FinalProject_group085.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - Final Project \n",
    "# Effect of Education Attainment on Lowering Poverty Rates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal of our project was to determine whether there is a correlation between the education level and the poverty rate in the United States. We analyzed high school attendance data and the poverty by region data from United States Census Bureau from 1959 to 2019. After performing a linear regression test and EDA, we were not able to find a significant correlation between education level and poverty rates. However, it is important to note the fact that many confounding variables were not included in our consideration. If we have a cleaner dataset without those confounding variables, it is possible to lead to a different outcome. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "- Shan Mo\n",
    "- Sherry Zeng\n",
    "- Tianqi Xia\n",
    "- Jingxi Chen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='research_question'></a>\n",
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To what extend does high school education attainment play a role in the lowering of poverty rates in the United States?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='background'></a>\n",
    "\n",
    "## Background & Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As of 2021, the COVID-19 pandemic has resulted in the skyrocketing of unemployment, the rise of poverty, and a steady increase in economic inequality. However, poverty has not been a recent issue that emerged from the pandemic--- it has been an issue that world leaders are trying to find a solution to for decades. People living in extreme poverty, as defined by the World Bank, are those that live on 1.90 USD per day, while those that live on between 1.0 USD and 3.10 USD per day belong in the moderate poor class. In 2019, about 14.4% of underaged Americans, 9.4% aged 18-64, 8.9% of those over 64, and 10.5% of all Americans live in poverty. The education attainment rate we will be focusing on is the completion of a high school degree or higher. In 2019, about 90.1% of Americans aged 25 or older have a high school degree and 36.0% have a bachelor's degree or higher. \n",
    "\n",
    "In creating the formula for eradicating poverty, education is often seen playing a big role. Previous studies have shown a correlation between education and poverty; countries with a larger proportion of the population living in poverty tend to have lower mean years in schooling. Moreover, in the United States, families with parents having a higher degree in school are shown to be less likely to live in poverty households. This trend is also only seen in countries with lower GDP growth, such as Pakistan, where an increase in educational achievements is linked with a lower possibility of poverty. \n",
    "\n",
    "\n",
    "References:\n",
    "- 1)https://ourworldindata.org/extreme-poverty\n",
    "- 2)https://nces.ed.gov/programs/coe/indicator_cce.asp\n",
    "- 3)https://www.researchgate.net/publication/215442733_Impact_of_education_on_poverty_reduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the findings of previous studies and unconfirmed claims, we hypothesize that a higher rate of high school education attainment will result in a lower poverty rate in the United States. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "- Percent of People 25 Years and Over Who Have Completed High School or College: Selected Years 1940 to 2019\n",
    "- https://www.census.gov/data/tables/time-series/demo/educational-attainment/cps-historical-time-series.html\n",
    "- 63 sets of observations\n",
    "- Documented by the United States Census Bureau, this data table shows the percentage of Americans 25 years and over who has completed a high school degree or higher. Although the table includes data from 1940, some records for several earlier years are missing. \n",
    "\n",
    "\n",
    "- Poverty of People by Reigion\n",
    "- https://www2.census.gov/programs-surveys/cps/tables/time-series/historical-poverty-people/hstpov9.xlsx\n",
    "- 55 sets of observations\n",
    "- Also taken from the United States Census Bureau, this table shows the number and percentage of people living in povery from 1959 to 2019 with regard to regions. The statistics for the total polulation are also included. It organizes the data by year. There are no missing data from this dataset. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Import seaborn and apply its plotting styles\n",
    "import seaborn as sns\n",
    "sns.set(style=\"white\", font_scale=2)\n",
    "\n",
    "# import matplotlib\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "# set plotting size parameter\n",
    "plt.rcParams['figure.figsize'] = (10, 5)\n",
    "\n",
    "# Statmodels & patsy\n",
    "import patsy\n",
    "import statsmodels.api as sm\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from scipy.stats import skewnorm\n",
    "from scipy.stats import pearsonr\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the two datasets, we found on the United States Census Bureau official website, there were invalid data contained in the first few rows. Also, the high school attendance dataset contained data from 1940 to 1958, which were not included in the poverty rate dataset. Thus, we need to exclude these data in order to perform a consistent correlation test. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We cleaned the high school attendence dataset first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(284, 34)\n"
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       "      <td>Table  A-2.  Percent of People 25 Years and Ov...</td>\n",
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       "      <th>3</th>\n",
       "      <td>Age and year</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>All races</td>\n",
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       "      <td>Non-Hispanic White</td>\n",
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       "  Table with row headers in column A and column headers in rows 5 through 6.  \\\n",
       "0  Table  A-2.  Percent of People 25 Years and Ov...                           \n",
       "1                  (Noninstitutionalized population)                           \n",
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       "3                                       Age and year                           \n",
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       "\n",
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       "4       Male     Female               Total  ...   \n",
       "\n",
       "                                  Unnamed: 24 Unnamed: 25 Unnamed: 26  \\\n",
       "0                                         NaN         NaN         NaN   \n",
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       "2                                         NaN         NaN         NaN   \n",
       "3  Non-Hispanic White alone or in combination         NaN         NaN   \n",
       "4                                       Total        Male      Female   \n",
       "\n",
       "                     Unnamed: 27 Unnamed: 28 Unnamed: 29  \\\n",
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       "2                            NaN         NaN         NaN   \n",
