
    {
     "data": {
      "image/png": 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s3ryZqKgoLly4QFJSEu7u7pQsWTKr4hMREckW6UqIyWxtbVOdM1RERCQ3SldC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\neqUVV1Zv+3GcO3cOg8HAq6++StmyZTN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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d95cce3898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bootstrap_AB_test(data1850, \"V14\",\"V40_new\", 10000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### /3/ Does the year matter?\n",
    "\n",
    "Try the other years, 1851 and 1852, and see what result you get. What do you notice?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Anon\\Anaconda3\\lib\\site-packages\\datascience\\tables.py:49: FutureWarning: Two-argument __init__ is deprecated. Use Table().with_columns(...)\n",
      "  warnings.warn(\"Two-argument __init__ is deprecated. Use Table().with_columns(...)\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observed difference in means:  197.766124171\n",
      "Bootstrap empirical P-value:  0.0007\n"
     ]
    },
    {
     "data": {
      "image/png": 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+mddff52zZ88CkJiYSOvWrdmxY4feAYSEhDB8+HBGjBiBu7s7QUFBODg4sHbt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\np6dH5V+rvLu6uqivr1e97+bmxsaNG5EkySKM8ZP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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d95cdab588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Your turn \n",
    "#For 1851, your turn\n",
    "#bootstrap_AB_test(..)\n",
    "\n",
    "bootstrap_AB_test(data1852, \"V14\",\"V40_new\", 10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Anon\\Anaconda3\\lib\\site-packages\\datascience\\tables.py:49: FutureWarning: Two-argument __init__ is deprecated. Use Table().with_columns(...)\n",
      "  warnings.warn(\"Two-argument __init__ is deprecated. Use Table().with_columns(...)\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observed difference in means:  107.377149877\n",
      "Bootstrap empirical P-value:  0.1995\n"
     ]
    },
    {
     "data": {
      "image/png": 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MC+dG+O00t3Pvnu6qIpJT2ZwQP/jgA1q2bImPjw+dOnXCYDAQEBDA7t27+e67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\nQCAQCAD4D8HNHZlxScatAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d95cdeefd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bootstrap_AB_test(data1851, \"V14\",\"V40_new\", 10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>V1</th> <th>V2</th> <th>V3</th> <th>V4</th> <th>V5</th> <th>V6</th> <th>V7</th> <th>V8</th> <th>V9</th> <th>V10</th> <th>V11</th> <th>V12</th> <th>V13</th> <th>V14</th> <th>V15</th> <th>V16</th> <th>V17</th> <th>V18</th> <th>V19</th> <th>V20</th> <th>V21</th> <th>V22</th> <th>V23</th> <th>V24</th> <th>V25</th> <th>V26</th> <th>V27</th> <th>V28</th> <th>V29</th> <th>V30</th> <th>V31</th> <th>V32</th> <th>V33</th> <th>V34</th> <th>V35</th> <th>V36</th> <th>V37</th> <th>V38</th> <th>V39</th> <th>V40</th> <th>V41</th> <th>V42</th> <th>V43</th> <th>V44</th> <th>V45</th> <th>V46</th> <th>V47</th> <th>V15_new</th> <th>V40_new</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>NO2 </td> <td>AB  </td> <td>2   </td> <td>1851</td> <td>999 </td> <td>TR  </td> <td>2   </td> <td>SMA </td> <td>LA  </td> <td>1   </td> <td>999 </td> <td>1   </td> <td>1   </td> <td>500 </td> <td>2   </td> <td>19  </td> <td>1   </td> <td>0   </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>0   </td> <td>1   </td> <td>9999</td> <td>99  </td> <td>99  </td> <td>1   </td> <td>99  </td> <td>0   </td> <td>0   </td> <td>0   </td> <td>292 </td> <td>1   </td> <td>3817</td> <td>1      </td> <td>0      </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>NO2 </td> <td>AB  </td> <td>2   </td> <td>1851</td> <td>999 </td> <td>HA  </td> <td>1   </td> <td>999 </td> <td>BO  </td> <td>2   </td> <td>MOR </td> <td>1   </td> <td>1   </td> <td>1062</td> <td>1   </td> <td>21  </td> <td>1   </td> <td>0   </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>0   </td> <td>1   </td> <td>9999</td> <td>99  </td> <td>99  </td> <td>1   </td> <td>99  </td> <td>0   </td> <td>3   </td> <td>9   </td> <td>181 </td> <td>1   </td> <td>3818</td> <td>0      </td> <td>0      </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>NO2 </td> <td>AB  </td> <td>3   </td> <td>1851</td> <td>999 </td> <td>FO  </td> <td>1   </td> <td>999 </td> <td>CR  </td> <td>1   </td> <td>999 </td> <td>1   </td> <td>1   </td> <td>525 </td> <td>2   </td> <td>19  </td> <td>4   </td> <td>0   </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>0   </td> <td>1   </td> <td>9999</td> <td>99  </td> <td>99  </td> <td>2   </td> <td>2   </td> <td>0   </td> <td>0   </td> <td>0   </td> <td>267 </td> <td>1   </td> <td>3819</td> <td>1      </td> <td>1      </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>NO2 </td> <td>AB  </td> <td>3   </td> <td>1851</td> <td>999 </td> <td>BU  </td> <td>1   </td> <td>999 </td> <td>HA  </td> <td>1   </td> <td>999 </td> <td>1   </td> <td>1   </td> <td>625 </td> <td>2   </td> <td>29  </td> <td>2   </td> <td>0   </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>0   </td> <td>1   </td> <td>9999</td> <td>99  </td> <td>99  </td> <td>1   </td> <td>99  </td> <td>0   </td> <td>0   </td> <td>0   </td> <td>267 </td> <td>1   </td> <td>3820</td> <td>1      </td> <td>0      </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>NO2 </td> <td>AB  </td> <td>4   </td> <td>1851</td> <td>999 </td> <td>BO  </td> <td>1   </td> <td>999 </td> <td>LA  </td> <td>2   </td> <td>RAP </td> <td>1   </td> <td>1   </td> <td>1180</td> <td>1   </td> <td>20  </td> <td>1   </td> <td>0   </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>0   </td> <td>2   </td> <td>550 </td> <td>8   </td> <td>99  </td> <td>1   </td> <td>99  </td> <td>0   </td> <td>3   </td> <td>9   </td> <td>181 </td> <td>1   </td> <td>3821</td> <td>0      </td> <td>0      </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>NO2 </td> <td>AB  </td> <td>5   </td> <td>1851</td> <td>999 </td> <td>GE  </td> <td>1   </td> <td>999 </td> <td>AU  </td> <td>1   </td> <td>999 </td> <td>1   </td> <td>1   </td> <td>700 </td> <td>1   </td> <td>24  </td> <td>1   </td> <td>0   </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>0   </td> <td>1   </td> <td>9999</td> <td>99  </td> <td>99  </td> <td>1   </td> <td>99  </td> <td>2   </td> <td>0   </td> <td>0   </td> <td>267 </td> <td>1   </td> <td>3823</td> <td>0      </td> <td>0      </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>NO2 </td> <td>AB  </td> <td>6   </td> <td>1851</td> <td>999 </td> <td>RO  </td> <td>1   </td> <td>999 </td> <td>PI  </td> <td>1   </td> <td>999 </td> <td>1   </td> <td>1   </td> <td>265 </td> <td>2   </td> <td>40  </td> <td>1   </td> <td>0   </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>0   </td> <td>1   </td> <td>9999</td> <td>99  </td> <td>99  </td> <td>2   </td> <td>99  </td> <td>0   </td> <td>0   </td> <td>0   </td> <td>267 </td> <td>1   </td> <td>3827</td> <td>1      </td> <td>1      </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>NO2 </td> <td>AB  </td> <td>7   </td> <td>1851</td> <td>999 </td> <td>MA  </td> <td>1   </td> <td>999 </td> <td>WO  </td> <td>1   </td> <td>999 </td> <td>1   </td> <td>1   </td> <td>650 </td> <td>2   </td> <td>21  </td> <td>1   </td> <td>0   </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>0   </td> <td>1   </td> <td>9999</td> <td>99  </td> <td>99  </td> <td>1   </td> <td>99  </td> <td>0   </td> <td>0   </td> <td>0   </td> <td>267 </td> <td>1   </td> <td>3828</td> <td>1      </td> <td>0      </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>NO2 </td> <td>AB  </td> <td>10  </td> <td>1851</td> <td>999 </td> <td>TH  </td> <td>1   </td> <td>999 </td> <td>BR  </td> <td>2   </td> <td>STA </td> <td>1   </td> <td>1   </td> <td>900 </td> <td>1   </td> <td>27  </td> <td>1   </td> <td>0   </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>0   </td> <td>1   </td> <td>9999</td> <td>99  </td> <td>99  </td> <td>1   </td> <td>99  </td> <td>5   </td> <td>0   </td> <td>0   </td> <td>267 </td> <td>1   </td> <td>3829</td> <td>0      </td> <td>0      </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>NO2 </td> <td>AB  </td> <td>10  </td> <td>1851</td> <td>999 </td> <td>BO  </td> <td>1   </td> <td>999 </td> <td>BO  </td> <td>1   </td> <td>999 </td> <td>1   </td> <td>1   </td> <td>900 </td> <td>1   </td> <td>27  </td> <td>2   </td> <td>0   </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>9   </td> <td>99  </td> <td>0   </td> <td>1   </td> <td>9999</td> <td>99  </td> <td>99  </td> <td>1   </td> <td>99  </td> <td>0   </td> <td>0   </td> <td>0   </td> <td>267 </td> <td>1   </td> <td>3833</td> <td>0      </td> <td>0      </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (75 rows omitted)</p"
      ],
      "text/plain": [
       "V1   | V2   | V3   | V4   | V5   | V6   | V7   | V8   | V9   | V10  | V11  | V12  | V13  | V14  | V15  | V16  | V17  | V18  | V19  | V20  | V21  | V22  | V23  | V24  | V25  | V26  | V27  | V28  | V29  | V30  | V31  | V32  | V33  | V34  | V35  | V36  | V37  | V38  | V39  | V40  | V41  | V42  | V43  | V44  | V45  | V46  | V47  | V15_new | V40_new\n",
       "NO2  | AB   | 2    | 1851 | 999  | TR   | 2    | SMA  | LA   | 1    | 999  | 1    | 1    | 500  | 2    | 19   | 1    | 0    | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 0    | 1    | 9999 | 99   | 99   | 1    | 99   | 0    | 0    | 0    | 292  | 1    | 3817 | 1       | 0\n",
       "NO2  | AB   | 2    | 1851 | 999  | HA   | 1    | 999  | BO   | 2    | MOR  | 1    | 1    | 1062 | 1    | 21   | 1    | 0    | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 0    | 1    | 9999 | 99   | 99   | 1    | 99   | 0    | 3    | 9    | 181  | 1    | 3818 | 0       | 0\n",
       "NO2  | AB   | 3    | 1851 | 999  | FO   | 1    | 999  | CR   | 1    | 999  | 1    | 1    | 525  | 2    | 19   | 4    | 0    | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 0    | 1    | 9999 | 99   | 99   | 2    | 2    | 0    | 0    | 0    | 267  | 1    | 3819 | 1       | 1\n",
       "NO2  | AB   | 3    | 1851 | 999  | BU   | 1    | 999  | HA   | 1    | 999  | 1    | 1    | 625  | 2    | 29   | 2    | 0    | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 0    | 1    | 9999 | 99   | 99   | 1    | 99   | 0    | 0    | 0    | 267  | 1    | 3820 | 1       | 0\n",
       "NO2  | AB   | 4    | 1851 | 999  | BO   | 1    | 999  | LA   | 2    | RAP  | 1    | 1    | 1180 | 1    | 20   | 1    | 0    | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 0    | 2    | 550  | 8    | 99   | 1    | 99   | 0    | 3    | 9    | 181  | 1    | 3821 | 0       | 0\n",
       "NO2  | AB   | 5    | 1851 | 999  | GE   | 1    | 999  | AU   | 1    | 999  | 1    | 1    | 700  | 1    | 24   | 1    | 0    | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 0    | 1    | 9999 | 99   | 99   | 1    | 99   | 2    | 0    | 0    | 267  | 1    | 3823 | 0       | 0\n",
       "NO2  | AB   | 6    | 1851 | 999  | RO   | 1    | 999  | PI   | 1    | 999  | 1    | 1    | 265  | 2    | 40   | 1    | 0    | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 0    | 1    | 9999 | 99   | 99   | 2    | 99   | 0    | 0    | 0    | 267  | 1    | 3827 | 1       | 1\n",
