U.S.-Mexican Border Exploration Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Independent Data Exploration: U.S.-Mexican Border "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datascience import * # a UC Berkeley developed wrapper of Pandas library for beginning students in Data Science\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plots\n",
    "plots.style.use('fivethirtyeight')\n",
    "\n",
    "#from client.api.notebook import Notebook\n",
    "#ok = Notebook('exploration.ok')\n",
    "#_ = ok.auth(inline=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Independent Data Exploration\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1) Data Summary\n",
    "The three following data setes represent information available from U.S. Border Patrol and Security regarding the United States-Mexican border. The wall data set represents the lengths of wall built, the year theat they were built, type, and subtype. Our quantiative variable is manifest in the length of wall built (in feet) and our categorical data is manifest in the type and subtype of the wall sections. The other two data sets include the total number of apprehensions of illegal crossings at the the U.S-Mexican border by U.S. Border Patrol (table name = apprehensions) as well as the number of BP Staff within the different sectors of the U.S.-Mexican border (table name = staffing). The latter two tables contain only quantitative variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Fiscal Year</th> <th>Big Bend</th> <th>Del Rio</th> <th>El Centro</th> <th>El Paso</th> <th>Laredo</th> <th>Rio Grande Valley</th> <th>San Diego</th> <th>Tucson</th> <th>Yuma</th> <th>Southwest Border Total</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>2016       </td> <td>6366    </td> <td>23078  </td> <td>19448    </td> <td>25634  </td> <td>36562 </td> <td>186830           </td> <td>31891    </td> <td>64891 </td> <td>14170</td> <td>408870                </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2015       </td> <td>5031    </td> <td>19013  </td> <td>12820    </td> <td>14495  </td> <td>35888 </td> <td>147257           </td> <td>26290    </td> <td>63397 </td> <td>7142 </td> <td>331333                </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2014       </td> <td>4096    </td> <td>24255  </td> <td>14511    </td> <td>12339  </td> <td>44049 </td> <td>256393           </td> <td>29911    </td> <td>87915 </td> <td>5902 </td> <td>479371                </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2013       </td> <td>3684    </td> <td>23510  </td> <td>16306    </td> <td>11154  </td> <td>50749 </td> <td>154453           </td> <td>27496    </td> <td>120939</td> <td>6106 </td> <td>414397                </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2012       </td> <td>3964    </td> <td>21720  </td> <td>23916    </td> <td>9678   </td> <td>44872 </td> <td>97762            </td> <td>28461    </td> <td>120000</td> <td>6500 </td> <td>356873                </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2011       </td> <td>4036    </td> <td>16144  </td> <td>30191    </td> <td>10345  </td> <td>36053 </td> <td>59243            </td> <td>42447    </td> <td>123285</td> <td>5833 </td> <td>327577                </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2010       </td> <td>5288    </td> <td>14694  </td> <td>32562    </td> <td>12251  </td> <td>35287 </td> <td>59766            </td> <td>68565    </td> <td>212202</td> <td>7116 </td> <td>447731                </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2009       </td> <td>6360    </td> <td>17082  </td> <td>33521    </td> <td>14999  </td> <td>40569 </td> <td>60989            </td> <td>118721   </td> <td>241673</td> <td>6951 </td> <td>540865                </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2008       </td> <td>5391    </td> <td>20761  </td> <td>40961    </td> <td>30312  </td> <td>43658 </td> <td>75473            </td> <td>162390   </td> <td>317696</td> <td>8363 </td> <td>705005                </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2007       </td> <td>5536    </td> <td>22920  </td> <td>55883    </td> <td>75464  </td> <td>56714 </td> <td>73430            </td> <td>152460   </td> <td>378239</td> <td>37992</td> <td>858638                </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (47 rows omitted)</p"
      ],
      "text/plain": [
       "Fiscal Year | Big Bend | Del Rio | El Centro | El Paso | Laredo | Rio Grande Valley | San Diego | Tucson | Yuma  | Southwest Border Total\n",
       "2016        | 6366     | 23078   | 19448     | 25634   | 36562  | 186830            | 31891     | 64891  | 14170 | 408870\n",
       "2015        | 5031     | 19013   | 12820     | 14495   | 35888  | 147257            | 26290     | 63397  | 7142  | 331333\n",
       "2014        | 4096     | 24255   | 14511     | 12339   | 44049  | 256393            | 29911     | 87915  | 5902  | 479371\n",
       "2013        | 3684     | 23510   | 16306     | 11154   | 50749  | 154453            | 27496     | 120939 | 6106  | 414397\n",
       "2012        | 3964     | 21720   | 23916     | 9678    | 44872  | 97762             | 28461     | 120000 | 6500  | 356873\n",
       "2011        | 4036     | 16144   | 30191     | 10345   | 36053  | 59243             | 42447     | 123285 | 5833  | 327577\n",
       "2010        | 5288     | 14694   | 32562     | 12251   | 35287  | 59766             | 68565     | 212202 | 7116  | 447731\n",
       "2009        | 6360     | 17082   | 33521     | 14999   | 40569  | 60989             | 118721    | 241673 | 6951  | 540865\n",
       "2008        | 5391     | 20761   | 40961     | 30312   | 43658  | 75473             | 162390    | 317696 | 8363  | 705005\n",
       "2007        | 5536     | 22920   | 55883     | 75464   | 56714  | 73430             | 152460    | 378239 | 37992 | 858638\n",
       "... (47 rows omitted)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apprehensions = Table().read_table('Apprehensions.csv')\n",
    "apprehensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>wall_type</th> <th>vbr_type</th> <th>year</th> <th>length_ft</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Ped      </td> <td>Primary </td> <td>1962</td> <td>715.31   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Ped      </td> <td>Primary </td> <td>1970</td> <td>24385.6  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Ped      </td> <td>Primary </td> <td>1970</td> <td>55.45    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Ped      </td> <td>Primary </td> <td>1970</td> <td>302.4    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Ped      </td> <td>Primary </td> <td>1990</td> <td>78.13    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Ped      </td> <td>Primary </td> <td>1990</td> <td>430.93   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Ped      </td> <td>Primary </td> <td>1991</td> <td>1915.88  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Ped      </td> <td>Primary </td> <td>1991</td> <td>1515.17  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Ped      </td> <td>Primary </td> <td>1991</td> <td>4445.99  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Ped      </td> <td>Primary </td> <td>1991</td> <td>2394.66  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (3611 rows omitted)</p"
      ],
      "text/plain": [
       "wall_type | vbr_type | year | length_ft\n",
       "Ped       | Primary  | 1962 | 715.31\n",
       "Ped       | Primary  | 1970 | 24385.6\n",
       "Ped       | Primary  | 1970 | 55.45\n",
       "Ped       | Primary  | 1970 | 302.4\n",
       "Ped       | Primary  | 1990 | 78.13\n",
       "Ped       | Primary  | 1990 | 430.93\n",
       "Ped       | Primary  | 1991 | 1915.88\n",
       "Ped       | Primary  | 1991 | 1515.17\n",
       "Ped       | Primary  | 1991 | 4445.99\n",
       "Ped       | Primary  | 1991 | 2394.66\n",
       "... (3611 rows omitted)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wall = Table().read_table('the-wall.csv')\n",
    "wall.sort('year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Year</th> <th>Big Bend\r",
       "(formerly Marfa)</th> <th>Del Rio</th> <th>El Centro</th> <th>El Paso</th> <th>Laredo</th> <th>Rio Grande\r",
       "Valley (formerly\r",
