statkclee/chap03soln-kor.ipynb

## chap03soln-kor.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "통계적 사고 (2판) 연습문제 ([thinkstats2.com](thinkstats2.com), [think-stat.xwmooc.org](http://think-stat.xwmooc.org))<br>\n",
    "Allen Downey / 이광춘(xwMOOC)\n",
    "\n",
    "여성 응답자 파일을 읽어들인다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import chap01soln\n",
    "resp = chap01soln.ReadFemResp()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "응답자 가구에서 18세 이하 자녀수, <tt>numkdhh</tt>에 대한 PMF를 생성하라."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import thinkstats2\n",
    "pmf = thinkstats2.Pmf(resp.numkdhh)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PMF를 화면에 표시하라."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xaf2f680c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaf31498c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import thinkplot\n",
    "thinkplot.Pmf(pmf, label='numkdhh')\n",
    "thinkplot.Show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<tt>BiasPmf</tt>를 정의하시오."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def BiasPmf(pmf, label=''):\n",
    "    \"\"\"Returns the Pmf with oversampling proportional to value.\n",
    "\n",
    "    If pmf is the distribution of true values, the result is the\n",
    "    distribution that would be seen if values are oversampled in\n",
    "    proportion to their values; for example, if you ask students\n",
    "    how big their classes are, large classes are oversampled in\n",
    "    proportion to their size.\n",
    "\n",
    "    Args:\n",
    "      pmf: Pmf object.\n",
    "      label: string label for the new Pmf.\n",
    "\n",
    "     Returns:\n",
    "       Pmf object\n",
    "    \"\"\"\n",
    "    new_pmf = pmf.Copy(label=label)\n",
    "\n",
    "    for x, p in pmf.Items():\n",
    "        new_pmf.Mult(x, x)\n",
    "        \n",
    "    new_pmf.Normalize()\n",
    "    return new_pmf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "응답자 대신에 자녀를 설문조사하면 관측되듯이, 가구 자녀수에 대한 편향된 Pmf를 생성하시오."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "biased = BiasPmf(pmf, label='biased')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "실제 Pmf와 편향된 Pmf를 동일축으로 화면에 표시하시오."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xaf2e730c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaf3147ac>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.PrePlot(2)\n",
    "thinkplot.Pmfs([pmf, biased])\n",
    "thinkplot.Show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "두 Pmf의 평균을 계산하시오."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Pmf Mean: ', 1.0242051550438309)\n",
      "('Biased Pmf Mean', 2.4036791006642821)\n"
     ]
    }
   ],
   "source": [
    "print('Pmf Mean: ',pmf.Mean())\n",
    "print('Biased Pmf Mean', biased.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": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
	{
	"cells": [
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"통계적 사고 (2판) 연습문제 ([thinkstats2.com](thinkstats2.com), [think-stat.xwmooc.org](http://think-stat.xwmooc.org))<br>\n",
	"Allen Downey / 이광춘(xwMOOC)\n",
	"\n",
	"여성 응답자 파일을 읽어들인다."
	]
	},
	{
	"cell_type": "code",
	"execution_count": 19,
	"metadata": {
	"collapsed": false
	},
	"outputs": [],
	"source": [
	"%matplotlib inline\n",
	"\n",
	"import chap01soln\n",
	"resp = chap01soln.ReadFemResp()"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"응답자 가구에서 18세 이하 자녀수, <tt>numkdhh</tt>에 대한 PMF를 생성하라."
	]
	},
	{
	"cell_type": "code",
	"execution_count": 20,
	"metadata": {
	"collapsed": false
	},
	"outputs": [],
	"source": [
	"import thinkstats2\n",
	"pmf = thinkstats2.Pmf(resp.numkdhh)"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"PMF를 화면에 표시하라."
	]
	},
	{
	"cell_type": "code",
	"execution_count": 21,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"image/png": 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	"text/plain": [
	"<matplotlib.figure.Figure at 0xaf2f680c>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	},
	{
	"data": {
	"text/plain": [
	"<matplotlib.figure.Figure at 0xaf31498c>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"import thinkplot\n",
	"thinkplot.Pmf(pmf, label='numkdhh')\n",
	"thinkplot.Show()"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"<tt>BiasPmf</tt>를 정의하시오."
	]
	},
	{
	"cell_type": "code",
	"execution_count": 22,
	"metadata": {
	"collapsed": false
	},
	"outputs": [],
	"source": [
	"def BiasPmf(pmf, label=''):\n",
	" \"\"\"Returns the Pmf with oversampling proportional to value.\n",
	"\n",
	" If pmf is the distribution of true values, the result is the\n",
	" distribution that would be seen if values are oversampled in\n",
	" proportion to their values; for example, if you ask students\n",
	" how big their classes are, large classes are oversampled in\n",
	" proportion to their size.\n",
	"\n",
	" Args:\n",
	" pmf: Pmf object.\n",
	" label: string label for the new Pmf.\n",
	"\n",
	" Returns:\n",
	" Pmf object\n",
	" \"\"\"\n",
	" new_pmf = pmf.Copy(label=label)\n",
	"\n",
	" for x, p in pmf.Items():\n",
	" new_pmf.Mult(x, x)\n",
	" \n",
	" new_pmf.Normalize()\n",
	" return new_pmf"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"응답자 대신에 자녀를 설문조사하면 관측되듯이, 가구 자녀수에 대한 편향된 Pmf를 생성하시오."
	]
	},
	{
	"cell_type": "code",
	"execution_count": 23,
	"metadata": {
	"collapsed": false
	},
	"outputs": [],
	"source": [
	"biased = BiasPmf(pmf, label='biased')"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"실제 Pmf와 편향된 Pmf를 동일축으로 화면에 표시하시오."
	]
	},
	{
	"cell_type": "code",
	"execution_count": 24,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"image/png": 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	"text/plain": [
	"<matplotlib.figure.Figure at 0xaf2e730c>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	},
	{
	"data": {
	"text/plain": [
	"<matplotlib.figure.Figure at 0xaf3147ac>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"thinkplot.PrePlot(2)\n",
	"thinkplot.Pmfs([pmf, biased])\n",
	"thinkplot.Show()"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"두 Pmf의 평균을 계산하시오."
	]
	},
	{
	"cell_type": "code",
	"execution_count": 25,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"('Pmf Mean: ', 1.0242051550438309)\n",
	"('Biased Pmf Mean', 2.4036791006642821)\n"
	]
	}
	],
	"source": [
	"print('Pmf Mean: ',pmf.Mean())\n",
	"print('Biased Pmf Mean', biased.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": "2.7.10"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 0
	}