       "NO2  | AB   | 7    | 1851 | 999  | MA   | 1    | 999  | WO   | 1    | 999  | 1    | 1    | 650  | 2    | 21   | 1    | 0    | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 0    | 1    | 9999 | 99   | 99   | 1    | 99   | 0    | 0    | 0    | 267  | 1    | 3828 | 1       | 0\n",
       "NO2  | AB   | 10   | 1851 | 999  | TH   | 1    | 999  | BR   | 2    | STA  | 1    | 1    | 900  | 1    | 27   | 1    | 0    | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 0    | 1    | 9999 | 99   | 99   | 1    | 99   | 5    | 0    | 0    | 267  | 1    | 3829 | 0       | 0\n",
       "NO2  | AB   | 10   | 1851 | 999  | BO   | 1    | 999  | BO   | 1    | 999  | 1    | 1    | 900  | 1    | 27   | 2    | 0    | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 9    | 99   | 0    | 1    | 9999 | 99   | 99   | 1    | 99   | 0    | 0    | 0    | 267  | 1    | 3833 | 0       | 0\n",
       "... (75 rows omitted)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1851"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## /II/ Let's look at some of what we've covered over the semester"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "/1/ In visualizing the data, we noted recorded sales peaked in some years -- a start of a temporal approach, or way of thinking. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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xY83u4yRSy/WiG5i1ZINF+z9e7aNCNERUU4qLZHR0NDZs2IAxY8ZYTPvtt9+wdu1a9O7d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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d95cfa7e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Another way to present the information is in percent format\n",
    "data = Table.read_table(\"https://github.com/data-8/history-connector/raw/gh-pages/Data1.csv\")\n",
    "viz_salesbyyear = data.select([\"V4\"])\n",
    "viz_salesbyyear.hist(bins=np.arange(1800, 1870, 1), normed=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "/2/ We noted a pretty normal distribution.... ONCE we excluded some of the messy values, the 99999s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3d4ednR2USqXO8rKyMr2rVFNkZ2dj8eLF+Oc//4nJkye32r69Bt3azhaXwtnZ2eT+Qvra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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d95cffa898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PriceRecorded=data.where(data[\"V14\"] != 99999)\n",
    "SingularRecord=PriceRecorded.where(np.logical_and(PriceRecorded.column('V12')==1, \n",
    "                                                PriceRecorded.column('V13')==1))\n",
    "SingularRecord.sort('V14', descending=True)\n",
    "SingularRecord.hist('V14', bins=np.arange(0,2501, 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "/3/ We noted an interesting phenomenon with age, and the selectivity of records. Also, would you rewrite this code?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JlqvJY12IiIi0pXUhmzhxovrfX331lcb8tLQ0TJw4ESqVSt0mk8lYyIiISK+0LmQccYOI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d95d197c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = Table.read_table(\"https://github.com/data-8/history-connector/raw/gh-pages/Data1.csv\")\n",
    "data.relabel('V16', 'principal slave age')\n",
    "principal_age = data.where(data.column(\"principal slave age\") !=99).column(\"principal slave age\")\n",
    "my_bins=np.arange(np.min(principal_age), np.max(principal_age)+2, 1)\n",
    "data.hist(\"principal slave age\", bins=my_bins)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "/4/ And we noted -- an outlier. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>COUNTY</th> <th>PARISH</th> <th>RELIEF</th> <th>INCOME</th> <th>UNEMP</th> <th>COTTIND</th> <th>ALLOTMNT</th> <th>LONDON</th> <th>FARMERS</th> <th>WORKHSE</th> <th>CHILDALL</th> <th>SUBSIDY</th> <th>LABRATE</th> <th>ROUNDSMN</th> <th>GRAIN</th> <th>DENSITY</th> <th>WEALTH</th> <th>POP</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1     </td> <td>1     </td> <td>20.36 </td> <td>41.32 </td> <td>0.14 </td> <td>0      </td> <td>0       </td> <td>31.5  </td> <td>0.55   </td> <td>1      </td> <td>1       </td> <td>1      </td> <td>1      </td> <td>0       </td> <td>16.04</td> <td>0.31   </td> <td>7.31  </td> <td>2140</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1     </td> <td>2     </td> <td>29.09 </td> <td>32.74 </td> <td>0.07 </td> <td>1      </td> <td>1       </td> <td>31.5  </td> <td>0.33   </td> <td>0      </td> <td>1       </td> <td>0      </td> <td>0      </td> <td>0       </td> <td>13.54</td> <td>0.21   </td> <td>3.9   </td> <td>206 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1     </td> <td>5     </td> <td>14.93 </td> <td>30.99 </td> <td>0.05 </td> <td>0      </td> <td>1       </td> <td>31.5  </td> <td>0.18   </td> <td>1      </td> <td>0       </td> <td>0      </td> <td>0      </td> <td>0       </td> <td>11.81</td> <td>0.24   </td> <td>3.5   </td> <td>1053</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1     </td> <td>8     </td> <td>24.12 </td> <td>31.38 </td> <td>0.06 </td> <td>0      </td> <td>0       </td> <td>31.5  </td> <td>0.4    </td> <td>1      </td> <td>1       </td> <td>0      </td> <td>0      </td> <td>0       </td> <td>11.32</td> <td>0.3    </td> <td>2.15  </td> <td>2602</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1     </td> <td>10    </td> <td>18.21 </td> <td>32.74 </td> <td>0.03 </td> <td>0      </td> <td>1       </td> <td>31.5  </td> <td>0.21   </td> <td>1      </td> <td>0       </td> <td>0      </td> <td>0      </td> <td>0       </td> <td>13.2 </td> <td>0.25   </td> <td>4.64  </td> <td>901 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1     </td> <td>11    </td> <td>20.73 </td> <td>32.74 </td> <td>0.1  </td> <td>0      </td> <td>1       </td> <td>31.5  </td> <td>0.74   </td> <td>1      </td> <td>1       </td> <td>0      </td> <td>0      </td> <td>0       </td> <td>13.78</td> <td>0.38   </td> <td>3.49  </td> <td>1223</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1     </td> <td>12    </td> <td>8.12  </td> <td>33.06 </td> <td>0.07 </td> <td>0      </td> <td>1       </td> <td>31.5  </td> <td>0.32   </td> <td>1      </td> <td>1       </td> <td>0      </td> <td>0      </td> <td>1       </td> <td>15.85</td> <td>0.27   </td> <td>6.1   </td> <td>1140</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1     </td> <td>13    </td> <td>14.02 </td> <td>35.47 </td> <td>0.01 </td> <td>0      </td> <td>0       </td> <td>31.5  </td> <td>0.11   </td> <td>1      </td> <td>1       </td> <td>0      </td> <td>0      </td> <td>0       </td> <td>12.39</td> <td>0.45   </td> <td>3.73  </td> <td>1820</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1     </td> <td>17    </td> <td>18.42 </td> <td>32.74 </td> <td>0.08 </td> <td>0      </td> <td>0       </td> <td>31.5  </td> <td>0.59   </td> <td>1      </td> <td>1       </td> <td>1      </td> <td>1      </td> <td>0       </td> <td>11.43</td> <td>0.27   </td> <td>3.28  </td> <td>1432</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1     </td> <td>18    </td> <td>34.55 </td> <td>32.23 </td> <td>0.26 </td> <td>0      </td> <td>1       </td> <td>31.5  </td> <td>0.31   </td> <td>1      </td> <td>1       </td> <td>0      </td> <td>1      </td> <td>0       </td> <td>14.47</td> <td>0.31   </td> <td>4.46  </td> <td>866 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (301 rows omitted)</p"
      ],
      "text/plain": [
       "COUNTY | PARISH | RELIEF | INCOME | UNEMP | COTTIND | ALLOTMNT | LONDON | FARMERS | WORKHSE | CHILDALL | SUBSIDY | LABRATE | ROUNDSMN | GRAIN | DENSITY | WEALTH | POP\n",
       "1      | 1      | 20.36  | 41.32  | 0.14  | 0       | 0        | 31.5   | 0.55    | 1       | 1        | 1       | 1       | 0        | 16.04 | 0.31    | 7.31   | 2140\n",
       "1      | 2      | 29.09  | 32.74  | 0.07  | 1       | 1        | 31.5   | 0.33    | 0       | 1        | 0       | 0       | 0        | 13.54 | 0.21    | 3.9    | 206\n",
       "1      | 5      | 14.93  | 30.99  | 0.05  | 0       | 1        | 31.5   | 0.18    | 1       | 0        | 0       | 0       | 0        | 11.81 | 0.24    | 3.5    | 1053\n",
       "1      | 8      | 24.12  | 31.38  | 0.06  | 0       | 0        | 31.5   | 0.4     | 1       | 1        | 0       | 0       | 0        | 11.32 | 0.3     | 2.15   | 2602\n",
       "1      | 10     | 18.21  | 32.74  | 0.03  | 0       | 1        | 31.5   | 0.21    | 1       | 0        | 0       | 0       | 0        | 13.2  | 0.25    | 4.64   | 901\n",
       "1      | 11     | 20.73  | 32.74  | 0.1   | 0       | 1        | 31.5   | 0.74    | 1       | 1        | 0       | 0       | 0        | 13.78 | 0.38    | 3.49   | 1223\n",
       "1      | 12     | 8.12   | 33.06  | 0.07  | 0       | 1        | 31.5   | 0.32    | 1       | 1        | 0       | 0       | 1        | 15.85 | 0.27    | 6.1    | 1140\n",
       "1      | 13     | 14.02  | 35.47  | 0.01  | 0       | 0        | 31.5   | 0.11    | 1       | 1        | 0       | 0       | 0        | 12.39 | 0.45    | 3.73   | 1820\n",
       "1      | 17     | 18.42  | 32.74  | 0.08  | 0       | 0        | 31.5   | 0.59    | 1       | 1        | 1       | 1       | 0        | 11.43 | 0.27    | 3.28   | 1432\n",
       "1      | 18     | 34.55  | 32.23  | 0.26  | 0       | 1        | 31.5   | 0.31    | 1       | 1        | 0       | 1       | 0        | 14.47 | 0.31    | 4.46   | 866\n",
       "... (301 rows omitted)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = Table.read_table(\"https://github.com/data-8/history-connector/raw/gh-pages/Data4.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def standard_units(any_numbers):\n",
    "    \"Convert any array of numbers to standard units.\"\n",
    "    return (any_numbers - np.mean(any_numbers))/np.std(any_numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QQoyid4J/9NFHFX+OjIzEn3/+iZycHLS0tOCuu+6Cj4+PWQIkhBBiHIP6wStzcXHB6NGj\nTRgKIYQQU9KZ4MvLyw3aWWhoaK+CIYQQYjo6E3xSUpLKlMA9oZGshBBiPXQm+PXr1ysSfHt7O1av\nXg03NzdMmjQJ/v7+qK6uxp49eyAQCPDmm2/2ScCEEEL0ozPBT5s2TfHnhQsXIikpCdnZ2SpX9QsW\nLMDUqVNRUFBgvigJIYQYTO+BTrt378bMmTPVSjYMw2DWrFnYtWuXyYMjhBBiPL0TvFAoxO3btzW+\nVltbi5aWFpMFRQghpPf0TvAjR47EkiVLcPbsWZXtubm5WLp0KUaOHGny4AghhBhP737wK1euxKRJ\nk3D//fcjODgY/v7+qKmpQUVFBcLDw7Fy5UpzxkkIIcRAeif4iIgI5OTkYPv27cjJyUF1dTViY2Mx\nbNgwZGZmgsfjmTNOQgghBjJoJCuPx8OMGTMwY8YMc8VDCCHERGhNVkIIYSm9E3x7ezuysrIwdOhQ\nBAUFwdvbW+V/fTXZ2Nq1a+Hl5UVLBBJCSA/0LtEsXrwYmzdvxv3334+JEyfC3t7enHFplJOTg23b\ntiEhIaHPj00IIbZG7wT/448/YtGiRZg3b54549GqsbERzz//PDZs2ICsrCyLxEAIIbbEoIFOQ4cO\nNWcsOv3zn//E5MmTqb89IYToSe8EP2HCBJw8edKcsWi1bds2lJSU4N///rdFjk8IIbZI7xLN888/\njxdffBEcDgfjxo2Dl5eX2nsiIiJMGRsAoKioCEuWLMGBAwfA4ejf6aewsNDksVgKm9oCUHtsAdva\nxJb2GLr2NcPn82X6vFE5oWubI94c88Fv374dr7zyikpyl0gkYBgGdnZ2uHXrFqsHWRUWFrJqQXNq\nj/VjW5vY1h5D6H0Frzw3fF966KGHkJqaqrJtzpw5iI6Oxty5c1md3AkhpDf0TvDKc8P3JXd3d7i7\nu6tsc3Z2hqenJwYOHGiRmAghxBbY5EhWS9xJEEKIrTFoLpra2lrs2rULRUVFEIlEKq8xDIP169eb\nNDht9u3b1yfHIYQQW6Z3gi8sLMTYsWMhkUggFArh4+ODhoYGSCQSeHp6qpVRCCGEWJbeJZrFixcj\nNTUV165dg0wmw86dO1FVVYWPPvoIzs7O+Prrr80ZJyGEEAPpneDPnTuHZ599Fg4ODgAAmUwGLpeL\np556CrNnz8aiRYvMFiQhhBDDGTRVgaenJzgcDtzd3VFXV6d4LSUlBefOnTNLgIQQQoyjd4IPCwtD\nVVUVgM7RVHv27FG8duDAAXh4eJg+OkIIIUbTO8GPHj0aR44cAQC8/PLLyM7OxpAhQ5Ceno5PPvnE\nYv3kCSGEaKZ3L5p33nkHbW1tAIDJkyfD0dERP/zwA1paWvDiiy/SMn6EWCF+owD5hWWIiwmDp4er\