sliderule_dsi_inferential_statistics_exercise_1 (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What is the True Normal Human Body Temperature? \n",
    "\n",
    "#### Background\n",
    "\n",
    "The mean normal body temperature was held to be 37$^{\\circ}$C or 98.6$^{\\circ}$F for more than 120 years since it was first conceptualized and reported by Carl Wunderlich in a famous 1868 book. But, is this value statistically correct?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"span5 alert alert-info\">\n",
    "<h3>Exercises</h3>\n",
    "\n",
    "<p>In this exercise, you will analyze a dataset of human body temperatures and employ the concepts of hypothesis testing, confidence intervals, and statistical significance.</p>\n",
    "\n",
    "<p>Answer the following questions <b>in this notebook below and submit to your Github account</b>.</p> \n",
    "\n",
    "<ol>\n",
    "<li>  Is the distribution of body temperatures normal? \n",
    "    <ul>\n",
    "    <li> Although this is not a requirement for CLT to hold (read CLT carefully), it gives us some peace of mind that the population may also be normally distributed if we assume that this sample is representative of the population.\n",
    "    </ul>\n",
    "<li>  Is the sample size large? Are the observations independent?\n",
    "    <ul>\n",
    "    <li> Remember that this is a condition for the CLT, and hence the statistical tests we are using, to apply.\n",
    "    </ul>\n",
    "<li>  Is the true population mean really 98.6 degrees F?\n",
    "    <ul>\n",
    "    <li> Would you use a one-sample or two-sample test? Why?\n",
    "    <li> In this situation, is it appropriate to use the $t$ or $z$ statistic? \n",
    "    <li> Now try using the other test. How is the result be different? Why?\n",
    "    </ul>\n",
    "<li>  At what temperature should we consider someone's temperature to be \"abnormal\"?\n",
    "    <ul>\n",
    "    <li> Start by computing the margin of error and confidence interval.\n",
    "    </ul>\n",
    "<li>  Is there a significant difference between males and females in normal temperature?\n",
    "    <ul>\n",
    "    <li> What test did you use and why?\n",
    "    <li> Write a story with your conclusion in the context of the original problem.\n",
    "    </ul>\n",
    "</ol>\n",
    "\n",
    "You can include written notes in notebook cells using Markdown: \n",
    "   - In the control panel at the top, choose Cell > Cell Type > Markdown\n",
    "   - Markdown syntax: http://nestacms.com/docs/creating-content/markdown-cheat-sheet\n",
    "\n",
    "#### Resources\n",
    "\n",
    "+ Information and data sources: http://www.amstat.org/publications/jse/datasets/normtemp.txt, http://www.amstat.org/publications/jse/jse_data_archive.htm\n",
    "+ Markdown syntax: http://nestacms.com/docs/creating-content/markdown-cheat-sheet\n",
    "\n",
    "****\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(65, 3)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "df = pd.read_csv('data/human_body_temperature.csv')\n",
    "df[df.gender=='F'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy as sc\n",
    "import scipy.stats.mstats as mst\n",
    "import scipy.stats as stats\n",
    "\n",
    "numpy = np\n",
    "\n",
    "%matplotlib inline\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAD8CAYAAAB9y7/cAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKAMvSY36P7GjTycE6rr+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10eaddd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "H =  ax.hist(df['temperature'], bins=5, alpha=.5, histtype='stepfilled')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x112111190>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+QUEvZvQVNq3NQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111ff0610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = plt.plot(df['temperature'])\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98.24923076923078"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_mean = df['temperature'].mean()\n",
    "sample_mean\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NormaltestResult(statistic=2.7038014333192031, pvalue=0.2587479863488254)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = mst.normaltest(df['temperature'])\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "temp = df['temperature']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Question 3</h1>\n",
    "<p1>sample size > 30, so z-test is appropriate </p1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature</th>\n",
       "      <th>heart_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>98.393846</td>\n",
       "      <td>74.153846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>98.104615</td>\n",
       "      <td>73.369231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        temperature  heart_rate\n",
       "gender                         \n",
