FinalProject_group159.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cogs 108 - Final Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "UFO sightings have long been a source of intrigue in popular culture. From alien encounters to covert\n",
    "military operations there are many theories regarding the various sources of these sighting reports. Since 1969,\n",
    "the National UFO Reporting Center has documented over 100,000 UFO sightings. We hope to demystify this\n",
    "phenomenon with a thorough analysis of these UFO sightings in relation to common socioeconomic\n",
    "indicators (educational attainment and unemployment). If correlations can be found between UFO sighting report frequency and education / unemployment, perhaps some light might be shed on the origin these sightings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Names\n",
    "\n",
    "- Nicholas Paz\n",
    "- Christopher Wong\n",
    "- Wesley Ruan\n",
    "- Timothy Nordahl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In an arbitrary county in California, is there a relationship between the frequency of UFO sighting reports and educational attainment / unemployment rates?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background and Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Background: \n",
    "This question was of interest to our group because we all shared a fascination with science fiction, specifically the paranormal. When the idea of using UFO sightings came up, we all wanted to determine what, generally speaking, causes people to report UFO sightings. While brainstorming we all agreed that education and employment would be the most likely causes for the as a form of escapism in belief or misinterpretation of what is observed above and our project hypothesis was born as we attempted to prove just that.\n",
    "\n",
    "Prior Work: \n",
    "https://www.psychologytoday.com/us/blog/fulfillment-any-age/201206/what-psychology-can-tell-you-about-your-extraterrestrial-beliefs\n",
    "https://theconversation.com/why-are-people-starting-to-believe-in-ufos-again-61717\n",
    "\n",
    "Both of the articles linked above reference surveys and scholarly articles utilizing surveys highly relevant to the question we have outlined that we are, unfortunately, unable to access without purchasing them. \n",
    "The first article by Psychology Today discusses two studies performed by Viren Swami, Adrian Furnham, Tanja Haubner, Stefan Stieger, J. Pietschnig, & Martin Voracek measuring people's beliefs on 37 extraterrestrial topics across almost 600 people in Britain and Austria in the first study and measuring correlation between extraterrestrial beliefs paranormal beliefs, superstition, schizotypy, and demographics across over 400 people in the second study. The results of these studies, according to Psychology Today, indicate that belief in extraterrestrials is highly correspondent with political beliefs, religious beliefs, and those with more education were less likely to believe.\n",
    "\n",
    "The second article by The Conversation discusses a scholarly article authored by Christopher D. Bader, Joseph O. Baker, and F. Carson Mencken discussing the paranormal as it relates to American religion and culture. Their findings indicated that those more socially connected are less likely to report paranormal belief, as are those who receive lower income as opposed higher income.\n",
    "\n",
    "Both of these articles attempt to answer our question using survey responses from Americans and abroad rather than using data based on geographic regions as we did"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypothesis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Null Hypothesis\n",
    "\n",
    "There will not be a significant correlation between UFO sighting report frequency and educational attainment / unemployment.  As educational attainment rises and unemployment drops in a county, the frequency of UFO sighting reports will be unaffected. \n",
    "\n",
    "### Alternate Hypothesis\n",
    "\n",
    "There will be a significant correlation between UFO sighting report frequency and educational attainment / unemployment.  As educational attainment rises and unemployment drops in a county, the frequency of UFO sighting reports will decrease. \n",
    "\n",
    "## Prediction\n",
    "\n",
    "We predict that we will find a correlation between education / unemployment and the frequency of UFO sightings in a county."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- NUFORC Reports \n",
    "- https://data.world/timothyrenner/ufo-sightings/workspace/file?filename=nuforc_reports.csv\n",
    "- 112,092 observations\n",
    "\n",
    "This dataset is an accumulation of the UFO sighting reports documented by the National UFO Reporting Center in roughly the last 40 years. For each sighting it contains information regarding the date, time, shape, duration, location, etc.\n",
    "\n",
    "- Educational attainment for the U.S., States, and counties, 1970-2017\n",
    "- https://www.ers.usda.gov/data-products/county-level-data-sets/download-data/\n",
    "- 3288 observations\n",
    "\n",
    "This dataset contains information provided by the Economic Research Service regarding educational attainment statistics at a county level in the United States. It has stats on multiple levels of education; high school completion and higher learning.\n",
    "\n",
    "- Population estimates for the U.S., States, and counties, 2010-18\n",
    "- https://www.ers.usda.gov/data-products/county-level-data-sets/download-data/\n",
    "- 3276 observations\n",
    "\n",
    "This dataset contains information provided by the Economic Research Service regarding population estimate statistics at a county level in the United States. \n",
    "\n",
    "- Unemployment and median household income for the U.S., States, and counties, 2007-18\n",
    "- https://www.ers.usda.gov/data-products/county-level-data-sets/download-data/\n",
    "- 3283\n",
    "\n",
    "This dataset contains information provided by the Economic Research Service regarding unemployment statistics at a county level in the United States. It also contains some median household income statistics.\n",
    "\n",
    "Each of these datasets contains information that can be categorized at a county level. The goal of this project will be to create a single dataframe in which each county in California is associated with a frequency of UFO sighting reports as well as education and unemployment data. Conclusions will then be drawn from those observations. Population data will be used to normalize the frequency of UFO sighting reports for a given county."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from bs4 import BeautifulSoup\n",
    "import requests\n",
    "import itertools\n",
    "from sklearn import linear_model\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "sns.set()\n",
    "sns.set_context('talk')\n",
    "pd.set_option('precision', 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "us_states=[\n",
    "    \"AL\", \"AK\", \"AZ\", \"AR\", \"CA\", \"CO\", \"CT\", \"DC\", \"DE\", \"FL\", \"GA\", \n",
    "    \"HI\", \"ID\", \"IL\", \"IN\", \"IA\", \"KS\", \"KY\", \"LA\", \"ME\", \"MD\", \"MA\", \n",
    "    \"MI\", \"MN\", \"MS\", \"MO\", \"MT\", \"NE\", \"NV\", \"NH\", \"NJ\", \"NM\", \"NY\", \n",
    "    \"NC\", \"ND\", \"OH\", \"OK\", \"OR\", \"PA\", \"RI\", \"SC\", \"SD\", \"TN\", \"TX\", \n",
    "    \"UT\", \"VT\", \"VA\", \"WA\", \"WV\", \"WI\", \"WY\"\n",
    "]\n",
    "year_range = [2010,2011,2012,2013,2014,2015,2016]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a dictionary from CA cities to their respective counties\n",
    "page_link = 'https://www.counties.org/cities-within-each-county'\n",
    "page_response = requests.get(page_link, timeout=5)\n",
    "page_content = BeautifulSoup(page_response.content, \"html.parser\")\n",
    "\n",
    "ca_city_to_county = {}\n",
    "ca_counties = [] \n",
    "current_county=page_content.find('h3')\n",
    "\n",
    "for i in range(58):\n",
    "    ca_counties.append(current_county.string.strip())\n",
    "    next_county = current_county.next_sibling.next_sibling.next_sibling.next_sibling\n",
    "    cities_in_county = current_county.next_sibling.next_sibling\n",
    "    for city in cities_in_county:\n",
    "        ca_city_to_county[city.string.strip()] = current_county.string.strip()\n",
    "    current_county = next_county\n",
    "_ = ca_city_to_county.pop('',None)\n",
    "\n",
    "for i, county in enumerate(ca_counties):\n",
    "    ca_counties[i] = county.replace('City & County','County')\n",
    "    if 'County' not in county: ca_counties[i] += ' County'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract date from date_time column\n",
    "def extract_year(str_in):\n",
    "    date_string = str_in.split('T')[0]\n",
    "    [year,month,day] = date_string.split('-')\n",
    "    return int(year)\n",
    "\n",
    "# extract month from date_time column\n",
    "def extract_month(str_in):\n",
    "    date_string = str_in.split('T')[0]\n",
    "    [year,month,day] = date_string.split('-')\n",
    "    return int(month)\n",
    "    \n",
    "# extract time of day from date_time column\n",
    "def extract_time(str_in):\n",
    "    time_string = str_in.split('T')[1]\n",
    "    [hour,minute,second] = time_string.split(':')\n",
    "    return float(hour) + float(minute) / 60\n",
    "\n",
    "# get county from CA city\n",
    "def get_county(str_in):\n",
    "    try: return ca_city_to_county[str_in]\n",
    "    except: return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>date_time</th>\n",
       "      <th>shape</th>\n",
       "      <th>duration</th>\n",
       "      <th>city_latitude</th>\n",
       "      <th>city_longitude</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>time</th>\n",
       "      <th>county</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>2014-11-30T15:30:00</td>\n",
       "      <td>sphere</td>\n",
       "      <td>2-3 minutes</td>\n",
       "      <td>34.05</td>\n",
       "      <td>-118.28</td>\n",
       "      <td>2014</td>\n",
       "      <td>11</td>\n",
       "      <td>15.5</td>\n",