npcMhfUzvBO/g4KB4wAoADzzwAB544AGzBEUI6T1+owCvLN4IfpMQnu4uWL9kDiX5O4zeJZrk5GRc\nvHhR42v5+flITk42WVCEkN7LLywDv0kIF2dH8JuEyC8qs3RIpI/pneDLysrQ3t6u8bW2tjaUl5eb\nLChCSO/FxYTB090FwhYRPN1dEBcdZumQSB8zaKoCbXPAnDt3jnrREIuhOrNmnh6uWL9kDvKLyhAX\nTefmTqQzwW/YsAGbNm0C0Jncp0yZorbYdmtrKxoaGvDYY4+ZL0pCtKA6s26eHq7ISIuzdBjEQnQm\n+IiICIwaNQoAsGPHDqSkpMDHx0flPQ4ODhg4cCCefvpp80VJiBaa6syU0AjppDPBP/jgg3jwwQcV\nf58/f75ZluUjxFjyOrP8Ct5a68y2UkbiNwpw9nIx/PyDrDpOoh+9a/AbN25U29bQ0IDS0lLExsaq\ndKEkpK/YQp3ZVspI8jgrq29j3/+dt9o4if707kWzatUq/Oc//1H8/cSJE0hKSsKYMWOQlpaG69ev\nmyVAQnoirzNbazKyle6K8jidHO2tOk6iP70T/HfffadSnnn33XeRkJCA7Oxs+Pn5YdmyZeaIjxCb\nZyvdFeVxtorarTpOoj+9SzSVlZWIjIwEANy+fRu5ubnYu3cv7r77brS3t2PhwoVmC5IQW2YLZSTg\nrzh/PXwS4+7NsNo4if70TvAcDgcdHR0AOsszjo6OSE9PBwD4+vqioaHBPBESwgK20l3R08MVKfH9\nKbmzhN4lmtjYWHz77bcQCATIzs7GiBEjwOPxAAAVFRXw9fU1W5CEEEIMp/cV/Pz58zF16lTs3LkT\nPB4Pu3fvVrx28OBBmouGEEKsjN4J/r777sOpU6dw/vx5JCUloX///orXMjIykJCQYJYACSGEGMeg\nuWgiIiI0DnSaOXOmqeIhhBBiIjpr8Hl5eQbvUCQS4dq1a0YHRIgt4DcKcPJMPpoErZYOhRCtdCb4\nBx98EFOmTMFvv/0GqVSqc0fl5eVYs2YNkpOTceDAAZMGSYg1kY/4fH/9t1jy8W7wGwWWDokQjXSW\naHJycrBs2TJMnToVbm5uGDp0KBISEuDr6wsHBwfw+XyUlJQgNzcXV65cQXh4OJYuXYrHH3+8r+In\npM8pj0y9XddAE5wRq6Uzwffr1w8bNmzAu+++i+zsbBw6dAgbN25Ea+tft6Xh4eHIyMjAu+++i/vu\nu0/rnPGEWCNjJgFTnuDMzcWJRnwSq8Xw+XyZoR/i8/loa2uDt7e3oi88MY/CwkLExMRYOgyTsab2\n9GYSMH6jAPlFZXDgiJGWkmTmSPuWNX1HpsC29hhC74FOyjw9PREQEEDJndi03kwCJh+Z6u7qZMYI\nCekdoxI8IWxgK5OAEWIsg/rBE3aylcUoTM1WJgEzl56+996+TiyPEvwdzlYWozAXW5kEzNR6+t57\n+zqxDlSiucPZymIUxLR6+t57+zqxDpTg73BUh9ZNPmLVkMFMxnymr8XFhMHZyQG3qurg7OSg9r33\n9Lug341t6LFEc/ToUXzyySe4ceMGPDw8MHnyZLzwwgt9ERvpA3d6HVoXY8oQtlS6YMCAYTr/v7ue\nfhf0u7ENOq/gDx06hEmTJuHEiRNwdnZGSUkJFi1ahDVr1vRVfKQPWPuappZiTBnCVkoX+YVlELaK\nEBTgA2GrSGOcPf0u6Hdj/XQm+DVr1iA9PR2XLl3CoUOHcPnyZTz99NNYt24dZDKDx0cRYlOMKUPY\nSunCVuIkvaNzJGt0dDQ++ugj/O1vf1Nsq6ioQEJCAi5cuIDQ0NA+CfJOxrZReLbWHvmI1e5lCHkX\nQQc79ZGs2j5jbbTFacx3ZM1dJm3tN2dKOmvwdXV1CAgIUNkWGBgIAKivr++TBL927Vrs378fRUVF\nsLe3x5AhQ/DOO+8gNjbW7McmRFM3SuU6O5cDfPlBpEpSs5Wul6aK05aeO9xpeuxFY+nJw06ePInZ\ns2fj119/xb59+8DlcjFp0iTw+XyLxkXuXMp19mZhq9XW2fuKrTx3uBP12ItmypQpsLe3V9v+xBNP\nqM1Fc+nSJdNF1mXXrl0qf//0008RFhaGU6dOYfz48SY/nrWy5lvgOw3NJqlK+XxQPd+66EzwmZmZ\nfRWH3pqbmyGVSuHp6WnpUPoM3QJbF+Uugg4c8R3/XVCXSeulM8Fv3Lixr+LQ28KFC5GcnIxhw4ZZ\nOpQ+o+kW2BZqvGwmr18XFhZaOhSrYCvPHe40NjWS9a233sLp06fx1VdfWfzZgCF6O7KxN13abGFU\nJSHEPHR2kywpKTFoZxEREb0MR7tFixZhz5492L9/P6Kionp8v7VcWTUJWrHk491oFrbCzcUJi199\nzKg5xJsErbheWoWo8EC9P2+qYxNCrIOh3T11lmhSUlIMulKur6836OD6WrBgAfbu3at3cgcMPxHm\ncvJMPsRSwNfHC8IWEdqkXINiU+7Da+jKQb09tjmwrU8y29oDsK9NbGuPIXrsRePq6oqHH34Yw4cP\nt0hZZN68efjuu++QnZ0Nd3d31NTUAABcXFzg4uLS5/EYypI9DKh3AyF3Np0lmu3bt+Obb77B8ePH\nERYWhieeeAKZmZno379/nwXo5eWl8R+WBQsWYMGCBX0Wh740dWfszcjG3l59mHJUpSm6aiq3hw1d\nP9l4dci2NrGtPYbQa9HtiooKfPPNN/juu+9QWFiIoUOHIjMzE5MnT4aHh0dfxGkTzNGd0Vp+nKZq\nm7w9bOn6aS3fjymxrU1sa48h9OpFExwcjLlz5+LUqVM4ePAgkpKSsGTJEgwcOBArVqwwd4w2g80j\n+kzdNjafK0KshcFL9qWlpcHOzg4SiQTbtm0zy+hVW8Xmmrep22aOc8WGko8cm9pCLEfvBH/z5k3s\n3LkT3377La5du4Zhw4Zh9erVmDx5sjnjsylsHtFn6raZen9sKfkA7GoL+UtHhxi3G5oQ5O/dZ8fU\nmeCbm5uxd+9efPvttzhx4oTFHrTaEjaP6DN120y5PzaN9mVTW+5UUqkUxWVVyL1QhLOXinD2YhEu\nFpRgcHwkfvl6aZ/FoTPBDxw4EFwuFxMnTsT8+fORkZGh6NEilUrV3s/h2NTAWMIibCqPWaItyiWh\nnl6nuwl1t6rrkHuxCOcuFuHspes4e6kITc0tau87n18MsVgCLteuT+LSmeBbW1sBADt27MCOHTt0\n7ohhGNTV1ZkuMkIMwKbyWF+3pXtJ6PWZE3S+fqeXjPiNApy7fF3l6ryqtkGvz7aK2nGlqByJgyLM\nG2QXnQneGvuZE6INm8pjfdmW7iWh66VVKqOm7+SSUauoDReu3Oi8Kr9QiLOXruN6aaXR+wvy90bN\n7b5by0Jngl+4cGFfxUEIsZDuJaGo8ECdr9ty+UsXsViCK0XlOHepCLkXO6/M8wvLIJGol6P14eHu\ngpT4KKQlRiMlIRqpiVHoF+Bj4qh1M7ibJGEXNtVWbbEt1hBz95JQbU2lztdt5dxqIj/fsdGh4DcJ\nOxN5V5nlfH4xWkXtRu3X0cEeSbERSE2MQWpCFFIToxEZFmjx55I6E/z06dPx3nvvITIyUrFt/fr1\nmDJlCnx9fRXbLl++jNmzZ+PkyZPmi5SYHJtqq7bYFmuKWbkkVFuj+3VbVFXbgGOnLmHlpp2orWuE\nsFWEjg6JUfvicDiIjQlFakJ019V5FOJiwsDjWd/1ss6I/ve//+H1119X/F0ikeDtt9/GyJEjVRJ8\na2srCgoKzBclMQs21VZtsS22GLMtaGwWIu/SdUVvllNnC1BT12j0/vqHBiI1MQopXQk9KbY/XJwd\nTRix+Rj8T45M1uPUNaQPaLu1N+SWn0211d62xRKlEjadf0sRtbXjUkGJSqml8MYto/fn7+OJ1KRo\npCZEIS0xBikJUfD2dDNhxH3L+u4pSI+03dobesvPptpqb9piqVIJm85/X5BIJLhaXIGzXQ9Az14q\nwuWrZegQi43an5uLEwYn/PUQNC0xGsGBPja1WlxPKMHbIG239sbc8tt6bVWZsW2xZKmETefflGQy\nGUoranDu4nXkXuzsnnj+cjGErSKj9mfP4yJxUNdD0MTOq/PoiCCLPwQ1tx4TvKZ/zdj0L5wt0nZr\nT7f8xqHzZnm1dY2KEsvZrtGgdQ1NRu2LYRgMigrpvCpPioavuz0euH8k7O15Jo7a+umcD97Lywt+\nfn6wt7dXbKuoqIC/vz94vL9OVnt7O27fvm22JfvuZNrmsta2kIcpF/gwB33n5u7rmrix562v5xrv\ni/Ni7jY1C1uRd/m6ytV5+a1ao/cXFuzfVTOPRkpiNJLjIuHm8tfaw3fyfPA6r+AzMzP7Kg5iIG23\n9my45bdETdwWzps1davUV3t7By5fK+0aOHQdZy8W4mpxhdGdNXy83BVdE+UPQf18aNEhbXQm+I0b\nN/ZVHIQoUPdBzaz9vEilUhTeuNU56ZbSDIrtHcY9BHVxckRyfCTSEqORmhCN1KRohPXzoxKxAegh\nK1FjbBmg+4yExpYSqCaumTWdF5lMhpuVtzv7mneVWfIuXUezsNWo/fG4XMQPDFNclaclxmBAZD/Y\n2fXNrItspTPBl5eXG7Sz0NDQXgVDLM/YMoDy51ycHCGDDC2tbUaVEqj7oGaWPC/1/GZF18TOaXGv\no6bO+EmzBkQGKxJ5akIUEgZFwNHBvucPEoPoTPBJSUl63w7RdMHsYGwZQPlzldV1kMmAfoE+RpcS\nbKEmbgm07wlEAAAgAElEQVR9cV5aRe04eSZf5eq8pLza6P2FBPoiJfGv/uaD4yPh4eZiwoiJNjoT\n/Lp163rcwY8//ohDhw6ZLCBiWcaWAZQ/F+TvAxlkELaIVPbBbxTg7OVi+PkHGXX1aQ0Tc+nDXHGa\nY78dHWJcLixTLFSRe7EQBYXlkBr5ENTT3RVpSdEqV+cBfl4midWWWeq3q7ObpC6HDh3C0qVLkZeX\nh/T0dPz73//GiBEjTB3fHc8SXbyM7TKo/DkAKvuQl3Aqq28jKMDX4LKNtfYg6f79mCtOU+xX2zJy\nojbjZlB0crTH4LhIpHQ9BE1LjEZEaIDVPQS1dDdJS/52DX7I+ueff+K9997Dn3/+iYSEBHz77bcY\nN26cOWIjFmJsGaD755T/LC/hODnaG1W2sfYeJHLmitOY/eq7jJw+7Ow4iIvpfAiamtg5He6gqNA+\nW3rOllnyt6t3gj9//jyWLl2KQ4cOISoqClu2bMHkyZPNGRthEXkJR34Fb2gPEGvqQaKLueLsab+9\nWUZOE0cHe3h7ueG5KeMxYmgcEgf1h7OTQ2+bcUey5G+3xxLNtWvXsGzZMvz4448ICQnB/PnzMW3a\nNNbP4WAtLH17aUr8RgF+PXwS4+7NML4Gr6V0JK9xBgf4oKK6Tu9aZ29ro5q+H7VSlYlqr/L9RoYE\n4vK1Uhw4ehbVtQ24dLW0V8vIBfp5ITWxs8SSmhgNRiqCvZMba3owWcN/Q5YaYa7zCv6ll17Czp07\n4ePjg6ysLMyaNUtligJCDOHp4YqU+P5G/8C1lY7kNc66+ibcKK9