       "F         98.393846   74.153846\n",
       "M         98.104615   73.369231"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('gender').mean()\n",
    "#will want to look at the difference in means, and plot it against a null hypothesis that there is no difference\n",
    "#then plot the difference in sample means distribution where the mean is equal to 0, and see how far away our mean is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Question 2</h1>\n",
    "<p1>Part 1: The sample size is large enough to use a Z Table for our tests (130 > 30) </p1>\n",
    "<p2>Part 2: The observations are independent from one another </p2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Question 5 </h3>\n",
    "<p1> We will be using a difference in means hypothesis test for this experiment </p1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06430441683789101"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pandas by default returns the sample standard deviation, i.e. denom is (sqrt(n-1))\n",
    "samp_std = temp.std()\n",
    "#estimating with sample std\n",
    "samp_dist_mean_samp_mean = samp_std/np.sqrt(130)\n",
    "samp_dist_mean_samp_mean\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "###what is the probability that the true population mean is actually 98.6\n",
    "###can also check - h0: the "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.0027207302320931825, 0.58118226869354717)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###what is the likilihood that our sampled mean is within \"\"\"\"\"\" of the population mean...\n",
    "z_score =stats.norm.ppf(.975)\n",
    "z_score\n",
    "###the variance of our sampling distribution is equal to the sum of each of these sampling distributions (x1, x2)\n",
    "##each variance of the sampling distribution is equal to the variance of the population distribution (x1, x2, etc)\n",
    "##divided by the size of the sample (x1, x2, etc)\n",
    "#### WE DON'T KNOW THE VARIANCE OF THE POPULATION DISTRIBUTION\n",
    "### WE CAN REPLACE THEM WITH THE SAMPLE VARIANCES FOR EACH SAMPLED DISTRIBUTION, BECAUSE N > 30\n",
    "## The sample variance will be the sample_std of each population, squared\n",
    "sample_dist_std = np.sqrt((male_temp_samp_std/ male_n) + (female_temp_samp_std/female_n))     #sum of\n",
    "_conf_interval = (-z_score*sample_dist_std, z_score*sample_dist_std)\n",
    "_distance = z_score*sample_dist_std\n",
    "_distance\n",
    "plus_minus(diff_in_means, _distance)\n",
    "#95% chance that the true mean is within z_score*sample_dist_std of the sampled mean\n",
    "####95% chance that the true difference in means is within z_score*sample_dist_std of the sampled difference in means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10577135326480434"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z_prob = stats.norm.ppf(.95)\n",
    "dist_from_mean=z_prob*samp_dist_mean_samp_mean\n",
    "dist_from_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "###h0: The true mean of the population is 98.6 \n",
    "###h1: The true population mean is not 98.6\n",
    "###Assume that the true mean of the population is 98.6 ==> If the probability of the true population mean being \n",
    "### 98.6 is less than 5% [P(population_mean | h0) < 5%], then we reject the null hypothesis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.289230769230727"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff_in_means =abs(female_temp_samp_mean - male_temp_samp_mean)\n",
    "\n",
    "diff_in_means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(98.143459415965978, 98.355002122495577)"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### we have confidence that there is a 95% 'chance'/probability/something that the mean of the population is \n",
    "###between 98.143... and 98.355..\n",
    "def plus_minus(sample_mean, interval):\n",
    "    return (sample_mean-interval, sample_mean + interval)\n",
    "\n",
    "\n",
    "plus_minus(sample_mean, dist_from_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "male_df = df[df.gender=='M']\n",
    "male_n = male_df.shape[0]\n",
    "male_temp_samp_std = male_df['temperature'].std()\n",
    "male_temp_samp_mean = male_df['temperature'].mean()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "female_df = df[df.gender=='F']\n",
    "female_n = female_df.shape[0]\n",
    "female_temp_samp_std = female_df['temperature'].std()\n",
    "female_temp_samp_mean = female_df['temperature'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14895758379526172"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###h0: There is no difference in means of men and women; i.e. mean(P1)-mean(P2) == 0\n",
    "###h1: There is a difference in means; mean(P1) != mean(P2); mean(P1)-mean(P2) != 0\n",
    "\n",
    "\n",
    "\n",
    "#probably going to randomize and take two halves\n",