       "3  Black alone or in combination         NaN         NaN   \n",
       "4                          Total        Male      Female   \n",
       "\n",
       "                     Unnamed: 30 Unnamed: 31 Unnamed: 32 Unnamed: 33  \n",
       "0                            NaN         NaN         NaN         NaN  \n",
       "1                            NaN         NaN         NaN         NaN  \n",
       "2                            NaN         NaN         NaN         NaN  \n",
       "3  Asian alone or in combination         NaN         NaN         NaN  \n",
       "4                          Total        Male      Female         NaN  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ed_data = pd.read_csv('https://raw.githubusercontent.com/COGS108/group085_wi21/main/education-attainment.csv?token=ASNGQOAMJ4WS5BYMM6GPTFTAKSQDY')\n",
    "print(ed_data.shape)\n",
    "ed_data.head()\n",
    "\n",
    "#read in the raw csv file of education attainment rates "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the dataset is organized by race, gender, and different education attainment degrees. However, since we only want to focus on all Americans who are 25 years of age with at least a high school degree, we will be dropping the other columns containing data we do not need. Also, the column names of the data subset will also be renamed so they are informative. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>High school degree attainment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>90.1</td>\n",
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       "      <td>2018</td>\n",
       "      <td>89.8</td>\n",
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      ],
      "text/plain": [
       "     Year High school degree attainment\n",
       "0    2019                          90.1\n",
       "1    2018                          89.8\n",
       "2    2017                          89.6\n",
       "3    2016                          89.1\n",
       "4    2015                          88.4\n",
       "5    2014                          88.3\n",
       "6    2013                          88.2\n",
       "7  2012 1                          87.6\n",
       "8    2011                          87.6\n",
       "9    2010                          87.1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#these rows contain data for those who are 25 years old or above with at least four years of high school education. \n",
    "ed_data = ed_data[9:71]\n",
    "\n",
    "#these columns are the year and the corresponding percentage of educational attainment for the entire population \n",
    "ed_data = ed_data.iloc[:, 2:4]\n",
    "\n",
    "#renaming the columns for a better idea of what they store \n",
    "ed_data = ed_data.rename(columns = {'Unnamed: 2':'Year',\n",
    "                           'Unnamed: 3':'High school degree attainment'\n",
    "                            })\n",
    "#reseting the index \n",
    "ed_data.reset_index(drop=True, inplace=True)\n",
    "\n",
    "ed_data.head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have the subset of data that we want. The first column is the year in which the data is describing and the second column is the percentage of people with at least four years of high school. Since we want to graph and analyze the data, they must be in numerical format. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year                             object\n",
       "High school degree attainment    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the type of data in dataframe \n",
    "ed_data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since percentage of high school degree attainment is in decimal form, it will be converted to a float type object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Year</th>\n",
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       "      <td>2019</td>\n",
       "      <td>90.1</td>\n",
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       "      <td>2018</td>\n",
       "      <td>89.8</td>\n",
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       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>89.6</td>\n",
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       "      <td>2016</td>\n",
       "      <td>89.1</td>\n",
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       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>88.4</td>\n",
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      "text/plain": [
       "   Year  High school degree attainment\n",
       "0  2019                           90.1\n",
       "1  2018                           89.8\n",
       "2  2017                           89.6\n",
       "3  2016                           89.1\n",
       "4  2015                           88.4"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert string to float\n",
    "ed_data['High school degree attainment'] = pd.to_numeric(ed_data['High school degree attainment'])\n",
    "ed_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of the data for the year number is followed by a footnote number with a space in between them. To only return a numerical type of the year number, the space and the footnote number will be remove. First, the specific data with footnote numbers is identified and then manipulated. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#check which data has more than four digits and remove the footnote annotation by replacing and slicing \n",
    "for item in ed_data['Year']:\n",
    "    if len(item)>4:\n",
    "        year = item[0:4]\n",
    "        ed_data = ed_data.replace([item],year)\n",
    "    else:\n",
    "        continue\n",
    "        \n",
    "ed_data['Year'] = pd.to_numeric(ed_data['Year'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, the type of the two data will both be in numerical format; Year is a interger type and high school degree is a float type. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Year                               int64\n",
      "High school degree attainment    float64\n",
      "dtype: object\n"
     ]
    },
    {
     "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>Year</th>\n",
       "      <th>High school degree attainment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>90.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "      <td>89.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>89.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>89.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>88.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014</td>\n",
       "      <td>88.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013</td>\n",
       "      <td>88.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2012</td>\n",
       "      <td>87.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2011</td>\n",
       "      <td>87.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2010</td>\n",
       "      <td>87.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  High school degree attainment\n",
       "0  2019                           90.1\n",
       "1  2018                           89.8\n",
       "2  2017                           89.6\n",
       "3  2016                           89.1\n",
       "4  2015                           88.4\n",
       "5  2014                           88.3\n",