       "McAllen)</th> <th>San Diego</th> <th>Tucson</th> <th>Yuma</th> <th>Total</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1992</td> <td>108                      </td> <td>317    </td> <td>217      </td> <td>627    </td> <td>364   </td> <td>418                                 </td> <td>1009     </td> <td>300   </td> <td>195 </td> <td>3555 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1993</td> <td>134                      </td> <td>297    </td> <td>198      </td> <td>608    </td> <td>353   </td> <td>393                                 </td> <td>992      </td> <td>287   </td> <td>182 </td> <td>3444 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1994</td> <td>128                      </td> <td>299    </td> <td>194      </td> <td>643    </td> <td>337   </td> <td>392                                 </td> <td>1290     </td> <td>282   </td> <td>182 </td> <td>3747 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1995</td> <td>133                      </td> <td>399    </td> <td>190      </td> <td>746    </td> <td>421   </td> <td>474                                 </td> <td>1433     </td> <td>407   </td> <td>185 </td> <td>4388 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>134                      </td> <td>406    </td> <td>189      </td> <td>841    </td> <td>415   </td> <td>507                                 </td> <td>1965     </td> <td>702   </td> <td>174 </td> <td>5333 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1997</td> <td>135                      </td> <td>471    </td> <td>249      </td> <td>918    </td> <td>451   </td> <td>760                                 </td> <td>2281     </td> <td>875   </td> <td>175 </td> <td>6315 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1998</td> <td>164                      </td> <td>589    </td> <td>383      </td> <td>972    </td> <td>628   </td> <td>1107                                </td> <td>2274     </td> <td>1013  </td> <td>227 </td> <td>7357 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1999</td> <td>144                      </td> <td>639    </td> <td>423      </td> <td>957    </td> <td>685   </td> <td>1166                                </td> <td>2136     </td> <td>1328  </td> <td>228 </td> <td>7706 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2000</td> <td>196                      </td> <td>758    </td> <td>502      </td> <td>1036   </td> <td>806   </td> <td>1368                                </td> <td>2053     </td> <td>1548  </td> <td>313 </td> <td>8580 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2001</td> <td>194                      </td> <td>893    </td> <td>589      </td> <td>1086   </td> <td>921   </td> <td>1451                                </td> <td>2004     </td> <td>1686  </td> <td>323 </td> <td>9147 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2002</td> <td>253                      </td> <td>972    </td> <td>683      </td> <td>1122   </td> <td>969   </td> <td>1484                                </td> <td>1807     </td> <td>1626  </td> <td>323 </td> <td>9239 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2003</td> <td>230                      </td> <td>950    </td> <td>755      </td> <td>1188   </td> <td>1025  </td> <td>1524                                </td> <td>1972     </td> <td>1838  </td> <td>358 </td> <td>9840 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2004</td> <td>239                      </td> <td>933    </td> <td>734      </td> <td>1094   </td> <td>981   </td> <td>1439                                </td> <td>1651     </td> <td>2104  </td> <td>331 </td> <td>9506 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2005</td> <td>220                      </td> <td>907    </td> <td>684      </td> <td>1330   </td> <td>937   </td> <td>1380                                </td> <td>1562     </td> <td>2324  </td> <td>547 </td> <td>9891 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2006</td> <td>246                      </td> <td>935    </td> <td>713      </td> <td>1741   </td> <td>926   </td> <td>1431                                </td> <td>1671     </td> <td>2595  </td> <td>774 </td> <td>11032</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2007</td> <td>336                      </td> <td>1138   </td> <td>894      </td> <td>2251   </td> <td>1206  </td> <td>1822                                </td> <td>2019     </td> <td>2806  </td> <td>825 </td> <td>13297</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2008</td> <td>421                      </td> <td>1427   </td> <td>1080     </td> <td>2506   </td> <td>1636  </td> <td>2063                                </td> <td>2328     </td> <td>3049  </td> <td>932 </td> <td>15442</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2009</td> <td>682                      </td> <td>1682   </td> <td>1187     </td> <td>2712   </td> <td>1863  </td> <td>2422                                </td> <td>2570     </td> <td>3318  </td> <td>972 </td> <td>17408</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2010</td> <td>672                      </td> <td>1650   </td> <td>1181     </td> <td>2752   </td> <td>1858  </td> <td>2441                                </td> <td>2594     </td> <td>3353  </td> <td>987 </td> <td>17535</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2011</td> <td>667                      </td> <td>1626   </td> <td>1164     </td> <td>2738   </td> <td>1871  </td> <td>2504                                </td> <td>2669     </td> <td>4239  </td> <td>969 </td> <td>18506</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2012</td> <td>693                      </td> <td>1655   </td> <td>1168     </td> <td>2718   </td> <td>1879  </td> <td>2546                                </td> <td>2623     </td> <td>4176  </td> <td>954 </td> <td>18546</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2013</td> <td>623                      </td> <td>1598   </td> <td>1141     </td> <td>2631   </td> <td>1804  </td> <td>3086                                </td> <td>2572     </td> <td>4135  </td> <td>911 </td> <td>18611</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2014</td> <td>588                      </td> <td>1537   </td> <td>1074     </td> <td>2522   </td> <td>1785  </td> <td>3064                                </td> <td>2546     </td> <td>4052  </td> <td>859 </td> <td>18156</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2015</td> <td>549                      </td> <td>1536   </td> <td>1004     </td> <td>2364   </td> <td>1670  </td> <td>3056                                </td> <td>2430     </td> <td>3991  </td> <td>804 </td> <td>17522</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2016</td> <td>511                      </td> <td>1439   </td> <td>927      </td> <td>2240   </td> <td>1670  </td> <td>3135                                </td> <td>2325     </td> <td>3834  </td> <td>829 </td> <td>17026</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "staffing = Table().read_table('Staffing.csv')\n",
    "staffing = staffing.with_column(\"Year\", np.arange(1992, 2017))\n",
    "staffing.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization of some quantitative variable(s) of the data that summarizes the data effectively and short observational description."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>wall_type</th> <th>vbr_type</th> <th>length_ft</th> <th>year_0</th> <th>miles</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Veh      </td> <td>Temporary</td> <td>162.67   </td> <td>2004  </td> <td>0.0308087 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Veh      </td> <td>Temporary</td> <td>133.7    </td> <td>2006  </td> <td>0.025322  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Veh      </td> <td>Temporary</td> <td>232.08   </td> <td>2006  </td> <td>0.0439545 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Veh      </td> <td>Temporary</td> <td>26.99    </td> <td>2001  </td> <td>0.00511174</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Veh      </td> <td>Temporary</td> <td>21.73    </td> <td>2001  </td> <td>0.00411553</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Veh      </td> <td>Permanent</td> <td>35402.6  </td> <td>2006  </td> <td>6.70504   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Veh      </td> <td>Permanent</td> <td>682.69   </td> <td>2007  </td> <td>0.129297  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Veh      </td> <td>Temporary</td> <td>663.24   </td> <td>2007  </td> <td>0.125614  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Veh      </td> <td>Permanent</td> <td>1294.28  </td> <td>2007  </td> <td>0.245129  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Veh      </td> <td>Permanent</td> <td>2122.21  </td> <td>2007  </td> <td>0.401934  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (3589 rows omitted)</p"
      ],
      "text/plain": [
       "wall_type | vbr_type  | length_ft | year_0 | miles\n",
       "Veh       | Temporary | 162.67    | 2004   | 0.0308087\n",
       "Veh       | Temporary | 133.7     | 2006   | 0.025322\n",
       "Veh       | Temporary | 232.08    | 2006   | 0.0439545\n",
       "Veh       | Temporary | 26.99     | 2001   | 0.00511174\n",
