chap04ex.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Examples and Exercises from Think Stats, 2nd Edition\n",
    "\n",
    "http://thinkstats2.com\n",
    "\n",
    "Copyright 2016 Allen B. Downey\n",
    "\n",
    "MIT License: https://opensource.org/licenses/MIT\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function, division\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import nsfg\n",
    "import first\n",
    "import thinkstats2\n",
    "import thinkplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examples\n",
    "\n",
    "One more time, I'll load the data from the NSFG."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "live, firsts, others = first.MakeFrames()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And compute the distribution of birth weight for first babies and others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Firsts 4413 4363\n",
      "Others 4735 4675\n"
     ]
    }
   ],
   "source": [
    "first_wgt = firsts.totalwgt_lb\n",
    "first_wgt_dropna = first_wgt.dropna()\n",
    "print('Firsts', len(first_wgt), len(first_wgt_dropna))\n",
    " \n",
    "other_wgt = others.totalwgt_lb\n",
    "other_wgt_dropna = other_wgt.dropna()\n",
    "print('Others', len(other_wgt), len(other_wgt_dropna))\n",
    "\n",
    "first_pmf = thinkstats2.Pmf(first_wgt_dropna, label='first')\n",
    "other_pmf = thinkstats2.Pmf(other_wgt_dropna, label='other')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the PMFs on the same scale, but it is hard to see if there is a difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a0c6410>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "width = 0.4 / 16\n",
    "\n",
    "# plot PMFs of birth weights for first babies and others\n",
    "thinkplot.PrePlot(2)\n",
    "thinkplot.Hist(first_pmf, align='right', width=width)\n",
    "thinkplot.Hist(other_pmf, align='left', width=width)\n",
    "thinkplot.Config(xlabel='Weight (pounds)', ylabel='PMF')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`PercentileRank` computes the fraction of `scores` less than or equal to `your_score`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def PercentileRank(scores, your_score):\n",
    "    count = 0\n",
    "    for score in scores:\n",
    "        if score <= your_score:\n",
    "            count += 1\n",
    "\n",
    "    percentile_rank = 100.0 * count / len(scores)\n",
    "    return percentile_rank"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If this is the list of scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = [55, 66, 77, 88, 99]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And you got the 88, your percentile rank is 80."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PercentileRank(t, 88)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Percentile` takes a percentile rank and computes the corresponding percentile. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Percentile(scores, percentile_rank):\n",