       "6  2013                           88.2\n",
       "7  2012                           87.6\n",
       "8  2011                           87.6\n",
       "9  2010                           87.1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(ed_data.dtypes)\n",
    "ed_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The process described above will be applied to the dataset for poverty rates dataset as well. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>Table with row headers in column A and column headers in rows 4 through 6</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>Unnamed: 10</th>\n",
       "      <th>Unnamed: 11</th>\n",
       "      <th>Unnamed: 12</th>\n",
       "      <th>Unnamed: 13</th>\n",
       "      <th>Unnamed: 14</th>\n",
       "      <th>Unnamed: 15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Table 9. Poverty of People by Region: 1959 to ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(Population in thousands. Population as of Mar...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Year</td>\n",
       "      <td>Total</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>South</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>West</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Total</td>\n",
       "      <td>Below poverty</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Total</td>\n",
       "      <td>Below poverty</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Total</td>\n",
       "      <td>Below poverty</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Total</td>\n",
       "      <td>Below poverty</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Total</td>\n",
       "      <td>Below poverty</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Number</td>\n",
       "      <td>Percent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Number</td>\n",
       "      <td>Percent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Number</td>\n",
       "      <td>Percent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Number</td>\n",
       "      <td>Percent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Number</td>\n",
       "      <td>Percent</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Table with row headers in column A and column headers in rows 4 through 6  \\\n",
       "0  Table 9. Poverty of People by Region: 1959 to ...                          \n",
       "1  (Population in thousands. Population as of Mar...                          \n",
       "2                                               Year                          \n",
       "3                                                NaN                          \n",
       "4                                                NaN                          \n",
       "\n",
       "  Unnamed: 1     Unnamed: 2 Unnamed: 3 Unnamed: 4     Unnamed: 5 Unnamed: 6  \\\n",
       "0        NaN            NaN        NaN        NaN            NaN        NaN   \n",
       "1        NaN            NaN        NaN        NaN            NaN        NaN   \n",
       "2      Total            NaN        NaN  Northeast            NaN        NaN   \n",
       "3      Total  Below poverty        NaN      Total  Below poverty        NaN   \n",
       "4        NaN         Number    Percent        NaN         Number    Percent   \n",
       "\n",
       "  Unnamed: 7     Unnamed: 8 Unnamed: 9 Unnamed: 10    Unnamed: 11 Unnamed: 12  \\\n",
       "0        NaN            NaN        NaN         NaN            NaN         NaN   \n",
       "1        NaN            NaN        NaN         NaN            NaN         NaN   \n",
       "2    Midwest            NaN        NaN       South            NaN         NaN   \n",
       "3      Total  Below poverty        NaN       Total  Below poverty         NaN   \n",
       "4        NaN         Number    Percent         NaN         Number     Percent   \n",
       "\n",
       "  Unnamed: 13    Unnamed: 14 Unnamed: 15  \n",
       "0         NaN            NaN         NaN  \n",
       "1         NaN            NaN         NaN  \n",
       "2        West            NaN         NaN  \n",
       "3       Total  Below poverty         NaN  \n",
       "4         NaN         Number     Percent  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read in the raw csv file of poverty rates\n",
    "pov_data = pd.read_csv(\"https://raw.githubusercontent.com/COGS108/group085_wi21/main/hstpov9.csv?token=ASNGQOGCXYEFFDAV7PWFTHDAKSQUO\")\n",
    "pov_data.head()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Percentage of Poverty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "      <td>11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017 (21)</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>12.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Year Percentage of Poverty\n",
       "0       2019                  10.5\n",
       "1       2018                  11.8\n",
       "2  2017 (21)                  12.3\n",
       "3       2017                  12.3\n",
       "4       2016                  12.7"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Although the origianl data also includes data with regards to regions, we will be focusing on the population statistics only.\n",
    "# Excludes the unwanted rows\n",
    "pov_data = pov_data[5 : 60]\n",
    "\n",
    "# Filter out the columns of the Year and the corresponding poverty data\n",
    "pov_data = pov_data.iloc[:, [0,3]]\n",
    "\n",
    "# Rename the columns with readable names\n",
    "pov_data = pov_data.rename(columns = {\"Table with row headers in column A and column headers in rows 4 through 6\" : \"Year\", \n",
    "                                \"Unnamed: 3\" : \"Percentage of Poverty\"})\n",
    "\n",
    "# Reset index for further steps\n",
    "pov_data.reset_index(drop=True, inplace=True)\n",
    "\n",
    "pov_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year                     object\n",
       "Percentage of Poverty    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pov_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year                       int64\n",
       "Percentage of Poverty    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#transform Percentage of Poverty to numerical format\n",
    "pov_data['Percentage of Poverty'] = pd.to_numeric(pov_data['Percentage of Poverty'])\n",
    "\n",
    "#check which data has more than four digits and remove the footnote annotation by replacing and slicing \n",
    "for item in pov_data['Year']:\n",
    "    if len(item)>4:\n",
    "        year = item[0:4]\n",
    "        pov_data = pov_data.replace([item],year)\n",
    "\n",
    "    else:\n",
    "        continue\n",
    "        \n",
    "pov_data['Year'] = pd.to_numeric(pov_data['Year'])\n",
    "\n",
    "#check element types again\n",
    "pov_data.dtypes\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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",
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       "    }\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>Year</th>\n",
       "      <th>Percentage of Poverty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "      <td>11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>12.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Percentage of Poverty\n",
       "0  2019                   10.5\n",
       "1  2018                   11.8\n",