CRGggfL3de6x1mqs2Ko/TFPs3\n9TJy7m7OnYOGEqIUE291X0bOGhIi21iqV5jOBP/NN9+AYRgEBgZiz5492LNnj9b3MgyDn376yeQB\nEtITeY1TIpWiRdQOiVSqV63T3LVRQ/cvk8lQUl5tsmXkHOx5SI7rr5gK11qWkSN9R2eCz8jIsLon\n4nc6S3cV5DcKcOpcAcAwuGvwwB6vkPsiVnmNs66+Cc6O9rDjcODp7oJgfx+cPJOv9fj61LW7x6/v\nNn32X13boJLMz126joZGgVHngMPhYFB0iKJrYmpitNUuI6eNpX/bbGR0N0nSN5Rvly3dVZDfKMAL\nCz9GzoVrYGRAWnIMPst6TWMM2mI11+2/vMYZ7O+Dipo6BPv74F+rtvV4rnTNytk9fgBq264XF+OD\nrb9oPY58/yGBvrhRXq0YDXr2YhEqqowfGBgW7If+oYG4e1g8MobEITku0mTLyFmqa665ftt3csnJ\ndv55JxbvKphfWIbKmjrIZDLIAFRW12uNoa9jVa5xhocG4OSZfL2Or602qil+yKC2rby8Um1bakK0\nyjJy5y5dx7XiCqPb5u/jqRgJmprQOYjIx8vd6P1ZI0v/ttlKZ4I/cuSI9g9yufD3979j/2U0hiG3\n95pYuqtgXEwYgvx9cLOqDowMCArwVolBuS2aYtU1krU3t+eaPtvbc6Xt8923cdEODodBcWkl2tvF\nWLDsCxQUlaNDLDHoeHLyZeQUD0ETohAS5KtSKuU3CnSWnszB3OUTS/+22arHBT8YhlGbu1n5xxYY\nGIi3334bU6ZMMV+ULKDvLX/3/3g0jZS05CRc/EYBTuddhYwB7koeqPIPlab2KXcX1DaStTe357o+\n29tz1f3zMpkMlwpK8L/DOaivb8Kla2U4d6nI6Iegfy0j1/kANDUhGjH9++l8CNoXZbq+Gp3bnbl+\n21Si0WLfvn1aX5NIJKiqqsLevXsxZ84ceHp6YsKECSYPkC30veW39jVUPT1cMe6eNLXt2m6x5bHK\nSyaaRrL25vZc12d7e646xBI0C1qx6b//6/UyckBn3Xzk0ISuNUGjETcgHA4GLiNniVJGXx3T0r9t\nNtKZ4EeOHNnjDp588kk888wz+OijjyjB66DvLb+t6ukWW9dI1t7cnpvq1t7Uy8g52PPg6eEKdzdn\nONrzEBzog0+Wv9rrK1NLlDKofGK7TNKLZt++fXj55ZdRVlZmiphYS9MtaE+jMw8cPoHx944w2YRV\npqijahs12tMttq6RrDrXmNXw3OJU3lVAJsOgqFAUXC8HOIxKyUgXcywjJ++amJoYjaiwINTU8zVO\numYK5i7T9TQ619a6MN7JJRqTJPjff/8dU6ZMQVVVlSliIvir7mns7Iva9tfbOqqxo0blDPmPTVtd\n//mFHyH3fCEkMhnsuVxEhQfCR0sM5l5GzsvNDveMGMaq8SJsS4hsa48hTNJN8syZM7Sak4n1dvZF\nbfvrbR3V2FGjpooZss7umVLIIBaL0dHRoYjhcmEpwvr5m2wZOaBzANGwwQMw/dExSE2MxsDIYJVl\n5AoLC1mV3Am79CrBi8Vi/PTTT1i3bh1efPFFU8VkU0zdfUy5/KGpZq3teD3FERzgA6lEiqYmIXy8\n3RXdFg3dl7weW13bAC6HA5lUprMua+j50dbV0sXJEc2CVgyKDEFQgDduVdWBw7EDA6Ci6jZaWtvx\n9D9Wo66hWZ/TrFFUeBCGJMcgIiQAn2//GR1iCVycHLDqrWfR3NKKfv7eiuQuj9PBzrg7AV3nXj5S\neFBkiEETp5mSqct5NDq1U1+fD50lmvj4eK1XJxKJBHV1dRCLxRg9ejR27NgBBwfzTSe6efNmfPzx\nx6iursagQYOwfPlyDB8+3GzH04epu49139+yN2fgVO55Rc1a2/F6ikP++u36JjAM8Mn7r8LD3cWo\nfQFAaXk1XnzrY4g7JODZc7Fp2SsIDw3Q6/zU1lRqvV3WVpI5nXcVn2T/hNq6RkjEEoT280fe5euo\nvm38mqDBgT5ITYxGbHQojp66BAAqpabS8mocOJqL4SmxWPHJTq3dW7kc4MsP3jTJ4iXKI4UhlYHL\n5SAyvJ9BJTBTyD13QefoXH1ZeuS1nLWUaCxxPnRewY8aNUprgudyufDz88Pdd9+N0aNHmyM2he+/\n/x6LFi3C2rVrkZ6ejs8//xyPP/44Tp06heDgYLMeWxdTdx/rvr+KmjqV2Re1Ha+nOOSve7i7QNgi\nQkVNHSqq64zaFwBUVNfBzs4O7m5/7U9Tgte0Lz937d0C/ypLOeBm5W2s/mw3GptacOz0JZU1QfML\nDVsM3tPdVbFIRWpC54PQwK5l5E6eycfJ3Ctq7Q0PDcDz0/6mcUSscvfW23UNJlu8RHmksFgsgai9\nw6wlMG2KStVH5/amnEejUztZ4nzoTPCbNm0y68H1tXHjRkyfPh1PPfUUAGDlypU4dOgQvvjiCyxe\nvNhicZm6+5im/dXWVOp8XZ84DOmiqU+b9G13T+0BVJeR++PsFVy6WoKmphZIZTLkXS42+Bw6Othj\ncHykIpn3tIxcb8+dm4uTyUbJKo8U5nHt4MTlKSZO68uuidHhQSb5XVP3SlWWOB9WP9lYR0cHgoKC\nsGXLFjzyyCOK7W+++SauXLmC/fv3WzA603cfKy2vxoEjuRh/TxrCQwP0HsmqTxdFxWRcXXVdADh1\n/iogleGulEEGjQDlNwoUnx0UFaqyT+UaY/f2nPjzDG7WCPHz4TO4XlqJkpvVaBYY9xBUvoxcwsBw\n+Pl6YsI9aRg2eKDey8jJYxueGovm1tYez53yAh7BAZ2TmjlwxEhLSerxWN1rr7q+R/lI4UH9Q1BR\nU9fnXRMLCwvh5x9kkt+1NXSvtJYSDdD358PqE3xVVRViY2Px008/qdTcV65ciV27duH06dMWjM60\nDK1Z93b/y96codeMi7r2pdxd0sPNBTLI0NLaBk93Fyx48XHMW7YZVbUNnQs7y9CrunlUeFBnf/Ok\nzjlaerOMXGl5NcZP/xdaRO1wdrTHga+XaSwzKTP2+7GWWrS+rCkhmgLb2mMImk3Sihhas+7t/g8c\nze31FAHy7pIdYjGKSirQ0to5L0tDowD7fzP+H197HhepidG4f2QK0pKikRIfZdKkeOBILlpE7XCw\n56FF1I4DR3Px/LS/6fyMsd8P1aKJpVh9gvfx8YGdnR1qampUttfW1sLf31/r5woLC00WQ5OgFUWl\nlYgOD4K7q1Ov9nO+oASMjEFSbLjavhzsxOBygNt1DXBzcYIDRwyAp9aWiup6HM+5gpFDYxEc4K33\n8R3sxIBUghtlt+Dv7YEB4T5qx1M+lnK7m4Wt2H/oLGSQ4m+jU1HX0Iya2w24Xd8EYYsIuReKjD4v\nLs4OiI8JRVR4AC4UlIHDYeDj6YbFrz6mOEe1NZWordH+XRj6HQ2I8IE91w6tojY42vMwINwHuecu\n6NyHru9H0/Hl2wJ8PSGTSlBUfBNOTvZob23W+Ps01e9MG0P2b8r/fqwBW9pj6J2I1ZdoAOD+++9H\nYmIiPvjgA8W2IUOGYNKkSfj3v/9t1mObcgSofASmjAGGJg3Ap1nqc5N0r9F1v700prTQPYbK6noE\nBXjjs6zXAGgeSi9vd0OjABKJFJcKSiFoMX7AkJw9j4tB0aGoqm3o7AYYGoSvPpwL7675zXuausHY\nrp2ayLtCjh+VprXbaHeavh8//yCdM4U6OzmgtbUN5/NvwM6Og2GDB6p99+Yu4xiyf7aVNNjWHkPY\nxOKML7/8MrZv346vvvoK165dw4IFC1BdXY1nnnnG7MfWOJrSyP3IR2DKZDJU1tRp3Jd8Rj1t//Fp\nKi0YEkNLaxv6BfqgpbUN+UVlaserrm3AT/+Xg4XLt+L46cs4fe4q/jxbYHRyZxgGvt7uiIoIwkdL\nXsSRb95F1qKZGBAZjMRB/QEGKCi+qVf7tX0Xxn5H8q6Q4aEBeu9DU3yaPqu8rbK6HhVVt2HH5QAM\nNH73pvqdaWPu/RPrZPUlGgCYPHkyGhoasGbNGlRXVyM2NhY7d+5ESEiI2Y9tqq5NcTFhihGYMgYI\n8vfRuC/l3hYAcDynAMW3mhTrn46/Jw1rP/9ecQU/flSazoVElCcE696WkEBf/HToNH49ehZVtQ24\nWFDSq2XknJ0ccPewBNwor0JVTT04HAb2PHtEhnfOV/Pw/emoralEXEyIxnPa03qvxnYT7Wm//EYB\nmoWtsOdyUVldB18vD1RV1+PAkVyd687KFzBJTxusdvzyilo0NDRBJGpHUIA3xB0SNOYXa/3uzdmF\njt8oQLOgBc5ODhC2iCzeZZFGt/YdmyjRWJqpujbJuxYyMmCYlkSjfFsv7pDg7MVC2NnZqax/2lNp\nAYBaDxcvD1c8P+0BnM8vxtFTl1B6swbXSys1hWkQOzsO3vnHVMREB2N4SqyiZCJv58BI1a5+8tvl\n7udU3/VeDe0m2tN+lXsDXS+9BS8PN9TxmyGRSMFhGJ1xKE8Gt+zNGYp2NjYJO8toLW3g8uywd/M7\nCA320/nd62pDbyj/plycHDErc1yPs26as6RhiR5Fd3KJxiau4C3NVAsReHq4Yvwo9cUy5JRvo29V\n1aGlRQQZAClkKuufyksLADSOtJRIpKioqgO/UYA6vgBNghtoFbXj58NnjIrbjsOBs7MD2to7ENrP\nD01NLbhd3wgejwuJRIJbtfV47blJWtup6RlB93Oq73qv2r4Lbdt72q9ybyBRuxgMh0Fraxs4dhxw\nuXZa4+g+GVxFTZ3iPd/tO9pZRnO0R1t7B/44dwWJcf11fve62tAbyr8pYYsIbi5OFr1qph5FfYsS\nvBVRvk2X39bXNTSBA0Zt/VO52OhQ2PO4KCmvRodYjHfXZOPS1RK0tLYp3tNhwNS49jwuEgZFIC0x\nGikJ0RjQvx8+2PwD6huacaO8Cn7eHgjy90JjnhASsQRcrh2mPXKvSdqua71Xc+1Xfs7r6pvg7GgP\nV2cnuDg7okMs0XnedS1goqmMZinWNprU2uJhOyrRWJnu5Zft3x9Au4SDyeMzEB4agNq6Rpztmte8\nt8vIMQyD/qEBiAwLxD3piRgxNB7xA/9aRk6ljl9T1zkKtqsMUV5Ri+y9hzHtkXuRGNdfrwXFdS1g\nwm8U4NCJPBQWVyAmKhj3ZQzWuh/5tlN5VyEUtsLFxanHWrmmdWSVX1eM8u1q59XimxrXne3eHm0L\nmCh/j8p3MLrqz71dlF0bQ0s/5i5p9PVozju5REMJ3op0r0++9fKTmPOvj8FvbkVbWwc4HKZXD0Gd\nHO1x7/AkJMdF4vAfFwAG8PXSPFOhIbVSfRYUl4+a1bbotrYupLoW/cjJuwZBSyvcnJ0wZPAAjbVy\nU9F2Pnq7gEn3ZwG6zmFfjYBlW0JkW3sMQSUaK9He3oE9B/7AlcJytLa1g98owP9+Ow1j//V1d3XG\nXSkD4efjgbz8Yvj7ekIsluCVWQ8DMuBYzmWddVBDaqX6LCguHzWrbdFteRdSyP7qRqhtdkv5oh8d\nYglkUhk6xBKttXJTMUXtWNc+TLUoOyHKKMH3grGLWQyKCkFxWRV+PnwGtXWNuHyttFfLyDnY88Dl\n2qGtvQNcOw5S4qOwff0CeHm6qfXMqaquhwydS881NQnBMAzcnJxw8ky+SumhsqYeorZ2iETt4HHt\nEOzvo7XdmhYTAVRnqxw/Kg1HT11CeUU1HBwcUFldD36jAJ4erjq7kGqq2TY2CeFozwPXjgOGw4DH\ntTNZzV4b5Vo9wzAq58PQfWiqP+uatbI3x+wL1O3RelGJxkj6ljBkMhluVt7G8dOXsPrT77uG9rdC\nLJEafezY6FAMT4vFoKgQ/PDrH7hytQzNLa2QSaVwdnLA0MEDsXXNG6r16vNX8elXPyHvSjEYGRA7\nIBRt7WJAKkN5Za1ibdVlb87Am+9vQe75QoilUjAMg5iIIAT4eeksvSgvJiKvOXevtZaWV2P6a1ko\nr2qAXbcuiLq6kHafzVHerVEileLpR++Dn7+n3gtu94Z8oROZ7K/FQQydDK6nkbrdX9N0TGvqVmgL\nE6lRiYYYTNvtdj2/uevhZ1HnQs8Xr6OmzvgZFMOD/eDj5Y78wjI4OthDIpVi5pNjFYtR/Pf7/4NY\nKoFMJgPDcMAwHNyub1K5nff0cIWbsxNuNzQqugtWVNbBzdUJjg72KmurHjiSqyiXSCTy/TLaSy9H\nOksvyouJyBN8925/FdV1aOsQg2HUu37q6kKqvB95t1D3ruNF9g/qs7KFfKGT3kwGp6srpKbXNB3T\nmso01O3RulGCN1JcTBhcXRxRUVUHiViCjdv2Y85bG1RWHjKUvT0X3h5uePrv9yE5NhJt7e24VVUJ\nHs8F76//Bu0dHXB1dlKMXm0WtMDX2x03b90Gh9MBiVgCUVs7XJwd1NZwDQ7wUekuGBEaAAYMqm43\nwIHHg1DYCq+ukbK/ncjDrao6cLlc2HO5aotOqJRe7knDz7+fwbXrNxERGqCzTBIXEwZvD1dUVnf+\ng+fr7Y5gfx+V8pAyeU8ZyDrnqweAZmErXJwc1UZk9mbtV32vOPVZwMTUrL1bobXHd6ejBK+njg4x\n8gvLFF0Tcy8WoqCoHFJpZ4XL0GXkPNxcEB7qD4GgFQ4OPPj7eOLlGQ9h2OCBAIAXFn6MU3lXIRC0\nAAwgkwH2XC4GRnVOz6A8OvE/b07Hhi/34er1CnSIJTh78TrKK2pV3ufp7oKVb81SdP8b1D8Eb76/\nBZB1zo0DhgEDBh7uLvgs6zWtI1EBYP2SOYpSQmOTEJevlkHY2gphSxsam4Q6E6ZMBsggAwNALJZg\n/vtfQNgq0tirRLlnzeDYSHB5dmhpbYOzkwNem/mwopRjaJnA2LKCp4erSts9PVxRW9Pjx3pF0zGt\nibXHd6ejBK+B8jJy8j7nFwtKOhetMAKHw8DV2QljRiTjofvvQmpCFPqHBeKP3Ct4f/23ilGGrq6d\nowxPnslHZU3nguZSqRQAAzCAHZeDxua/yiLyz4lE7WhqbgEAMExn4szeexgPj01XuX2uqKnDuHs6\nyyAnz+SjpbUNLs5OaBfXwdXZEcJWkeIWW9dIVOVSwnf7jkLU3g5nJ0eI2nXPq55fWIaGRgF4vM6f\nXUXVbbi6OCEowKfHnjWlN6vh7OyIfoE+KudK/l5DygS9KSuYY7SpNR7TENYe352MEnyXQ8fzcCLn\nMs5euo6zl4oUCdNQDAM4OznCx8sNz/x9LH7/8wIkUim8PFzx4bsvqFzh9LQ2Z9mt2+BwOF35nQGX\ny0WQvw/G35OGo6cvqfRQ2XfwFKpqGyCTATz7ztGlocF+PfbauN01gtPYtT8NGbUZFxMGPx8P1DUI\nIGOAiJBA8HhcjRNgde9ZEx4SAC7PTut7DSkTUFmB3CmoF02Xx19YhoPHzhn8uciwQKQlRmNQdCg8\n3F0wckg86puaVSbROnX+KoQC9RGX8hozA/WeI6Xl1dix5zAqqqpxV2oCnJwd4ObspCjhyD83MDIE\nBcU3IRC2oq6+CXlXipE0qD/6hwZgUFQoCq6XAxymc33PrlklVUZJKo3g1LUmqabZGOV1bDdnJ/xx\n7oraqE1Nfj9+CnkFtxAe7I97M5LR2PWgVr5mq9pxlXrWAJrnrldui75lAr3WsNWjRs/GHhpsaxPb\n2mMIuoLvkpIQ3WOCD/D1RGpiNNISY5CaGIWU+Ci1vua//3FBrab7xY5f1WYzBFTr4/IEBnQml/nv\nf4GcC9cglUhQXS9U6U7YfcbJ813T0A6OjQQAHDicC6lMCh6Xh8hw9bVS5fEp31prS8zaZmPsHr8+\ndWx+owAffvETxNLOB7WpCdGKNWGPnr6ktg9NPWsM6YGii67320LXP0L0QQm+S1pStMrf3d2ckRIf\n1TnpVmI0UhOi0S/AGwzDqH22pxGKmmYz1DVKUfkzUqlqd8LuM04KW1oVdeqSm1UAOrsgisVStHe0\nQiKVdu0L6BeoXuvuiTHx69pXs7AVvj5eKqNbra2LHXX9I2xBCb5LdHgQJoxOQ3R4EMbenYoWUeeU\nsdoWiFBelKNZ0AIe1w63quoUIyqVyxeODvaATAYOw0FQgDeC/X2Qe7EQLS0i1Dc0IbSfv0p3weCA\nznVo29ra0dEhAc+Og9Nnr8LNyQmVNfVobBKgrKIG/QJ8wOPagd8oBDgMggN9IRCK0NAoBJfLAYfh\noeZ2A8L6+cPJyQGNTUKIJRLFKFKgs9RTU8tHXUMTJk/IULuSd3N2QnuHGG1tHWhra4ddP3+4OTnh\n0Ik8cMCgsrpO4wIW8rKOoEUE167SVFxMGNxcnBR1dPmzgwv5xYiO6Ndjl8fS8mqt5RxD6dpXcIAP\nJGIJGpuE8FUamWsKNOqT9CWqwUO1S55EJgPPzg5iiRgchqN1gQh5iYQBg6ZmIa6XVsHXxw2hQf5Y\n+dYsxejOkvIqhAb5ARwGLz71NwxJiMH897/A6bwCCIQiODjwkBIfBWenzl4s8rJLzoWraBaIFDE6\nOzlALBbD0cEeTYJWcDgM7Ow4iI8JB49nhxl/vx/7fjuNpubOuc3//sBIrPx0F1pb2+Di7Igv17yB\ndz/MRuGNW+AwDJLjIiGDDOcuXUdjkxAcOw58PF1xcPtyRcKTr//a2CREa1uHIhYPNxdIpVK0tbcj\nMjQQYaEBaufohYUf43TeVQhaWuHq5IShKZ2TgV0vLkablKuYkfK+qYsg7pCAy7PDoe3LERrsp7E8\n0pu1aLvTtS/lBUAYDoNNy17ReRy2jfoE2FezZlt7DGETa7Kam3KXPLFYjGZBC8RiqcpoS+X3Kq+1\nWVlT17VYRDtcnJ0gbBUpSg8SiRQtonYwHAYO9jwE+XujorquqwuktLP/OTq7C1bW1Cn2WXqzGm2i\nDpUY2zvE6OiQoFXU1VVTBog7JBC1t8PBwR4tojYIW0Vwd3eBvT0PxeWVaO8Qw9nZEaL2Dvx46FRn\nN8+uUaQlN6tQUl6NtvYOxYRmgpY2lTVe5eu/doglKrE0CTr/QZBIpACHUazvqnI+a+oglnRNBiYR\nK86ju6uTYuRq9p7/U8wpL+nq2qlt7dDerEXbna59yY/v7u4CDoeDihrjZ+/sjtZFJX2NEjz+6pLH\n6eqK6Obq3Fni0LDgg7yLnbBFhKAAbwT5+3SueKTU1XD8qDR4urvAzo6j1gVR3gWys4TSebyIkEAE\n+fso9hkeEgBHJ3uVGO15XPB4dnBytAcDdE6yxbODq7OTyjHl5Y9pk8bAuWtFIWdHe0x75F4E+fuA\nYRhwwCAiJBARoQFwsOdB/lTB1dlB5aHm+HvS4OxoD3ueaiXP3dUFUokUXK4d3LqOr9bF0d8HXDu7\nzjjtuBonA5s2aUxncpf8tXCI8vlV3q88Fnl7erOIhq59aTu+KZhz34RoQiWaLt3XEdW04IPye5Un\nv9LU1VBXF0T5AhTNXXOZd+8CCACn866iuLwKp89expMTx6D0Vg2Gp8Ti1u161NbwcZvfhPsyBqO5\ntVXtmMqTeykvOtF94QsAOHX+qmJ/8kVFlMn34Whvj2/3HcFrMx/BoOhQHDiai+EpsSrH736OTudd\nhUDYChdXJ8V57H67fDH/hsrCId3Pr1oNXsMiGsbQtS9Dulwaevvf14tdGINtJQ22tccQlOCtHNt+\nnNQe68e2NrGtPYagEg0hhLAUJXhCCGEpSvCEEMJSlOAJIYSlKMETQghLUYInhBCWogRPCCEsRQme\nEEJYihI8IYSwFCV4QghhKUrwhBDCUpTgCSGEpSjBE0IIS1GCJ4QQlqIETwghLEUJnhBCWIoSPCGE\nsBQleEIIYSmrTvB8Ph/z58/HsGHDEBQUhISEBMydOxcNDQ2WDo0QQqyeVSf4yspKVFVVYcmSJfjj\njz/w2Wef4eTJk3juuecsHRohhFg9rqUD0CU2NhZfffWV4u8RERF47733MGXKFAgEAri6Wueq9IQQ\nYg2s+gpek6amJjg4OMDZ2dnSoRBCiFWzqQTP5/Px/vvvY8aMGeBwbCp0Qgjpcwyfz5f19UGXLl2K\nNWvWaH2dYRjs27cPI0aMUGwTCoX4+9//Dh6Ph127dsHe3r4vQiWEEJtlkQTf0NCAuro6ne8JCQmB\no6MjgL+SO4fDwc6dO6k8QwgherBIgjeEQCDA448/DgDYvXs3JXdCCNGTVfeiEQgEmDx5MoRCIbKz\ns0p/wfkAABDsSURBVCEQCCAQCAAAXl5e4PF4Fo6QEEKsl1Un+Ly8POTm5gIA0tLSAAAymUxjjZ4Q\nQogqqy/REEIIMQ4r+xqycYqDbdu2YeLEiQgPD4eXlxfKy8stHZLBNm/ejOTkZAQGBmL06NH4448/\nLB2S0U6ePInMzEzExcXBy8sLO3bssHRIRlu7di3GjBmDsLAwREdHY8qUKbhy5YqlwzLa5s2bMWLE\nCISFhSEsLAzjxo3Dr7/+aumwTGbt2rXw8vLC/Pnze3wvKxM8G6c4aGlpwX333YdFixaBYRhLh2Ow\n77//HosWLcK8efNw7NgxDBs2DI8//jgqKiosHZpRhEIh4uPjkZWVZfMP/k+ePInZs2fj119/xb59\n+8DlcjFp0iTw+XxLh2aU4OBgvPfeezh69Ch+//13jBo1CtOmTUN+fr6lQ+u1nJwcbNu2DQkJCXq9\n/44p0Rw8eBBTpkxBaWmpTU9xkJeXhzFjxuD8+fMIDQ21dDh6u//++5GYmIgPPvhAsS0tLQ2TJk3C\n4sWLLRhZ74WEhGDVqlXIzMy0dCgmIRQKERYWhu3bt2P8+PGWDsck+vfvj3fffRczZsywdChGa2xs\nxOjRo/Hxxx8jKysLcXFxWLlypc7PsPIKXhOa4sByOjo6kJeXh9GjR6tsHzNmDE6dOmWZoIhWzc3N\nkEql8PT0tHQovSaVSrF79260tLRg2LBhlg6nV/75z39i8uTJGDlypN6fsepeNKZCUxxYVl1dHSQS\nCfz9/VW2+/n54ciRIxaKimizcOFCJCcn23RCzM/Px7hx4yASieDq6oqvv/4asbGxlg7LaNu2bUNJ\nSQm2bNli0OdsKtstXboUXl5eWv/n7e2NEydOqHxGKBQiMzMTwcHB+M9//mOhyDUzpj2EmNNbb72F\n