    "\n",
    "#OR find an interval in which there is a 95% chance (confident at least) that the true mean falls within\n",
    "\n",
    "sample_dist_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "class HypothesisTest(object):\n",
    "    \"\"\"Represents a hypothesis test.\"\"\"\n",
    "\n",
    "    def __init__(self, data):\n",
    "        \"\"\"Initializes.\n",
    "\n",
    "        data: data in whatever form is relevant\n",
    "        \"\"\"\n",
    "        self.data = data\n",
    "        self.MakeModel()\n",
    "        self.actual = self.TestStatistic(data)\n",
    "        self.test_stats = None\n",
    "\n",
    "    def PValue(self, iters=1000):\n",
    "        \"\"\"Computes the distribution of the test statistic and p-value.\n",
    "\n",
    "        iters: number of iterations\n",
    "\n",
    "        returns: float p-value\n",
    "        \"\"\"\n",
    "        self.test_stats = numpy.array([self.TestStatistic(self.RunModel())\n",
    "                                       for _ in range(iters)])\n",
    "        #print self.test_stats\n",
    "        #print self.actual\n",
    "        count = sum(self.test_stats >= self.actual)\n",
    "        \n",
    "        \"\"\"print count\n",
    "        print iters\n",
    "        print int(count)/int(iters)\"\"\"\n",
    "        \n",
    "        return float(count) / float(iters)\n",
    "\n",
    "    def MaxTestStat(self):\n",
    "        \"\"\"Returns the largest test statistic seen during simulations.\n",
    "        \"\"\"\n",
    "        return max(self.test_stats)\n",
    "\n",
    "    def PlotHist(self, label=None):\n",
    "        \"\"\"Draws a Cdf with vertical lines at the observed test stat.\n",
    "        \"\"\"\n",
    "        ys, xs, patches = pyplot.hist(ht.test_stats, color=COLOR4)\n",
    "        pyplot.vlines(self.actual, 0, max(ys), linewidth=3, color='0.8')\n",
    "        pyplot.xlabel('test statistic')\n",
    "        pyplot.ylabel('count')\n",
    "\n",
    "    def TestStatistic(self, data):\n",
    "        \"\"\"Computes the test statistic.\n",
    "\n",
    "        data: data in whatever form is relevant\n",
    "        \"\"\"\n",
    "        raise UnimplementedMethodException()\n",
    "    def MakeModel(self):\n",
    "        \"\"\"Build a model of the null hypothesis.\n",
    "        \"\"\"\n",
    "        pass\n",
    "\n",
    "    def RunModel(self):\n",
    "        \"\"\"Run the model of the null hypothesis.\n",
    "\n",
    "        returns: simulated data\n",
    "        \"\"\"\n",
    "        raise UnimplementedMethodException()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "class DiffMeansPermute(HypothesisTest):\n",
    "    \"\"\"Tests a difference in means by permutation.\"\"\"\n",
    "\n",
    "    def TestStatistic(self, data):\n",
    "        \"\"\"Computes the test statistic.\n",
    "\n",
    "        data: data in whatever form is relevant\n",
    "        \"\"\"\n",
    "        group1, group2 = data\n",
    "        test_stat = abs(group1.mean() - group2.mean())\n",
    "        return test_stat\n",
    "    \n",
    "    \n",
    "    def MakeModel(self):\n",
    "        \"\"\"Build a model of the null hypothesis.\n",
    "        \"\"\"\n",
    "        group1, group2 = self.data\n",
    "        self.n, self.m = len(group1), len(group2)\n",
    "        self.pool = numpy.hstack((group1, group2))\n",
    "\n",
    "    def RunModel(self):\n",
    "        \"\"\"Run the model of the null hypothesis.\n",
    "\n",
    "        returns: simulated data\n",
    "        \"\"\"\n",
    "        numpy.random.shuffle(self.pool)\n",
    "        data = self.pool[:self.n], self.pool[self.n:]\n",
    "        return data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "means permute temp difference \n",
      "('p-value =', 0.028)\n",
      "('actual =', 0.289230769230727)\n",
      "('ts max =', 0.47076923076923549)\n"
     ]
    }
   ],
   "source": [
    "data = (male_df['temperature'], female_df['temperature'])\n",
    "ht = DiffMeansPermute(data)\n",
    "p_value = ht.PValue(iters=1000)\n",
    "print('\\nmeans permute temp difference ')\n",
    "print('p-value =', p_value)\n",
    "print('actual =', ht.actual)\n",
    "print('ts max =', ht.MaxTestStat())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#list(male_df['temperature'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(98.120621935554993, 98.377839602906562)"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###Margin of error\n",
    "sample_mean\n",
    "approx_pop_std = temp.std()/np.sqrt(temp.shape[0])\n",
    "margin_of_error = approx_pop_std*2 #two standard deviations\n",
    "### ^^^ Not EXACTLY 95%. it's an approximate, but two standard deviations is closer to 95.4\n",
    "\n",
    "plus_minus(sample_mean, margin_of_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}