       "2  2017                   12.3\n",
       "3  2017                   12.3\n",
       "4  2016                   12.7"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pov_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Year                             0\n",
      "High school degree attainment    0\n",
      "dtype: int64\n",
      "Year                     0\n",
      "Percentage of Poverty    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#check for missing values\n",
    "print(ed_data.isnull().sum())\n",
    "print(pov_data.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no missing datas from the subset that we have extracted. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results \n",
    "\n",
    "## Descriptive Analysis \n",
    "Before analyzation, lets get a better idea of what each dataset looks like. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(62, 2)\n"
     ]
    },
    {
     "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>Year</th>\n",
       "      <th>High school degree attainment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>62.000000</td>\n",
       "      <td>62.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1987.919355</td>\n",
       "      <td>71.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>19.083336</td>\n",
       "      <td>16.083835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1947.000000</td>\n",
       "      <td>33.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1973.250000</td>\n",
       "      <td>60.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1988.500000</td>\n",
       "      <td>76.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2003.750000</td>\n",
       "      <td>85.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>90.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year  High school degree attainment\n",
       "count    62.000000                      62.000000\n",
       "mean   1987.919355                      71.550000\n",
       "std      19.083336                      16.083835\n",
       "min    1947.000000                      33.100000\n",
       "25%    1973.250000                      60.150000\n",
       "50%    1988.500000                      76.550000\n",
       "75%    2003.750000                      85.050000\n",
       "max    2019.000000                      90.100000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#shape and descriptive information of education dataset\n",
    "print(ed_data.shape)\n",
    "ed_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(55, 2)\n"
     ]
    },
    {
     "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>Year</th>\n",
       "      <th>Percentage of Poverty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>55.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1993.509091</td>\n",
       "      <td>13.421818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16.277538</td>\n",
       "      <td>2.137036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1959.000000</td>\n",
       "      <td>10.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1980.500000</td>\n",
       "      <td>12.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1994.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2007.500000</td>\n",
       "      <td>14.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>22.400000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year  Percentage of Poverty\n",
       "count    55.000000              55.000000\n",
       "mean   1993.509091              13.421818\n",
       "std      16.277538               2.137036\n",
       "min    1959.000000              10.500000\n",
       "25%    1980.500000              12.200000\n",
       "50%    1994.000000              13.000000\n",
       "75%    2007.500000              14.350000\n",
       "max    2019.000000              22.400000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#shape and descriptive information of poverty dataset\n",
    "print(pov_data.shape)\n",
    "pov_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean percentage of high school degree attainment is 70.49% and the mean percentage of poverty is 13%. However, the record for education attainment starts from 1940 and 1959 for poverty percentage. Before merging the two datasets, let's take a look at the general trend of the records.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8629e6acd0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot a line graph of the general trend of education attainment percentage\n",
    "sns.lineplot(x='Year', y='High school degree attainment', data=ed_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8629d04e90>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot a line graph of the general trend of poverty percentage\n",
    "sns.lineplot(x='Year', y='Percentage of Poverty', data=pov_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that there is a trend in an increase in education attainment rate as the year increases. However, the trend for poverty rate fluctuates quite a lot, starting with a sharp decrease followed by a number of fluctuations. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA\n",
    "To analyze our data, first we will have merge the two subsets into one. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64, 3)\n"
     ]
    },
    {
     "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>Year</th>\n",
       "      <th>High school degree attainment</th>\n",
       "      <th>Percentage of Poverty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>90.1</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "      <td>89.8</td>\n",
       "      <td>11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>89.6</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>89.6</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>89.1</td>\n",
       "      <td>12.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>1959</td>\n",
       "      <td>43.7</td>\n",
       "      <td>22.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>1957</td>\n",
       "      <td>41.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>1952</td>\n",
       "      <td>38.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>1950</td>\n",
       "      <td>34.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>1947</td>\n",
       "      <td>33.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  High school degree attainment  Percentage of Poverty\n",
       "0   2019                           90.1                   10.5\n",
       "1   2018                           89.8                   11.8\n",
       "2   2017                           89.6                   12.3\n",
       "3   2017                           89.6                   12.3\n",
       "4   2016                           89.1                   12.7\n",
       "..   ...                            ...                    ...\n",
       "59  1959                           43.7                   22.4\n",
       "60  1957                           41.6                    NaN\n",
       "61  1952                           38.8                    NaN\n",
       "62  1950                           34.3                    NaN\n",
       "63  1947                           33.1                    NaN\n",
       "\n",
       "[64 rows x 3 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#left merge of two subsets \n",