    "    scores.sort()\n",
    "    for score in scores:\n",
    "        if PercentileRank(scores, score) >= percentile_rank:\n",
    "            return score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The median is the 50th percentile, which is 77."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Percentile(t, 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a more efficient way to compute percentiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Percentile2(scores, percentile_rank):\n",
    "    scores.sort()\n",
    "    index = percentile_rank * (len(scores)-1) // 100\n",
    "    return scores[index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's hope we get the same answer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Percentile2(t, 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Cumulative Distribution Function (CDF) is almost the same as `PercentileRank`.  The only difference is that the result is 0-1 instead of 0-100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def EvalCdf(sample, x):\n",
    "    count = 0.0\n",
    "    for value in sample:\n",
    "        if value <= x:\n",
    "            count += 1\n",
    "\n",
    "    prob = count / len(sample)\n",
    "    return prob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = [1, 2, 2, 3, 5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can evaluate the CDF for various values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.2, 0.6, 0.8, 0.8, 1.0)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EvalCdf(t, 0), EvalCdf(t, 1), EvalCdf(t, 2), EvalCdf(t, 3), EvalCdf(t, 4), EvalCdf(t, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's an example using real data, the distribution of pregnancy length for live births."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a0c69d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cdf = thinkstats2.Cdf(live.prglngth, label='prglngth')\n",
    "thinkplot.Cdf(cdf)\n",
    "thinkplot.Config(xlabel='Pregnancy length (weeks)', ylabel='CDF', loc='upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Cdf` provides `Prob`, which evaluates the CDF; that is, it computes the fraction of values less than or equal to the given value.  For example, 94% of pregnancy lengths are less than or equal to 41."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.94064276344556186"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf.Prob(41)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "`Value` evaluates the inverse CDF; given a fraction, it computes the corresponding value.  For example, the median is the value that corresponds to 0.5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf.Value(0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In general, CDFs are a good way to visualize distributions.  