       "Veh       | Temporary | 21.73     | 2001   | 0.00411553\n",
       "Veh       | Permanent | 35402.6   | 2006   | 6.70504\n",
       "Veh       | Permanent | 682.69    | 2007   | 0.129297\n",
       "Veh       | Temporary | 663.24    | 2007   | 0.125614\n",
       "Veh       | Permanent | 1294.28   | 2007   | 0.245129\n",
       "Veh       | Permanent | 2122.21   | 2007   | 0.401934\n",
       "... (3589 rows omitted)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walls_with_years = wall.where('year', are.not_equal_to('TBD'))\n",
    "cleaned_wall = walls_with_years.with_column('year_0', walls_with_years.column('year').astype(np.int)).drop('year')\n",
    "cleaned_wall_with_miles = cleaned_wall.with_column('miles', cleaned_wall.column('length_ft') / 5280)\n",
    "cleaned_wall_with_miles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KLYJWVbOk9QolEtwkuAkhhJOw7EvzUYKb5QolZZhMJrvlyxFJcBNCCCdgNBqtmhurFk5W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\n+f/yHhnfg8oumwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11879ac88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cleaned_wall_with_miles.group('year_0', sum).plot('year_0', 'miles sum')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot shows the length of walls built in each year in miles from 1960 to 2015. We can see that there was a large portion of the wall that was constructed from 2006 through 2010, during Bush's term and the first year of Barack Obama's term. There was also some wall construction in the 90's during President Bill Clinton's presidential term."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization of categorical variable(s) of the data in a way that summarizes the data effectively and short observational description."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IOiMjA3PmzJELaQDw8PDA7NmzkZGRUeeNSyQSZGdnIyYmBt7e3jAzM0OXLl0wYcIEfPjh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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1f944a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wall_type_bar = cleaned_wall_with_miles.group('vbr_type', sum).barh('vbr_type','miles sum')\n",
    "wall_type_bar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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V0EzmJgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1f944eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wall_type_bar = cleaned_wall_with_miles.group('wall_type', sum).barh('wall_type','miles sum')\n",
    "wall_type_bar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sections of the wall are divided first into pedestrian and vehicle categories. Pedestrian walls are further divided into the subtypes of primary, secondary, and tertiary. Primary walls are usually made of corrugated steel landing mats; secondary walls are typically composed of fencing and barbed wire; tertiary walls are made of high chain-link fencing. The latter of the last composes less than 15 miles of the entire wall.\n",
    "\n",
    "The first graph shows the miles of each subtype of wall constructed. The second graph shows the amount of pedestrian and vehicle wall constructed. We can see that most of the wall is of primary pedestrian categorization. It should be noted that secondary and tertiary walls were built next to primary walls to reinforce security. Much of the length of America's wall comes from reinforcement of the primary wall's built (rather than extending the wall to areas with no walls at all)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summarize some aspect of the data in a table that involves joining the two tables and write a short observational description."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Year</th> <th>Big Bend\r",
       "(formerly Marfa)</th> <th>Del Rio</th> <th>El Centro</th> <th>El Paso</th> <th>Laredo</th> <th>Rio Grande\r",
       "Valley (formerly\r",
       "McAllen)</th> <th>San Diego</th> <th>Tucson</th> <th>Yuma</th> <th>Total</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1992</td> <td>108                      </td> <td>317    </td> <td>217      </td> <td>627    </td> <td>364   </td> <td>418                                 </td> <td>1009     </td> <td>300   </td> <td>195 </td> <td>3555 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1993</td> <td>134                      </td> <td>297    </td> <td>198      </td> <td>608    </td> <td>353   </td> <td>393                                 </td> <td>992      </td> <td>287   </td> <td>182 </td> <td>3444 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1994</td> <td>128                      </td> <td>299    </td> <td>194      </td> <td>643    </td> <td>337   </td> <td>392                                 </td> <td>1290     </td> <td>282   </td> <td>182 </td> <td>3747 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1995</td> <td>133                      </td> <td>399    </td> <td>190      </td> <td>746    </td> <td>421   </td> <td>474                                 </td> <td>1433     </td> <td>407   </td> <td>185 </td> <td>4388 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1996</td> <td>134                      </td> <td>406    </td> <td>189      </td> <td>841    </td> <td>415   </td> <td>507                                 </td> <td>1965     </td> <td>702   </td> <td>174 </td> <td>5333 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1997</td> <td>135                      </td> <td>471    </td> <td>249      </td> <td>918    </td> <td>451   </td> <td>760                                 </td> <td>2281     </td> <td>875   </td> <td>175 </td> <td>6315 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1998</td> <td>164                      </td> <td>589    </td> <td>383      </td> <td>972    </td> <td>628   </td> <td>1107                                </td> <td>2274     </td> <td>1013  </td> <td>227 </td> <td>7357 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1999</td> <td>144                      </td> <td>639    </td> <td>423      </td> <td>957    </td> <td>685   </td> <td>1166                                </td> <td>2136     </td> <td>1328  </td> <td>228 </td> <td>7706 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2000</td> <td>196                      </td> <td>758    </td> <td>502      </td> <td>1036   </td> <td>806   </td> <td>1368                                </td> <td>2053     </td> <td>1548  </td> <td>313 </td> <td>8580 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2001</td> <td>194                      </td> <td>893    </td> <td>589      </td> <td>1086   </td> <td>921   </td> <td>1451                                </td> <td>2004     </td> <td>1686  </td> <td>323 </td> <td>9147 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (15 rows omitted)</p"
      ],
      "text/plain": [
       "Year | Big Bend\n",
       "(formerly Marfa) | Del Rio | El Centro | El Paso | Laredo | Rio Grande\n",
       "Valley (formerly\n",
       "McAllen) | San Diego | Tucson | Yuma | Total\n",
       "1992 | 108                       | 317     | 217       | 627     | 364    | 418                                  | 1009      | 300    | 195  | 3555\n",
       "1993 | 134                       | 297     | 198       | 608     | 353    | 393                                  | 992       | 287    | 182  | 3444\n",
       "1994 | 128                       | 299     | 194       | 643     | 337    | 392                                  | 1290      | 282    | 182  | 3747\n",
       "1995 | 133                       | 399     | 190       | 746     | 421    | 474                                  | 1433      | 407    | 185  | 4388\n",
       "1996 | 134                       | 406     | 189       | 841     | 415    | 507                                  | 1965      | 702    | 174  | 5333\n",
       "1997 | 135                       | 471     | 249       | 918     | 451    | 760                                  | 2281      | 875    | 175  | 6315\n",
       "1998 | 164                       | 589     | 383       | 972     | 628    | 1107                                 | 2274      | 1013   | 227  | 7357\n",
       "1999 | 144                       | 639     | 423       | 957     | 685    | 1166                                 | 2136      | 1328   | 228  | 7706\n",
       "2000 | 196                       | 758     | 502       | 1036    | 806    | 1368                                 | 2053      | 1548   | 313  | 8580\n",
       "2001 | 194                       | 893     | 589       | 1086    | 921    | 1451                                 | 2004      | 1686   | 323  | 9147\n",
