{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inference for categorical data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In August of 2012, news outlets ranging from the Washington Post to the Huffington Post ran a story about the rise of atheism in America. The source for the story was a poll that asked people, \"Irrespective of whether you attend a place of worship or not, would you say you are a religious person, not a religious person or a convinced atheist?\" This type of question, which asks people to classify themselves in one way or another, is common in polling and generates categorical data. In this lab we take a look at the atheism survey and explore what’s at play when making inference about population proportions using categorical data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import io\n",
    "import requests\n",
    "\n",
    "df_url = 'https://raw.githubusercontent.com/akmand/datasets/master/openintro/atheism.csv'\n",
    "url_content = requests.get(df_url, verify=False).content\n",
    "atheism = pd.read_csv(io.StringIO(url_content.decode('utf-8')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>nationality</th>\n",
       "      <th>response</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>non-atheist</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>non-atheist</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>non-atheist</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>non-atheist</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>non-atheist</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88027</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>non-atheist</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88028</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>non-atheist</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88029</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>non-atheist</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88030</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>non-atheist</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88031</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>non-atheist</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>88032 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       nationality     response  year\n",
       "0      Afghanistan  non-atheist  2012\n",
       "1      Afghanistan  non-atheist  2012\n",
       "2      Afghanistan  non-atheist  2012\n",
       "3      Afghanistan  non-atheist  2012\n",
       "4      Afghanistan  non-atheist  2012\n",
       "...            ...          ...   ...\n",
       "88027      Vietnam  non-atheist  2005\n",
       "88028      Vietnam  non-atheist  2005\n",
       "88029      Vietnam  non-atheist  2005\n",
       "88030      Vietnam  non-atheist  2005\n",
       "88031      Vietnam  non-atheist  2005\n",
       "\n",
       "[88032 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atheism"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We create a new dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "us12 = atheism[(atheism.nationality == 'United States') & (atheism.year == 2012)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>nationality</th>\n",
       "      <th>response</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49925</th>\n",
       "      <td>United States</td>\n",
       "      <td>non-atheist</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49926</th>\n",
       "      <td>United States</td>\n",
       "      <td>non-atheist</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49927</th>\n",
       "      <td>United States</td>\n",
       "      <td>non-atheist</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49928</th>\n",
       "      <td>United States</td>\n",
       "      <td>non-atheist</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49929</th>\n",
       "      <td>United States</td>\n",
       "      <td>non-atheist</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50922</th>\n",
       "      <td>United States</td>\n",
       "      <td>non-atheist</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50923</th>\n",
       "      <td>United States</td>\n",
       "      <td>non-atheist</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50924</th>\n",
       "      <td>United States</td>\n",
       "      <td>non-atheist</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50925</th>\n",
       "      <td>United States</td>\n",
       "      <td>non-atheist</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50926</th>\n",
       "      <td>United States</td>\n",
       "      <td>non-atheist</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1002 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         nationality     response  year\n",
       "49925  United States  non-atheist  2012\n",
       "49926  United States  non-atheist  2012\n",
       "49927  United States  non-atheist  2012\n",
       "49928  United States  non-atheist  2012\n",
       "49929  United States  non-atheist  2012\n",
       "...              ...          ...   ...\n",
       "50922  United States  non-atheist  2012\n",
       "50923  United States  non-atheist  2012\n",
       "50924  United States  non-atheist  2012\n",
       "50925  United States  non-atheist  2012\n",
       "50926  United States  non-atheist  2012\n",
       "\n",
       "[1002 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate the proportion of atheist in us12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0499001996007984\n"
     ]
    }
   ],
   "source": [
    "ath_us12 = us12[us12.response == 'atheist']\n",
    "print(len(ath_us12)/len(us12))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We will construct a 95% confidence interval for the proportion of atheists in the United States in 2012 but first we must be confident that conditions for inference are met.\n",
    "## np>10 and n(1-p)>10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of successes (atheist) = 50.0\n",
      "Number of failures (non-atheist) = 952.0\n",
      "Standard error = 0.006878629122390021\n",
      "95% confidence interval = (0.0364183342579056, 0.0633820649436912)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import norm\n",
    "\n",
    "conf_lvl = 0.95\n",
    "z_value = norm.ppf((1-(1-conf_lvl)/2))  # the Z value for the specified confidence level\n",
    "prbs = us12.response.value_counts(normalize = True)  # the probabilities for the response of atheist and non-atheist\n",
    "se = np.sqrt(prbs.prod()/len(us12))  # the standard error\n",
    "\n",
    "# construct a 95% confidence interval for the proportion of atheists in the United States in 2012\n",
    "ci1 = prbs['atheist'] - z_value * se\n",
    "ci2 = prbs['atheist'] + z_value * se\n",
    "\n",
    "# print the results\n",
    "print(f'Number of successes (atheist) = {len(us12)*prbs[1]}')\n",
    "print(f'Number of failures (non-atheist) = {len(us12)*prbs[0]}')\n",
    "print(f'Standard error = {se}')\n",
    "print(f'{int(conf_lvl*100)}% confidence interval = {ci1, ci2}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0269637306857856\n"
     ]
    }
   ],
   "source": [
    "print(2*z_value*se) #the margin of error for the estimate of the proportion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 1: Calculate confidence intervals for the proportion of atheists in 2012 in two other countries of your choice, and report the associated margins of error. (First you must test the conditions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How does the proportion affect the margin of error?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First we will define an array p that is a sequence from 0 to 1 with each number separated by 0.01. We can then create a array of the margin of error (me) associated with each of these values of p using the familiar approximate formula ( ME = 2×SE). Lastly, we plot the two vectors against each other to reveal their relationship."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 318,
       "width": 624
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "n = 1000\n",
    "p = np.arange(0, 1, 0.01)\n",
    "me = 2 * np.sqrt(p * (1 - p)/n)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "plt.style.use('ggplot')\n",
    "plt.rcParams['figure.figsize'] = (10,5)\n",
    "plt.plot(p, me)\n",
    "plt.xlabel('Population Proportion')\n",
    "plt.ylabel('Margin of Error')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can investigate the interplay between n and p and the shape of the sampling distribution by using simulations. To start off, we simulate the process of drawing 5000 samples of size 1040 from a population with a true atheist proportion of 0.1. For each of the 5000 samples we compute and then plot a histogram to visualize their distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 336,
       "width": 620
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = 0.1\n",
    "n = 1040\n",
    "p_hats = np.zeros(5000)\n",
    "\n",
    "for i in range(5000):\n",
    "    samp = np.random.choice(['atheist', 'non_atheist'], size = n, replace = True, p = [p, 1-p])\n",
    "    p_hats[i] = sum(samp == 'atheist')/n\n",
    "\n",
    "plt.hist(p_hats)\n",
    "plt.xlabel('p_hats')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('p = 0.1, n = 1040')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 2: Repeat the above simulation three more times but with modified sample sizes and proportions:\n",
    "## n=400 and p=0.1\n",
    "## n=1040 and p=0.2\n",
    "## n=400 and p=0.2\n",
    "## Plot the 3 histograms and describe the three new sampling distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}