    "ed_pov = pd.merge(ed_data, pov_data, how='left', left_on='Year', right_on='Year')\n",
    "print(ed_pov.shape)\n",
    "ed_pov"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the number of rows for the two subsets are not equal, merging the data will result in missing values. The rows with missing values will be dropped. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year                              0\n",
       "High school degree attainment     0\n",
       "Percentage of Poverty            10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for missing rows \n",
    "ed_pov.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(54, 3)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop NaNs and check shape of subset\n",
    "ed_pov = ed_pov.dropna(how='any')\n",
    "ed_pov.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>Year</th>\n",
       "      <th>High school degree attainment</th>\n",
       "      <th>Percentage of Poverty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>90.1</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "      <td>89.8</td>\n",
       "      <td>11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>89.6</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>89.6</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>89.1</td>\n",
       "      <td>12.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  High school degree attainment  Percentage of Poverty\n",
       "0  2019                           90.1                   10.5\n",
       "1  2018                           89.8                   11.8\n",
       "2  2017                           89.6                   12.3\n",
       "3  2017                           89.6                   12.3\n",
       "4  2016                           89.1                   12.7"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ed_pov.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f8629b81bd0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Histogram of high school degree attainment\n",
    "ed_pov.hist(column = \"High school degree attainment\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f8629ba27d0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Histogram of percentage of poverty\n",
    "ed_pov.hist(column = \"Percentage of Poverty\", bins = 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can tell from the distribution of percentage of poverty that there is an outlier in our merged dataset. Since the outlier would drastically affect the result of our analysis, the row with the outlier will be dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "ed_pov = ed_pov[ed_pov['Percentage of Poverty'] < 20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we take a look at the histogram again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f8629cf2350>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Histogram of high school degree attainment\n",
    "ed_pov.hist(column = \"High school degree attainment\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f8629a8be10>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Histogram of percentage of poverty\n",
    "ed_pov.hist(column = \"Percentage of Poverty\", bins = 20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>High school degree attainment</th>\n",
       "      <th>Percentage of Poverty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>90.1</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "      <td>89.8</td>\n",
       "      <td>11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>89.6</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>89.6</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>89.1</td>\n",
       "      <td>12.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015</td>\n",
       "      <td>88.4</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014</td>\n",
       "      <td>88.3</td>\n",
       "      <td>14.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013</td>\n",
       "      <td>88.2</td>\n",
       "      <td>14.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013</td>\n",
       "      <td>88.2</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2012</td>\n",
       "      <td>87.6</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2011</td>\n",
       "      <td>87.6</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2010</td>\n",
       "      <td>87.1</td>\n",
       "      <td>15.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2009</td>\n",
       "      <td>86.7</td>\n",
       "      <td>14.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2008</td>\n",
       "      <td>86.6</td>\n",
       "      <td>13.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2007</td>\n",
       "      <td>85.7</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2006</td>\n",
       "      <td>85.5</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2005</td>\n",
       "      <td>85.2</td>\n",
       "      <td>12.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2004</td>\n",
       "      <td>85.2</td>\n",
       "      <td>12.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2003</td>\n",
       "      <td>84.6</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2002</td>\n",
       "      <td>84.1</td>\n",
       "      <td>12.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2001</td>\n",
       "      <td>84.1</td>\n",
       "      <td>11.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2000</td>\n",
       "      <td>84.1</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1999</td>\n",
       "      <td>83.4</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1998</td>\n",
       "      <td>82.8</td>\n",
       "      <td>12.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1997</td>\n",
       "      <td>82.1</td>\n",
       "      <td>13.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1996</td>\n",
       "      <td>81.7</td>\n",
       "      <td>13.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1995</td>\n",
       "      <td>81.7</td>\n",
       "      <td>13.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1994</td>\n",
       "      <td>80.9</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1993</td>\n",
       "      <td>80.2</td>\n",
       "      <td>15.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1992</td>\n",
       "      <td>79.4</td>\n",
       "      <td>14.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1991</td>\n",
       "      <td>78.4</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1990</td>\n",
       "      <td>77.6</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1989</td>\n",
       "      <td>76.9</td>\n",
       "      <td>12.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1988</td>\n",
       "      <td>76.2</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1987</td>\n",
       "      <td>75.6</td>\n",
       "      <td>13.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1986</td>\n",
       "      <td>74.7</td>\n",
       "      <td>13.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1985</td>\n",
       "      <td>73.9</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1984</td>\n",
       "      <td>73.3</td>\n",
       "      <td>14.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1983</td>\n",
       "      <td>72.1</td>\n",