       "... (15 rows omitted)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "staffing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>year_0</th> <th>length_ft sum</th> <th>miles sum</th> <th>Apprehensions Total</th> <th>Staffing</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1992  </td> <td>80960.4      </td> <td>15.3334   </td> <td>1145574            </td> <td>3555    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1993  </td> <td>22341.5      </td> <td>4.23135   </td> <td>1212886            </td> <td>3444    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1994  </td> <td>38139.2      </td> <td>7.22334   </td> <td>979101             </td> <td>3747    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1995  </td> <td>34300.4      </td> <td>6.49629   </td> <td>1271390            </td> <td>4388    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1996  </td> <td>11721.2      </td> <td>2.21993   </td> <td>1507020            </td> <td>5333    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1997  </td> <td>23289.6      </td> <td>4.4109    </td> <td>1368707            </td> <td>6315    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1998  </td> <td>158366       </td> <td>29.9935   </td> <td>1516680            </td> <td>7357    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1999  </td> <td>56037.9      </td> <td>10.6132   </td> <td>1537000            </td> <td>7706    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2000  </td> <td>32446.6      </td> <td>6.14519   </td> <td>1643679            </td> <td>8580    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2001  </td> <td>48.72        </td> <td>0.00922727</td> <td>1235718            </td> <td>9147    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2002  </td> <td>4952.72      </td> <td>0.938015  </td> <td>929809             </td> <td>9239    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2004  </td> <td>6539.96      </td> <td>1.23863   </td> <td>1139282            </td> <td>9506    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2005  </td> <td>24853.8      </td> <td>4.70716   </td> <td>1171396            </td> <td>9891    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2006  </td> <td>275977       </td> <td>52.2685   </td> <td>1071972            </td> <td>11032   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2007  </td> <td>853654       </td> <td>161.677   </td> <td>858638             </td> <td>13297   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2008  </td> <td>1.79707e+06  </td> <td>340.354   </td> <td>705005             </td> <td>15442   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2009  </td> <td>672502       </td> <td>127.368   </td> <td>540865             </td> <td>17408   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2010  </td> <td>35649.1      </td> <td>6.75173   </td> <td>447731             </td> <td>17535   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2011  </td> <td>59891.8      </td> <td>11.3431   </td> <td>327577             </td> <td>18506   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2012  </td> <td>24981.6      </td> <td>4.73136   </td> <td>356873             </td> <td>18546   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2013  </td> <td>1540.14      </td> <td>0.291693  </td> <td>414397             </td> <td>18611   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2014  </td> <td>5418.73      </td> <td>1.02627   </td> <td>479371             </td> <td>18156   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2015  </td> <td>8531.31      </td> <td>1.61578   </td> <td>331333             </td> <td>17522   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "apprehensions_and_wall = cleaned_wall_with_miles.group('year_0', sum).join(\n",
    "    'year_0', apprehensions, 'Fiscal Year').select([0, 1, 2, 3, 4, 14]).relabeled(\n",
    "    'Southwest Border Total', 'Apprehensions Total')\n",
    "\n",
    "apprehensions_and_wall_and_staffing = apprehensions_and_wall.join(\n",
    "    'year_0', staffing, 'Year').select([0, 3, 4, 5, 15]).relabeled('Total', 'Staffing')\n",
    "\n",
    "apprehensions_and_wall_and_staffing.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Umn9M8wtLUVhc1sYRhBBFwynRCoVCZGRkAACGDx+OPXv2oLi4GKWlpQgODoaxsXE7Z2h2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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1f8bee10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "apprehensions_and_wall_and_staffing.plot(\"year_0\", \"Apprehensions Total\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RI0cCAL7//nu4uLggLi4Oo0aNwtmzZ3H9+nWkpaXB0tISAPDFF19gwYIF+Oyzz6Cvr4+f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\nNCwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117ce7ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "apprehensions_and_wall_and_staffing.plot('year_0', 'Staffing')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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C1DyldijZvn07qalFv3Tfffddrl27VmFB1SYPdyq5ePnxB3On3clk/vcbyL9775HHFhZq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\nA69UqjB7rgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117a53208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "apprehensions_and_wall_and_staffing.with_column('Apprehensions/Staffing', apprehensions_and_wall_and_staffing.column('Apprehensions Total')/apprehensions_and_wall_and_staffing.column('Staffing')).plot('year_0', 'Apprehensions/Staffing')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KXLt2jZ1OkJ6ezj4Wr8nKlStRUlKC33//HWPHjsVbb70Ff3//OsdiY2MDiUSC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\nCCGEEEI4zP8DNUR7AYbFMIAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11795c860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "apprehensions_and_wall_and_staffing.with_column(\n",
    "    \"Staffing x 100\",\n",
    "    apprehensions_and_wall_and_staffing.column(4) * 100).drop(1,2,4).plot(\"year_0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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v346vvvoKkyZNwoMHD7B3714A5b2o1tbWKCwsRGhoKDp37gwdHR3o6urWyeey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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11552aeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "apprehensions_and_wall_and_staffing.select(0,1,3).move_to_start(\n",
    "    \"Apprehensions Total\").plot(\"year_0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jnf79++Prr7/mXksk/F/mK1euxOnTp7Fnzx6Ymppi1apVmDx5Mi5cuAChUAiF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11869e320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "apprehensions_and_wall_and_staffing.with_column(\n",
    "    \"Staffing x 100\",\n",
    "    apprehensions_and_wall_and_staffing.column(4) * 100).select(\n",
    "    0,1,3,5).move_to_start(\"Apprehensions Total\").plot(\"year_0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We joined with the wall data, information about border patrol staffing (total number of Southwest Border Patrol staff) along with the number of apprehensions at the Southwest border of the United States (U.S - Mexico border) per Fiscal Year. We can see that over time, total apprehensions has decreased while staffing has increased. By visualizing the ratio of total apprehensions to staffing, we can see a general negative trend (seen in line plots above and scatter plot below in Part 7). \n",
    "\n",
    "Furthermore, we also see (in the very last line plot) a large influx of wall being built in 2008 as well as a significant decrease in total apprehensions during 2008 (albeit the decrease in apprehensions decreased steadily between 2005 to 2010). The final line plot shows the total number of apprehensions from 1992 to 2015 against both length of wall build (in ft.) and number of Border Patrol staffing (multiplied by 100 to normalize to scale). Around the year 2001 (September 11th) border staffing and security was increase and apprehensions decreased. The largest decline in apprehensions occured between 2005 and around 2012 where we also see a significant increase in Border Patrol staffing and wall being built. These graphics coincide with the numbers seen in the apprehensions_and_wall_and_staffing joined and grouped table we created."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hypothesis related to the data and the corresponding null hypothesis.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hypothesis: There is indeed a relationship between Border Patrol staffing and the number of Apprehensions at the U.S. - Mexican border. \n",
    "\n",