       "      <td>Los Angeles County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>Vallejo</td>\n",
       "      <td>CA</td>\n",
       "      <td>2013-06-30T00:00:00</td>\n",
       "      <td>other</td>\n",
       "      <td>2 minutes</td>\n",
       "      <td>38.12</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>2013</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Solano County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>825</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>2016-10-27T21:00:00</td>\n",
       "      <td>oval</td>\n",
       "      <td>2 minutes</td>\n",
       "      <td>34.05</td>\n",
       "      <td>-118.28</td>\n",
       "      <td>2016</td>\n",
       "      <td>10</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Los Angeles County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>Corona</td>\n",
       "      <td>CA</td>\n",
       "      <td>2012-04-18T21:00:00</td>\n",
       "      <td>circle</td>\n",
       "      <td>Seconds</td>\n",
       "      <td>33.86</td>\n",
       "      <td>-117.58</td>\n",
       "      <td>2012</td>\n",
       "      <td>4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Riverside County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1861</th>\n",
       "      <td>Atwater</td>\n",
       "      <td>CA</td>\n",
       "      <td>2015-11-05T16:00:00</td>\n",
       "      <td>light</td>\n",
       "      <td>4 seconds</td>\n",
       "      <td>37.35</td>\n",
       "      <td>-120.61</td>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Merced County</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             city state            date_time   shape     duration  \\\n",
       "185   Los Angeles    CA  2014-11-30T15:30:00  sphere  2-3 minutes   \n",
       "666       Vallejo    CA  2013-06-30T00:00:00   other    2 minutes   \n",
       "825   Los Angeles    CA  2016-10-27T21:00:00    oval    2 minutes   \n",
       "896        Corona    CA  2012-04-18T21:00:00  circle      Seconds   \n",
       "1861      Atwater    CA  2015-11-05T16:00:00   light    4 seconds   \n",
       "\n",
       "      city_latitude  city_longitude  year  month  time              county  \n",
       "185           34.05         -118.28  2014     11  15.5  Los Angeles County  \n",
       "666           38.12         -122.23  2013      6   0.0       Solano County  \n",
       "825           34.05         -118.28  2016     10  21.0  Los Angeles County  \n",
       "896           33.86         -117.58  2012      4  21.0    Riverside County  \n",
       "1861          37.35         -120.61  2015     11  16.0       Merced County  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create California UFO Sighting Dataframe\n",
    "df_ufo = pd.read_csv('nuforc_reports.csv')\n",
    "\n",
    "# general cleaning\n",
    "df_ufo = df_ufo.drop(columns=['summary','report_link','text','posted','stats'])\n",
    "df_ufo = df_ufo.dropna()\n",
    "df_ufo = df_ufo[df_ufo['state'].isin(us_states)]\n",
    "df_ufo = df_ufo[df_ufo['city_longitude']<-60]\n",
    "df_ufo = df_ufo[df_ufo['city_latitude']>0]\n",
    "\n",
    "# create time and day columns\n",
    "df_ufo['year'] = df_ufo['date_time'].apply(extract_year)\n",
    "df_ufo['month'] = df_ufo['date_time'].apply(extract_month)\n",
    "df_ufo['time'] = df_ufo['date_time'].apply(extract_time)\n",
    "\n",
    "# create california df\n",
    "df_ca = df_ufo[df_ufo['state']=='CA']\n",
    "df_ca = df_ca[df_ca['year'].isin(year_range)]\n",
    "df_ca['county'] = df_ca['city'].apply(get_county)\n",
    "df_ca.dropna()\n",
    "\n",
    "df_ca.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>6</th>\n",
       "      <th>FIPS</th>\n",
       "      <th>State</th>\n",
       "      <th>Area_name</th>\n",
       "      <th>Rural_urban_continuum_code_2013</th>\n",
       "      <th>Urban_influence_code_2013</th>\n",
       "      <th>Metro_2013</th>\n",
       "      <th>Civilian_labor_force_2007</th>\n",
       "      <th>Employed_2007</th>\n",
       "      <th>Unemployed_2007</th>\n",
       "      <th>Unemployment_rate_2007</th>\n",
       "      <th>...</th>\n",
       "      <th>Civilian_labor_force_2017</th>\n",
       "      <th>Employed_2017</th>\n",
       "      <th>Unemployed_2017</th>\n",
       "      <th>Unemployment_rate_2017</th>\n",
       "      <th>Civilian_labor_force_2018</th>\n",
       "      <th>Employed_2018</th>\n",
       "      <th>Unemployed_2018</th>\n",
       "      <th>Unemployment_rate_2018</th>\n",
       "      <th>Median_Household_Income_2017</th>\n",
       "      <th>Med_HH_Income_Percent_of_State_Total_2017</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>6001</td>\n",
       "      <td>CA</td>\n",
       "      <td>Alameda County</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>745198</td>\n",
       "      <td>710144</td>\n",
       "      <td>35054</td>\n",
       "      <td>4.7</td>\n",
       "      <td>...</td>\n",
       "      <td>840239</td>\n",
       "      <td>809372</td>\n",
       "      <td>30867</td>\n",
       "      <td>3.7</td>\n",
       "      <td>848215</td>\n",
       "      <td>822769</td>\n",
       "      <td>25446</td>\n",
       "      <td>3</td>\n",
       "      <td>95550</td>\n",
       "      <td>1.3e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>6003</td>\n",
       "      <td>CA</td>\n",
       "      <td>Alpine County</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>493</td>\n",
       "      <td>454</td>\n",
       "      <td>39</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>550</td>\n",
       "      <td>516</td>\n",
       "      <td>34</td>\n",
       "      <td>6.2</td>\n",
       "      <td>547</td>\n",
       "      <td>522</td>\n",
       "      <td>25</td>\n",
       "      <td>4.6</td>\n",
       "      <td>55755</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>6005</td>\n",
       "      <td>CA</td>\n",
       "      <td>Amador County</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>17703</td>\n",
       "      <td>16675</td>\n",
       "      <td>1028</td>\n",
       "      <td>5.8</td>\n",
       "      <td>...</td>\n",
       "      <td>14727</td>\n",
       "      <td>13997</td>\n",
       "      <td>730</td>\n",
       "      <td>5</td>\n",
       "      <td>14706</td>\n",
       "      <td>14117</td>\n",
       "      <td>589</td>\n",
       "      <td>4</td>\n",
       "      <td>60588</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>6007</td>\n",
       "      <td>CA</td>\n",
       "      <td>Butte County</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>101228</td>\n",
       "      <td>94489</td>\n",
       "      <td>6739</td>\n",
       "      <td>6.7</td>\n",
       "      <td>...</td>\n",
       "      <td>102230</td>\n",
       "      <td>96327</td>\n",
       "      <td>5903</td>\n",
       "      <td>5.8</td>\n",
       "      <td>102712</td>\n",
       "      <td>97619</td>\n",
       "      <td>5093</td>\n",
       "      <td>5</td>\n",
       "      <td>48634</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>6009</td>\n",
       "      <td>CA</td>\n",
       "      <td>Calaveras County</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>20725</td>\n",
       "      <td>19394</td>\n",
       "      <td>1331</td>\n",
       "      <td>6.4</td>\n",
       "      <td>...</td>\n",
       "      <td>21064</td>\n",
       "      <td>20070</td>\n",
       "      <td>994</td>\n",
       "      <td>4.7</td>\n",
       "      <td>21351</td>\n",
       "      <td>20506</td>\n",
       "      <td>845</td>\n",
       "      <td>4</td>\n",
       "      <td>58536</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "6    FIPS State         Area_name Rural_urban_continuum_code_2013  \\\n",
       "202  6001    CA    Alameda County                               1   \n",
       "203  6003    CA     Alpine County                               8   \n",
       "204  6005    CA     Amador County                               6   \n",
       "205  6007    CA      Butte County                               3   \n",
       "206  6009    CA  Calaveras County                               6   \n",
       "\n",
       "6   Urban_influence_code_2013 Metro_2013 Civilian_labor_force_2007  \\\n",
       "202                         1          1                    745198   \n",
       "203                         4          0                       493   \n",
       "204                         4          0                     17703   \n",
       "205                         2          1                    101228   \n",
       "206                         6          0                     20725   \n",
       "\n",
       "6   Employed_2007 Unemployed_2007 Unemployment_rate_2007  ...  \\\n",
       "202        710144           35054                    4.7  ...   \n",
       "203           454              39                    7.9  ...   \n",
       "204         16675            1028                    5.8  ...   \n",
       "205         94489            6739                    6.7  ...   \n",
       "206         19394            1331                    6.4  ...   \n",
       "\n",
       "6   Civilian_labor_force_2017 Employed_2017 Unemployed_2017  \\\n",
       "202                    840239        809372           30867   \n",
       "203                       550           516              34   \n",
       "204                     14727         13997             730   \n",
       "205                    102230         96327            5903   \n",
       "206                     21064         20070             994   \n",
       "\n",
       "6   Unemployment_rate_2017 Civilian_labor_force_2018 Employed_2018  \\\n",
       "202                    3.7                    848215        822769   \n",
       "203                    6.2                       547           522   \n",
       "204                      5                     14706         14117   \n",
       "205                    5.8                    102712         97619   \n",
       "206                    4.7                     21351         20506   \n",
       "\n",
       "6   Unemployed_2018 Unemployment_rate_2018 Median_Household_Income_2017  \\\n",
       "202           25446                      3                        95550   \n",