       "      <td>15.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1982</td>\n",
       "      <td>71.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1981</td>\n",
       "      <td>69.7</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1980</td>\n",
       "      <td>68.6</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1979</td>\n",
       "      <td>67.7</td>\n",
       "      <td>11.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1978</td>\n",
       "      <td>65.9</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1977</td>\n",
       "      <td>64.9</td>\n",
       "      <td>11.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1976</td>\n",
       "      <td>64.1</td>\n",
       "      <td>11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1975</td>\n",
       "      <td>62.5</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1974</td>\n",
       "      <td>61.2</td>\n",
       "      <td>11.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1973</td>\n",
       "      <td>59.8</td>\n",
       "      <td>11.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1972</td>\n",
       "      <td>58.2</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1971</td>\n",
       "      <td>56.4</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>1970</td>\n",
       "      <td>55.2</td>\n",
       "      <td>12.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>1969</td>\n",
       "      <td>54.0</td>\n",
       "      <td>12.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  High school degree attainment  Percentage of Poverty\n",
       "0   2019                           90.1                   10.5\n",
       "1   2018                           89.8                   11.8\n",
       "2   2017                           89.6                   12.3\n",
       "3   2017                           89.6                   12.3\n",
       "4   2016                           89.1                   12.7\n",
       "5   2015                           88.4                   13.5\n",
       "6   2014                           88.3                   14.8\n",
       "7   2013                           88.2                   14.8\n",
       "8   2013                           88.2                   14.5\n",
       "9   2012                           87.6                   15.0\n",
       "10  2011                           87.6                   15.0\n",
       "11  2010                           87.1                   15.1\n",
       "12  2009                           86.7                   14.3\n",
       "13  2008                           86.6                   13.2\n",
       "14  2007                           85.7                   12.5\n",
       "15  2006                           85.5                   12.3\n",
       "16  2005                           85.2                   12.6\n",
       "17  2004                           85.2                   12.7\n",
       "18  2003                           84.6                   12.5\n",
       "19  2002                           84.1                   12.1\n",
       "20  2001                           84.1                   11.7\n",
       "21  2000                           84.1                   11.3\n",
       "22  1999                           83.4                   11.9\n",
       "23  1998                           82.8                   12.7\n",
       "24  1997                           82.1                   13.3\n",
       "25  1996                           81.7                   13.7\n",
       "26  1995                           81.7                   13.8\n",
       "27  1994                           80.9                   14.5\n",
       "28  1993                           80.2                   15.1\n",
       "29  1992                           79.4                   14.8\n",
       "30  1991                           78.4                   14.2\n",
       "31  1990                           77.6                   13.5\n",
       "32  1989                           76.9                   12.8\n",
       "33  1988                           76.2                   13.0\n",
       "34  1987                           75.6                   13.4\n",
       "35  1986                           74.7                   13.6\n",
       "36  1985                           73.9                   14.0\n",
       "37  1984                           73.3                   14.4\n",
       "38  1983                           72.1                   15.2\n",
       "39  1982                           71.0                   15.0\n",
       "40  1981                           69.7                   14.0\n",
       "41  1980                           68.6                   13.0\n",
       "42  1979                           67.7                   11.7\n",
       "43  1978                           65.9                   11.4\n",
       "44  1977                           64.9                   11.6\n",
       "45  1976                           64.1                   11.8\n",
       "46  1975                           62.5                   12.3\n",
       "47  1974                           61.2                   11.2\n",
       "48  1973                           59.8                   11.1\n",
       "49  1972                           58.2                   11.9\n",
       "50  1971                           56.4                   12.5\n",
       "51  1970                           55.2                   12.6\n",
       "52  1969                           54.0                   12.1"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the dataset we will be using to perform inferential analysis on\n",
    "ed_pov"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the outliers are removed from each histogram, the resulting dataset is the one we will be using to perform inferential analysis on. To see if there is a correlation between the two factors, a scatter plot can be useful to visual the relationship between them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Percentage of Poverty')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot of high school degree attainment and percentage of poverty\n",
    "plt.scatter(x='High school degree attainment', y='Percentage of Poverty', data=ed_pov)\n",
    "plt.xlabel('High school degree attainment')\n",