    "We chose to analyze the relationship between Border Patrol (BP) staffing and apprehensions per year instead of the amount of wall being built and apprehensions based on the visualizations above showing clearer trends between staffing and apprehensions since 1992. This was further evidenced by the scatter plot between each variable produced below and their corresponding r values where the r value for apprehensions and BP staffing shows a strong negative correlation (-0.86) and the r value for apprehensions and the wall shows almost no relationship (-0.18). By testing the nature of the relationship between BP staffing and apprehensions, we may be able to see if an increase in border security by way of Border Patrol staffing actually deters people from crossing the U.S. Mexico border illegally. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pzWKxBWd+q7gsKJ+2pXzajjPnslw9szl58iQpKSmEhYUZx6pUqUKHDh3YtWsX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\niIiI3anYiIiI3anYiIiI3f0/l3KbkiAPJI4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117a7a3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "just_app_and_wall = apprehensions_and_wall_and_staffing.select(\"miles sum\", \"Apprehensions Total\")\n",
    "\n",
    "just_app_and_wall.scatter(\"miles sum\", \"Apprehensions Total\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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10xk5eXt7c/nyZc6fPw9ASkqKznFK+5Tuo7KyCCGE0HM22r///W9ycnJ4+eWX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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1f9af400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "just_app_and_staffing = apprehensions_and_wall_and_staffing.select('Staffing', 'Apprehensions Total')\n",
    "just_app_and_staffing.scatter(0, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Null hypothesis: The slope of the true line between apprehensions and border patrol staffing is 0; There is no relationship.\n",
    "##### Alternative hypothesis: The slope of the true line is not 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Statistical test for the hypothesis and short conclusion.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def standard_units(arr):\n",
    "    standard_units = (arr - np.mean(arr)) / np.std(arr)\n",
    "    return standard_units \n",
    "\n",
    "def correlation(tbl):\n",
    "    first_column_su = standard_units(tbl.column(0))\n",
    "    second_column_su = standard_units(tbl.column(1))\n",
    "    r = np.mean(first_column_su*second_column_su)\n",
    "    return r\n",
    "\n",
    "def fit_line(tbl):\n",
    "    r = correlation(tbl)\n",
    "    slope = r * ((np.std(tbl.column(1)))/(np.std(tbl.column(0))))\n",
    "    intercept = np.mean(tbl.column(1)) - (slope * np.mean(tbl.column(0)))\n",
    "    return make_array(slope, intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.85869306081751784"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation(just_app_and_staffing) # r value for BP staffing and apprehensions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.17740326201878953"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation(just_app_and_wall) # r value for wall and apprehensions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a histogram of slopes of 1000 samples of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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o+fPnw97eHr///jvCwsLKze/atStOnjxZ7ULu3r2L0tJSPkKJiIgAaDBI4vTp05gzZw7M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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117c28e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resample_slopes = make_array()\n",
    "for i in np.arange(1000):\n",
    "    sample = just_app_and_staffing.sample(23)\n",
    "    resample_line = fit_line(sample)\n",
    "    resample_slope = resample_line.item(0)\n",
    "    resample_slopes = np.append(resample_slope, resample_slopes)\n",
    "\n",
    "Table().with_column(\"Slope estimate\", resample_slopes).hist() # DO NOT CHANGE THIS LINE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we compute a 95% confidence interval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95% confidence interval for slope: [-84.9151, -51.5885]\n"
     ]
    }
   ],
   "source": [
    "lower_end = percentile(2.5, resample_slopes)\n",
    "upper_end = percentile(97.5, resample_slopes)\n",
    "print(\"95% confidence interval for slope: [{:g}, {:g}]\".format(lower_end, upper_end))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus, because 0 is not within the 95% confidence interval, we can reject the null hypothesis. We can conclude that there is not no relationship."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prediction problem related to the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predict apprehensions in a year given staffing number of 12000 using linear regression. That is, using the best fit line between the two variables that we generated above, generate the corresponding y values (apprehensions) given the x values (BP staffing) that fall on our best fit line. We will then calculate the residual errors and root mean squared error (RMSE) to assess our model's performance quantitatively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Application of prediction technique to the problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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59UlISKBQoUKULl1aYyT0tPM8L4soWJ98VIeD38+j4Qea9xq//WE3zbp6E3v2\nLz0kE+LN9tRi0717d7Uv+YLy4MEDPD09OXPmDJGRkdjZ2am1Ozk5YWZmRlRUlOq169evc/78eZyd\nnQGoX78+58+fV5uCHBUVReHChXFyclL12bt3r9qxo6Ki+OCDDzAzM8Pc3BwnJye18zzq8+g82mQR\nBc/W2pKQ8Z74j/HE3Ex98H7pyg0+6TGORau2yW6gQhQgU19f3ykFecK0tDTOnTtHXFwc3377LdWr\nV6dUqVJkZWVRvHhxevXqxfHjx1m1ahUlS5YkPT2d9PR0TE1NMTMzo0iRIty6dYvly5dTs2ZN7ty5\nw8iRIylVqhRTp07FxMSEKlWqEBkZyZ49e6hRowbnzp3D29sbd3d32rVrB8Dbb79NSEgIt2/f5q23\n3mL79u3MnTuXGTNmUK1aNQBKlixJQEAAZcuWpUiRIsyePZtDhw6xaNEiLCwstMpizJKSkjTuRxmL\n5ORkWrVoyKdN63LwtzMk/Wf5mNzcXPYciuXYyQs0b1CLEsWK6i1nXEIyfrO/Yc22KPYdOUndmvZk\nZtwzys/dmH9eJLvuPXXqs64cOHBA9YX/Xx4eHvj6+lK7du083xcaGqqaIp2RkcHEiRPZtGkTGRkZ\nNG3alLlz56rNPrt69Sre3t7s37+fIkWK4ObmxowZMyhcuLCqT3R0NOPHj+fcuXOULVuWESNG0KdP\nH7XzhoeHM3/+fOLi4nB0dMTf35/GjRur2rXJYqyM5VpwXv6bPf1eBn5zvuabTbs0+pWxKsXiGV/x\n6Uf6mXU5fGqY2ozDCnalGeLxsVF+7q/Lz4uxMZbsBV5shPEwlh/ivOSVfeuuwwyfGkZKappG/4Ff\ntGXKiJ4UKWxeUBGBvJ+lmjSoo1F+7q/bz4uxMJbsxn2dR4gX0P6ThkRvCqZR3eoabUtW/8THPXw5\n99fVPN6pO7KAp3hTaFVsLl26xO+//6769/3795k6dSpdu3Zl2bJlOgsnRH6rWLYMkeFT8Rvqgamp\n+o//6fOXaeExhq837iywBT1lAU/xptDqOZsxY8bw/vvvq1YTmD59OsuXL6d69eqMHz8ehUJBv379\ndBr0TSc7QeYfU1NTxvR3p5lzLbzGzuPfG4+nt9/PyGLk9KXsPhTLgsmDKW2p25HGowU8/+tO8m2d\nnlMIfdBqZHPmzBnVVN7c3FzWrVvHlClT2LdvH97e3nzzzTe6zCiQnSB1oX5tBw5snIt7m4802n7c\nfZQmbiOR0r0LAAAgAElEQVQ5cOy0HpIJ8frRqtjcuXNH9czNyZMnSUlJUa2M3KRJE65cuaK7hAKQ\nnSB1xaJkcZYFjCBsxleUeGJlgRvxSXzuNZnpC1bz4EH2U47wfLJVhBBaFhsbGxv+/vtvAPbs2cPb\nb7+tmtr76BkYoVtyI1l3FAoFHp+3YP+GudSp+a5am1KpZG7497Tu7cfla7de6vgyKhVCy2LTunVr\npk2bxoQJE1i0aBEdOnRQtZ09e5YqVaroKp/4P7mRrHtVK5Vjx8qZjOzbSWNtu99OXeQj99Gs/1H7\njQIfkVGpEFpOEJgyZQqZmZns2bOH1q1bM3r0aFXbzz//rLFUv8h/ed1IFvnP3MyMycN70LxBbQb6\nzedm/ONNAe+m32fA+PnsPhTLnPH9KFWimFbHlK0ihJCHOsUzGMvDYnnJj+yJyakMnRzKz3uPabRV\nqWhHeOBIPqz1/M0F4xNS8F+8lsQU7WYSGuvnbqy5QbIXBIPdYkAIfbO2KsWa+b6s2LATvznfkJH5\neDfQy9fiaNlrPOMHd2NEn47PvG8po1IhXqDYrFmzhu+//55r166RkZGh1qZQKIiNjc33cELom0Kh\noG/XVjSqW52+Y+dx9uLjmZc5OblMX7iGqCMnWTpzGBXKltFjUiEMm1bFZtasWQQEBODo6Mj777+P\nuXnBrh8lhL45vluJPWuCmDRvFcvWbFdriz52mibuo1g4dQifucjWEkLkRati8+233zJw4EACAgJ0\nnUcIg1WksDmzfL1waejEkEmLSExOVbUl30mjx4ggPN0/Zaa3J8WKFn7GkYR482g19Tk5OZlWrVrp\nOosQRqFVsw85uCmY5g1qabR9vfEXWniM4dT5f/SQTAjDpVWxady4MadPy7IdQjxS1qY0m5dMYvqo\nLzErpH6B4Pzf13DtPpYlq38qsAU9hTB0WhWbgIAAVq9ezdq1a0lMTCQ3N1fjjxBvGhMTE77q3YFd\n3wXwTuVyam1ZD7LxDYqg69CZ3E6U5WmE0KrY1K1blz///JMhQ4Zgb29PmTJl1P7Y2NjoOqcQBsup\n+jvsWz+HLzpoPtz8y4HjNHYbxZ5DMltTvNm0miDg4+OjsXyHEOKxEsWKEjptKK6NnBgxfQmpd++p\n2uITU+g0cBpDv/ycicO+oLC5mc7zyJYUwtBoVWzGjRun6xxCvBY6tWpC3fffY8D4EI6cOKfWtmjV\nNvbHnCIiaBT2b1fQaY5Hi3+aKBSqxT/lwVKhTy+8LXRaWhrXrl0jPT1dF3mEMHqVK9jyY8R0fAd1\nxcRE/X+xk+f+oVlXb1Zt/lWnkwdk8U9haLQuNrt376Z58+ZUrlyZWrVqUalSJVxcXIiKitJlPiGM\nUqFCpvgO6spPK6ZTsZz6ygL3MjIZNmUxnmPmkpKappPzy5YUwtBoVWx2795Nly5dSE9PZ8yYMcyd\nOxdvb2/S0tJwd3eXgiPEUzSs40j0xmA6fNpIo23LL4do4j6KQ7+fzffzypYUwtBoterzJ598gqWl\nJevXr1e7LJCbm0vXrl25c+cOv/zyi06DioJnLKvJ5sXQsiuVSlZv3cPYgAjS76uvLWhiYoJ3Pzd8\nBrhTqJCpwWXXlrHmBsleELQa2Zw+fRovLy+N688mJib07duXU6dO6SScEK8LhUJBjw6u7Fs/h9qO\nVdXacnNzmbV0A237TODK9Xg9JRRCt7QqNubm5ty9m/cNxrS0NFmYUwgtvVulPLu+C+CrXu012o7G\nnuejLqP45eAfekgmhG5pVWyaNGnCzJkzuXz5strrV69eJTAwkI8++kgX2YR4LZmbmTF9dC82L5mE\nrbX6sy+pd+/hF7yGwRMXcjf9vp4SCpH/tCo2U6dOJTU1lXr16tG6dWs8PT1p06YNdevW5c6dO0yd\nOlXXOYV47bg0cuLgpmBaNq2r0bZmaxTNunpz4swlPSQTIv9pVWzeffddDh48yIABA8jKyuKPP/4g\nMzOTgQMHcuDAAd555x1d5xTitWRjbcm6heMJ8u2rsbLA3//e5JOe45i/4gdZf1AYPa136ixbtiwz\nZszQZRYh3kgKhYIB3dvS+MMaeI2dx7m/rqrasrNzmBzyLVFH/mDJzGGUtSmtx6RCvLwXXkFACKEb\nNd+rwp41s+j8aQONtr1HTtLYbRQ/7z2mh2RCvLqnFpt27dpx4cIF1d+f9efzzz/X+oQHDx6kW7du\nODo6YmlpyerVq9XalUolAQEBVKtWjbJly9K2bVv+/PNPtT4pKSn079+fSpUqUalSJfr3709Kivoy\n7mfOnKFNmzaULVsWR0