       "203              25                    4.6                        55755   \n",
       "204             589                      4                        60588   \n",
       "205            5093                      5                        48634   \n",
       "206             845                      4                        58536   \n",
       "\n",
       "6   Med_HH_Income_Percent_of_State_Total_2017  \n",
       "202                                   1.3e+02  \n",
       "203                                        78  \n",
       "204                                        84  \n",
       "205                                        68  \n",
       "206                                        82  \n",
       "\n",
       "[5 rows x 56 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create California Unemployment Dataframe\n",
    "df_ua = pd.read_excel('Unemployment.xls')\n",
    "df_ua.columns = df_ua.iloc[6]\n",
    "df_ua = df_ua[df_ua['State']=='CA']\n",
    "df_ua['Area_name'] = df_ua['Area_name'].apply(lambda name: name.replace(', CA', '')) \n",
    "df_ua['Area_name'] = df_ua['Area_name'].apply(lambda name: name.replace('County/city', 'County')) \n",
    "df_ua = df_ua[df_ua['Area_name'].isin(ca_counties)]\n",
    "df_ua.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>3</th>\n",
       "      <th>FIPS Code</th>\n",
       "      <th>State</th>\n",
       "      <th>Area name</th>\n",
       "      <th>2003 Rural-urban Continuum Code</th>\n",
       "      <th>2003 Urban Influence Code</th>\n",
       "      <th>2013 Rural-urban Continuum Code</th>\n",
       "      <th>2013 Urban Influence Code</th>\n",
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       "      <th>...</th>\n",
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       "      <th>Percent of adults with a bachelor's degree or higher, 2000</th>\n",
       "      <th>Less than a high school diploma, 2013-17</th>\n",
       "      <th>High school diploma only, 2013-17</th>\n",
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       "      <th>Bachelor's degree or higher, 2013-17</th>\n",
       "      <th>Percent of adults with less than a high school diploma, 2013-17</th>\n",
       "      <th>Percent of adults with a high school diploma only, 2013-17</th>\n",
       "      <th>Percent of adults completing some college or associate's degree, 2013-17</th>\n",
       "      <th>Percent of adults with a bachelor's degree or higher, 2013-17</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>06001</td>\n",
       "      <td>CA</td>\n",
       "      <td>Alameda County</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>217589</td>\n",
       "      <td>197371</td>\n",
       "      <td>87675</td>\n",
       "      <td>...</td>\n",
       "      <td>28</td>\n",
       "      <td>35</td>\n",
       "      <td>141961</td>\n",
       "      <td>203796</td>\n",
       "      <td>284098</td>\n",
       "      <td>509294</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>25</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>06003</td>\n",
       "      <td>CA</td>\n",
       "      <td>Alpine County</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>101</td>\n",
       "      <td>98</td>\n",
       "      <td>57</td>\n",
       "      <td>...</td>\n",
       "      <td>34</td>\n",
       "      <td>28</td>\n",
       "      <td>83</td>\n",
       "      <td>277</td>\n",
       "      <td>243</td>\n",
       "      <td>208</td>\n",
       "      <td>10</td>\n",
       "      <td>34</td>\n",
       "      <td>30</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>06005</td>\n",
       "      <td>CA</td>\n",
       "      <td>Amador County</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>2911</td>\n",
       "      <td>2314</td>\n",
       "      <td>1214</td>\n",
       "      <td>...</td>\n",
       "      <td>37</td>\n",
       "      <td>17</td>\n",
       "      <td>2978</td>\n",
       "      <td>7832</td>\n",
       "      <td>11745</td>\n",
       "      <td>6398</td>\n",
       "      <td>10</td>\n",
       "      <td>27</td>\n",
       "      <td>41</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>06007</td>\n",
       "      <td>CA</td>\n",
       "      <td>Butte County</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>24482</td>\n",
       "      <td>17743</td>\n",
       "      <td>8511</td>\n",
       "      <td>...</td>\n",
       "      <td>36</td>\n",
       "      <td>22</td>\n",
       "      <td>16411</td>\n",
       "      <td>33023</td>\n",
       "      <td>57637</td>\n",
       "      <td>38847</td>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>40</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>06009</td>\n",
       "      <td>CA</td>\n",
       "      <td>Calaveras County</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4164</td>\n",
       "      <td>2721</td>\n",
       "      <td>1127</td>\n",
       "      <td>...</td>\n",
       "      <td>40</td>\n",
       "      <td>17</td>\n",
       "      <td>3421</td>\n",
       "      <td>9450</td>\n",
       "      <td>14775</td>\n",
       "      <td>6604</td>\n",
       "      <td>10</td>\n",
       "      <td>28</td>\n",
       "      <td>43</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "3   FIPS Code State         Area name 2003 Rural-urban Continuum Code  \\\n",
       "203     06001    CA    Alameda County                               1   \n",
       "204     06003    CA     Alpine County                               8   \n",
       "205     06005    CA     Amador County                               6   \n",
       "206     06007    CA      Butte County                               3   \n",
       "207     06009    CA  Calaveras County                               6   \n",
       "\n",
       "3   2003 Urban Influence Code 2013 Rural-urban Continuum Code  \\\n",
       "203                         1                               1   \n",
       "204                         4                               8   \n",
       "205                         4                               6   \n",
       "206                         2                               3   \n",
       "207                         6                               6   \n",
       "\n",
       "3   2013 Urban Influence Code Less than a high school diploma, 1970  \\\n",
       "203                         1                                217589   \n",
       "204                         4                                   101   \n",
       "205                         4                                  2911   \n",
       "206                         2                                 24482   \n",
       "207                         6                                  4164   \n",
       "\n",
       "3   High school diploma only, 1970 Some college (1-3 years), 1970  ...  \\\n",
       "203                         197371                          87675  ...   \n",
       "204                             98                             57  ...   \n",
       "205                           2314                           1214  ...   \n",
       "206                          17743                           8511  ...   \n",
       "207                           2721                           1127  ...   \n",
       "\n",
       "3   Percent of adults completing some college or associate's degree, 2000  \\\n",
       "203                                                 28                      \n",
       "204                                                 34                      \n",
       "205                                                 37                      \n",
       "206                                                 36                      \n",
       "207                                                 40                      \n",
       "\n",
       "3   Percent of adults with a bachelor's degree or higher, 2000  \\\n",
       "203                                                 35           \n",
       "204                                                 28           \n",
       "205                                                 17           \n",
       "206                                                 22           \n",
       "207                                                 17           \n",
       "\n",
       "3   Less than a high school diploma, 2013-17  \\\n",
       "203                                   141961   \n",
       "204                                       83   \n",
       "205                                     2978   \n",
       "206                                    16411   \n",
       "207                                     3421   \n",
       "\n",
       "3   High school diploma only, 2013-17  \\\n",
       "203                            203796   \n",
       "204                               277   \n",
       "205                              7832   \n",
       "206                             33023   \n",
       "207                              9450   \n",
       "\n",
       "3   Some college or associate's degree, 2013-17  \\\n",
       "203                                      284098   \n",
       "204                                         243   \n",
       "205                                       11745   \n",
       "206                                       57637   \n",
       "207                                       14775   \n",
       "\n",
       "3   Bachelor's degree or higher, 2013-17  \\\n",
       "203                               509294   \n",
       "204                                  208   \n",
       "205                                 6398   \n",
       "206                                38847   \n",
       "207                                 6604   \n",
       "\n",
       "3   Percent of adults with less than a high school diploma, 2013-17  \\\n",
       "203                                                 12                \n",
       "204                                                 10                \n",
       "205                                                 10                \n",
       "206                                                 11                \n",