They are not as noisy as PMFs, and if you plot several CDFs on the same axes, any differences between them are apparent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZeeSVzNbsxksKH8083phnIGb16uZ123l5cLdXMltLGDMpRMSNUxWIWb0qT0UdLJVok7yS2ZrauFNSJZ0j6X5JL6R/No62baZZMwuJZUfO8Upma2pjthTSaagXA5cA95OMLSwDPiUpIuKL+YdoVpzq/ZjL3HVkzWq8lsJfAW+PiLsi4tmI+HVEfA94B/Ch/MMzK9ZIpS3Mmtl4SeF3IqKv+mB67HfyCMisnri0hbWa8ZLCb/bznFnTcWkLawXjTUl9paSfjHBcwO/lEI9Z3SiPJwyUSsMWrZk1s/F+0k8ADgOeqDo+H3gql4jM6kR5PGGwVEJtexrVno5qzWy87qPPAs9GxM+zf4Bn03NmTas8nhDp2gRIEoKno1ozG6+lcFhEPFR9MCIekrQwl4jM6kh5FXN5POEflh9TZDhmuRuvpXDoGOcOrGUgZvWkPJ7gAnjWasZLChsl/WX1QUkfADblE5JZ8bIF8MrrE9xtZK1gvO6ji4FvSDqbPUmgB+gA3p5nYGZFGml9glcxWysYryDevwN/JOn1wJL08P9NVzWbNbXq8QSzVjChydcRcRdwV86xmBUuu3eCxxOsFY1bJdWslZT3TgCPJ1hrclIwyygnhIHM3gng8QRrHV67b5bKlskeLJU48cikdeCuI2slubYUJJ0u6VFJ2yStGuO6P5Q0JOmdecZjNpZsmexsWQt3HVkryS0pSGoHrgKWA4uB90paPMp1/wP4Tl6xmE3EaGWy3XVkrSTPlsJJwLaI2BERA8BNwIoRrrsQ+BrwdI6xmO0TT0O1VpVnUpjH8Oqq/emxCknzSBbBXZNjHGYTNlAq8UJnZ9FhmBWm6NlHlwMfi4jfjnWRpPMkbZS0cefOnVMUmrWiwVKJyGy76UFmazV5zj56kmTfhbLu9FhWD3CTkl/COcBbJA1FxL9mL4qIa4FrAXp6eiK3iK1lrVnfx8AICcGDzNZq8kwKG4CjJC0iSQbvAc7KXhARi8qPJd0A3FadEMymws3rtjNYmgZAm0RHSS6TbS0pt6QQEUOSLgBuB9qB6yNii6Tz0/Or83pvs32xZn0fLw/uJkodQDLzyC0Ea1W5Ll6LiLXA2qpjIyaDiFiZZyxmo8muT2iTOHzmdE9DtZZV9ECzWeFGW59g1opc5sJaVrYiapnXJ1irc0vBWla2IipQqYhq1srcUrCWlU0IB0xr51B3HZm5pWAG8MEzl7rryAwnBWtR2TLZAHc+9kzlsVcxWytzUrCWlB1gjgM6GBjas1DeaxSslTkpWEvKjid0HXZI5XFHSV6jYC3NScFa3pxDDqw8divBWp1nH1nLKY8nDJRKDJaG/wq4lWCtzi0Faznl8YTBUmnYtpseYDZzUrAWVB5PCKlS1sJlss0S7j6ylpLtOoI9ZS1cJtss4ZaCtZRs11G5rIW7jcz2cFKwljJS15G7jcz2cPeRtYRsRdTqriPPODLbwy0FawnZiqjuOjIbnZOCtYTsCma1tbnryGwU7j6yppctfjdQKrHsyD2JwF1HZsO5pWBNL1v8bnfHtMpjdx2Z7c0tBWt6Lw/urpS0mDdrRuW4u47M9uaWgrWEwVKJkCozjlwN1WxkTgrWEiKz/7JLWpiNzt1H1tTWrO+rrEsoc0kLs9G5pWBN7eZ12yvlsdskDy6bjcNJwZray4O7K11HR8ya7m4js3E4KVjLOHzmdA8um43DScGa1kjjCWY2NicFa1oeTzDbd04K1rQ8nmC279y2tqZTLpOd7TryeILZxOTaUpB0uqRHJW2TtGqE82dL+omkhyTdK+mEPOOx1lAuk+2uI7N9l1tSkNQOXAUsBxYD75W0uOqynwGvi4jjgU8A1+YVj7WO7O5qbekOa+46MpuYPLuPTgK2RcQOAEk3ASuAh8sXRMS9mevXA905xmMtoDzjqNxKKJfJdteR2cTk2X00D3gi87w/PTaavwC+NdIJSedJ2ihp486dO2sYojWb8oyjcisBXCLbbF/UxewjSa8nSQofG+l8RFwbET0R0dPV1TW1wVlDqZ5x5OJ3Zvsmz+6jJ4H5mefd6bFhJP0BcB2wPCJ25RiPNbnqxWqHz5zu4ndm+yjPlsIG4ChJiyR1AO8Bbs1eIGkB8HXgfRHxWI6xWAvwYjWzycutpRARQ5IuAG4H2oHrI2KLpPPT86uBvwdmA1crafIPRURPXjFZc3t5cDdR6gC8WM1sf+W6eC0i1gJrq46tzjz+APCBPGOw1jBS15FnHJntu7oYaDabLHcdmdWGy1xYQyuXtHgu5DpHZjXgloI1tJFKWizomuGuI7P95KRgDWvN+j6XtDCrMXcfWcMqV0IdLJVok1zSwqwG3FKwhlXuNoq0hQAuaWE2WU4K1rAG0oQAyRRUl7Qwmzx3H1lDWrO+rzK4DEkLwSUtzCbPLQVrOGvW9/GF7z1WaSW0yS0Es1pxS8EaRnlNQnnGUdkRs7x62axWnBSsIZRbB2Xlkhbds2dw+MzpRYVl1nScFKwh3Lxue+XxQKnEUEcH3bOmVwaYzaw2nBSs7pUXqZXXJMybc9Cw1oHHE8xqx0nB6tqa9X1cd/cOBjs7K6uWswlh+eIujyeY1ZCTgtWl7KByOSEAwxapveHoOU4IZjXmpGB1p9I6KE2rbJoDyaDygq4ZTgZmOXJSsLqRLYM9MG1a5Xi50N2CrhleoGaWMycFqxsjJYTylFOXsDCbGk4KVhfWrO8blhDKrYNze1/hriKzKeSkYIUaqcuoXAbbM4vMpp6TghUimwwGS9Mqs4sgmWHkhGBWDCcFK8SN9/TxYvvwZOAuI7PiOSnYlLnsW49wx6M72R1AW3vleHZ2kaebmhXLScFylV2E9kJmEVpZm8Srj+lyMjCrE04KVnMjlbjO7pJW1i544zFzuMRrD8zqhpOCTUp1AigXrYtSx4g/XW0SC+bM4H/92aumOFIzmwgnBdsve80eypSjqFYeMygXslu+uGuqwjSzfeSkYOMangAy3UD7kAjARezMGoGTgg2T7Q6qdAVJ+5QAfPM3a1xOCi1itP2NgeE3f0gSwAR+MjyV1Kz55JoUJJ0OXAG0A9dFxKVV55WefwvwIrAyIu7PM6ZmMtqNfq+bfNkEb/YjcQIwaw25JQVJ7cBVwGlAP7BB0q0R8XDmsuXAUemfk4Fr0r9bxlif4LPyuNGPJtsd5K4gs9aSZ0vhJGBbROwAkHQTsALIJoUVwBciIoD1kg6VNDcifpljXDU36kDsROVwYx/JSIO/Zb75mxnkeyuaBzyRed7P3q2Aka6ZB9Q8Kbzjk98Z95rnDzxw/99gjIHYPI12o/dN3sz2R0MMNEs6DzgPYMGCBQVHU3tjfYLP8o3ezPKWZ1J4Epifed6dHtvXa4iIa4FrAXp6eqK2YdaGB2LNrBnkmRQ2AEdJWkRyo38PcFbVNbcCF6TjDScDz+Y1nvC1v3tTHi9rZtZUcksKETEk6QLgdpIpqddHxBZJ56fnVwNrSaajbiOZknpuXvGYmdn4ch1TiIi1JDf+7LHVmccBfCjPGMzMbOLaig7AzMzqh5OCmZlVOCmYmVmFk4KZmVU4KZiZWYWSCUCNQ9JO4Of7+GVzgGdyCKdW6jm+eo4NHN9k1XN89RwbNF58r4iIcbc9bLiksD8kbYyInqLjGE09x1fPsYHjm6x6jq+eY4Pmjc/dR2ZmVuGkYGZmFa2SFK4tOoBx1HN89RwbOL7Jquf46jk2aNL4WmJMwczMJqZVWgpmZjYBTZ0UJJ0u6VFJ2yStKjqeLEnzJd0l6WFJWyRdVHRMI5HULukBSbcVHUu1dPvWWyQ9ImmrpNcUHVOZpI+k/6+bJX1VUmfB8Vwv6WlJmzPHZkn6rqSfpn/PrLP4PpX+3/5E0jckHVpP8WXO/bWkkDSniNjSGEaMT9KF6b/hFkn/cyKv1bRJQVI7cBWwHFgMvFfS4mKjGmYI+OuIWAy8GvhQncVXdhGwteggRnEF8O2IOBY4gTqJU9I84MNAT0QsISkd/55io+IG4PSqY6uAOyPiKODO9HlRbmDv+L4LLImIPwAeA/52qoPKuIG940PSfOBNwONTHVCVG6iKT9LrgRXACRFxHPDpibxQ0yYF4CRgW0TsiIgB4CaSf6C6EBG/jIj708fPkdzQ5hUb1XCSuoEzgOuKjqWapEOAPwb+GSAiBiLi18VGNUwJOFBSCZgO/KLIYCLibuBXVYdXADemj28E/nRKg8oYKb6I+E5EDKVP15PszFiIUf79AD4L/A1Q6ODsKPH9FXBpRLycXvP0RF6rmZPCPOCJzPN+6uymWyZpIfAq4IfFRrKXy0l+4H9bdCAjWATsBD6fdm9dJ2lG0UEBRMSTJJ/KHgd+SbKj4HeKjWpEh2V2OnwKOKzIYMbxfuBbRQeRJWkF8GRE/LjoWEZxNHCKpB9K+jdJfziRL2rmpNAQJB0EfA24OCL+X9HxlEl6K/B0RGwqOpZRlIBlwDUR8SrgBYrt/qhI++ZXkCSuI4AZkv6s2KjGlm54VZdTESV9nKS79ctFx1ImaTrwd8DfFx3LGErALJLu6Y8C/yJJ431RMyeFJ4H5mefd6bG6IWkaSUL4ckR8veh4qvQCfyKpj6Tr7T9J+lKxIQ3TD/RHRLl1dQtJkqgHbwR+FhE7I2IQ+DrwRwXHNJJ/lzQXIP17Qt0LU0nSSuCtwNlRX/PnjyRJ+j9Of0e6gfslHV5oVMP1A1+PxI9IWvzjDoY3c1LYABwlaZGkDpKBvlsLjqkizdj/DGyNiMuKjqdaRPxtRHRHxEKSf7vvRUTdfNqNiKeAJyQdkx56A/BwgSFlPQ68WtL09P/5DdTJIHiVW4Fz0sfnAGsKjGUvkk4n6b78k4h4seh4siLioYj43YhYmP6O9APL0p/LevGvwOsBJB0NdDCBAn5NmxTSAaoLgNtJfiH/JSK2FBvVML3A+0g+gT+Y/nlL0UE1mAuBL0v6CbAU+GTB8QCQtl5uAe4HHiL5PSt09aukrwL3AcdI6pf0F8ClwGmSfkrSurm0zuK7EjgY+G76+7F6zBeZ+vjqxijxXQ/8XjpN9SbgnIm0tryi2czMKpq2pWBmZvvOScHMzCqcFMzMrMJJwczMKpwUzMyswknBGoKkz0q6OPP8dknXZZ5/RtIl47zGvRN4n76Rql1KOlXSqAvQJP2ppMJWt0p6fpzzdxRZBdUah5OCNYp7SFcFS2ojWZl5XOb8HwFj3vQjYjKrik9l7FXJfwNcPYnXz9sXgQ8WHYTVPycFaxT3AuX9Eo4DNgPPSZop6QDglSSLxZD0UUkb0jr8/1h+gfKnaUltkq5O68x/V9JaSe/MvNeFku6X9JCkY9OChecDH0kXUZ2SDSxdLfpyRDyTPr9B0mpJGyU9ltaRQlKnpM+nr/tAWtoYSSslXZl5vdsknVqOWdI/SfqxpPWSDkuPL5J0X/pa/y3ztXMl3Z3GuTkT663Ae/f7X99ahpOCNYSI+AUwJGkBySf2+0iqyr4G6AEeiogBSW8CjiIpnb4UOFHSH1e93JnAQpJ9Nt7HnmRT9kxELAOuAf5zRPQBq4HPRsTSiFhXdX0vaULKWJjGcAawWskmOx9KvpU4nuQGfaPG33xnBrA+Ik4A7gb+Mj1+BUkxwONJKrGWnQXcHhFLSfaYeJDkTf8DOEDS7HHez1qck4I1kntJEkI5KdyXeX5Pes2b0j8PkNyojyVJElmvBf5PRPw2rVVzV9X5cnHCTSQ39/HMJSnjnfUv6ev/FNiRxvFa4EsAEfEI8HOS8sZjGQDKu95l4+kFvpo+/mLm+g3AuZL+K3B8uldH2dMkVVvNRuWkYI2kPK5wPEn30XqST/nZ8QQB/z39RL80In4/Iv55H9/n5fTv3STlh8fzG6D6E391/Zix6skMMfx3Mftag5l6NdXx7PWa6WYrf0xSEfgGSX9e9bq/GSMOMycFayj3kpRR/lVE7I6IXwGHkiSGclK4HXh/uk8FkuZJ+t2q17kHeEc6tnAYySDyeJ4jKc42kq3A71cde1f6+kcCvwc8CqwDzk7jOhpYkB7vA5am188sYN6yAAABGUlEQVQn6XYazz3s2eLz7PJBSa8A/j0i/jfJjnnL0uMCDk/fy2xUTgrWSB4imXW0vurYs+VB3nSHs68A90l6iKRaafXN/GskpY4fJunOuR94dpz3/ibw9pEGmkn6+l+V3njLHgd+RLJb2PkR8RLJ7KS2NK6bgZXpVon3AD9L4/kce49PjOQikn29H2L4joKnktT4fwB4N8nYA8CJJGMTQ5iNwVVSrSVJOigink8HXn8E9E6mFr6kK4BvRsQdkm4AbouIW2oU7qSl8d0aEXcWHYvVt4n0l5o1o9skHUqy8cgnarA5yieBkycfVm42OyHYRLilYGZmFR5TMDOzCicFMzOrcFIwM7MKJwUzM6twUjAzswonBTMzq/j/6bL/SsDUZnYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0bd56910>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "first_cdf = thinkstats2.Cdf(firsts.totalwgt_lb, label='first')\n",
    "other_cdf = thinkstats2.Cdf(others.totalwgt_lb, label='other')\n",
    "\n",
    "thinkplot.PrePlot(2)\n",
    "thinkplot.Cdfs([first_cdf, other_cdf])\n",
    "thinkplot.Config(xlabel='Weight (pounds)', ylabel='CDF')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we can see that first babies are slightly, but consistently, lighter than others.\n",
    "\n",
    "We can use the CDF of birth weight to compute percentile-based statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "weights = live.totalwgt_lb\n",
    "live_cdf = thinkstats2.Cdf(weights, label='live')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, the median is the 50th percentile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.375"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median = live_cdf.Percentile(50)\n",
    "median"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The interquartile range is the interval from the 25th to 75th percentile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6.5, 8.125)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iqr = (live_cdf.Percentile(25), live_cdf.Percentile(75))\n",
    "iqr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the CDF to look up the percentile rank of a particular value.  For example, my second daughter was 10.2 pounds at birth, which is near the 99th percentile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98.827174153573807"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "live_cdf.PercentileRank(10.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we draw a random sample from the observed weights and map each weigh to its percentile rank."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample = np.random.choice(weights, 100, replace=True)\n",
