CH4/templates/07_stat.student_tests.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Критерии Стьюдента"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import scipy\n",
    "from statsmodels.stats.weightstats import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Treatment effects of methylphenidate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В рамках исследования эффективности препарата метилфенидат 24 пациента с синдромом дефицита внимания и гиперактивности в течение недели принимали либо метилфенидат, либо плацебо. В конце недели каждый пациент проходили тест на способность к подавлению импульсивных поведенческих реакций. На втором этапе плацебо и препарат менялись, и после недельного курса каждый испытуемые проходили второй тест.\n",
    "\n",
    "Требуется оценить эффект применения препарата.\n",
    "\n",
    "Pearson D.A, Santos C.W., Casat C.D., et al. (2004). Treatment effects of methylphenidate on cognitive functioning in children with mental retardation and ADHD. Journal of the American Academy of Child and Adolescent Psychiatry, 43(6), 677–685."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('ADHD.txt', sep = ' ', header = 0)\n",
    "data.columns = ['Placebo', 'Methylphenidate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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yuuDYx4DvTJ9O12OPjS0YEak71QwaTD5vWW4Ov3Av4o6OjlH3Ih7qZ089ReuQ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdb240cb490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot.scatter('Placebo', 'Methylphenidate', c = 'r', s = 30)\n",
    "pylab.grid()\n",
    "pylab.plot(range(100), c = 'black')\n",
    "pylab.xlim((20, 80))\n",
    "pylab.ylim((20, 80))\n",
    "pylab.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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A3wMvalzzyLJ7vRtobPd6N9DY7vVuoLXRejfQ1u71buDI0jrwTwBuW7Z+e7dNkjRnW9a7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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdaf3de3f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Methylphenidate'].plot.hist()\n",
    "data['Placebo'].plot.hist()\n",
    "pylab.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Одновыборочный критерий Стьюдента"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Исходя из того, что способность к подавлению испульсивных поведенческих реакций измеряется по шкале [0, 100], можно предположить, что при хорошей калибровке теста средняя способоность к подавлению реакций в популяции составляет 50. Тогда для того, чтобы проверить гипотезу о том, что пациенты в выборке действительно в среднем хуже справляются с подавлением импульсивных реакций (нуждаются в лечении), давайте проверим, что их способность к подавлению реакций отличается от средней (не равна 50). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$H_0\\colon$ среднее значение способности к подавлению испульсивных поведенческих реакций равно 50.\n",
    "\n",
    "$H_1\\colon$ не равно."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_1sampResult(statistic=-4.4380033419051284, pvalue=0.00018894293251080175)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_1samp(data.Placebo, 50.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95% confidence interval: [35.223273, 44.276727]\n"
     ]
    }
   ],
   "source": [
    "print \"95%% confidence interval: [%f, %f]\" % zconfint(data.Placebo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Двухвыборочный критерий Стьюдента (независимые выборки)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для того, чтобы использовать двухвыборочный критерий Стьюдента, убедимся, что распределения в выборках существенно не отличаются от нормальных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dyfmH1YMnnoAPPuDMp/5F81ZDqWhLV239KlJlEpqz3f0ddz8v8vl6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\nQsgmpapgx9AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdaf3dcca90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.figure(figsize=(12,8))\n",
    "pylab.subplot(2,2,1)\n",
    "stats.probplot(data.Placebo, dist=\"norm\", plot=pylab)\n",
    "pylab.subplot(2,2,2)\n",
    "stats.probplot(data.Methylphenidate, dist=\"norm\", plot=pylab)\n",
    "pylab.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Критерий Шапиро-Уилка:\n",
    "\n",
    " $H_0\\colon$ способности к подавлению импульсивных реакций распредлены нормально\n",
    " \n",
    " $H_1\\colon$ не нормально."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shapiro-Wilk normality test, W-statistic: 0.857487, p-value: 0.003020\n"
     ]
    }
   ],
   "source": [
    "print \"Shapiro-Wilk normality test, W-statistic: %f, p-value: %f\" % stats.shapiro(data.Placebo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shapiro-Wilk normality test, W-statistic: 0.916445, p-value: 0.048768\n"
     ]
    }
   ],
   "source": [
    "print \"Shapiro-Wilk normality test, W-statistic: %f, p-value: %f\" % stats.shapiro(data.Methylphenidate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "С помощью критерия Стьюдента проверим гипотезу о развенстве средних двух выборок."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Критерий Стьюдента:\n",
    "\n",
    "$H_0\\colon$ средние значения способности к подавлению испульсивных поведенческих реакций одинаковы для пациентов, принимавших препарат, и для пациентов, принимавших плацебо.\n",
    "\n",
    "$H_0\\colon$ не одинаковы."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-1.4521635018159091, pvalue=0.15329199774047089)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.ttest_ind(data.Placebo, data.Methylphenidate, equal_var = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95% confidence interval: [-1.914589, 11.831256]\n"
     ]
    }
   ],
   "source": [
    "cm = CompareMeans(DescrStatsW(data.Methylphenidate), DescrStatsW(data.Placebo))\n",
    "print \"95%% confidence interval: [%f, %f]\" % cm.tconfint_diff()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Двухвыборочный критерий Стьюдента (зависмые выборки)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для того, чтобы использовать критерй Стьюдента для связанных выборок, давайте проверим, что распределение попарных разностей существенно не отличается от нормального."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MCgk2bpzMsCWiZA+Eb2Bm2wM9gTfiOYiIZJc6ld2YNClYx6JnT+jVC6ZMgXPPVcKoJ6Ik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\n/wE/BaZnqoGwIwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdaf1504c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stats.probplot(data.Placebo - data.Methylphenidate, dist = \"norm\", plot = pylab)\n",
    "pylab.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Критерий Шапиро-Уилка:\n",
    "\n",
    "$H_0\\colon$ попарные разности распределены нормально.\n",
    "\n",
    "$H_1\\colon$ не нормально."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shapiro-Wilk normality test, W-statistic: 0.979806, p-value: 0.892254\n"
     ]
    }
   ],
   "source": [
    "print \"Shapiro-Wilk normality test, W-statistic: %f, p-value: %f\" % stats.shapiro(data.Methylphenidate - data.Placebo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Критерий Стьюдента:\n",
    "\n",
    "$H_0\\colon$ средние значения способности к подавлению испульсивных поведенческих реакций одинаковы для пациентов, принимавших препарат, и для пациентов, принимавших плацебо.\n",
    "\n",
    "$H_1\\colon$ не одинаковы."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_relResult(statistic=3.2223624451230406, pvalue=0.003771488176381471)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_rel(data.Methylphenidate, data.Placebo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95% confidence interval: [1.775236, 8.141431]\n"
     ]
    }
   ],
   "source": [
    "print \"95%% confidence interval: [%f, %f]\" % DescrStatsW(data.Methylphenidate - data.Placebo).tconfint_mean()"
   ]
  },
  {
   "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
}