       "207                                                 10                \n",
       "\n",
       "3   Percent of adults with a high school diploma only, 2013-17  \\\n",
       "203                                                 18           \n",
       "204                                                 34           \n",
       "205                                                 27           \n",
       "206                                                 23           \n",
       "207                                                 28           \n",
       "\n",
       "3   Percent of adults completing some college or associate's degree, 2013-17  \\\n",
       "203                                                 25                         \n",
       "204                                                 30                         \n",
       "205                                                 41                         \n",
       "206                                                 40                         \n",
       "207                                                 43                         \n",
       "\n",
       "3   Percent of adults with a bachelor's degree or higher, 2013-17  \n",
       "203                                                 45             \n",
       "204                                                 26             \n",
       "205                                                 22             \n",
       "206                                                 27             \n",
       "207                                                 19             \n",
       "\n",
       "[5 rows x 47 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creat California Educational Attainment Dataframe\n",
    "df_ed = pd.read_excel('Education.xls')\n",
    "df_ed.columns = df_ed.iloc[3] \n",
    "df_ed = df_ed[df_ed['State']=='CA']\n",
    "df_ed = df_ed[df_ed['Area name'].isin(ca_counties)]\n",
    "df_ed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>State</th>\n",
       "      <th>Area_Name</th>\n",
       "      <th>Rural-urban_Continuum Code_2003</th>\n",
       "      <th>Rural-urban_Continuum Code_2013</th>\n",
       "      <th>Urban_Influence_Code_2003</th>\n",
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       "      <th>ESTIMATES_BASE_2010</th>\n",
       "      <th>...</th>\n",
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       "      <th>R_NET_MIG_2011</th>\n",
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       "      <th>R_NET_MIG_2014</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>06001</td>\n",
       "      <td>CA</td>\n",
       "      <td>Alameda County</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1510271</td>\n",
       "      <td>1510258</td>\n",
       "      <td>...</td>\n",
       "      <td>-7.1</td>\n",
       "      <td>-6</td>\n",
       "      <td>5.2</td>\n",
       "      <td>8.7</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>06003</td>\n",
       "      <td>CA</td>\n",
       "      <td>Alpine County</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1175</td>\n",
       "      <td>1175</td>\n",
       "      <td>...</td>\n",
       "      <td>61</td>\n",
       "      <td>-21</td>\n",
       "      <td>-59</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>-42</td>\n",
       "      <td>1.8</td>\n",
       "      <td>-23</td>\n",
       "      <td>61</td>\n",
       "      <td>-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>06005</td>\n",
       "      <td>CA</td>\n",
       "      <td>Amador County</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>38091</td>\n",
       "      <td>38091</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>24</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>-7.9</td>\n",
       "      <td>-8.5</td>\n",
       "      <td>6.7</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>32</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>06007</td>\n",
       "      <td>CA</td>\n",
       "      <td>Butte County</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>220000</td>\n",
       "      <td>220002</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>7.3</td>\n",
       "      <td>-1</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>7.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>7.3</td>\n",
       "      <td>11</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>06009</td>\n",
       "      <td>CA</td>\n",
       "      <td>Calaveras County</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>45578</td>\n",
       "      <td>45578</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>-4.2</td>\n",
       "      <td>-5.4</td>\n",
       "      <td>0.78</td>\n",
       "      <td>2.8</td>\n",
       "      <td>8.9</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 149 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "1     FIPS State         Area_Name Rural-urban_Continuum Code_2003  \\\n",
       "194  06001    CA    Alameda County                               1   \n",
       "195  06003    CA     Alpine County                               8   \n",
       "196  06005    CA     Amador County                               6   \n",
       "197  06007    CA      Butte County                               3   \n",
       "198  06009    CA  Calaveras County                               6   \n",
       "\n",
       "1   Rural-urban_Continuum Code_2013 Urban_Influence_Code_2003  \\\n",
       "194                               1                         1   \n",
       "195                               8                         4   \n",
       "196                               6                         4   \n",
       "197                               3                         2   \n",
       "198                               6                         6   \n",
       "\n",
       "1   Urban_Influence_Code_2013 Economic_typology_2015 CENSUS_2010_POP  \\\n",
       "194                         1                      0         1510271   \n",
       "195                         4                      5            1175   \n",
       "196                         4                      4           38091   \n",
       "197                         2                      0          220000   \n",
       "198                         6                      5           45578   \n",
       "\n",
       "1   ESTIMATES_BASE_2010  ... R_DOMESTIC_MIG_2017 R_DOMESTIC_MIG_2018  \\\n",
       "194             1510258  ...                -7.1                  -6   \n",
       "195                1175  ...                  61                 -21   \n",
       "196               38091  ...                  32                  24   \n",
       "197              220002  ...                  10                 7.3   \n",
       "198               45578  ...                  10                   2   \n",
       "\n",
       "1   R_NET_MIG_2011 R_NET_MIG_2012 R_NET_MIG_2013 R_NET_MIG_2014  \\\n",
       "194            5.2            8.7             10             11   \n",
       "195            -59             15             16            -42   \n",
       "196           -4.5           -7.9           -8.5            6.7   \n",
       "197             -1            3.6            3.5            7.1   \n",
       "198           -4.2           -5.4           0.78            2.8   \n",
       "\n",
       "1   R_NET_MIG_2015 R_NET_MIG_2016 R_NET_MIG_2017 R_NET_MIG_2018  \n",
       "194             10            3.6          -0.53           0.41  \n",
       "195            1.8            -23             61            -21  \n",
       "196             12             14             32             25  \n",
       "197            4.9            7.3             11            8.5  \n",
       "198            8.9             11             11            2.6  \n",
       "\n",
       "[5 rows x 149 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create California Population Dataframe\n",
    "df_pop = pd.read_excel('PopulationEstimates.xls')\n",
    "df_pop.columns = df_pop.iloc[1]\n",
    "df_pop = df_pop[df_pop['State']=='CA']\n",
    "df_pop = df_pop[df_pop['Area_Name'].isin(ca_counties)]\n",
    "df_pop.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize California UFO Sightings\n",
    "\n",
    "# Create scatter plot of city coordinates in CA\n",
    "df_ca.plot(\n",
    "    kind='scatter',\n",
    "    x='city_longitude',\n",
    "    y='city_latitude',\n",
    "    figsize=(10,15),\n",
    "    title='Location of Reports (California)'\n",
    ");plt.show()\n",
    "\n",
    "# Create bar graph of UFO sighting reports per county\n",
    "df_ca['county'].value_counts().plot(\n",
    "    kind='bar',\n",
    "    figsize=(20,10),\n",
    "    title='Reports by County (California)'\n",
    ");plt.show()\n",
    "\n",
    "# Create bar graph of reported shapes of UFOs sighted in CA\n",
    "df_ca['shape'].value_counts().plot(\n",
    "    kind='bar',\n",
    "    figsize=(20,10),\n",
    "    title='UFO Shape (California)'\n",
    ");plt.show()\n",
    "\n",
    "# Create a histogram of time of day of reported UFO sightings in CA\n",
    "df_ca['time'].plot(\n",
    "    kind='hist',\n",
    "    bins=48,\n",
    "    figsize=(20,10),\n",
    "    title='Time of Sighting (California)'\n",
    ");plt.show()\n",
    "\n",
    "# Create a bar graph of UFO sightings in CA by year since 2010\n",
    "df_ca['year'].value_counts().sort_index().plot(\n",
    "    kind='bar',\n",
    "    sort_columns=True,\n",
    "    figsize=(20,10),\n",
    "    title='Reports by Year (California)'\n",
    ");plt.show()\n",
    "\n",
    "# Create a bar graph of UFO sightings in CA by month\n",
    "df_ca['month'].value_counts().sort_index().plot(\n",
    "    kind='bar',\n",
    "    sort_columns=True,\n",
    "    figsize=(20,10),\n",
    "    title='Reports by Month (California)'\n",
    ");plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use county level data to create bivariate data points. We will compare ufo sighting frequncy with educational attainment and with unemployment. Then using logistic regression we will attempt to find correlations between our variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the pop estimate of a county from a year\n",