dHgoKCNJYH2bp1K87Oztja2uLs7ExkZKROsgihrWJFC+M7oCPfhYzFyqKE\nWlticioewwIY47+c+xmZekooxMt5arH57xdzbm4uSqXyqX9e5Hpyeno61atXJzAwkKJFi2q0z58/\nn9DQUIKCgtizZw82NjZ07NhRbeq1l5cXJ0+eZOPGjWzatImTJ08yYMAAVXtqaiodO3bE1taWPXv2\nEBgYyMKFC1m0aJGqT0xMDH369MHd3Z0DBw7g7u5O7969+e233/I1ixAv4zMXZw5umsdH9WpqtC1f\n9zOu3cdy9uIVPSQT4uVotYKArlSoUIFZs2bxxRdfAA8LXLVq1ejXrx/e3t4A3L9/H3t7e6ZPn46n\npyfnz5/H2dmZHTt20KDBw8sNhw8fpnXr1hw7dgx7e3siIiKYMmUKFy5cUBW02bNns2LFCs6ePYtC\nocDT05Pk5GS2bNmiytO+fXvKlClDREREvmUxZsbyZHJeXpfsOTk5LPhmKzND15KdnaPWr0hhc2aM\n7kXfrq0MYguQ1+UzNzbGkt2g7tlcuXKFuLg4XFweb0JVtGhRGjVqxNGjR4GHI5ISJUrg7Oys6tOg\nQQOKFy+u1qdhw4ZqIydXV1du3rzJlSsPfxs8duyY2nke9Xl0jPzKIsSrMDU1ZWTfTuxc6U+VinZq\nbRmZWXj7L6f78EASk1P1lFAI7Wg1G+2nn34iOTmZHj16APDvv//St29f/vzzT1xcXFi8eDElSpR4\nzlGeLy4uDkBj508bGxtu3rwJQHx8PNbW1mq/ySkUCsqUKUN8fLyqT/ny5TWO8aitSpUqxMXF5Xme\nR8fIryx5uXjx4rM+BoNiTFmf9DplL1UEvg4YzJyIrfy097ha2897j+Hc4SumDutG/VrvFmRMDa/T\nZ25MDCH780ZXWhWbOXPm0KFDB9W//fz8uHHjBr169WL9+vUEBgbm67ToJy8JKJVKjS/0Jz2vz6N7\nUM/r8+Rr+ZHlScYw5AXjGZ7n5XXNvnrB+2z8aT+jZixVW2EgIfkuQ6eFM9yzA+OHdMPcTPe7gT7p\ndf3MDZ2xZNfqMto///xDjRo1gIf3LXbt2sXMmTOZOXMmkyZN4scff8yXMHZ2Dy8TPDkqSEhIUI0w\nbG1tSUhIUJvAoFQqSUxMVOuT1zHg8UjFzs7umefJryy6EJeQzPCpYfQcNYvhU8OIT5DZb28S97ZN\nObBxLh++r/4Fo1QqCVnxAy2/HM/f/97UUzoh8qZVscnMzKRIkSLAw/sU2dnZtGjRAni4usCtW7fy\nJUzlypWxs7NT2x8nIyODw4cPq+6L1K9fn7S0NGJiYlR9YmJiSE9PV+tz+PBhMjIeL+UeFRVFuXLl\nqFy5MgD16tXT2IcnKipKdYz8yqILj7b8zcrKVm35K94sVSqW5edvZuLdz01jFH3izF807TKaNVuj\ndLobqBAvQqtiU6lSJY4cOQI8vH/j5OSEhYUFALdv36ZUqVJanzAtLY2TJ09y8uRJcnNzuXbtGidP\nnuTq1asoFAoGDRpESEgI27Zt4+zZswwePJjixYvj5uYGgIODAx9//DEjR47k2LFjxMTEMHLkSFq2\nbKkaSrq5uVG0aFEGDx7M2bNn2bZtGyEhIQwePFj1P+bAgQPZv38/wcHBXLhwgeDgYA4cOMCgQQ/3\nac+vLLogW/4KADOzQkz4qjuREdOoYGet1pZ2L4PBExfiNXYed+7KFu5C/7QqNr179yYwMJDmzZsT\nERFBz549VW3Hjh3DwcFB6xOeOHGCpk2b0rRpU+7fv09AQABNmzbF398fgOHDhzN48GDGjBlDixYt\nuHXrFps3b6Zkycfb2i5fvpyaNWvSqVMnOnfuTM2aNVm6dKmq3cLCgh9++IGbN2/SokULxowZw5Ah\nQxg6dKiqj7OzMytWrGDt2rU0btyYdevWsWLFCj788ENVn/zIoguy5a/4ryYf1iB6UzDtXDVXHvh+\nRzQfuY8m5o/zekgmxGNaP2ezYcMGjh07Rp06dfDweLzF7IgRI3B2dlZ7TehWfEIK/ovXkphyl9IW\nJfEb4oFtGcvnv/EFGcuNx7y8idmVSiWrvv8V31kR3M/IUmszNTVh7MAujPbqjKmpaX5FVfMmfuaG\nwFiy6/WhTmHYjOWHOC9vcvbzf1+j79hgTp+/rNHWsE51lgUM561y+T+B5U3+zPXJWLK/8EOdt2/f\n5urVqxp/hBCGwaFqRX79LpBBPT7TaDt8/CxN3Eax9ZdDekgm3mRaPWeTmpqKr68vP/zwA5mZeS8A\nmJSUlK/BhBAvr0hhcwJ8+uDS0InBExdyO+mOqu3O3XR6ec/hy04fE+DTh+LFiugxqXhTaFVsvL29\niYyMpEePHtSoUQNzc3Nd5xJC5INPPqrDwe/nMchvAbsPxaq1rdr8K4eP/0l40EhqO1bVU0LxptCq\n2OzZs4dp06bRr18/XecRQuQzW2tLNi6eQNjqn5gy71seZGer2i5evs4nPXyZPKIng75oi4mJQS2X\nKF4jWv1kKZVKo7gBJYS+GerqDiYmJgzp2Y5fVwdiX6WCWlvWg2z8Zn+N++AZxCUk6ymheN1pVWw6\nderEzz//rOssQhg9Q1/dobZjVfaum02vzp9otO0+FEsTt1HsOnA8j3cK8Wq0uozm4uLCuHHjSEtL\n49NPP8XSUvOZjmbNmuV7OCGMjTGs7lC8WBHmTx6ES8PaDJsaprbCwO2kO7gPmcHAL9oydeSXFDYv\n+AU9xetJq2LTvXt34OEeL2vWrFG9rlAoVCscy2w0IR6u7nAjPgkThcLgV3do/2kj6tZ6j/7jQjj0\n+1m1tiWrfyL6tzNEBI3CoWpFPSUUrxOtik1kZKSucwjxWvAb4qGxuoMhq1i2DJHhUwmO2Exg2Hpy\nch5v8X76/GWad/MmwKcPvTp/YhC7gQrjpVWxadKkia5zCPFasC1jScikQfqO8UJMTU0Z09+dZs61\n8Bo7j39vPN5W435GFiOmLWH3wVgWTBmElQGP1IRhe6F5jomJiezYsYM1a9aQnPxw1kpGRga5ubnP\neacQwtDVr+3AgY1zcWv9kUZb5O4jNO48kgPHTushmXgdaD31eeLEiVSvXh0PDw+GDh3KlStXgIf3\nc+bMmaPTkEKIgmFRsjjLA0cQNuMrSjyxssCN+CQ+95rMjIVrePAg+ylHECJvWhWb4OBgli9fjo+P\nD7t371bbkKlVq1bs3LlTZwGFEAVLoVDg8XkL9m+Yywc13lFrUyqVzFm+iTaeE7h8LX82TRRvBq2K\nzapVq/Dx8WH06NHUrl1bra1q1ar8888/OgknhNCfqpXKsXOVPyP7dtKYHHDs5AU+ch/Nhp/26Smd\nMDZaFZubN2+qbSr2X2ZmZty7dy9fQwkhDIO5mRmTh/dgy9LJlLWxUmu7m36f/uPmM2D8fFLT5DtA\nPJtWxaZcuXL8+eefebadPn2aypUr52soIYRhadagFgc3zaN183oabet/3EfTLqM5c1G2GhFPp1Wx\n6dChA7NmzeLIkSOq1xQKBZcuXSI0NJROnTrpLKAQwjBYW5VizXxf5ozvR5HC6iu/X74WR1+/xQSH\nf09OTo6eEgpDplWx8fX15b333qNNmzbUqVMHgN69e9OoUSOqVq3KyJEjdRpSCGEYFAoFXt1as2dN\nENXfraTWlpOTy7QFq+nQfyo34hL1lFAYKq2KTdGiRfnxxx9ZvHgxzs7ONG/enDp16hASEsKWLVtk\nfxsh3jDV7Suze00Q/Txaa7QdOHaaxm4j+XHP0ecex1BXyRb5T5GSkqJ8fjfxJjKWvc3zItkLzs97\njzFk0iKS8lh0tI97S2Z496ZY0cJ5vnf41DC1teQq2JXWywoMxvaZ/5exZJedkoQQr6R183oc3DSP\n+rXe1WhbsXEnLt19OH3hcp7vNYZVskX+0KrYZGVlERgYSL169ShXrhylS5dW+2Ntba3rnEIIA1bO\ntjQLJ/Zl2sgvKVTIVK3t3F9Xce0+liWrf1J7IBwerpKd+//XDH2VbPFqtFqIc+LEiYSHh/Pxxx/T\nrl07uUcjhNBgYmLCMM8OfFS/Jl6+8/jryk1VW2bWA3yDIog6HEvotKGUKW0BGN8q2eLlaVVstm3b\nxrhx4/D29tZ1HiGEkfugxrvsWz+HsYERrN6yR61t5/7faew2krAZw3Bp5GSUq2SLl6PVZbT09HTq\n1dN8mEsIIfJSolhRQqcNJSJoFKVKFFNri0tIodPAaUycu5KsBw/0lFAUNK2KTatWrTh06JCuswgh\nXjOdWzfhwMZgnJ0cNNoWrtzKJz3GcfGf63pIJgraUy+jXb58WfX3/v37M3DgQExMTPj000+xsrLS\n6F+lShVd5BNCGLnKFWz5acUMZi/byOxlm9T2v/rjz79p1tWboHFe9OjgIruBvsae+pyNlZWV2n/4\nR7NInvbDkJSUpIN4Qp+MZf5+XiR7wdMm96Hfz9J/XAjXbiVotHVs2Yh5EwdhWaq4riI+lbF+5mA8\n2Z86sgkNDS3IHCo5OTkEBASwYcMG4uLisLOzo0uXLvj6+lKo0MO4SqWSwMBAVq5cSUpKCnXr1mXO\nnDk4OjqqjpOSkoKPjw87duwAHl4KnDVrFpaWlqo+Z86cYcyYMRw/fhwrKyt69+6Nj4+PWkHdunUr\n/v7+/PPPP7z99ttMmDCBdu3aqdq1ySKEeKhR3epEbwpmxLQlbPlF/dL8DzsPcezkBZYHjKRhHfn/\n53Xz1GLTvXv3gsyhEhISQnh4OGFhYVSvXp0zZ84waNAgzM3N8fHxAWD+/PmEhoYSGhqKvb09s2bN\nomPHjhw7doySJR/O0/fy8uLatWts3LgRhULBsGHDGDBgAOvXrwcgNTWVjh070qhRI/bs2cPFixcZ\nMmQIxYoV46uvvgIgJiaGPn36MG7cONq1a0dkZCS9e/dm586dqi0XtMkihHjMslQJvp49GtfGHzA2\nIJx7GZmqtms3E2jbZyJj+rsxpr+7xjM7wni90AoCubm5nD17lujoaNLT03USKCYmhlatWtG6dWsq\nV65MmzZtaN26Nb///jvwcCQRFhbGiBEjaN++PdWrVycsLIy0tDQ2bdoEwPnz5/n1118JCQnB2dmZ\n+vXrM2/ePHbu3MnFixcB2LhxI/fv31cVtfbt2zN8+HAWL16sumQYFhbGRx99hLe3Nw4ODnh7e9Ok\nSRPCwsK0ziKE0KRQKOjZ0ZV96+dQ27GqWltubi5BSzbwWd+J/HsjXk8JRX7TutgsX76c9957j8aN\nG/P555+rvrS7d+/OkiVL8i1QgwYNiI6O5sKFCwCcO3eOAwcO8MknnwBw5coV4uLicHFxUb2naNGi\nNGrUiKNHHy78FxMTQ4kSJXB2dlY7bvHixdX6NGzYkKJFi6r6uLq6cvPmTa5cuQLAsWPH1M7zqM+j\nY2iTRQjxdPZvV+CXbwMY+uXnGm1HTpyjifsoNu+I1kMykd+0eqhz5cqV+Pr60qNHD1xcXOjdu7eq\nrWHDhmzbto2BAwfmS6ARI0aQlpaGs7MzpqamZGdn4+3tjZeXFwBxcXEA2NjYqL3PxsaGmzcfPrEc\nHx+PtbW12r0XhUJBmTJliI+PV/UpX768xjEetVWpUoW4uLg8z/PoGNpkycujQm0MjCnrkyR7wXvZ\n3L3aN8ahsg2TF64nKSVN9Xrq3Xv08Qnmhx378e7z+VMX9MwPxvqZg2Fkf94kBa2KTWhoKEOHDmXq\n1KkaGyO99957LFy48OUTPmHz5s2sW7eO8PBwqlWrxqlTp/D19aVSpUp8+eWXqn5PzopTKpUaxeVJ\nz+uT14y7551H2z7/ZQwzR8B4ZrnkRbIXvFfNbW9vTyuXxgyZtIhfDhxXa4vc8xtn/7pOeOBIPqih\nueDnqzLWzxyMJ7tWl9GuXLmicTnpkWLFinHnzp18CzRp0iSGDh1K586dqVGjBt26dWPIkCHMmzcP\nADs7OwDV6OKRhIQE1QjD1taWhIQEtUX/lEoliYmJan3yOgY8HqnY2dk98zzaZBFCaM/G2pL1i/wI\n8u1LYXMztba/rtzk057jWfD1FrVndYRx0KrYWFtb8++//+bZdunSJcqVK5dvge7du4epqfoMFFNT\nU9UPV+XKlbGzsyMqKkrVnpGRweHDh1X3aOrXr09aWhoxMTGqPjExMaSnp6v1OXz4MBkZGao+UVFR\nlCtXjsqVKwNQr149tfM86vPoGNpkEUK8GIVCwYDubdm9JgiHqhXV2h5kZzNp3io6DZzGrdvybJ8x\n0Xq5mlmzZqmtKqBQKEhMTGTx4sW0bds23wK1atWKkJAQdu7cyZUrV4iMjCQ0NJTPPvtMdd5BgwYR\nEhLCtm3bOHv2LIMHD6Z48eK4ubkB4ODgwMcff8zIkSM5duwYMTExjBw5kpYtW6qGm25ubhQtWpTB\ngwdz9uxZtm3bRkhICIMHD1ZdAhs4cCD79+8nODiYCxcuEBwczIEDBxg0aJDWWYQQL6fme1WIWjub\nPu4tNdr2HjlJY7dR7Nj3mx6SiZeh1U6dSUlJfPrpp1y/fp26dety6NAhnJ2duXDhAjY2NuzcuRML\nC4t8CXT37l1mzpzJjz/+SEJCAnZ2dnTu3BkfHx+KFCkCPH6Q8ptvvlF7kLJ69eqq4yQnJzN27Fh+\n/vlnAFq3bp3nQ53e3t4cP34cS0tLPD09GTt2rMZDnTNmzODy5cuqhzo///zxzBltshgrY7kWnBfJ\nXvB0mfvHPUf5anIoyXfSNNr6ebRm2sgvKVrk5ScPGOtnDsaTXettoe/evUtYWBh79uzh9u3blC5d\nGldXVwYPHkypUqV0nVPogbH8EOdFshc8Xee+fiuBAX4LiD52WqOt+ruViJg1Csd3K73UsY31Mwfj\nyf7cy2g5OTmcOnWKzMxM1fIvv//+O7t27cLX11cKjRCiQFQoW4atyyYzadgXmJqqf3WdvfQvLTx8\niFi/Q2M3UGEYnltsFAoFLVq04OTJkwWRRwghnsrU1JRRXp3ZudKfKhXt1NoyMrMYPXMZ3YcHkpic\nqqeE4mmeW2xMTEyoUKGCzpanEUKIF/VhrffYv2EuXT9rptH2895jNHEfxb6jp/SQTDyNVrPRPD09\nCQsLIysrS9d5hBBCK6VKFGOp/3CWBQynZPGiam0345Po0H8KU0K+5cGDbD0lFP+l1QoCaWlpXL58\nGScnJ1xdXbGzs9N4yn78+PE6CymEEE/TpW0z6td2wGvsPH479XjZFqVSSciKH9gfc4rwwJFUrZR/\nzwOKF6fVbLS8duZUO4hCIZunvYaMZZZLXiR7wdN37gcPsglasoG54d9rTBIoUawIs8f3o1u75nku\nJaXv7K/CWLJrNbJJTk7WdQ4hhHglZmaFmPBVd5o1qMWAcSHciH/8C3DavQwGTVjInkOxzPHrj0XJ\ngt8N9E33QvvZCCGEofuoXk0Ofj+Pz1w1l4zauP0AH7mPJuaP83pI9mbTamTzyP79+zl27Bg3b96k\nXLly1KtXj6ZNm+oqmxBCvBQri5J8G+zDyu93MW7WCu5nPJ7c9O+NeFr39sN3UFdG9e2ksRaj0A2t\nL6P16tWL6OhoFAoFlpaWpKSkoFQqadKkCStXrnzufR0hhChICoWC3m6f0uADR/qODebMhSuqtpyc\nXGYuWkvU4T9YFjBCjynfHFpdRhs7diwnTpxg6dKl3Lp1i7/++otbt26xZMkSYmNjGTt2rK5zCiHE\nS6n2zlvsXh3EoB6fabQd+v0sTdxGsefw05/JiUtIZvjUMHqOmsXwqWHEJ6ToMu5rS6tis2PHDiZN\nmoS7uztmZg/3mDAzM6NLly74+fmxY8cOnYYUQohXUaSwOQE+fdgQ6kcZK/UltlJS0xg75zuGTw0j\n/V6Gxnv9Q9dxIz6JrKxsbsQn4b94bUHFfq1oVWxMTEx455138myzt7fHxETmGQghDN+nH9Xl4Pfz\ncGnkpNG28vtdtPAYw8lz/6i9nnTnLib/ny5tolCQmHK3QLK+brSqEm3atGHz5s15tn3//ff5up+N\nEELokl0ZKzYtnsAM796YFVK/bX3hn+t8/MVYQr+NVG3YWNqiJLn/f24nV6mktEXJAs/8OjD19fWd\n8rxOOTk5REREsHfvXrKysoiLi+PIkSNMnz6d3377jUGDBnHz5k2uXLnClStXqFKliu6TC51LSkrC\n2tpa3zFeimQveMaUW6FQUL+2Ay2b1uXgb2dI+s9oJSc3lz2HYvn91EWaN6xN03rv88eff5H1IBtb\na0v8hnhQvFgRPaZXZyyfe76tIAAPl4eQ1QReH8byZHJeJHvBM9bc6fcyGDxhHlt/PabRZlPagrAZ\nX/Fxkzp6SKYdY/nctZr6HBkZqescQgihF8WLFWHCIDc6tWrGsKlh3Ln7eIX720l3cBs8g0E9PmPK\niJ4UNjfTY1LjplWxadKkia5zCCGEXrX/tBF13ren/7j5HD5+Vq0t7LsfOXDsNBFBo3CoWlFPCY3b\nC00jS01N5ejRo2zZsoWjR4+SmiobFAkhXh9vlbPhx4ipjB/STWM30NPnL9O8mzcrN+2S3UBfgtbF\nJigoiJo1a9K6dWs8PT1p1aoVNWvWZPbs2brMJ4QQBcrU1BSfAV3Y/vUMKpW3VWu7n5HF8GlhfDlq\nNsl3ZAr0i9Cq2Pj7+xMYGEjHjh354YcfOHToEFu2bKFDhw4EBAQQEBCg65xCCFGgnJ2qcWDjXDq3\n0nAgKcAAACAASURBVLyNELn7CE3cRhH92xk9JDNOWhWbVatWMXToUObPn0+zZs1wdHSkWbNmLFiw\ngMGDB7Ny5Upd5xRCiAJnUbI44UEjWTz9K4oXVZ/ufD0ukXZ9JzFj0RrZDVQLWhWb1NRUXF1d82z7\n+OOP5d6NEOK1pVAo6N6+Bfs3zOGDGuorqSiVSuYs20QbzwlcvnZLTwmNg1bFpm7duhw/fjzPtuPH\nj1O3bt18DSWEEIbmncrl2bnKnxF9Omrs9nns5AU+ch/Nxp/26ymd4dOq2AQFBfHdd9+xYMECrly5\nwv3797ly5Qrz58/nu+++Y/bs2eTm5qr+CCHE68jczIwpI3qyZelkytqoP+x+N/0+/caFMGD8fO6m\n39dTQsP1QisI5LV396NVA1QHVChITEzMx4hCX4zlyeS8SPaCZ6y54eWyJyanMmTSInbs+02j7e23\nyhIeOJK67+v+8zCWz12rhzp9fHzyLDRCCPGmsrYqxdoF44hYv4MJc1eSkfl4N9B/rt6iZa/x+A3x\nYFjv9rIbKFoWm3Hjxuk6hxBCGB2FQoFXt9Y0qlsdr7HzOHvpX1VbdnYOU+d/x55DsSz1H055O8Nf\nLFOXXnkjmujoaIYMGZIfWYQQwihVt6/M7jVB9OvWWqPtwLHTNHYbyU9RMXpIZjheqtj8/fffzJw5\nk1q1atGuXTu2bNmSr6Fu3brFwIEDeeedd7Czs8PZ2Zno6GhVu1KpJCAggGrVqlG2bFnatm3Ln3/+\nqXaMlJQU+vfvT6VKlahUqRL9+/cnJUV9O9czZ87Qpk0bypYti6OjI0FBQRrLUGzduhVnZ2dsbW1x\ndnbWWJRUmyxCiNdf0SKFmT2+H2sXjKO0pfqeN8l30vhieCCjZyzlfkamnhLql9bF5s6dO3zzzTe0\natWKDz/8kLlz52JpaUlwcHC+frmmpKTQsmVLlEolGzZs4OjRo8yaNQsbGxtVn/nz5xMaGkpQUBB7\n9uzBxsaGjh07cvfu4+UjvLy8OHnyJBs3bmTTpk2cPHmSAQMGqNpTU1Pp2LEjtra27Nmzh8DAQBYu\nXMiiRYtUfWJiYujTpw/u7u4cOHAAd3d3evfuzW+//fZCWYQQb47WzetxcNM8mjnX0miL2LCTFh4+\nnL5wueCD6dkzZ6Pl5uby66+/sm7dOn7++WcyMjIoV64cn332GeHh4URGRtK4ceN8DTRt2jQOHjzI\nzp0782xXKpVUq1aNfv364e3tDcD9+/ext7dn+vTpeHp6cv78eZydndmxYwcNGjQA4PDhw7Ru3Zpj\nx45hb29PREQEU6ZM4cKFCxQtWhSA2bNns2LFCs6ePYtCocDT05Pk5GS1kVv79u0pU6YMERERWmUx\nZsYyyyUvkr3gGWtu0E323NxcFq3cxrSFq8nOzlFrK2xuxrRRX9Lfo80rT74yls/9qSObCRMm4Ojo\nSLdu3dixYwefffYZ33//PadPn2b8+PE6W/X0p59+om7dunh6evLuu+/SpEkTli1bpjrflStXiIuL\nw8XFRfWeokWL0qhRI44ePQo8HJGUKFECZ2dnVZ8GDRpQvHhxtT4NGzZUFRoAV1dX1Y6jAMeOHVM7\nz6M+j46hTRYhxJvJxMSEYZ4d2PVtwP/au/fwmK798ePvJJIUwchEJhdCNBES1C0XqqXiUqIcl1Cl\nWkpUKNITl/PluB1NxC2UiNLoSV1KRXsi6tKLVFPSJqpKc1ApcVplIiKIu2T//vDLsCUklZkko5/X\n88zzyF5r9v6sPdt8Zq1ZszaN3ZxVZTdv3Wbq/DhefiuCnNxLlRRhxXrobLSYmBgsLCzo1q0bsbGx\n2NvbG8pMOQ06KyuLuLg4QkNDmTRpEkeOHGHq1KkAhISEoNfrAVTDakV/nz17FoDs7Gy0Wm2x3/84\nODiQnZ1tqOPi4lJsH0VljRo1Qq/Xl3icon2UJZaSnDhxogxnomowp1gfJLFXPHONG0wXu50NrI0Y\ny6K4RLYn/6Aq2/3ND/j/bQJzJgzC/5kmj32MqnDeS+tdPTTZDBs2jMTERD7//HPatWvHgAEDePnl\nl02+NE1hYSGtW7dm1qxZADzzzDOcPHmS999/n5CQEEO9BxNeST8ufVBpdYp6T6XVeXBbWerczxy6\nvGA+3fOSSOwVz1zjhoqJff2y5mzd+S1h/1rF5fxrhu0X8q4wfm4cb73Wl39OeAUb6z93N1BzOe8P\nHUZbvnw5v/zyC6tXr6ZVq1asXbuWbt264efnx7Jly0zWu9HpdHh5eam2NWnShN9//91QDhh6F0Vy\ncnIMPQxHR0dycnJUQ32KonDhwgVVnZL2Afd6Kjqd7pHHKUssQghRZEDPjqRsWYJ/K69iZcvjE+k2\n7B9kZv1RCZGZ3iNnoz311FMEBwfzySefkJGRwcyZM7GysiI6OhpFUZgzZw6bN2/mxo0bRgsoICCA\nzMxM1bbMzEwaNGgAQMOGDdHpdCQnJxvKb9y4QWpqquE7Gj8/P/Lz80lLuzevPS0tjatXr6rqpKam\nqmJPTk7G2dmZhg0bAuDr66s6TlGdon2UJRYhhLhfQ1dHPls7jyljBmFpqX4L/unoSToNDmf9f756\n4u4GajVt2rTZZaloZ2dHQEAAo0aNonv37tjY2LB//362bNnCmjVrCAsLM0pA9evXJyoqCktLS5yc\nnNi7dy/z5s0jLCyMtm3bYmFhQUFBAdHR0Xh4eFBQUMD06dPR6/UsXboUW1tbHBwcOHDgAAkJCbRs\n2ZIzZ84QFhZGmzZtDNOfn376aT744AOOHDmCp6cnqampzJw5k0mTJhkShbOzMxEREVhbW6PVaomP\nj2fDhg0sW7YMFxeXMsViznJzc9FqzfNXzxJ7xTPXuKHiY7e0tOQ5v+Y859ucvd8fVg2r3b5zhx3J\n6fxy6nc6BzzDU7Y2j9yXuZz3Mi3E+TC3b99m586dbNq0iY0bNxotqN27dzN37lwyMzOpX78+o0eP\nZsyYMYahO0VRmD9/Pv/+97/Jy8ujbdu2LFq0CG9vb8M+Ll68yNSpU9m5cycAPXv2ZMGCBWg0GkOd\njIwMwsPDOXjwIBqNhhEjRjB16lTVEGFiYiLz5s0jKysLd3d3ZsyYQZ8+fQzlZYnFXJnLWHBJJPaK\nZ65xQ+XGnnc5n4lzYkn8IrVYWX1nB96fH0ZA62YPfb65nPdyJRvxZDOXi7gkEnvFM9e4ofJjVxSF\ndZ9+xbT5cVx7YIUBS0tLpowJJnz0QKpVK76gZ2XHXlblXhtNCCFE+VhYWDC8f1f2bl5Ey6buqrLC\nwkLmx26m9xv/5H9/ZD9kD1WfJBshhKgiPN1d+WL9fMYP71Os7Lsfj9Ex+G0+3b2vEiIrP0k2QghR\nhdjaWDMv/HW2xv4TR61GVXb5yjVGTF7M+Fkx5F8zr7uBSrIRQogqKPDZ1uxLWEL359oUK1v/6Vd4\ndx3NS6Nm8k7sVrJz8krYQ9UiyUYIIaqoeloNm1dMZ/7UN7CxVi/4cjn/Gt+mZ3Do6CneiTHebGBT\nkWQjhBBVmIWFBW8ODeKrjVF4Na6vKlMUhawz5/ksOZ1z53MrKcKykWQjhBBmoIWXO8kfLWRkcI9i\nZTm5l3h24Nvs/uZACc+sGiTZCCGEmahR3ZYl/xzDijnjsLVRL9h54eJlBo+PYMr897lx81YlRfhw\nkmyEEMLMDOsXyMHtMXT0bV6sbPXGHXR5ZSpHM/9XCZE9nCQbIYQwQ65ODiSunkXoKz2wslK/lf/3\nxGleGDKFuM27qsyCnpJshBDCTFlZWTFiQBd2x0fQqL5OVXbj5i3+/s5qhoVFkZt3pZIivEeSjRBC\nmLl2LZvwzceLGdS7U7Gyz/ak8ezAMPZ+f6QSIrtHko0QQjwBatvVYHXERN6LmEitmtVVZWezc/lb\nyGzmLFvP7dt3KiU+STZCCPEEGdy7E998vJi2zdUrQSuKQnTcJ/R47f849du5Co9Lko0QQjxh3Bs4\nsSv+Hf4+aoDq/lwAB3/O5Lngt9mU9HWFxiTJRgghnkDW1tX454ShbHt/Di6O9qqy/Gs3eHP6uwx/\newGXrlytkHgk2QghxBPsOd/mfJsQTVAXv2Jl2778jucHhZP+03GTxyHJRgghnnD2mlqsj55Ki6bu\nWFqq3/ZPn9Hz4uvTWbQ6gYKCApPFIMlGCCH+AiwsLGjj44F/q6bYPTBbraCgkHkrNtJn9Gx+P5dj\nkuNLshFCiL+I6eOG4NXYlef9WtDMw61Y+b4DGXQc+DbbvvzO6MeuVnoVIYQQTwJHBw1LZ441/P15\nyg+EzlhOzsXLhm15l/N5Y8oSDn4WQwPnekY7tvRshBDiL6r7c23ZtzWaLh1aqbbPnPCKURMNSM9G\nCCHMlj7nIu/EbqUAS+zr1GL6uCE4Omj+1D50DnVJWDmDleu3M2fpejr6+jBueB+jxyrJRgghzFRE\nzCbO517Gzs6OP7JziVj5kWqYrKwsLS0ZP7wPHdv54FSvbrEZa8YgyUYIIcxU7qUrhhUCLC0suFDO\n1Z1beT9tjLBKJN/ZCCGEmbKvU8twv5pCRcG+Tq1KjujhJNkIIYSZmj5uCI72tbGxqYaLoz3Txw2p\n7JAeSobRhBDCTDk6aPi/sQPw9PQsvXIlk56NEEIIk6vyyWbx4sVoNBomT55s2KYoCpGRkTRt2hQn\nJyeCgoI4evSo6nl5eXmEhITg5uaGm5sbISEh5OXlqepkZGTQq1cvnJycaNasGVFRUcXu152YmIi/\nvz+Ojo74+/uTlJSkKi9LLEIIYQ70OReZOCeWV99ewMQ5sWTn5JX+pDKq0skmPT2d+Ph4fHx8VNuX\nLVtGTEwMUVFR7Nmzh3r16tGvXz+uXLk3E2PUqFEcPnyYLVu2kJCQwOHDhxkzZoyh/PLly/Tr1w9H\nR0f27NnD/PnzWb58OStWrDDUSUtLY+TIkQQHB5OSkkJwcDCvv/46Bw4c+FOxCCGEOYiI2cQf2bnc\nunXHMJXaWKpssrl06RKjR49m+fLlaDT3fqSkKAqxsbFMmjSJvn374u3tTWxsLPn5+SQkJABw/Phx\nvvzyS5YuXYq/vz9+fn5ER0eze/duTpw4AcCWLVu4fv06sbGxeHt707dvXyZOnMjKlSsNvZvY2Fie\ne+45wsPD8fLyIjw8nI4dOxIbG1vmWIQQwlzkXrqCpRGnUt+vyiabojfwTp06qbafPn0avV5Ply5d\nDNuqV69Ohw4d+P7774G7PRI7Ozv8/f0NdQICAqhZs6aqTvv27ale/d7qp4GBgZw9e5bTp08Dd3tW\n9x+nqE7RPsoSixBCmAv7OrUoNNFU6io5Gy0+Pp6TJ0/y3nvvFSvT6/UA1KunXrenXr16nD17FoDs\n7Gy0Wq3qdqgWFhY4ODiQnZ1tqOPi4lJsH0VljRo1Qq/Xl3icon2UJZaSFPWuzIE5xfogib3imWvc\nILEDDOnlz3sffU7elWvUqVWDIb38y7zv0mbEVblkc+LECebOncvOnTuxsbF5aL0H76utKEqx5PKg\n0uoUDZ+VVufBbWWpcz9zmKYId18Lc4n1QRJ7xTPXuEFiL+IJBPi2Mcq+HlTlhtHS0tK4cOEC7du3\nR6vVotVq2bdvH++//z5arRZ7+7v30i7qXRTJyckx9DAcHR3JyclRzSxTFIULFy6o6pS0D7jXU9Hp\ndI88jk6nKzUWIYQQVTDZBAUFsX//flJSUgyP1q1bM2DAAFJSUvDw8ECn05GcnGx4zo0bN0hNTTV8\nR+Pn50d+fj5paWmGOmlpaVy9elVVJzU1lRs3bhjqJCcn4+zsTMOGDQHw9fVVHaeoTtE+GjZsWGos\nQgghquAwmkajUc0+A6hRowZ169bF29sbgLFjx7J48WI8PT3x8PBg0aJF1KxZk4EDBwLg5eVF165d\nCQsLY9myZSiKQlhYGD169DB0NwcOHEhUVBShoaGEh4eTmZnJ0qVLmTJlimEI7M0336RXr14sWbKE\n3r17s337dlJSUti1axdwd/istFiEEEJUwWRTFhMnTuT69etMnjyZvLw82rZtyyeffEKtWvdmTqxZ\ns4apU6fSv39/AHr27MmCBQsM5XXq1OHTTz8lPDycF154AY1Gw7hx4xg/fryhjr+/P2vXrmXevHlE\nRkbi7u7O2rVradeu3Z+KRQgh/uos8vLylNKrib8i+dK0cphr7OYaN0jsFcEsezZCCCGMS59zkYiY\nTeReuvLYd/18lCo3QUAIIUTFM+VSNSDJRgghBKZdqgYk2QghhMC0S9WAJBshhBDcveunq87eZHf9\nlAkCQghhpvQ5F3kndisFWJb7S31HBw1LZ441coT3SM9GCCHMVETMJs7nXi73l/qmvGlaEUk2Qghh\npnIvXTGseFKeL/VNPRMNJNkIIYTZsq9Ty7DgcHm+1Df1TDSQZCOEEGZr+rghONrXLveX+qaeiQYy\nQUAIIcyWo4OG/xs7oNzL1UwfN4SIlR9xIe/e6gHGJslGCCH+4kw9Ew1kGE0IIUQFkGQjhBDC5CTZ\nCCGEMDlJNkIIIUxOko0QQgiTk2QjhBDC5CTZCCGEMDlJNkIIIUzOIi8vT6nsIIQQQjzZpGcjhBDC\n5CTZCCGEMDlJNkIIIUxOko0QQgiTk2QjhBDC5CTZPKEiIyPRaDSqR5MmTQzliqIQGRlJ06ZNcXJy\nIigoiKNHj6r2kZeXR0hICG5ubri5uRESEkJenvre5BkZGfTq1QsnJyeaNWtGVFSU4c6Bj6tFixbF\nYtdoNAwaNKhMbTNm+0qzb98+Xn75ZZo1a4ZGo2HDhg0miaMs5zkxMRF/f38cHR3x9/cnKSnpsWO/\nffs2s2bNokOHDri4uODl5cWoUaP47bffVPsICgoq9lqMHDnSJO0ra+wAY8eOLRZX165dVXVu3rzJ\n5MmTady4MS4uLrz88sucOXNGVee3335j8ODBuLi40LhxY6ZMmcKtW7dUdb799ls6deqETqfjmWee\nYe3atQ+Nuyyxl3TtazQawsPDK6V9xiLJ5gnm6enJ8ePHDY/9+/cbypYtW0ZMTAxRUVHs2bOHevXq\n0a9fP65cuXc72FGjRnH48GG2bNlCQkIChw8fZsyYMYbyy5cv069fPxwdHdmzZw/z589n+fLlrFix\nolxxJycnq+Leu3cvFhYW/O1vfytT24zVvrK4evUq3t7ezJ8/n+rVqxcrr6jznJaWxsiRIwkODiYl\nJYXg4GBef/11Dhw48FixX7t2jZ9++onw8HD27t3Lxo0bOXPmDAMHDuTOnTuqukOHDlW9FtHR0apy\nU1xHpZ13gM6dO6vi2rJli6r8H//4B0lJScTFxbFjxw6uXLnC4MGDKSgoAKCgoIDBgweTn5/Pjh07\niIuLY9u2bUyfPt2wj6ysLAYNGoSfnx/ffPMNb7/9NlOmTCExMfGxY78/5uPHj7Np0yYA1fVfUe0z\nJvmdzRMqMjKSbdu2kZqaWqxMURSaNm3K6NGjDZ+Wrl+/jqenJ//6178YMWIEx48fx9/fn127dhEQ\nEABAamoqPXv2JD09HU9PT+Li4pg9eza//PKL4T/NwoULWbt2Lf/973+x+P/3NC+vRYsW8e6773Ls\n2DFq1KjxyLYZs31/lqurKwsWLGDo0KFGjaMs53nEiBFcvHiR//znP4Z4+vbti4ODA3FxcX869pIc\nO3aMgIAA9u3bh4+PD3C3Z+Pt7c3ChQtLfE5FXEclxT527Fhyc3PZvHlzic+5dOkSHh4exMTEGHrM\nv//+Oy1atCAhIYHAwEC++OILBg0axJEjR6hfvz4AmzdvZsKECZw4cYLatWsza9YskpKSOHjwoGHf\nb731FseOHeOLL754ZNwPi/1BEyZMYP/+/aoPDhXVPmOSns0TLCsri2bNmtGyZUtGjhxJVlYWAKdP\nn0av19OlSxdD3erVq9OhQwe+//574O4nZTs7O/z9/Q11AgICqFmzpqpO+/btVZ/OAgMDOXv2LKdP\nnzZKGxRFYd26dQwePJgaNWqU2jZjtq+8KvI8p6enq45TVMdYbQEMvTGNRqPavnXrVho3bkxAQAAz\nZsxQ9doq8zpKTU3Fw8ODtm3bMmHCBM6fP28oO3ToELdv31ads/r16+Pl5aWKy8vLy/BGXBTXzZs3\nOXTokKFOSef9xx9/5Pbt248de5H8/Hw++eQTXnvttUppnzFJsnlCtWvXjpUrV7Jlyxbeffdd9Ho9\n3bt3Jzc3F71eD0C9evVUz6lXrx7Z2dkAZGdno9VqVZ8qLSwscHBwUNUpaR9FZcaQnJzM6dOnefXV\nV8vUNsBo7SuvijzPer3+kccpr1u3bjFjxgxefPFFXF1dDduDg4NZs2YNSUlJTJ48mW3btqleq8q6\njrp27cqqVatITExk3rx5/PDDD/Tp04ebN28a9mtlZYVWqy123EfFpdVqsbKyKjX2O3fucOHChceK\n/X4JCQncvHmTIUOGVEr7jKma0fcoqoRu3bqp/m7Xrh2tWrVi48aN+Pr6AhQbnlAUpdibwoNKq1P0\npa6xhtDi4+Np