    "plt.ylabel('Percentage of Poverty')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we see that there seems to be a weak trend of a positive correlation between the two factors. The poverty percentage seems to fluctuate in this graph as well. Thus, to examine the correlation further, an inferential analysis will be carried out. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inferential Analysis and Results "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To determine whether there is a correlation between the percentage of high school attainment and the poverty rate, we will first create a linear regression model using the Ordinary Least Square (OLS) estimation method. In this model, the predictor will be high school degree attainment and the outcome will be poverty percentage. By setting the predictors and the outcome variables this way, we will be examining the impact of educational attainment on poverty percentage. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>Percentage of Poverty</td> <th>  R-squared:         </th> <td>   0.078</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                     <td>OLS</td>          <th>  Adj. R-squared:    </th> <td>   0.060</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>Least Squares</td>     <th>  F-statistic:       </th> <td>   4.320</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>               <td>Wed, 17 Mar 2021</td>    <th>  Prob (F-statistic):</th>  <td>0.0427</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                   <td>23:54:05</td>        <th>  Log-Likelihood:    </th> <td> -84.936</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>        <td>    53</td>         <th>  AIC:               </th> <td>   173.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>            <td>    51</td>         <th>  BIC:               </th> <td>   177.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>                <td>     1</td>         <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>nonrobust</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                <td></td>                   <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                         <td>   10.4684</td> <td>    1.271</td> <td>    8.237</td> <td> 0.000</td> <td>    7.917</td> <td>   13.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>High school degree attainment</th> <td>    0.0336</td> <td>    0.016</td> <td>    2.079</td> <td> 0.043</td> <td>    0.001</td> <td>    0.066</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.784</td> <th>  Durbin-Watson:     </th> <td>   0.255</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.410</td> <th>  Jarque-Bera (JB):  </th> <td>   1.203</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.012</td> <th>  Prob(JB):          </th> <td>   0.548</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.262</td> <th>  Cond. No.          </th> <td>    593.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                              OLS Regression Results                             \n",
       "=================================================================================\n",
       "Dep. Variable:     Percentage of Poverty   R-squared:                       0.078\n",
       "Model:                               OLS   Adj. R-squared:                  0.060\n",
       "Method:                    Least Squares   F-statistic:                     4.320\n",
       "Date:                   Wed, 17 Mar 2021   Prob (F-statistic):             0.0427\n",
       "Time:                           23:54:05   Log-Likelihood:                -84.936\n",
       "No. Observations:                     53   AIC:                             173.9\n",
       "Df Residuals:                         51   BIC:                             177.8\n",
       "Df Model:                              1                                         \n",
       "Covariance Type:               nonrobust                                         \n",
       "=================================================================================================\n",
       "                                    coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------------------\n",
       "const                            10.4684      1.271      8.237      0.000       7.917      13.020\n",
       "High school degree attainment     0.0336      0.016      2.079      0.043       0.001       0.066\n",
       "==============================================================================\n",
       "Omnibus:                        1.784   Durbin-Watson:                   0.255\n",
       "Prob(Omnibus):                  0.410   Jarque-Bera (JB):                1.203\n",
       "Skew:                          -0.012   Prob(JB):                        0.548\n",
       "Kurtosis:                       2.262   Cond. No.                         593.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y is the outomce and x is the predictor\n",
    "y = ed_pov['Percentage of Poverty']\n",
    "x = ed_pov['High school degree attainment']\n",
    "\n",
    "#adds a constant to our model \n",
    "x = sm.add_constant(x)\n",
    "\n",
    "#fit the model and show results\n",
    "model = sm.OLS(y, x).fit() \n",
    "predictions = model.predict(x)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results reveal several important points. First, the effect size is shown by Coef = 0.0336, indicating that when high school degree attainment increases by 1, the value of poverty percentage increases by 0.0336. Moreover, the constant indicates that when the educational attainment rate is 0, the baseline percentage of poverty is 10.4684. Through these values, we can create a linear regression line and evaluate how well it fits out data. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___Poverty Percentage = 0.0336 * High school degree attainment + 10.4684___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create same scatter plot\n",
    "plt.scatter(x='High school degree attainment', y='Percentage of Poverty', data=ed_pov)\n",
    "\n",
    "#use the values derived from the linear regression model and plot it on the scatter plot \n",
    "xs = np.arange(ed_pov['High school degree attainment'].min(), ed_pov['High school degree attainment'].max())\n",
    "ys = 0.0336 * xs + 10.4684\n",
    "plt.plot(xs, ys, '--k', linewidth=2, label='Model')\n",
    "\n",
    "#set lables and legend\n",
    "plt.xlabel('High school degree attainment')\n",
    "plt.ylabel('Percentage of Poverty');\n",
    "plt.legend();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the regression line, there is only a slight slope of the regression line. This indicates that the correlation between education attainment and poverty percentage does not have a strong relationship. However, it must be noted that we do see a positive correlation between them, where when one increases, so does the other. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way to examine the degree of correlation between the two factors is by determining the Pearsons correlation. The closer the absolute value of the Pearson correlation is to 1, the stronger the correlation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearsons correlation coefficient = 0.279, (p=0.043)\n"
     ]
    }
   ],
   "source": [
    "#pearsonr to evaluate Pearson's correlation \n",
    "y = ed_pov['Percentage of Poverty']\n",
    "x = ed_pov['High school degree attainment']\n",
    "pearsonr(x, y)\n",
    "print('Pearsons correlation coefficient = %.3f, (p=%.3f)' % (pearsonr(x, y)[0], pearsonr(x, y)[1]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Pearson correlation is 0.279 and the p-value is 0.043. The Pearson correlation is very small, implying that the correlation between education attainment and poverty percentage is weak. p-value <0.05 indicates that the probability to observe this correlation is statistically significant. \n",
    "\n",
    "The results of the analysis show that as educational attainment increases, so the poverty percentage. A positive correlation between the two factors is observed. However, the correlation is not strong since the effect size and the Pearson correlation are both very small. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   - Data Permission   \n",
    "     \n",