    "def get_pop_estimate(row):\n",
    "    r = df_pop.loc[df_pop['Area_Name']==row['county']]\n",
    "    return r['POP_ESTIMATE_'+str(row['year'])].tolist()[0]\n",
    "\n",
    "# get the number of ufo sightings from a county from a year\n",
    "def get_sightings_num(row):\n",
    "    sightings = df_ca[(df_ca['county']==row['county']) & (df_ca['year']==row['year'])]\n",
    "    return sightings.shape[0]\n",
    "\n",
    "def get_unemployment(row):\n",
    "    r = df_ua.loc[df_ua['Area_name']==row['county']]\n",
    "    return r['Unemployment_rate_'+str(row['year'])].tolist()[0]\n",
    "\n",
    "def get_education(row):\n",
    "    r = df_ed.loc[df_ed['Area name']==row['county']]\n",
    "    return 100.0 - r['Percent of adults with less than a high school diploma, 2013-17'].tolist()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>county</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>sightings</th>\n",
       "      <th>sightings per cap x 100,000</th>\n",
       "      <th>unemployment</th>\n",
       "      <th>education</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alameda County</td>\n",
       "      <td>2010</td>\n",
       "      <td>1513043</td>\n",
       "      <td>17</td>\n",
       "      <td>1.12</td>\n",
       "      <td>10.9</td>\n",
       "      <td>87.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amador County</td>\n",
       "      <td>2010</td>\n",
       "      <td>37884</td>\n",
       "      <td>1</td>\n",
       "      <td>2.64</td>\n",
       "      <td>14.1</td>\n",
       "      <td>89.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Butte County</td>\n",
       "      <td>2010</td>\n",
       "      <td>219945</td>\n",
       "      <td>2</td>\n",
       "      <td>0.91</td>\n",
       "      <td>13.9</td>\n",
       "      <td>88.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Colusa County</td>\n",
       "      <td>2010</td>\n",
       "      <td>21437</td>\n",
       "      <td>1</td>\n",
       "      <td>4.66</td>\n",
       "      <td>20.8</td>\n",
       "      <td>69.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Contra Costa County</td>\n",
       "      <td>2010</td>\n",
       "      <td>1052559</td>\n",
       "      <td>15</td>\n",
       "      <td>1.43</td>\n",
       "      <td>11.0</td>\n",
       "      <td>89.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                county  year      pop  sightings  sightings per cap x 100,000  \\\n",
       "0       Alameda County  2010  1513043         17                         1.12   \n",
       "2        Amador County  2010    37884          1                         2.64   \n",
       "3         Butte County  2010   219945          2                         0.91   \n",
       "5        Colusa County  2010    21437          1                         4.66   \n",
       "6  Contra Costa County  2010  1052559         15                         1.43   \n",
       "\n",
       "   unemployment  education  \n",
       "0          10.9       87.5  \n",
       "2          14.1       89.7  \n",
       "3          13.9       88.8  \n",
       "5          20.8       69.4  \n",
       "6          11.0       89.1  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create analysis dataframe\n",
    "df_anal = pd.DataFrame()\n",
    "df_anal['county'] = ca_counties * len(year_range)\n",
    "df_anal['year'] = list(itertools.chain.from_iterable([[year] * len(ca_counties) for year in year_range]))\n",
    "df_anal['pop'] = df_anal.apply(get_pop_estimate,axis=1)\n",
    "df_anal['sightings'] = df_anal.apply(get_sightings_num,axis=1)\n",
    "df_anal['sightings per cap x 100,000'] = df_anal.apply(lambda row:(row['sightings'] / row['pop'])*100000,axis=1)\n",
    "df_anal['unemployment'] = df_anal.apply(get_unemployment,axis=1)\n",
    "df_anal['education'] = df_anal.apply(get_education,axis=1)\n",
    "df_anal = df_anal[df_anal['sightings']!=0]\n",
    "degrees_freedom = df_anal.shape[0] - 2\n",
    "df_anal.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
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nndRq22mnnaZhw4Zp69atHlUFt/3rX//SsmXLdNddd3ldClzw0ksv6cCBA/r6178uSWpoaJBhGB5XBTccPHhQktSrV69W2wsLC9WxY0d16NDBi7LggPz8fBUUFCR8zpYtW7RlyxZdffXVrc79rFmzFI/H9eqrrzpdJhyQyrkfOHCg+vfv32pbJBLR9OnTdfjwYe3atcvJEuGQVM59k+PHj+tHP/qRZs+ercGDBztcGZyWyrk/cOCAlixZohtvvFEFBQU6cuQIt+KniCaUAzZt2qQhQ4YoLy+v1fYzzzxThmFwf3DIGIah2tralP9HDMFmGIbuvfdeXX755RoxYoTX5cAFK1eu1Omnn64333xT06ZN01lnnaVJkyZp/vz5On78uNflwUETJ06UJN19993avHmzdu/erWXLljXfgs8tGeHy3nvvSZJGjx7danufPn3Ut2/f5scRHrW1tZLEvwFD4JlnnlF1dbW++tWvel0KXPLuu+/q6NGjKiws1Be/+EWNHTtW48aN05e//GV9+OGHXpfna8yEckAsFlOfPn3abS8qKpIkklAhs2zZMlVXV+v222/3uhS44MUXX9SWLVv00EMPeV0KXLJjxw5VVVXp29/+tv7zP/9TI0eO1Ouvv66FCxfqyJEjuvvuu70uEQ759Kc/ra9//et67LHHtGLFiubtX/va15gBE0KxWEzSJ//ea6moqIh//4XMvn37tGjRIk2aNEk9e/b0uhw4aN++fXrwwQd16623qnv37l6XA5c0NZruuecejR49Wg888IBqamq0YMEC3XDDDXrppZeUn5/vcZX+RBPKAYcPH1anTp3abT/55JMlSUeOHHG7JHhk69at+sEPfqCzzjqLFdJCoKGhQffff7/mzp2r3r17e10OXNLY2Kj9+/frG9/4hubOnStJuvDCC9XY2Kg//elPuuWWW/gAksMGDBigSZMm6T/+4z90yimn6I033tCvf/1r9ezZU9dee63X5cFFhw8flqR2t+VLJ/4NeOjQIbdLgkfi8bi++c1vqr6+Xt/5zne8LgcOe/DBB9WzZ09dc801XpcCFzXdkl9UVKSFCxc2p5+HDBmiuXPn6vnnn9cNN9zgZYm+RRPKAZ07d9axY8fabW9qPjU1o5DbYrGYvvKVr6hHjx761a9+xW0ZIfDII4+oU6dO+tKXvuR1KXBR586dJUmf/exnW23/3Oc+p1deeUXr16/XtGnTvCgNDvvzn/+s7373u3rllVeaE9AXXnihDMPQz372M11yySXq0aOHx1XCLU1/C8xmghw5cqT5ceS+e++9V//4xz80f/58lZSUeF0OHFRZWalnnnlGjzzyiDp25KN1mDT9TZ8xY0arz3nTpk1Tjx49tHbtWppQFvhU7ACryHVTTJuERO6rr6/XTTfdpPr6ej3xxBOm0XzklpqaGv3hD3/QrFmzVFtbq507d2rnzp06cuSIjh07pp07d2r//v1elwkHNP1+FxYWttre9N+c99z19NNPa9SoUe1uwT///PPV2NiozZs3e1QZvND0t6Dp33stxWIx/v0XEgsWLNDTTz+tb33rW+2+nEDueeCBBzRy5EgNHTq0+d9+e/fulXTi34a7d+/2uEI4xerff5LUs2dPHThwwO2SAoN2rQOGDx+uJ598UgcPHmw1nHzdunXNjyN3HTlyRDfffLM++OAD/f73v9fpp5/udUlwQV1dnY4dO6b58+dr/vz57R6/4IILdNNNN+mb3/ymB9XBSaNGjdLbb7+t6upqDRw4sHl7VVWVJHErXg6rra01Pb9NaWgG04dL02IUGzZs0KhRo5q3V1dXq6qqisUqQuCpp57Sr3/9a33xi1/UjTfe6HU5cMHu3bu1efNmXXDBBe0emzt3rgoLC/XWW295UBmc1vR3vrq6utX2eDyuWCzW6n8H0BpNKAfMmDFDv/3tb7Vo0SJ98YtflHQimv3CCy9o/PjxpkPLkRuOHz+u2267Tf/85z/18MMPa9y4cV6XBJcMGDDAdBj5L3/5SzU2Nup//ud/dNppp7lfGBw3Y8YMLVy4UIsXL25egMAwDC1atEhdu3bl70AOGzJkiN566y19+OGHGjRoUPP2P//5z+rQoQO34YTMsGHDdPrpp+vZZ5/VlVdeqQ4dOkiS/vSnPykajerCCy/0uEI46eWXX9YPf/hDfe5zn9O3v/1tr8uBS+666y41NDS02rZq1So9+eSTuuuuu/gyOocNHTpUxcXFeumll3TzzTc3j9x5+eWX1dDQoKlTp3pcoX/RhHLA2LFjNWPGDM2fP1+xWEyDBg3SkiVL9O9//1s/+clPvC4PDvrpT3+qFStW6LzzztO+ffu0dOnS5sfy8vI0ffp0D6uDk7p162Z6fv/whz+oQ4cOnPscNnr0aF1++eV67LHHVFdXp5EjR+rNN9/UP/7xD33rW99iZZQcduONN+pvf/ubrr32Wl133XXq0aOH3njjDf3tb3/TNddco169enldImz0/9u796iq6vSP42+8p3hBBSREJVsdlwKKY0LkUjlqgAleCQVE854x1SQa6dKZWTKhkzcGb2UFKqQ56KBgagorW44OjjOalxxLU5O0Aw6DFzQB4feH65w4ctf4GfZ5reWKs8+zv99n73O0evx+n7169Wrg3kNHALZv386//vUvWrVqRXh4OABz5szhlVdeYfLkyQwdOpSvv/6a5ORkQkJCcHV1fWS5y8Op7rM/fvw4c+bMoU2bNjz33HPs2LHD6vznn3++wi078stX3Wfv7e1d7hzzNiwvLy+tgKzHavJnfnR0NFOnTiU0NJThw4eTm5vL+vXr6d69O0FBQY8s9186m9LS0tJHncTj6M6dO6xYsYK0tDSuXbuGwWDgzTffxMfH51GnJnVo/PjxHD58uML3nJ2drR7hLb8O48eP5/r161YFSXn8FBYWsnr1alJTU7l69SodO3Zk4sSJelLOr8Dx48eJj4/n9OnT5Ofn4+zszOjRo5k8ebJlJYw8Hipb2Xb/v9/37dvHypUrOXfuHG3btmX06NHMnDlTTYvrseo++23btvH2229Xev6GDRvw8vKqq/SkDtX0931Z5u9DamqqilD1WE0/+y+++IL4+HjOnDlD8+bNGTRoEFFRUdjZ2f1/pVrvqAglIiIiIiIiIiJ1Tk/HExERERERERGROqcilIiIiIiIiIiI1DkVoUREREREREREpM6pCCUiIiIiIiIiInVORSgREREREREREalzKkKJiIiIiIiIiEidUxFKRERERERERETqnIpQIiIijzGj0cj48eMf+Pz4+HgMBgPZ2dkPnYvBYCA6OvqhxxERERGR+klFKBEREflZXL9+nfj4eLKysh51KlJP5OTksHz5ciZPnoy3t3eNCpWpqamMGDECDw8PfHx8mDdvHnl5eRXGfvnll0ycOBFPT0969+7N5MmTOX36dK1yrM0YJpOJOXPm4O3tjYeHB6NGjWLXrl0VxhYWFhIXF4fRaMTNzY3BgwezevVqioqKapWfiIhIfdLoUScgIiIidWf37t3/b3Ndv36dlStXEhkZiZeXV7n3jx8/ToMG+vsv+cn58+dZu3YtTk5OuLu788UXX1QZn5iYSGxsLH379mXevHn88MMPJCYmcuzYMf7617/SvHlzS+yxY8cYP348jo6OvP766wAkJSURGhrK5s2bMRgM1eZXmzHy8/MJDQ0lLy+PiRMn0qFDB9LT03njjTe4desWo0ePthr7jTfeICMjg9GjR+Pp6cnRo0eJi4vju+++Y9GiRTW+hyIiIvWJilAiIiKPsSZNmjzqFCyaNm36qFP4RSoqKqKkpORXeX969OjBoUOHaNu2LXl5eTz33HOVxubl5bFixQrc3d1JTEykYcOGALi7u/PKK6+wYcMGZsyYYYmPiYmhcePGJCcn4+joCEBAQAABAQEsXryYjz76qNr8ajPG+++/T3Z2NmvWrMFoNAIwZswYQkJC+POf/4y/vz8tWrQAYP/+/WRkZPDyyy9bVn4FBwfTqlUrEhISeOmll+jdu3dtbqWIiEi9oL+OFBERqYfu3LlDfHw8fn5+9OzZkz59+hAYGMjixYut4irrCfXxxx/j5+eHm5sbL7zwAklJSWzbtg2DwVDhdrrCwkKWLVtG//79cXNzIygoiP3791vez8rKYtCgQQCsXLkSg8GAwWCw/M84VNwTynzs6NGjhIeH06tXL7y8vJg3bx4FBQXl8jh8+DAhISF4eHjw/PPPExMTwzfffIPBYCA+Pt4SV1JSQmJiIoGBgZZtVH5+fsydO7fa7U7Z2dmW8dLT0wkMDMTd3Z2BAwcSHx9PcXFxuXNycnL4/e9/z8CBA3Fzc6Nfv37Mnz+f//73v1Zx5h5b33zzDbGxsfTv3x8PDw+OHTtWZU6lpaVs2bKF4OBgPD098fT0JDAwkLi4OEvMzZs3Wb58OcHBwXh5eeHm5saQIUNYsmQJt2/fthovKysLg8HAtm3b2LhxI35+fri7u+Pn58fGjRurzMUsOTkZg8HAqlWrrI6bTCa8vb0JCAjg1q1bVY5ha2tL27ZtazRfRkYGt2/fJjw83FKAgnvfcRcXF3bs2GE5dvHiRU6cOIG/v7+leATg6OiIv78/Bw8eJDc3t8r5ajtGeno6nTp1svrON2zYkPDwcPLz861+v6SlpQEwYcIEqznNr8tei4iIyONEK6FERETqoT/+8Y9s3bqVESNG4Onpyd27d7lw4UKN+jG9//77LF26lB49ejBr1ixu377Nhx9+iJ2dXaXnREdH06hRIyZNmkRRURHr16/n1VdfZffu3XTs2JGuXbvy9ttvExsby5AhQxgyZAiAZeVHVU6fPs2MGTMYNWoUw4YN4/Dhw6SkpNCgQQMWLlxoiTty5AiTJk2idevWTJs2jZYtW7Jr1y7+/e9/lxtzzZo1/OUvf8HX15exY8fSsGFDsrOzyczMpLCwkMaNG1ebV2ZmJpcuXSIsLIz27duTmZnJypUruXz5MrGxsZa4y5cvExISQlFREWPGjKFTp05cvHiRTZs2kZWVxdatW2nZsqXV2FFRUTRr1oxJkyYBYG9vX2Uus2fPJi0tjZ49ezJjxgxatmzJt99+y549eyzbxEwmEykpKbzwwgsMGzaMRo0acfjwYT744ANOnz7Nhx9+WG7cpKQkcnNzCQkJwdbWlvT0dGJiYrh27RqRkZFV5hQWFsY//vEPVq1ahZeXF3369KGkpISoqCgKCgpITEy02h73sE6cOAGAp6dnufd69uzJzp07KSgooEWLFlXG9urVi61bt3Lq1CkGDhz4QPPdP0ZOTg4mk4nAwMAKY83jDR061PKzo6MjTk5OVrFOTk44ODhY5hYREXncqAglIiJSD+3bt4/+/fuXW/lUnfz8fFauXMkzzzzDpk2bLFvAgoOD8ff3r/Q8Ozs71q5di42NDQBeXl4EBwfzySefMGvWLNq3b8/gwYOJjY3FYDAwfPjwGud05swZPvnkE3r27AnA2LFjuXnzJtu2bSM6OtpSyFq0aBE2NjZs3rwZFxcXAEJDQytc6bVv3z66du3K2rVrrY5HRUXVOK///Oc/pKSk0KNHDwDCw8OJjIxk27ZthISEWIoLCxcupLi4mNTUVDp06GA539/fn5CQEBITE/ntb39rNbZ521WjRtX/p9inn35KWloaQUFBLF682KqvVklJieVnFxcXPv/8c6sCW1hYGCtWrGDNmjUcP34cDw8Pq7HPnz/Prl27LHmHhoYSGhrKmjVrGDNmjNX1VCQmJoZTp04RFRXF9u3bSUpK4vDhw8yfP59u3bpVe221kZOTA2C1KsnM0dGR0tJScnJycHV1tcQ6ODhUGAv3inY1ma8mY1SXW9kY889PP/10hfM6Ojryww8/VJmbiIhIfaXteCIiIvWQra0tZ8+e5euvv67VeQcPHuTOnTuMGzfOqgeRvb19has4zCIiIiwFKAAPDw+aN2/OxYsXa5/8fXr16mUpQJl5e3tTXFzM999/D8DVq1c5ceIEgwYNshSgABo3bkxERES5MW1tbTGZTBw5cuSB8/Lx8bEUoABsbGyYMmUKAHv37gXgxo0bfP755xiNRpo0aUJeXp7ll7OzM506deLvf/97ubEnTJhQowIU/LR166233irX2L3s6yZNmlgKUMXFxVy7do28vDx8fHyAe095u19gYKBVoalJkyZMnDiR4uJiMjMzq82tdevWLFmyhNzcXKZOncqqVaswGo2Eh4fX6Npqw7ylsKI+Z+bv8o8//lhtrPnY/VsUazPf/WOY560qt7Lz/fjjj5X2a2vatKllPBERkceNVkKJiIjUQ3PnzmXOnDkEBgbi4uKCl5cXvr6+GI3GKp9Al52dDYCrq2u59yo6Zla28GNmZ2fH//73vwfIvvqx27RpA9xbuQVV5/3UU0+VO/bmm2/y6quvEhYWhoODA3379mXgwIH4+fnVuFl7165dyx0zr165dOkScG8lUUlJCSkpKaSkpFQ4TkXX16VLlxrlAPd6E9nb29O+fftqY5OTk9m8eTNnz561WiUFcO3atXLxNbnG6vTu3ZspU6awdu1a7O3teeedd2p0Xm098cQTwL3+ZM2aNbN6786dOwCW42Vj72c+Zo6pyXzVjWGet6JYc25l52vWrFmFseb4+69PRETkcaEilIiISD00ePBgMjMz2b9/P//85z85ePAgKSkp9OnTh4SEhJ/9qXhVFbYeVtkm0/crLS19oDE9PT3Zu3cvBw4cICsri6ysLNLT01mzZg0ff/yxpcj1sMz5BQUFMXLkyApjKnrqXV0UGRISEli0aBH9+vUjIiICBwcHGjdujMlkIjo6+oHvZXUKCws5cOAAcK9oeOXKlSr7iz0o87Y4k8lE586drd4zmUzY2NhYYsz/LLsFrmwsVLx1rqL5ajJG2dwqiy27rc/BwaHS7YAmk6na3EREROorbccTERGpp9q0acPw4cOJiYkhIyODKVOmcOTIETIyMio9x9nZGbi3gud+FR2rjbLb9X5uVeX97bffVnhOixYt8PPzY8GCBezcuZMFCxZw7ty5Slcs3e/cuXPljp09exb4aXVTp06dsLGxoaioCB8fnwp//eY3v6nRfJXp0qULubm5XL16tcq47du34+zszLp16wgODmbAgAH4+PjQrl27Ss+pyTVWZ9myZZw8eZLZs2dja2vL7373u2qfivcg3N3dATh69Gi597788ktcXV0t/cOqij127Bg2NjZWWy1rO9/9Yzg4OODo6Fjhlkfzkw/N45l/NplMXLlyxSr2ypUr5OTk4ObmVmVuIiIi9ZWKUCIiIvXM3bt3uX79utUxGxsbunfvDlS87crMx8eHJk2asGnTJss2IYDc3FxL76EHZX4SWlXzPyh7e3vc3NzIyMiw2iZWVFTEhg0bysXn5eWVO2YuGNQ0v4MHD3Lq1CnL69LSUj744APg3ko0uLclccCAAezdu9dSbCirtLS0wlxqw9yr69133y23xa7s6qYGDRpgY2Njday4uJh169ZVOnZaWppVE+zCwkISExNp2LAhvr6+1ea2f/9+EhMTGTlyJFOmTCE2NpYLFy5YPdXw5zJo0CCaNWtGcnIyd+/etRw3P8WwbE+zzp074+bmxu7du61WHJlMJnbv3o23t3e1TySs7Rgvvvgi3333nVUvrbt375KUlESrVq3o37+/5fiwYcMAWL9+vdWc5tdV9WcTERGpz7QdT0REpJ4pKCigX79+GI1GunfvTtu2bcnOzmbTpk20bt26yuKBnZ0dkZGRLFu2jHHjxhEUFMTt27fZsmULXbp04eTJkw+8osnOzo7OnTuzc+dOXFxcaN++PU888QRGo/FBL9XKW2+9xaRJkxg7dizjxo2jZcuW7Nq1i6KiIsB6JdbQoUPp1asXHh4eODg4kJuby5YtW2jcuDEvvvhijebr1q0bEyZMICwsDHt7ezIyMjh48CDDhw/H09PTEveHP/yB0NBQwsPDGT58ON27d6ekpIRLly6RkZHBiBEjyj0drzYCAgL47LPPSE1N5eLFixiNRlq1asWFCxc4cOAA6enpwL2n8S1dupSpU6cyZMgQbt68SXp6epUN0F1dXQkODmbs2LG0aNGC9PR0Tpw4wcyZM3Fycqoyr5ycHKKjo+ncuTPz588HwNfXl4iICDZs2EC/fv1qdK9Xr14N/NTc+8yZM5Zjzz77LM8++ywAbdu25fXXX2fx4sVMnDiRYcOGYTKZSEhI4KmnnmLChAlW486bN4+IiAjCwsIsjdKTkpIoLS0lOjraKjYrK4uIiAhGjhzJokWLHmiMadOmsWfPHmbNmsXLL7+Mo6Oj5X7GxMRga2triR04cCC+vr4kJCRw48YNevXqxbFjx0hJSSEoKIg+ffpUe99ERETqIxWhRERE6plmzZoxYcIEDh06xKFDhygoKMDBwQGj0cj06dOr7Sczffp0bG1t2bBhA0uWLOHJJ59k8uTJlJaWcvLkyYfqV7RkyRLeeecdli9fzu3bt3F2dv7ZilB9+/Zl3bp1LF++nPfee49WrVoREBBAYGAgL730klXvpUmTJrF//342btzIjRs3aNeuHT179mT69Ol069atRvMZjUZcXV157733OH/+PO3atWPmzJnMnDnTKs7JyYmtW7eybt06MjMz2bFjB02bNsXJyQlfX18CAgIe+tqXLl1Knz59SElJYdWqVTRo0ICOHTvi7+9viTF/hikpKfzpT3/C3t6egIAARo8ezdChQyscNzw8nJs3b5KUlMTly5d58sknmTt3brmCzv1KSkqYM2cON2/e5KOPPrJsgwOYPXs2R44cYcGCBXh4eFS7rS8uLs7q9VdffcVXX30FQGRkpKUIBfc+1zZt2pCYmGgp7Pj7+xMVFWWVA9xrmL5x40ZWrFhhmaN3797ExcWV+w4UFBQA5ftE1WYMOzs7Nm3axJIlS0hOTubWrVs8/fTTLF++vML7HxcXx+rVq0lLS2P79u04Ojry2muvMW3atCrvl4iISH1mU1pXXSpFRESkXlm4cCFJSUkcOHCg2q1KvyR79uzhtddeY9myZTVe5VSV7OxsBg0aRGRk5EOtYPolM6/8iY2NZdSoUY86nUcuNjaWv/3tb3z22Wc/W9N6ERERKU89oURERH5lyvaCMsvJySE1NZVnnnnmF1uAKi0tLZd7UVERCQkJNGrUiL59+z6izKS+O3DgADNmzFABSkREpI5pO56IiMivTFZWFu+++y5DhgyhQ4cOfP/992zZsoVbt24xa9asR51epQoLC/H19SUwMBBXV1fy8/P59NNPOXPmDFOnTv3FFs/kl2/nzp2POgUREZFfBRWhREREfmU6d+6Mi4sLW7ZsIT8/n6ZNm+Lm5sb06dPx8fF51OlVqlGjRgwYMICMjAxyc3MpLS3F1dWVBQsWEBYW9qjTExEREZFqqCeUiIiIiIiIiIjUOfWEEhERERERERGROqcilIiIiIiIiIiI1DkVoUREREREREREpM6pCCUiIiIiIiIiInVORSgREREREREREalzKkKJiIiIiIiIiEid+z/6mRIqSMZObwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot relationships between UFO sightings per capita and uemployment / education\n",
    "df_anal.plot(\n",
    "    kind='scatter',\n",
    "    x='sightings per cap x 100,000',\n",
    "    y='unemployment',\n",
    "    figsize=(20,10),\n",
    "    title='UFO Sightings per Capita vs. Unemployment Percentage'\n",
    ");plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above scatter plot shows unemployment plotted with sightings per capita x 100,000. From the scatter plot, its difficult to determine correlation. While unclear, we can see a higher density of lower values for unemployment and lower values for UFO sightings. While no pattern is readily discernible from the graph, correlation cannot be determined definitively without greater numerical analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_anal.plot(\n",
    "    kind='scatter',\n",
    "    x='sightings per cap x 100,000',\n",
    "    y='education',\n",
    "    figsize=(20,10),\n",
    "    title='UFO Sightings per Capita vs. Educational Attainment'\n",
    ");plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above scatter plot shows education plotted with sightings per capita x 100,000. From the scatter plot, its difficult to determine correlation, however, a positive correlation may be possible such that as the value for education increases, sightings per capita increase as well, despite the generally heavier density towards greater educational attainment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = 0.006769214959296644 * x + 10\n",
      "r^2 = 8.625566597530998e-06\n",
      "pearson correlation coefficient: 0.0029369314934846098\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = df_anal['sightings per cap x 100,000'].values.reshape(-1,1)\n",
    "Y = df_anal['unemployment'].values\n",
    "regr = linear_model.LinearRegression().fit(X,Y)\n",
    "pred = regr.predict(X)\n",
    "\n",
    "plt.scatter(X, Y,  color='black')\n",
    "plt.plot(X, pred, color='blue', linewidth=3)\n",
    "\n",
    "print(\"y = %s * x + %d\" % (regr.coef_[0], regr.intercept_))\n",
    "print(\"r^2 = \" + str(r2_score(Y, pred)))\n",
    "print(\"pearson correlation coefficient: \" + str(df_anal['sightings per cap x 100,000'].corr(df_anal['unemployment'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above scatter plot shows unemployment plotted with sightings per capita x 100,000, the line plotted shows the best fit line to the scatter plot. From the scatter plot, its difficult to determine correlation, however with the best fit line, we can see that correlation between the two is unlikely, though its this cannot be determined definitively without greater numerical analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = 0.31168125256126417 * x + 82\n",
      "r^2 = 0.006341076318498562\n",
      "pearson correlation coefficient: 0.07963087540959714\n"
     ]
    },
    {
     "data": {
      "image/png": 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engQFBeHQoSPYvHkLJkyYiG7duult6+npCaBrXdshQ4bo/MWjiY+PD6RSKaKiojFq1ChkZDyGzZu34Mcff9Jb95ZhGE4eKgDwzTcbEBkZabCNWCzGli3fIyYmRm+bmJhYbNnyvV4RDggIwMKF/0BUVBQkEgnEYjHc3NzQr19/DBw4EMePH9dyzRSLxdi2bRvi4uLUz6T5Ny6u/cvCGv70YrEYxcVb1X2JOr2r/fz88PLLr2DQoEGYPn0apk+fhpKSEqu4kpoLy7IoKSnB9OnTkJo61inGRBAAxwCuFStWYP369WhtbQXQbs4ZOXIk1q5di82bN2PIkCFYv349lixZgoMHDyIoKEjr+vT0dLi7uyM7O9usQVqrEAvLsti9eyfWrPkI1dXtUa2qurijRqXgwIG9OitrsSyL5cuX4Isv1uLWrXbPGm9vH/z1r3Mwf/7fDQZVyeVyvPvuYnz++afq+TOVtLR0rF37Jae27c9YijVrPu70jC/igQfG6hVhY8FfKjSDsjQjd+1VlUxXBbQxYx7Ep59+jMrKk05TEtPUKGhT4UsQkRDhy9xbJXK3oaEB1dXV8PHxQUREBJYvX47PPvsMx48fh7e3N7Zs2YIFCxagsLAQUVFRWtfef//9iI+Px8qVK816AGtX4NKV5yUtLQMAUFiY3+XYpk0bUV19Hg0N9fD19cOgQWEIC4tATc151NXVgWXb4xQYRgyFon01JxZLIJVKkZaWgQ8/fA9VVfqKtBvn3ntHYdOmLWZdywWWZTF27ChOcQCakbEqc4ojPwDmRBwLYUx8ER8hwpe5NyT8nJO0+fr6YtiwYep/HzlyBLGxsfD2bv/wDxnSnkemqqpKS/ivXbuGq1evqs/bC5W4b9yYgxMnyiGT1YJhGHTv3h1tbSxkMu09h6Kirhusuo5duXIFp09XcRqDSCTSeQ9TsbVrItfgL0A7KGvy5Ik2HRcXVIFd+tAcb0pKqsuOiSA0McvlpaSkBKdOncKMGTPUx8LCwjBw4EDk5eVp2TBzcnLAMAzGjh1r+Wg5IpPJkJo6Go8//hiKi4tw6dKvaGpqwu3bt/Hbb791EX1bYa00SGlpGSgr24E5c57C5MnjMWfOUygr22E1W7GpAVbOFBlrTsSxrXHGMRGEJkZX/EePHsW6deswYsQI+Pn5oaKiAkVFRRg/fjwefvhhrbZ/+9vf8Oyzz2LWrFkYN24czp8/j++++w6ZmZkYMGCAzR5CE1VOF0MrLj4RGRmFDz54F6dOaduvi4o2Wc1+bUrwF+BckbHmRBzbGmccE0FoYlT4e/XqBYZhsH79ety+fRv9+/fHwoULMW1a12Ci0aNHY/Xq1Vi9ejXeeecd+Pv749lnn8XcuXNtMnhdGPuZzSciI9tNZqdPnwKgPwmapfZrU4K/AONBWfbMlW9OxLGtccYxaUK1DJwbe7w+gkvLPGfOUyguLuJF1K4mQ4YMQV3ddQAdXjhKpRLTp08xem12dr5FtuKysh14/PHHTLomOztfbePX3OSytTeLuWO3dI5MwR5jMneD0d6vjxCx5eauNV8fl0rLzJfkbJr4+flhz54jOH26BqdP12Dz5u0YO/YhFBbm28VWnJw8BjExQzm11RXBq6Jz6mRbpmlWYU7Esa1xxjEBjnl9CO7Y8/URnPCbktPFWYiIiNT5E85etmKxWIzc3EK1ackQMTGxyMnZpHO85uT0txSxWIycnE2Ijo4F0DWIzNB4bYUzjglwzOtDcMeer4/gau6mp2daxYXSnoSE9NZ53J62YqlUit27D6mDv86fPw+5XA53dzcEB/dEeHgEMjKmGAzKUnmzGBtvQUGeVc0uUqkUpaV77RZExtcxOer1Ibhhz9dHcMKv+pnNpw1efUnMuHyJWTMTpVgsxtix4zB27DizrnekN4tYLEZKSqpTCZazjYm8jZwbe74+ghN+1c9sW7t0csn1z6VNv34DsHFjDj79dHWXCGKZTAY/Pz80NDTojAkQiRjExMSAZVk8/fSTWtHFYWHhyMiYYrOC53v2lKG4eBNqa2vRo4c/0tMzERAQ4NTeLK6Os3sbuTr2fH0E59WjQpXTZePGHJSX/4Ta2loolQowjBiBgYFwd/fAxYv/0btR4unphba2NrS1mZdfx9vbB5MnZyA/Pxd//PGHnj50F3gHoM7sqZnhs3O7yMhoKJUKnD2rP+W1LQqe6/M6GDBgIC5c+I/Re9jTw0aomONZ4oweUHzEVl491n59rJKrx5GYm6tHtTL97ruvceDAfjQ2NqqFVCVW3bv74ubNBpuO3xqIRCL4+voiJmaoOgfQ//3f/zMo+oB1c9VwyUGjysSq/xeKffPmCBVzc1QZev3o9eGGrYTf2q+PS7lzqtBM21BSsg2Nje1fHCpBUk0sH0QfaB93fX09nn32eaxd+yWUSqVR0QdsU/DckNdBU1MT+vVrj9J2Fm8Woh1n9TYi2rHn6yM4Gz/Q1R9WKGju6K9Z85FZ11kCV6+D+Ph4vPvuB07jzUJ04IzeRkQH9np9BCn8QkrboInmjn5NTbVZ11kCV6+DurrrTuXNQmjjbN5GhDb2eH0EKfxcVqZ8xNwdfVOvk8vlWLlyKXJzN+DWrUb4+HhjypRp8Pf3J68QgrAyV66IcOSIGEePinH4sAQXL3ZY4CdMaMWyZc3o3t26fQpS+PmYtoELmj77vXuHqMtfmnKdMc6dO4vU1FFoaurYuGpoqMfSpe/D3d3dbsXVCUIIKJXAr7+2C/vhwxIcPSrGlSvct1aLi92QmMhi5kzzvAv1IUjh5+IPyzdUO/qjR6dALpdz2tj980rExg7llBdGLpd3Ef3O50UiEUQikUGvA3vnoCEIR6FUAjU1jMaKXYxr16zrM3PPPdbPnSRI4edj2gZ9qL7ANHf0ly37AHK5nNP1pngCrFy5VK/oq1AqlQgKCsa1a1e7+PGTVwghNBQK4Nw5Ri3qR4+Kcf26Sth9bNKnj48SSUkskpLa8NhjbQgIsL7HvSCFX5W2QZ8/LF+QSoNw7733ddnRz83dwOl6Nzd3+Pr64vXX/8YpnzfX+0okEmRn53eJ3CWvEIJvsCxw+jSjFvUjRyS4eVO377u18PdX/CnsLBITWQwZooC980oKNoBLM8KUr+grsh4W1hcNDfWc78M1nzfX+/r5+eH8+Uu8KTotRGjuudHaClRWMjhyRKI2x/zxh22FPThYoRb1ESNYDBqkgMi2XerEKsXW+YZUKkVJyS6sWrUUq1evMmrCcDYMecf4+HibJPzGKnapIpzl8hZO9/P2ts1PXIIwlZYW4MQJMY4cEauFvbXVtirbp48S992nxLBhciQmtmHAAKVDhN0SBCv8mit+Ed9eFRj2jpkyZRqWLn3frHuqonhVPsLm/DLKyupadpMgbEFTE3D8eIeoHzlie8nq31+BpKQ2JCa2m2P69NE2inT82rKup409EaSpx1jOC2fHWE4OuVyOsLA+Zv2KYRgGEyemYe3aL82aJy8vL1RXX4a7uzuZGxyIUOa+sRH48UfVil2CH3+0/R5RWFiHGSYxkUXPnqZJIF/m3uVMPXyP3O3fv79B7xh3d3eUlu4z6HqpD80oXlPnycvLC6Wl++Du7m5Sn4TrcvMm8MMP7aJ+9KgYJ07YXtiHDGHVm6f33MNCKnX6ta3dEaTw8z1yNy4u3mga5cGDh6C6+jJWrVqGnJwNaGy8hZYWOZqadKeAVqG5d8B1nry87sDzz7+El16aT6JPaHHjBnD0qETt7nj6tO2FPTZWtWJvw/DhLHr0sHmXgkOQws/3yN26uuuc2rm7u2PBgkVYsGARAG75vDX3DrjO07Bhd6v7IFyL2lrRn7b1dhv7uXO2F/Y772z3YU9KYnHXXazV0xUQAhV+PkfuWpLrxlj8QufIWqrIRPzvf9p5Yv7zH9s7lA8f3qY2xQwbxqJbN5t3SXRCkMLP58hdS3LddC47aSyy1t41fQn7olQCly6J1KJ+9KgYly7ZVtgZRqkW9aQkFgkJLDw9bdolYQacvHouXryIlStXory8HDdv3kTv3r0xceJEPPnkk2qb7/Tp0/Hvf/+7y7Xjxo3DihUrLBqktb16RCIG0dExEImAqqpTBlbHMVAqlTh1ynBe/+joWCxYsAjr1q1GdXV7uuSwsDA888xzUCpF+PTTj1FZeRLNzU0GV9fWqoCkKjtpLJ+3pRV/+OLdIER8fb2gVAInTjTj8OGO4KTffrOtsHt4KDWiTtsQF6eAq2378OV9b1HpxWvXruGRRx6Bj48PpkyZAl9fXxw/fhxbtmzBo48+ig8//BBAu/BfuXIF8+bN07o+JCQEw4YNs+gBLI3c7bzyVUWwAuDUJiNjAs6cqdLZf2RkFPLzt3CuactlXNaqj2vr8fDlA8BXlErg558ZdXDSkSNi1NXZVti7devIE5OUxCImRgGJIO0C5sOX971Fwv/ZZ59h2bJl+P777xEWFqY+/uKLL2L37t2oqKiAm5sbpk+fjps3b6K4uNi6o4dlNXeNrXy5ttm9eyfWrPlIa0U/d+4LeOCBVJNX51xX5PbC3PHw5QPgrCgU7XliOqJOJaivt22woZ+fUi3qiYksIiMVoPRKpsGX971Ffvy3b9/+8yYBWscDAwMhkUi6CENbWxtaWlrQzQl2bLhUstHVRpXCoKAgD3V1dejRowdkslr89NNxtLa2R+vV1/+Op556AizbBpFIBD8/P8yYMQuvvPI3uLu7g2VZ7NpVijVrPlJXyxo0KAxz576IlJRUjB6dAqVS+afYypCfnwulUon770/G/v171H0HBgYiLS0DAFBYmK8+xiXpmjE6P2dgYCCeeWau+r4sy6KsbIfW+fT0TPUYOydps3Q8QqOtTZUnpl3UDx+2fZ6YwMCOBGAjRrAID3dMnhjCuTG64j906BBmzZqF5ORkvPTSS/D19cWPP/6It956C7NmzcLzzz8PoN3Uc+LECQBAa2srpFIppk2bhqefflpdLNhczF3xm4Mu04cpeHp6Ii+vCIsWLdBrHgoPj4BE4oYzZ6q6mFe8vLzQ1NSk/rdIJFIXiLemaciYiWf16nV4/vk5Os93HqMjTVWORC4HKiraE4CpsjvK5bZV2V692oX9gQcY3HuvEkFBTSTsdkYIK35Om7tr1qzBunXr0NzcrD724osv4rnnnlP/+x//+Ad69+6NiIgINDY24vvvv8ehQ4eQmZmJxYsXW/QASqUSra26ixFIJO0rzLY2y4sVsCyLkSOTUFFRYZErqKZY2xKGYRAXF4dDh46YtNI29pwMw8DDwwPNzc0mPYe543FWmpuBY8dEOHgQOHhQhP37RVAqbauy/fu3JwC79972v/37625nzfc9YRp8mXs3N7HePGWchH/Lli3YunUrxowZAz8/P+zbtw+FhYV46623kJWVpfe6l156CaWlpSgpKcHAgQPNfgB7CX9JSQkmTZpg8X3szebNW/DQQw9xbm/r5zR1PI7i9m3g6FERDh4U4cABEY4csf3SedCgdkFXiXtoqHn34Yv4CBG+zL0h4Tdq49+2bRveeust7NixA8HBwQCAsWPHQqlUYsmSJRg3bhx8fX11Xjtz5kzs2LEDx44ds1D49f+ssubPrm+++YZ3gV8Mw+Cbb75BUtIoztfY8jnNGY+tuHULOHasw4e9vNz2v0IGD2bVWR3vuYdFcLDhdVVDg3n98MXcIET4MvcBAd56zYBGhT87OxtRUVFq0VeRnJyMwsJCnDt3DsOHD9d5bc+ePQEADea+u+0MH1M9aCZdU6Fr01Zz89WWz6lrPLbi99+BH36QqL1iTp2yvbBHR2smAGuDv7/NuyQIq2NU+Ovq6uCv492t8m5hWf0/dy5fvgwAOq93RgIDA+1mn7cWnVMq6Nu0LSrapN58tWVKC2umeJDJRPjhh45ap2fP2l7Y4+NVK/b2BGB6fswSBK8xKvwDBgzA4cOHcenSJfTt21d9fNu2bRCLxerNXHd3d63MjSzLYt26dWAYBomJibYZvZVJS8vgXaoHzZQKLMsiK2syqqoq1ec0/6oqcC1YsNBmz2lKioerV0U4fLgjAVhNje2F/a67tBOAeXvbvEuCcDqMCv+sWbNw4MABZGVl4fHHH4evry/27duHAwcOYExMihMAACAASURBVMqUKQgICMCxY8cwf/58PPLII+jbty/++OMPbN++HVVVVZg9ezb69Oljj2dxOTonXTOWX19VgUskgtFkbp6epnv1dB7P5csidYGNI0fE+PVX2ycAU4l6UhKLO+9k4eVl8y4JgncYFf677roLubm5+Pjjj5GdnY36+nqEhIRg/vz5mDVrFgCgd+/eSEhIwM6dO1FXVweGYRAWFob3338fkyZNsvlDWIvCwnyn2dzl4sffOekal/z6DMOgsLDAaDI3Ln78IlEYlMr7AIwCcB+Uyr6orAR69bLNnLi5KdUbp0lJLOLjWXh42KYvghAynLJwxMbG4vPPP9d7vk+fPvjoo4+sNihH4Sybu1FRMYiIiFCnT0hLSwcgQmFhvsGUClzGr9p8lUqlKC3dqzdVA8OI8dFH+/H11zUoK2vCtWtD0NravlejKvpl7a2QO+7QFPY2xMYq4OZm3T4IghBoWmZzcYY8/gzDICIiAmvXftnl3Nixhn3jTcmvr1AA58654cKFR9DcPAFVVRL8/rsIhYWdr0gw4yl00727UssUExVFeWIIwhGQ8GvgDHn8Lcl/rz1+BkA8gPv//G8UgO5QKIDCQugQeMsJCFBoFbEePFgBC7N1EARhA0j4NTBWwcrWdN4cNURra3ueGFXyr6NHxWhuzgBgO1fU4GCFWtTbN089IBI5fyALQRDakPBrYKyCVXj4YPz66wW0tLTovYdqM9bT01Mrt5EmqnOGNmtbWoDy8g4f9iNHxGBZ26YUCA1VaOVi79dPaTABGCUHIwh+wilXj6OxZ3ZOwHB+epZlsWLFh/jXv9bj5s0GMAyDoKAghIT0gUTihqAgKdLTM3HffaOxb99unTn8R41KwY4dB/HJJ7/hl19G4ObNWKuN3RD9+7NaK/bQUMteer6ErgsRmnvHwZe5tzg7p6Oxt/Bbgxs3gKIiN+TmuuHkSfvsYIaHd4h6YiKLnj1t+9I669y7AjT3joMvc29RIRZCN9euiVBQIMHGjW52SSUAAJGRHaKemMgiMNDpv7MJgnBCSPj1cOWKCPn57Sv2Cxfs55ry5JNyJCe34a67WlFevlOdaM3f3x8iEYOion34+uubYBgGAwYMxKJFb2Ds2IcEkf8eMJ5gjiAIy3FZU8+1ayL1pumRI2KcP28fURk6lEVmZismTmzTu2I3tQpYePhgFBVts3vlK3tUP3PV6l7G4Iu5QYjwZe5d1sZ/6xbw5pseOHJEYrdV+913tyEzsw2PPtrKObOjZn3e6upq1Nf/jra2NpP6jYkZip0799l1VWzNDwDLskhNHa3XlZZhGERHx6K0dC+t/MEf8REifJl7l7Tx//47MGJEN9TVWVvwdwH4Gp6eO7Bz5zYMHjzEorvJZDJkZEzQW5+XK6dOncTevbsMFpZ3ZrgmmOPzMxKEsyDYuMpff2VMEn0vLyWSk9vw+ustKCm5jf/85zq8vO4AIOr03xgAG9DcXIfU1FGQy+Vmj7E9jXKaxaKvoqAgzyr3cQSqBHOGYBiG189IEM6CYFf8cXEKvPlmMxYv9gQAeHsrMWJER3BSVJQCEgNPv2TJUjQ1Gf4p19TUhFWrlmHBgkVmjbF9lXvSrGt1sXVrMQ4cGIjAwED4+flDIpFAKpXyYnPUlARzhPWgzXTXRLDCDwDPP9+K559vNeva3NwNnNrl5GwwW/itvXptbW1FXV0d6uo6xLFz9S1n3Rw1JcEcYR24VGtz1vcLYRmCNfVYyq1bujeTO9PYeMvsPjQF2lZ0rr5lqFSmI0lPz+S04jc3gR2hDddqbc76fiEsg4RfDz4+3GryeXv7mN1HYKD9Vq+am6POiCpBnj47v0jEIDY2jlMCO8I4qs10fV+2zv5+ISxD0KYeS5gyZRqWLn3faLurV68iMlJlV+8BsVgChaJ9lSQW67axq+yqV6/+ZtNn6Ixqc9QZvWKMJcjrXG2MsAyu1dqc9f1CWAYJvx7mzXsVn3yyyugGb1tbV7u6JiKRSMtmCkAtbiKO6S27deuGlJSxEIvF2L9/H27ebEBbW5tJ9XAB598cNVYVjETfetBmumtDwq8Hd3d3lJbuQ2rqKKPibwiVOFdVVWLKlDSIREBV1Smtc4aIjIxCfv6WLptsc+Y8ZXLRGD5sjorFYqSkpNIq08bQZrprQzZ+AwwePATV1ZeRlDTC4nspFAqcOnUSlZUnja60fH19kZQ0Ahs25GL37kM6PSvM2eSkzVFCBW2muzYk/EZwd3fHpUu/2q0/hmHwwANjsHnzdowdO06veSM5eQxiYoZyvi9tjhKa0Ga6a0PCzwGurp3WgKtdVSwWIze3EJGRUQbbqT7YtDlKaKLaTI+Obi8CpHqf0PvFNRB0kjZT0RfFuGDBy/jf//5r8f25IpVKMXLkfZwiKFmWxe7dpViz5mNUV1dDqVRCKm2P3BWLxWo/bLFYbPUoXr4kqxIi1pp7Q9XmSPR1w5f3vctm5zQFQymBJRKJydkyLcXSdMT2SHHMlw+AEKG5dxx8mXtDws/J1HPx4kXMmzcP9913H+Li4jBu3Dh89tlnXRKUlZeXIysrC0OHDsWIESPwz3/+0yKPGHthLIrRGqIvEjGIiRmK2NihRpORafZtTgQlRWUSBGEIowp07do1ZGRkoLKyEtOmTcOiRYsQFRWFZcuW4bXXXlO3O3v2LJ588km0tLRg4cKFSE9PR15eHl5++WWbPoA1MBbFaA1iYmKRm1uInJxC9OvXn/N15kRQUlQmQRCGMOrHX1xcjJs3byI7OxthYWEAgMzMTLS0tKCkpATvvvsu3NzcsHz5cvj5+eHbb79Ft27dAAChoaF4/fXXcfToUSQmJtr2SSyASxSjuYSG9sGSJcu1bKZDh8bh4sULnAOwTI2gpKhMgiAMYXTFf/v2bQBAQECA1vHAwEBIJBKIxWI0NjbiyJEjmDhxolr0AWDChAm44447sH37disP27pwiWI0lwED/oKUlFStjbLr16+bFHVragQlRWUSBGEIo8J/1113AQBee+01nDt3Dr/99hu2bNmCoqIizJ49GwzD4Oeff0ZbWxuio6O1rnV3d8eQIUNw9uxZ24zeSqiiGK2NvshHU/szNYKSy/0pKpMgXBejpp6RI0fipZdewrp167Bnzx718RdffBHPPfccgHYPEgA6vUSkUikqKvSX1OOCSNSxk94ZiaR9Ja3vPBeeeOIJk9MfcEGhUOCJJ57oMjZT+9N3H31wub+p99SFNeaeMA+ae8fBl7k3lAqMU66e0NBQ3H333RgzZgz8/Pywb98+fPzxx/D390dWVhaam5sBtK/wO+Ph4aE+76ykpqYiISEBFRXW2+BlGAZxcXEYO3asRf0Zuo8+jN3fnHsSBCEcjAr/tm3b8NZbb2HHjh0IDg4GAIwdOxZKpRJLlizBuHHj4OnZXt5QV/3ZlpYW9XlzUSr1+8ya4lPLsix27SrFmjUfoaamGgAwaFAY5s59Ef/6Vy6mTcvQWQrRlI1fkUgEpVKJfv36o2fPXkhNTdVZzu7bb/O1snR2tvmr+oyOjsW33+ajsdFwbd/OwWc9e/ZCv371uHDhP138+Lne01h/P/xwADk52fjf/34Dy7a7vOpLRU1YF13veyqjaB/45Mevb9VvVPizs7MRFRWlFn0VycnJKCwsxLlz59QmHpXJRxOZTIagoCAzhm1dZDIZMjIexZkzp7scP3r0CMLDB0Ms1m0XN+VXgFKphLu7Oy5c+A9+/fWi3nJ2nVMQ19bKoFB0FU8uEZSGgrX69x+IyMgo3Lp1y2pRmbr606RzKmoq32d7qIwiYQpGhb+urg7+/v5djre2tteyZVkWkZGRkEgkqKqq0jIfyOVynD17FuPHj7fikE2HZVlMmZLWRfQ1OX/+nNX6U/3y0Rc4VVq6F2Kx2CopiI0Fa126dBHdu3dX92kp+vrTRDMVtebzEraBa8AevQ6ECqOuJQMGDEBVVRUuXbqkdXzbtm0Qi8WIiIiAj48PEhMTUVxcrHb/BNpjAP744w88+OCD1h+5CezZU4ZTp7qacOyNLQKn7B2sZUqwGwWK2QcK2CNMxajwz5o1688VRRbWrFmD7777DrNnz8auXbuQkZGh9u9/+eWXcePGDUyfPh05OTlYsWIF3nnnHdx3331ISkqy+YMYoqAgz6H9d+bLLz+32r1UwVqGUAVr2as/W/VN6Mbe7wGC/xg19dx1113Izc3Fxx9/jOzsbNTX1yMkJATz58/HrFmz1O2ioqLw1VdfYenSpXjvvffg7e2Nxx57DK+88opNH4AL+soiOoo9e3ZBLpfr9IIyFXsHa5ka7EaBYraHAvYIU+HkzhkbG4vPPze+Sh02bBhyc3MtHpS1CQx0rkAlhUKBVauWYcGCRRbfy94l9Lj0Z6u+Cd1QGUXCVFyiEIszlo/LydlglfvYu4Qel/5s1TehGyqjSJiKSwh/TEycxbEE1qax8ZZV7mPvEnrG+rNl34RuqIwiYSqCF36WZTFt2mNOFz3s7e1jlfvYu4Sevv40Ef0ZNULl++wDlVEkTEXwFbjKynbg8ccfs9nYNNEsdWiMpKSRCAoKwvXr17tEWOqKwExLywAAFBbm64zKtHcJPZZlcezYwS6RuwwjQVAQ9+Azwjz0Re5SGUXbw6fIXZctvThnzlMoLi6yaZEVS9Esibh69To8//wcrQhMzZQOtiqjaA58+QAIEZp7x8GXube49CKfsWWufWuhGWGZmjpKHWymOq753UxlFAmCsBTBC7+tcu3bAoVCgaamJpOLtFBUJkEQpsAPRbQAU90P+QhFZRIEYQqCF36Vq5vIUFUCnkNRmQRBmILghV/l6hYTM9Ss6/nwhUFRmQRBmILghR+AOvd9dnY+Jk2ajKioGISG9kGfPn3RvXt3SCQSiEQiMAwDDw9PhIaGYsGCRbhypQ4bNji/CYWiMgmCMAXBu3NaCsuySE0djaqqSpvvFYhEDDw920tVcn1ZRCIGMTGxDsm1zhe3NiFCc+84+DL3Lu3OaSnGoiKBDnOQ6lhkZBQiI6O1zhlCM8KytHSf2iylOq55D4rKJAjCUmjFzxFdUZHt0bRKFBYWdImUBKC3rGJgYCAGDQpHTc151NVd7xJhqbuvdAAiFBbmO01UJl9WPkKE5t5x8GXuXTpyl7AdNPeOg+becfBl7snUQxAEQagh4ScIgnAxSPgJgiBcDBJ+giAIF4OEnyAIwsUg4ScIgnAxSPgJgiBcDBJ+giAIF0PCpdHChQtRVFSk9/yBAwcQHByM6dOn49///neX8+PGjcOKFSvMHyVBEARhNTgJf2ZmJhITE7WOKZVKvP322wgJCUFwcLD6eO/evTFv3jyttiEhIVYYKkEQBGENOAl/fHw84uPjtY4dP34cTU1NGD9+vNbx7t27Y8KECdYbIUEQBGFVzLbxf//99xCJRHjkkUe6nGtra8Pt27ctGhhBEARhGzit+DvT2tqK7du3Iz4+HqGhoVrnfvnlF8TFxaG1tRVSqRTTpk3D008/7bQFz+VyOVauXIrc3A24dasRPj7eeOyxqRg6NA5FRQWorj6PhoZ6dO/ui+7du0MkEkEslkAqlSI9PRPJyWO0smOyLIs9e8pQUJCHuro6BAYG6mxnCGvcgyAIQh9mZefcu3cvnnnmGbz11luYOnWq+vg//vEP9O7dGxEREWhsbMT333+PQ4cOITMzE4sXLzZ7kEqlEq2trM5zEkm7ELa16T5viDNnziAp6R40NZmeZU8kEkGpVCIhIQHFxVsRFBSE2tpaTJgwHuXl5WAYBgqFQv1Xs50hrHEPe2HJ3BOWQXPvOPgy925uYr31QMwS/vnz56O0tBQHDx5Ejx49DLZ96aWXUFpaipKSEgwcONDUrgDYRvjlcjmCggLNEn1NGIZBXFwc9u8/iPvvvxcVFRU6K3Wp2h06dETvqp1lWYwcmWTRPewJXz4AQoTm3nHwZe4NCb/Jpp7bt29j9+7dGDlypFHRB4CZM2dix44dOHbsmAXCrz/3tbm5sZcseddi0Qfa692Wl5dj8eJ3UF5ebrTd5s1bkZKSqrNNWdkOi+9hT/iSl1yI0Nw7Dr7MfUCAN/QVADTZ8L5r1y6d3jz66NmzJwCgoaHB1K5sSm7uBqvdi2EY5OR8Z3Qfg2EYFBToL95eUJBn8T0IgiCMYbLwb926FXfccQeSk5M5tb98+TIAwN/f39SubMqtW7orepmDQqHArVu3jBZjVygUkMnq9J6vq6uz+B4EQRDGMMnUc+PGDRw9ehQPP/wwvLy8tM41NjbC3d0d7u7u6mMsy2LdunVgGKZLAJg90eUlI5GY5dCkF4lEot6E1QfDMJBKA/WeDwwMtPgeBEEQxjBJ/UpKStDW1qbTzHP69GnMnz8fjzzyCPr27Ys//vgD27dvR1VVFWbPno0+ffpYbdCmIJPJkJU1GZWVFV28ZKzJ9evGV+EKhQLp6Zl6z6enZ6KoaJNF9yAIgjCGScK/detWBAQEICkpqcu53r17IyEhATt37kRdXR0YhkFYWBjef/99TJo0yWoDNgWWZZGVNRlVVZUAoBZ7a4s+F0QiBjExsRg9OkVvm+TkMYiNjUNVVaXOMXK5B0EQhDHMcue0NwqFEtev67bJG9phLyvbgccff8ymY9NH518XsbFxyMnZBKlUavA6Q79QuN7DXvDFu0GI0Nw7Dr7MfUCANxjGSu6cfELlJWPPFb5IJEJiYhJ69uwFmawOUml71O3o0SmcfO+lUilKS/di795dKCjIM+seBEEQhhC08HPxkrE2SqUSYrEb1q790ux7iMVipKSkOoWvPkEQwsM5E+hYCZWXjD0hrxuCIJwdQQt/enqm3Vf8CoUCf/nLIMyZ8xQmTx6POXOeQlnZDrCsc4d3EwThOgh6c5dlWaSmjsapUydhn8cUwcvLE01NTU6/MWsN+LLJJURo7h0HX+be0OauoFf8YrEYGzZshKenJ6f25pqFVImQvLw80dzcAqCr62hVVSWysibTyp8gCIcjaOEHgFOnKjglY4uKisHEiWnYsCEPiYld4xR0ERgYiJEj78ekSZOxYMFCNDU1QanUbVpSKBSorKzA3r27TBo/V5qamjBnzkz06xeMXr380a9fMObMmWmVRHR8Qy6XY8mSd5GQEImwsL5ISIjEkiXvQi6XO3poDoVlWZSV7SAzJCFsUw8AzJnzFIqLi4ymQZg4MU3tidOvXzAnwfTyugO//nrV7H6sxdGjhzFx4jid5iyRSITNm0uQmDjCqn0CzvmT99y5s0hNHaXz9fPy8kJp6T4MHjzEASOzLqbOPZ/iQ5wdZ3zf68JlTT2AeYnP5PJWTvdube1YQToqwVpTU5Ne0Qfa3UsnThznEit/uVyuV/SB9rlKTR3lcit/YxHsZIZ0PQQv/FxcOju7YLq7u3G6t5tbR0I6c/qxBvPmPWd041qpVOKVV563ar/OyMqVS41+wTU1NWHVqmV2GpFzsGdPGSordRf3AWxvhiScD8ELPxeXzs6Jzx588GFO9x43rqOdOf1Ygx07tnFqV1LCrR2f4VpjISfHerUY+ADVeSA6I3jhVyU+0/fGF4kYxMbGaSU+W7nyE70lyzquE2H58tUW9WMNzDFLCRWuNRYaG2/ZeCTOBdV5IDojeOEXi8XIydmE6OhYAB0um6q/MTGxyMnZpJUDx8vLC5s3l+gvVPznhqlmTQJz+rEG5pilhIqPjzendt7ePjYeiXPhKDMk4bwIXviB9sRnW7eWIi0tAx4eHhCLJfDw8EBaWga2bi3V6c1w99334PPPv0ZgYMeHob2gzAj88st/dXrJ+Pv7Y8GChUhMTEJAQCCkUimGD0/Ehg15KC3d26Ufa7jXmWOWEipTpkzj1C4ri1s7oeAoMyThvAjenRMw3cVPJpMhI2MCzpyp0nm/yMgo5Odv0RJyU93lrOVe19TUhP79exrc4BWJRLh48WqXqmmW4mxubXK5HGFhfQxu8Hp5eaG6+rJWpTg+YsrcqyLYjdV5KC3dS9lfOeBs73t9uLQ7p6kufu2ub2l6RR8Azpw5jSlT0tQrc1Pd5azpXmeOWUqouLu7o7R0n95nVX3J8130TcVRZkjCeRG88Jvq4rdrVykqK08ave+pUycRHt4PISEB6NNHapK7nLXd6xITR+DixauYPDkDXl53QCKRwMvrDkyenIGLF6/aJHjLWRk8eAiqqy9jwYJFCA3tAz8/P4SG9sGCBYtQXX1ZEMFb5qCq85CdnY+JE9Nw772jMHFiGrKz83WaIQlhI3hTT0JCJK5cuWK0j169emPXroNISroTDQ315g9WD5pRu46M8rUmfPnJK0Ro7h0HX+bepU09XF38rl27iilT0mwi+oC2uxy51xEE4UgEL/xcXfwUCgVOnTJu4jEXTXc5cq8jCMKRCF74ubr42RpNdzlyryMIwpEIXvjnzXvV7uUXdaEZteuoKF+CIAjABYTf3d0do0c/4NAx+Pn5abnLkXsdQRCOROLoAdiDmTNnY/fuMof1v3r1ui7ucir3ur17d6GgIA8yWR2k0kCkp2di9OgUEn2CIGwGJ3fOhQsXoqioSO/5AwcOIDg4GABQXl6ODz/8EGfOnIG3tzceeughzJ8/36IAIksjd7lFLsZAqQROnz5ltQLtQo+I5ItbmxChuXccfJl7Q+6cnFb8mZmZSExM1DqmVCrx9ttvIyQkRC36Z8+exZNPPolBgwZh4cKFuHr1Kr788ktcuXIFa9eutfAxzEdlWtGXIkFlWgHQpU1nRCKR0fz3ne8rRNEnCIK/cBL++Ph4xMfHax07fvw4mpqaMH78ePWx5cuXw8/PD99++y26desGAAgNDcXrr7+Oo0ePdvnysCdcTSud2wQGBmDQoHDU1JxHXd11SKWBmDhxMk6ePIGvv/4SDQ0NYBgGQUFBCAkJhZubO6RSKZlsCIJwWsyO3H377beRm5uLXbt2ITQ0FI2NjRg+fDhmzZqFV155Rd1OLpdj+PDhGD9+PBYvXmzWIC019RC2gebecdDcOw6+zL3VI3dbW1uxfft2xMfHIzQ0FADw888/o62tDdHR0Vpt3d3dMWTIEJw9e9acrgiCIAgrY5ZXz6FDh1BfX69l5pHJZACgM9mTVCpFRUWFmUMERKKOb9nOSCTtphR95wnbQXPvOGjuHQdf5t5QEUGzVvzff/893Nzc8NBDD6mPNTc3A4DOlLceHh7q8wR/kMvlWLx4MQYN+guCg6UYNOgvWLx4sTqFNUEQ/MTkFf/t27exe/dujBw5Ej169FAf9/T0BACdotDS0qI+bw5KpX57Gl/sbXxDV/Ga+vp6/N//vYPly5eitHQfhg9PAEBz7wjofe84+DL3AQHeelf9Jq/4d+3a1cWbB+gw8ahMPprIZDIEBQWZ2hXhIEwtXkMQBL8wecW/detW3HHHHUhOTtY6Hh4eDolEgqqqKowdO1Z9XC6X4+zZs12+KJwRlmWxa1cp1qz5CDU11QCAQYPCMHfui0hJSQXQXkSloCAPdXV1CAxsdwe9//5k7N+/p8vx5OQxnNw5WZbVeV+u15vznIb641q85oMPPsAbb7zh0GchCMJ0THLnvHHjBu699148/PDDWLJkSZfzf/3rX1FdXY2SkhK1H39+fj5ef/11fPXVV0hKSjJrkPZw5zRWZzc8fDAkEjHOnDndJQDMy8sLTU1NZtXOtVbtXVOe01h/qan3cype07dvX1RX/6Kee3s/iyvDF3ODEOHL3Bty5zRJ+Dds2IB33nkHX3zxBe69994u50+fPo0pU6YgLCwMGRkZuHr1Kr766isMHz4cn3/+udkPYGvhb0/pMIpTyUVTYBgG0dH6UzYYSyVh7HpT4drfr79e5FSQpkePHrh6tRYNDU12fxZXhy/iI0T4MvcWp2xQsXXrVgQEBOhduUdFReGrr77C0qVL8d5778Hb2xuPPfaYVkCXvehscggI6IjAvX79OgIDA5GWlgEAWLPmI6uLPtBRO3f58g/wyit/7yJ4qtq7xq7fu3eX2tRkCVz7CwjgVgDGx8fH5Htb61kIgjAfk4Q/Ly/PaJthw4YhNzfX7AFZA10mB10UFW2yy3g+/PB95OfnYciQKDQ23lLbvfPzcw2OD2hfKRcU5FlFLAsK8jj1J5VKcf268bKPTzzxpMn3ttazEARhPoJLy8yyLLKyJqOqqhIArJZp01IuXryAixcvAGgXwKKiTfD29rZr7V2utX4DA6Xw8rpocIPXy8sLf//7302+N9URJgjHI7hCLCqTg7MIvi5UY2tsNF4I3pq1d7nW+g0KCkJp6T69qbS9vLxQWrpPK1iP6ggTBH8QnPCrTA5CwZq1d02p9Tt48BBUV1/GggWLEBraB35+fggN7YMFCxahuvoyBg8eYva9CYJwLGZn57Qnpnj1TJ48HgcP7rfb2GyJtQu5cCtIw70/zbm39r0Jw/DFs0SI8GXurZ6d05nhYnJwNry9vQHYvvauLWv9Uh1hguAPglvxl5XtwOOPP2a3sVkKwzCYMGESMjKm2K32LsuyVqn1q2vlY617E4bhy6pTiPBl7q0WwOUoTBF+lcnh1KmTRkskOgvZ2fm8dHHkywdAiNDcOw6+zL1LmXo6mxy4wNU0pGrn5eUFkaFk14DR8+1tGMTGxmH06BRO/RMEQVgDwQk/0J4pdOfOffjgg+UGRb1XrxB88slnmDgxDffckwRfX1+d7Xx9/XDPPUmYODEN2dn5OHbsJGJihgLoasuOjR2KTz/9ApMmTca9947CuHGPYMCAgTrbkt2bIAhHIDhTT2fkcjlWrlyKf/3rC3Vh9IEDB2LRojcwZsxDWqJrin3aVm35BF9+8goRmnvHwZe5dykbP2E/aO4dB8294+DL3LuUjZ8gCIIwDAk/QRCEi8ELU49SqYS+UaqcZ5z/KYQHzb3joLl3HHyZe5FIv3chL4SfIAiCsB5k6iEIgnAxSPgJgiBcQ8KVWwAABgRJREFUDBJ+giAIF4OEnyAIwsUg4ScIgnAxSPgJgiBcDBJ+giAIF4OEnyAIwsUg4ScIgnAxSPgJgiBcDBJ+giAIF4OEnyAIwsUg4ScIgnAxJI4egLnI5XKsWrUKxcXFuHnzJgYPHoyXX34ZiYmJjh6aoDl27BieeOIJnedKSkrwl7/8xc4jEia1tbX45ptvcPLkSVRVVeGPP/7AN998g+HDh3dpu3v3bqxevRo1NTUICAhAeno6nnnmGUgkvP14Oxyu85+cnIz//ve/Xa6fPXs2Xn31VXsN12R4+85YuHAhdu7ciSeeeAL9+vVDUVERZs+ejW+//Rbx8fGOHp7gmTFjBqKiorSOBQcHO2g0wuPChQv4/PPP0a9fP0RERODEiRM62+3fvx/PPfcc7rnnHrzxxhs4f/48PvnkE/z+++9444037Dxq4cB1/gEgKioKM2bM0DoWHh5u6yFahpKHnDx5UhkeHq786quv1Meam5uVKSkpyqlTpzpuYC7ADz/8oAwPD1eWlZU5eiiC5tatW8obN24olUqlsqysTBkeHq784YcfurQbN26cctKkScq2tjb1seXLlysHDx6svHDhgr2GKzi4zv/o0aOVzz77rL2HZzG8tPHv2LEDbm5uyMjIUB/z8PBAeno6fvrpJ9TW1jpwdK5DY2Mj2traHD0MQeLt7Y0ePXoYbFNTU4OamhpkZmZCLBarj0+dOhUKhQI7d+609TAFC5f510Qul6OpybmLr2vCS+E/e/YsBgwYgG7dumkdj42NhVKpxNmzZx00MtdhwYIFuPPOOzF06FDMnDkTP//8s6OH5HKcOXMGABAdHa11PDg4GD179lSfJ2zL4cOHERcXh7i4OKSkpCAvL8/RQzIKL238MplMpz1ZKpUCAK34bYibmxtSU1Nx3333oUePHvj555/x5ZdfYurUqSgoKMCAAQMcPUSXQSaTAeh432silUrpc2AHwsPDMWzYMPTv3x+///47Nm7ciDfffBMNDQ14+umnHT08vfBS+Jubm+Hm5tbluIeHBwCgpaXF3kNyGRISEpCQkKD+9wMPPIDk5GRMnjwZq1evxrJlyxw4OteiubkZAODu7t7lnIeHB69MD3xl7dq1Wv9OS0vD1KlTsWbNGmRlZcHHx8dBIzMML009np6eaG1t7XJcJfiqLwDCPgwePBiJiYn44YcfHD0Ul8LT0xNAu325My0tLerzhP0Qi8WYMWMGmpqaDHoCORpeCr++n7Gqn75BQUH2HpLL06tXLzQ0NDh6GC6FysSjet9rIpPJ6HPgIHr27AkATv154KXwDx48GBcuXMDt27e1jp88eVJ9nrAvly9fNskLgrCcIUOGAACqqqq0jl+7dg1Xr15Vnyfsy+XLlwEA/v7+Dh6Jfngp/A8++CBaW1uRn5+vPiaXy1FYWIiEhAQKJLIhN27c6HLs+PHjOHbsGEaOHOmAEbkuYWFhGDhwIPLy8sCyrPp4Tk4OGIbB2LFjHTg64VNfXw+FQqF1rKWlBevXr0e3bt0QFxfnoJEZh5ebu0OHDsWDDz6IpUuXQiaToW/fvigqKsL//vc/vPfee44enqCZN28evLy8EB8fjx49eqC6uhp5eXno0aMHXnjhBUcPT1CsWbMGAPDLL78AAIqLi/HTTz+he/fumDZtGgDgb3/7G5599lnMmjUL48aNw/nz5/Hdd98hMzOTPKwsxNj879mzB2vXrkVqaipCQkJQX1+PoqIiXLx4EW+//XYXd3NnQqRUKpWOHoQ5tLS0YOXKldi6dSsaGhoQERGBV155BUlJSY4emqD55ptvsHXrVly6dAmNjY3w9/fHyJEj8cILL6B3796OHp6giIiI0Hk8JCQEe/bsUf97165dWL16NX755Rf4+/tj8uTJmDt3LuXqsRBj819VVYXVq1fjzJkzuHHjBtzd3REVFYWZM2di9OjRdh6tafBW+AmCIAjz4KWNnyAIgjAfEn6CIAgXg4SfIAjCxSDhJwiCcDFI+AmCIFwMEn6CIAgXg4SfIAjCxSDhJwiCcDFI+AmCIFwMEn6CIAgX4/8Dj8MyK8FiloMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = df_anal['sightings per cap x 100,000'].values.reshape(-1,1)\n",
    "Y = df_anal['education'].values\n",
    "regr = linear_model.LinearRegression().fit(X,Y)\n",
    "pred = regr.predict(X)\n",
    "\n",
    "plt.scatter(X, Y,  color='black')\n",
    "plt.plot(X, pred, color='blue', linewidth=3)\n",
    "\n",
    "print(\"y = %s * x + %d\" % (regr.coef_[0], regr.intercept_))\n",
    "print(\"r^2 = \" + str(r2_score(Y, pred)))\n",
    "print(\"pearson correlation coefficient: \" + str(df_anal['sightings per cap x 100,000'].corr(df_anal['education'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above scatter plot shows education plotted with sightings per capita x 100,000, the line plotted shows the best fit line to the scatter plot. From the scatter plot, its difficult to determine correlation, however with the best fit line, we can see that there may be a correlation between the two, though its significance cannot be determined without greater numerical analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we used linear regression to find if there is a statistically significant correlation between both unemployment values and education values and the UFO sightings per capita (multiplied by 100,000). We use linear regression because we expect a linear relationship between unemployment and UFO sightings as well as between education and UFO sightings (as unemployment goes up and educational attainment goes down, we expect UFO sightings to rise). To do so, we utilize the regression to find the best fit line for the data, the variance for the data, and the pearson correlation coefficient. The correlation coefficient shows the linear correlation between unemployment/education and UFO sightings while variance explains what percent of the data can be explained by the explained by the correlation coefficent. Using these variables we can find r-critical, which r must exceed to reject the null hypothesis for both sets of analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### UFO Sighting Frequency and Unemployment - Fail to Reject Null Hypothesis\n",
    "\n",
    "- n = 292. \n",
    "- df = 290\n",
    "- r-critical = 0.115\n",
    "- r-value = 0.0029369314934846098\n",
    "\n",
    "### UFO Sighting Frequency and Educational Attainment - Fail to Reject Null Hypothesis\n",
    "\n",
    "- n = 292. \n",
    "- df = 290\n",
    "- r-critical = 0.115\n",
    "- r-value = 0.07963087540959714\n",
    "\n",
    "\n",
    "In the case of both tests, the resulting r value is less than the r critical for a two-tailed hypothesis with an alpha of 0.05. Therefore we fail to reject our null hypothesis for both cases. It appears that there is not a detectable correlation between ufo sighting frequency and either unemployment or educational attainment rates per county."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ethics and Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this project, there are minimal ethical considerations. UFO sightings are deemed trivial by society and\n",
    "while interesting, probably are not of much importance. This project further does not deal with information regarding individual people.\n",
    "\n",
    "When we first came across this dataset we knew that even though it was the public record we still had to be careful about how we would utilize the information and present our findings. Even though public data is usually seen as low-threat, we were still aware of the possibility that sometimes publishing data on certain communities can lead to some consequences. This can lead to violating respect for communities and respect for persons -- which is also reiterated in the Belmont report. Regardless of the data — it can still lead to biases and how people interact with the place(s) that are discussed. However, since it is a public record there are not many other barriers of ethical consideration for specific interest groups that our group needs to worry about in the scope of our project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion and Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This project sought to eplore the data provided by the NUFORC regarding UFO sighting reports. In particular we investigated the relationships between the frequency of UFO sightings and socioeconomic indicators (education and unemployment). By Looking at California at a county level, we were able to produce a sizable sample of bivariate data points. Using logistic regression, we performed a hypothesis test.\n",
    "\n",
    "With n = 292, 290 degrees of freedom, and an r-critical value of 0.115 we failed to reject the null hypothesis since the R-values were r = 0.07963087540959714  and  r = 0.0029369314934846098 respectively. This analysis of our dataset led us to conclude that UFO sightings likely do not have a significant correlation to educational attainment and unemployment in California. \n",
    "\n",
    "It is important to note that there were some possible confounds with our dataset. For example, one of the confounds was that the data is only limited to populations within the state of California. Ideally, if we had more time we would like to diversify the data in order to hopefully generate results that are applicable to the entire population instead of a small niche."
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