06YNLVu2NGx7VNvGjx//yNj+bPuMoaLOc2nHeVx37twhJCSES5cu8dFHH6nKXn/9\ndcO/fXx8aNSoEYGBgRw6dIhWrVqVGFdJsRn7OhowYIAqrlatWtGiRQt2795Nnz59Hvq8srw2D243\n5f+B+Ph4goKCcHBwUG2vyPYZi/Rs/iLs7Oxo2rQpJ0+eRKfTAcU/Nebk5Bg+6Tg6OpKTk6OaEaQo\nChcuXFDVKWkfUPzT/OM4f/48O3bsKHEI4X73tw0wWvvKqyLPs06ne+RxHtedO3d44403yMjIIDEx\nEXt7+0fWb926NVZWVobXoipcRwDOzs64uLio4iooKCjW+3jwtXkwrgsXLlBQUFBq7NWqVSv1XJXm\n8OHD/Pjjj6Ve/2C69hmTJJu/iBs3bnDixAl0Oh0NGzZEp9ORnJysKk9NTTWMrfv5+ZGfn09aWpqh\nTlpaGlevXlXVSU1N5caNG4Y6ycnJODs707Bhw3LHvHHjRmxtbenfv3+Z2wYYrX3lVZHn2dfXV3Wc\nojrlacvt27cZMWIEGRkZJCUlGc7vo2RkZFBQUGCoWxWuI7j7Jnr27FlDXK1atcLa2lp1zs6cOWOY\n0FAU1/Hjx1XThZOTk7G1tTX02vz8/Pj6669Vx0pOTqZ169ZYW1uXK+b4+Hjc3Nzo3LlzpbXPmKym\nTZs22+h7FZVuxowZ2NjYUFhYSGZmJpMnT+bkyZNER0ej0WgoKCggOjoaDw8PCgoKmD59Onq9nqVL\nl2Jra4uDgwMHDhwgISGBli1bcubMGcLCwmjTpo1h2urTTz/NBx98wJEjR/D09CQ1NZWZM2cyadKk\ncr9hK4rCuHHj6NGjR7Epn49qW506dbCwsDBK+8oiPz+fY8eOodfrWbduHd7e3tSuXZtbt25Rp06d\nCjvPzs7OREREYG1tjVarJT4+ng0bNrBs2TJcXFz+dOw1a9bktdde4+DBg3z44YfUqlWLq1evcvXq\nVaysrLC2tubUqVOsXr2amjVrcuvWLdLS0pg0aRKurq7MmDEDS0tLk11Hj4rdysqKuXPnYmdnx507\ndzhy5AhvvfUWBQUFLFy4EFtbW5566inOnTvHmjVraN68OZcuXSIsLIzatWszZ84cLC0tadSoEUlJ\nSezZswcfHx+OHTtGeHg4wcHBvPTSSwC4u7uzdOlSzp8/T4MGDdixYweLFy9m3rx5NG3a9LGuGbg7\n9Tw0NJSQkBCeffbZYs+vqPYZVV5eniKPJ+/Rv39/xcnJSbG2tlacnZ2Vl156Sfnuu+8M5RcvXlSm\nTp2q6HQ6xdbWVunQoYOyf/9+1T5OnTqlDBo0SKlVq5ZSq1YtZdCgQUpWVpaqzr59+5T27dsrtra2\nik6nU6ZNm6ZcvHix3PFv27ZNAZSvvvrqT7fNmO0r7ZGUlKQAxR5Dhgyp8PMcHx+veHp6KtbW1kqT\nJk2UDz/88LFj/+mnn0osA5SYmBglLy9P+fnnn5UOHToodevWVWxsbBR3d3dlzJgxyqlTp0x+HT0q\n9rNnzypdunRRHBwcFGtra6V+/frKkCFDlJ9//lm1j3PnzimjR49W6tatq1SvXl3p0aNHsTpHjhxR\nevTooVSvXl2pW7euMnr0aEWv16vqbN++XWnZsqViY2OjuLm5KUuWLCnXNZOXl6esWLFCsbKyUo4e\nPVrs+RXdPmM95Hc2QgghTE6+sxFCCGFykmyEEEKYnCQbIYQQJifJRgghhMlJshFCCGFykmyEEEKY\nnCQbISrI9u3b6dmzJx4eHjg5OdG8eXNeeeUVvvzyS0OdlJQUIiMjKSwsfOzjpKWlERgYiIuLCxqN\nhsOHDwOwePFimjdvjlarpWPHjsDdFZwjIyPL1zAhykB+ZyNEBVi1ahXTpk1j2LBhBAUFUaNGDbKy\nsti9ezceHh7MnTsXuHsfoqioKMP6Wo/D39+fp556irlz51KjRg18fHw4evQogYGBTJgwgaCgIOzs\n7PDx8SE9PR0XFxfVSs5CmIKs+ixEBVixYgVBQUGqu0926tSJ1157rVy9mAcVFhZy4sQJ/v73v9Op\nUyfD9uPHjwMwcuRIGjVqZNhetAK4EKYmw2hCVICLFy8+dCFLS8u7/w2LejUADg4OhnvLF4mIiOD5\n55/Hzc2Nxo0b89JLL5Genm4o37BhA/b29hQWFrJw4UI0Gg0tWrQgKCiI0NBQ4O4CjfcPnT04jBYZ\nGYlGo+HXX39l0KBBuLq60rx5c6KiooolxUOHDtGzZ090Oh0+Pj4sXryYiIiIYjdXEwKkZyNEhWjT\npg0fffQRjRo1olevXnh4eBSrM3z4cP744w/WrVvHrl27sLKyUpWfPXuW0NBQXFxcuHbtGh9//DG9\nevUiOTmZ5s2b06NHD3bt2sWLL77Iq6++yvDhw7GxscHW1paPP/6YJUuWsG7dOpycnB66OGeRYcOG\n8corrxAaGsrOnTuJjIzE1dWVYcOGAXdXGe7bty/Ozs6sWrUKa2trVq5cyf/+9z/jnTTxRJFkxna5\nKgAAAy1JREFUI0QFiI6OZvjw4cycOZOZM2dib2/PCy+8wNChQw237nV1dTUkgXbt2hX7zmb58uWG\nfxcUFNC1a1cCAgJYt24dUVFRht4QgIuLi2qIrGjorGXLlmVatn/cuHGGxNK5c2dSUlLYunWrYVtM\nTAzXrl1j69athu97AgMDVTe5E+J+MowmRAXw8PAgJSWFzz77jPDwcFq0aMH27dvp378/CxcuLNM+\nvv76a3r37o27uztarRYHBwcyMzPJzMw0erw9evRQ/d2sWTN+//13w9/p6en4+vqqJhZUr16d7t27\nGz0W8WSQZCNEBbGysuLZZ59lxowZJCYmcujQIby9vYmKiiIvL++Rzz106BDBwcHUrFmT5cuX8+WX\nXxqGz+6/6Zix1K1bV/W3jY2N6jh6vb7Euzk6OjoaPRbxZJBkI0QlcXZ2Zvjw4dy5c4dff/31kXWT\nkpKoVq0a69evp3fv3rRr147WrVuXmqRMRafTcf78+WLbH7zNsBBFJNkIUQHuH4K634kTJ4B7PQJb\nW1sArl+/rqp37do1rKyssLCwMGzbu3fvQ/drar6+vqSnp6tuKXz9+nU+//zzSolHVH0yQUCICtCh\nQwc6duxI7969adiwIVeuXOGLL75g7dq19OvXjwYNGgDg5eUF3P1dTrdu3bCysqJ169Z07dqV2NhY\nQkNDGTp0KJmZmSxcuLDUWWWmMm7cOOLi4hgwYABTp07FxsaGmJgYbG1tVQlRiCLSsxGiAsyePZvC\nwkIiIiLo378/I0eOJC0tjdmzZ/Pee+8Z6r344ouMGjWKuLg4unXrxgsvvADcnekVFRXFd999x+DB\ng1m/fj2rVq3C3d29Utqj1WpJTExEo9Hw5ptvEh4eTufOnQkKCqJ27dqVEpOo2mS5GiGEURQUFPD8\n88+j1WrZtm1bZYcjqhgZRhNCPJZ58+bRuHFjGjRoQG5uLuvWrSMjI4MtW7ZUdmiiCpJkI4R4LBYW\nFixYsIBz585hYWGBj48PGzZsoFu3bpUdmqiCZBhNCCGEyckEASGEECYnyUYIIYTJSbIRQghhcpJs\nhBBCmJwkGyGEECYnyUYIIYTJ/T/layIzIcyiXQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1f9b85c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "apprehensions_and_wall_and_staffing.scatter('Staffing', 'Apprehensions Total', fit_line=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "901394.0"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the predicted apprension total given a staffing number using the regression line\n",
    "m_and_b = fit_line(just_app_and_staffing) # returns slope and intercept\n",
    "#Using y = mx+b\n",
    "staffing_12000 = np.round(m_and_b.item(0)*12000 + m_and_b.item(1))\n",
    "staffing_12000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1182446d8>]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lnDlzhoMHD+Lm5sZdd91lsCKwsbHB29tbt0x0cXHhqaeeYvbs2Xh5eeHm5sbMmTNp06aN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\nsDpJNkIIIaxOko0QQgirk2QjhBDC6v4PpfZFY44ESJEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118244a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "just_app_and_staffing\n",
    "predicted_y = m_and_b.item(0) * just_app_and_staffing.column(0) + m_and_b.item(1)\n",
    "apprehensions_and_wall_and_staffing.scatter('Staffing', 'Apprehensions Total')\n",
    "plots.plot(just_app_and_staffing.column(0), predicted_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use ordinary least squares linear regression as our method of prediction since the two features (apprehensions and BP staffing) seem to have a strong negative correlation (r = -.86) and since we were able to reject the null hypothesis by way of bootstrap statistical testing that the slope of the true line between apprehensions and staffing is 0, which indicates that there is not not a relationship between the two variables. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Quantitative evaluation of prediction technique and short conclusion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the chart, we can see that ~90000 seems to be a resonable number of apprehensions given a staffing of 1200 since it falls on the line of best fit. But when we investigate quantitatively, we see that linear regression may not have been the best method for predication and that there wasn't a linear relationship..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-323020.26542503, -263163.4748514 , -476597.76803887,\n",
       "       -141256.6127028 ,  157843.41349468,   85485.50950095,\n",
       "        303443.44844039,  347203.70150168,  512584.08022824,\n",
       "        142705.09594673, -157024.81155574,   70380.98949688,\n",
       "        128353.14831808,  105563.32809726,   44355.89882455,\n",
       "         34789.7836855 ,    2694.30379581,  -81909.86199564,\n",
       "       -136847.57052711, -104865.00857166,  -42975.34539406,\n",
       "         -8560.98763729, -199180.99463116])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "residuals = just_app_and_staffing.column(1) - predicted_y\n",
    "residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "217405.01715345035"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse = np.sqrt(np.mean(residuals ** 2))\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jIwPu7u7w9vYGAPTv3x8ZGRl45ZVXxG1nZGSI2/D29oarqysyMjLQp08fAEBV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\nvRrrpkyZIv7Z398fHTt2RFB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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11823e240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "just_app_and_staffing.select(0).with_column(\"residuals\", residuals).scatter(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Staffing mean:  Apprehensions Total\n",
      "424213 Staffing std. deviation:  Apprehensions Total\n",
      "964870\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ryanschaub/anaconda3/lib/python3.5/site-packages/datascience/tables.py:192: FutureWarning: Implicit column method lookup is deprecated.\n",
      "  warnings.warn(\"Implicit column method lookup is deprecated.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "print(\"Staffing mean: \", np.std(just_app_and_staffing.select(1)), \"Staffing std. deviation: \", \n",
    "      np.mean(just_app_and_staffing.select(1)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We were surprised to find that our rmse was as large as 217405, so, upon further investigation, after plotting the residual errors against the predictor variable, BP Staffing, to check whether linear regression was an appropriate prediction method, we see that there is not a non-associated \"blob\" of points present and, instead, there appears to be a pattern where the BP Staffing data seems to be higher when the residual erros approach zero. This indicates that linear regression was not the best method for prediction. Our mistake was not testing for whether linear regression was the appropriate prediction method before carrying out the prediction and assuming that a high r value of -.86 was enough grounds to justify linear regression as our prediction method."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}