06dP46uvvrLJZz1yAwYMwPHjx3Ho0CE8++yzePHFF1FQUGDpsIxSVFSEJUuWYPPmzQZfoNrUFfzL\nL7+MKVOm6HxPSEiI4s/KUxx88803Vjd/jaHtsVU+Pj6ws7NDTU2Nyvba2lq1q3piOYsWLcKePXuw\nf/9+hIWFWTqcXuFyuYiIiAAAJCcnIzc3Fxs3bsRHH31k2cCMcPr0adTX1+Ouu+5SbJNIJDh58iS2\nbt2KW7duaR30aVMJXn5lqw/lKQ6sdf4aQ9pjy3g8HgYPHozff/8djzzyiGL74cOHMWnSJAtGRuQW\nLFiAvXv3Yv/+/YiKirJ0OCYnlUrR1tZm6TCM8tBDDyE1NVVl25w5cxAdHY25c+fqHNFvUwleX2yc\n4qCmpgbV1dUoLCyETCZDQUEB+Hw+QkNDbeJh2Msvv4wXX3wRKSkpSE9Px5YtW1BdXY1nnnnG0qEZ\nRSgUori4GDKZDFKpFDdv3sTFixfh5eVlc3dd8+bNw3fffYfs7Gy4u7sr7rRcXFzg4uJi4egM95//\n/Afjxo1DcHAwBAIBdu7ciRMnTmDnzp2WDs0o7u7ucHd3V9nm7OwMT09PDBw4UOdnWdlN8vjx43j4\n4YdVttn6FAdZWVlYsWKFWl10w4YNNtM974svvsC6detQXV2N2NhYLF++HOnp6ZYOyyjHjx/HxIkT\n1b6PzMxMbNiwwUJRGcfLy0tjvX3BggVYsGCBBSLqnTlz5uD48eOoqamBu7s74uPj8Y9//EOtF5ct\nmzhxImJjY3vsJsnKBE8IIcTGetEQQgjRHyV4QghhKUrwhBDCUpTgCSGEpSjBE0IIS1GCJ4QQlqIE\nTwghLEUJnkW2b9+uMlmZv78/UlJSsGTJErVh2sePH9c5yVlTUxMAoKysDF5eXvjvf/+r9bjyfSnP\nDJmVlaV1/6NGjdIas3IM+sw0mZeXh379+qGqqsqg8/T111/r/X5TW758eZ9NUfHSSy8hKSmpT46l\nLCsrC97e3oq/NzY2IisrCxcuXDB4X5s2bUJGRoYpw7tjsHKqgjsZwzDYtm0b+vXrB4FAgP3792Pt\n2rUQCARYsWKF2ntXrlyJlJQUtf24ubkZfFxN2w4cOKA2A173eYGUY1bW0zBsAHj77bfx1FNPITAw\nUO9Yt2/fDolEgunTp+v9GVNiGKbPZmrsy2MpmzFjBsaOHav4e2NjI1asWIHg4GCD/8GZOXMmPvzw\nQ2zfvh1Tp041daisRgmehRITExUz6d1zzz0oKipCdna2WoKXyWSIiYlBWlpar48pk2keEJ2WlqbX\nFKfKMesrLy8Px48fx+rVqw36HJuIxWJwudb3n3FQUBCCgoIUf9f2+9CHo6MjpkyZgvXr11OCNxCV\naO4AycnJaGlpQV1dnaVDMamvvvoK8fHxGDBggMr2nTt3YtSoUQgJCUFYWBgyMjKwbds2AJ0z8504\ncQKnTp1SlIMmTpwIoHNhktdffx1DhgxBv379kJCQgNmzZ6OyslJl//ISS3FxMZ588kmEhIQgMTFR\n47wg58+fxwMPPIDAwEDEx8dj1apVGpPd559/jnHjxqF///4IDw/H2LFj1RaKlpfLtmzZgnfeeQex\nsbEICAhAY2MjAODIkSO45557EBgYiNTUVHz55Zd6nUf5frsvHC4vvSmvSfDggw/igQceUByrX79+\nyMjIwP79+zWeI/n+Bw8eDIZh8NprrylKcPLjHTp0COPHj0dYWBhCQkIwdOhQrFq1SmV/jz76KK5c\nuYKcnBy92kQ6Wd8//cTkSktL4e7urlITlZPJZJBIJCrbGIYx2cpXYrEYdnZ2Kts4HI5a2UAikajE\noU8M8sSg7I8//sALL7yAl156CUuWLIFMJsO1a9cUSXDt2rWYPXs2pFIp1q1bB5lMpihHNTQ0wN7e\nHosXL4afnx+qq6uxfv16TJgwATk5OYr1BOSxT58+HdOmTcOcOXPwyy+/YPny5QgJCVFcZdbX1+Ph\nhx9GUFAQPv30U/B4PHz88ccoLy9Xa0tZWRmmTZuGiIgISKVS/Pzzz5gyZQp27dqFMWPGqLx37dq1\nSElJwbp16yCRSODo6IirV6/iiSeeQGpqKrZu3Yq2tjYsX74cQqFQ7fwbovv3xDAMbty4gUWLFuGN\nN96At7c3Pv74Y8ycORM5OTmKuzDl0lBgYCD++9//4qmnnsLcuXPxwAMPAOhcJ7WkpARTp07FpEmT\nsGDBAvB4PBQXF6OkpETluElJSXBzc8Nvv/2GoUOHGt2eOw0leBaSJ8vm5mbs27cP+/fvR1ZWlsZa\n7KOPPqp2RRkbG4uTJ0/2Og6ZTIaAgACVbQzD4LnnnlO52pXJZBgyZIjK+9LT0/Hzzz9r3XdtbS3K\nysrUVpc/c+YMPD09sWzZMsU25VkEBwwYADc3N0gkErU5tqOjo1XKWFKpFMOGDUNCQgIOHjyIBx98\nUKUdr776qmImz3vuuQdHjhzB7t27FQl+w4YNEIlE+OGHHxTlitGjRyMxMVGtPUuWLFE5H6NGjUJR\nURG2bNmiluD9/f3VHhKvXr0abm5u+OGHH+Do6AgAGDp0KFJSUlRKJaZQX1+PX375RZHMk5KSMHDg\nQPzwww94/fXX1d5vb2+vqLuHh4erlASPHTuGjo4OrFmzBq6urgCAu+++W20fDMMgPj4eZ86cMWlb\n2I4SPMtoSpbPPfccnn32WY3vX7NmjdpDVicnJ5PEwjAMDh06pPYPi5+fn9r7srOzVRJRTw955WUT\nX19fle2pqang8/l4/vnn8dhjjyE9PR0eHh56x7xlyxZs3boVJSUlEAqFiviKiorU3jtu3DiVv8fF\nxeHixYuKv+fk5GDIkCEq7XJ2dsaECRPUyiF5eXlYvnw5zp07h9u3byv+0e1efgKAv/3tb2rbcnJy\nMHbsWEVyB4Dg4GDcddddKCsr06fpeouKilJ5XuLr6ws/Pz/cvHnT4H0lJSWBx+Nh1qxZmDZtGkaM\nGKH2nSof5/r168aGfUeiBM8yysmyrq4OGzZswObNmzFkyBA8+eSTau+PjIzE4MGDzRZPcnKyXuWe\n2NhYgx6yyrt9Ojg4qGwfMWIEvvzyS3z22Wd46qmnIJPJMGLECCxbtgzx8fE69/npp59i4f+3d3ch\nTX5xAMe/MuZSC2rJei9GZrqoixZBzIFkBUEsnmIQXSVBouXLRdSw0Fo0FCkaZVIM7cK78KW6GlYX\nJWFvEAWBYhdCVpq0ypyWMv8X4fN3Po+WZonz97k8O+c55xn4e87O+T0ej4f8/Hy2b9/OwoULiUQi\nZGVlMTAwoKk/NtUxPj4+ql5XVxc2m03TbuwxhZ2dnezdu5f09HQqKipYuXIlBoOB8+fP09bWpmk/\n9lfRSF96xx9aLJZpD/B6B8yMvfffZbVaqaurw+/3k5uby8DAAHa7nTNnzmjObUhISJhSH3OZBPgY\nNDpYOp1OHA4HJSUluFyuaZudz7SR/YTPnz9rPnO5XLhcLsLhMM3NzZSWluJ2u3n9+vWE12xoaCAz\nMxOv16uWdXR0THmMS5Ys0ZxDCz+D8Wj37t2jt7eXGzduRKV7hsNh3evqLbWN15de2Vgjs/7BwcGo\n8k+fPv2y7XTIyMggIyODwcFBWlpa8Pl8HDhwgJcvX0Y9REOhEIsXL/4nY4oVkkUT4+Lj4/F6vXR3\ndxMIBKI+m8786H+da7169WrmzZun2YwbLTExkV27dnHo0CE+fPigBiyTyaQ7EwyHw5rjHGtra6d8\nb1u3buXZs2e8e/dOLevr6yMYDGr6BaLSHdvb23n8+PGk+mpqaqK/v18te/v27W9dw2KxYDKZNA/A\nseP8EyO/tCaagRuNRpxOJwUFBfT19Wkerh0dHaSkpEzbmOYCmcHPAbt372bz5s1UVlZy5MgR9Y9t\neHiY1tZW3XM3bTZb1AtJL1680JwLCagbj+PlOT99+lSTxWEwGHRfrpoMo9GI3W7n+fPnUeU+n4+P\nHz/idDpZunQpnZ2dXLt2jU2bNqmz/vXr11NdXU1DQwNWq5X58+eTkpLCjh078Pv9XLx4EbvdzoMH\nD7h169aUx5iXl0cgEEBRFDweD0ajkStXrpCQkEAoFFLrZWZmYjAYyMnJ4dixY7x//56ysjJWrVpF\nJBL5rb6OHz9OY2MjiqKQn5/Pjx8/KC8v11220aMoCrW1taxdu5Z169YRDAaj0iP/lMViwWw2U19f\nj81mIykpiTVr1tDY2MijR4/YuXMnK1asoKenh0uXLrF8+XLS09PV9l++fKG9vZ2CgoJpG9NcIAF+\njjh9+jT79++nurqa3Nxc4Oes2+Px6Na/f/++ujYfFxdHTU0NNTU1mnojm17jzXJHUuJGS0pK0k0V\nnKx9+/ZRWlpKf3+/uvS0ZcsWrl+/zqlTpwiFQiQnJ5OVlUVxcbHarqioiDdv3lBYWMi3b99wOBzc\nuXOHEydO8PXrV6qqqvj+/TsOh4P6+no1h3u08e53dLnZbOb27dt4PB7y8vIwm81kZ2czNDQUlUWU\nlpZGIBDA5/Nx8OBBrFYrZ8+epampSRNkx+s3NTWVmzdvUlJSwuHDh1m2bBlFRUU8efKE5ubmX36X\nZWVlDA8PU15eTiQSQVEUKioqdPdtxntreaLvKC4ujsuXL3Pu3DkURWFoaIjKyko2btzI3bt38Xq9\n9PT0sGjRIrZt20YgEIjaXwkGg5hMJvbs2fPLexH/kzNZxazV29vLhg0buHDhAm63e6aHI/4it9tN\ncnIyVVVVMz2UWUXW4MWstWDBAgoLC/H7/TM9FPEXvXr1iocPH3Ly5MmZHsqsI0s0YlY7evQokUiE\nrq4u3fRBMft1d3dz9erVSf+vIiFLNEIIEbNkiUYIIWKUBHghhIhREuCFECJGSYAXQogYJQFeCCFi\nlAR4IYSIUf8BMCyUXzGPqx0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d9585b4438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Table().with_columns([\n",
    "    'UNEMP (standard units)',  standard_units(data.column('UNEMP')), \n",
    "    'RELIEF (standard units)', standard_units(data.column('RELIEF'))\n",
    "]).scatter(-1,0, fit_line=True, s=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, keep in mind that change happens over time, be mindful of what’s included and of what’s excluded, and of who’s doing the counting or recording, and, yes, try to note outliers.\n",
    "\n",
    "Thank you!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}