    "   The two datasets we used were web scraped off of the United States Census Bureau. It states that the content could be copied, modified, and redistributed. However, the site must be acknowledged as we linked all of the charts and data we scraped from the website. Unite States Census Bureau is also a public source of information on the Internet that is legal for use according to the copyleft licenses.  \n",
    "       \n",
    "       \n",
    "   - Data Discrepancy and biases  \n",
    "     \n",
    "   The dataset is still limited to our question. For the first dataset, the data contained some other information and columns which are unrelated to our question. We cleaned the dataset to eliminate race, gender, different education degrees and only extract the data of what percentage of people 25 years and over who have completed high school or college. Our dataset does have some bias. There is a discrepancy between the ranges of the two data. The first data collects information from 1940 to 2019, and the second data is collected from 1959 to 2019. The lack of time period in the second data may discredit our research. \n",
    "    We do have the confidence to believe that the datasets we included in our research are less biased as they are all facts and census investigation results. However, we can only have an overview of these data as there may be individuals that were initially excluded from the census result. It may be biased from the lens of the government.  \n",
    "    \n",
    "         \n",
    "   - Missing data  \n",
    "        \n",
    "   There are a few missing data for the first data set, that some information related to recent years are missing. Thus, the existence of missing data can exert some impact on the predictive trends. The politicians could make use of the data to support or go against certain political goals.      \n",
    "          \n",
    "          \n",
    "   - Data Privacy  \n",
    "     \n",
    "   In terms of data privacy, every individual’s information is protected by the United States Census Bureau. The commitment applies to individuals, households, and businesses. It followed FOIA for its protected data. Therefore, the origin of our first dataset is pretty reliable.  \n",
    "   \n",
    "   \n",
    "   - Implications:\n",
    "   \n",
    "   Since the results of our analysis demonstrated that as education attainment increases, so does the poverty rate. This does not mean that we are implying that in order to reduce poverty rates, the education attainment rate must also be reduced. Moreover, it does not mean that education has a negative effect. It must be noted that the implications of the results must be considered with caution. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Question of our research is to explore the correlation between the education level and the poverty rate in the United States. We started by trying to operationally define the term education level. We decided to use the high school attainment rate to define education level. After doing certain web research, we decided to use the datasets from the United States Census Bureau, which is official, confidential, and more precise with its protection of privacy. Therefore, we choose two datasets. One for exploring the percentage of high school attainments versus time and another is for examining the poverty rate versus years. Initially, we thought that there is a negative correlation between the education level and the poverty rate. The higher the education level is, the poverty rate seemed to decrease. However, once the data points were plotted, the results seem to be the opposite of what we had hypothesized. \n",
    "  \n",
    "\n",
    "However, we need to confirm the relationship between education attainment and the poverty rate in a more concrete way. Thus, a linear regression test is carried out to figure out what is the impact value of education on poverty and the whether the correlation between the two factors is considered significantly strong. The results of the OLS regression model indicated that the impact value of education on poverty was significant, with a 0.0336% increase in poverty rate when the education rate increases by 1%. The regression line for the dataset was \n",
    "___Poverty Percentage = 0.0336 * High school degree attainment + 10.4684___. In order to examine the strength of the regression model, we analyzed the Pearson correlation, which was 0.279. This indicated that the positive correlation between the poverty rate and education is not particularly strong. \n",
    "\n",
    "\n",
    "Thus, with the analytical results in mind, we reject our hypothesis. This demonstrated that the poverty rate would ___not___ decrease in poverty rate if education attainment increased. The results of the analysis were shocking in comparison to the background research (and common intuition) that we had before carrying out the analysis. However, it must be noted that although the results indicated that there would be an increase in the poverty rate if the education rate increased, the strength of this correlation is weak and so is the effect size. \n",
    "\n",
    "  \n",
    "It was important to note that there are certain limitations to our datasets. Specifically, the ideal number of observations is around 1000. However, it is theoretically impossible for us to gather more than 1000 observations. Also, there are many confounding variables that did not include in our initial research such as financial factors, political influence, or the stock market. We could not get a precise correlation between the education level and the poverty rate without excluding these confounding factors. We could only give an overview of this topic and anticipate future research and discussions. Another important idea that must be understood is that correlation does not equate to causation. There is simply a positive correlation observed, but it does not mean that one causes the other. \n",
    "\n",
    "Improvements could be made to our attempt at answering the question of whether education helps reduce poverty rates. Such improvements include the consideration of confounding elements such as the stock market and the overall economy of the United States during a specific year. Moreover, the range of the research could be narrowed down. For example, instead of looking at the United States as a whole, the datasets could be separated by states and the analysis will focus on the change in education attainment and the poverty percentage of the state from the 1940s to the 2020s. Another way of analysis could be used to examine this topic from another view as well. Instead of using a temporal analysis, we could carry out a more spatially focused one. An example would be to look at the individual education attainment and poverty rate at each state from 2019 and compare these data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Team Contributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Shan Mo  \n",
    "Background research, high school attendance data collection and cleaning, main data visualization and analysis, helped with conclusion and discussion\n",
    "- Sherry Zeng  \n",
    "Poverty rate data collection and cleaning, data analysis, content editing, conclusion and discussion editing.\n",
    "- Tianqi Xia  \n",
    "Ethics and Privacy editing, conclusion and discussion editing.\n",
    "- Jingxi Chen  \n",
    "Data visualization, EDA analysis"
   ]
  }
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