    "ranks = [live_cdf.PercentileRank(x) for x in sample]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting list of ranks should be approximately uniform from 0-1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0b94eb90>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rank_cdf = thinkstats2.Cdf(ranks)\n",
    "thinkplot.Cdf(rank_cdf)\n",
    "thinkplot.Config(xlabel='Percentile rank', ylabel='CDF')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That observation is the basis of `Cdf.Sample`, which generates a random sample from a Cdf.  Here's an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1c508050>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "resample = live_cdf.Sample(1000)\n",
    "thinkplot.Cdf(live_cdf)\n",
    "thinkplot.Cdf(thinkstats2.Cdf(resample, label='resample'))\n",
    "thinkplot.Config(xlabel='Birth weight (pounds)', ylabel='CDF')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This confirms that the random sample has the same distribution as the original data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Exercise:** How much did you weigh at birth? If you don’t know, call your mother or someone else who knows. Using the NSFG data (all live births), compute the distribution of birth weights and use it to find your percentile rank. If you were a first baby, find your percentile rank in the distribution for first babies. Otherwise use the distribution for others. If you are in the 90th percentile or higher, call your mother back and apologize."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54.870501948200776"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution goes here\n",
    "first_cdf.PercentileRank(7.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution goes here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Exercise:** The numbers generated by `numpy.random.random` are supposed to be uniform between 0 and 1; that is, every value in the range should have the same probability.\n",
    "\n",
    "Generate 1000 numbers from `numpy.random.random` and plot their PMF.  What goes wrong?\n",
    "\n",
    "Now plot the CDF. Is the distribution uniform?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1c91bb10>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution goes here\n",
    "rand_array = np.random.random(1000)\n",
    "pmf = thinkstats2.Pmf(rand_array)\n",
    "thinkplot.Pmf(pmf)\n",
    "thinkplot.Config(xlabel='Value', ylabel='PMF')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1c8c2810>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution goes here\n",
    "cdf = thinkstats2.Cdf(rand_array)\n",
    "thinkplot.Cdf(cdf)\n",
    "thinkplot.Config(xlabel='Value', ylabel='CDF')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution goes here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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