statkclee/chap02soln-kor.ipynb

## chap02soln-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 읽어들여 변수명을 표시하시오."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'caseid', u'rscrinf', u'rdormres', u'rostscrn', u'rscreenhisp',\n",
       "       u'rscreenrace', u'age_a', u'age_r', u'cmbirth', u'agescrn',\n",
       "       ...\n",
       "       u'pubassis_i', u'basewgt', u'adj_mod_basewgt', u'finalwgt', u'secu_r',\n",
       "       u'sest', u'cmintvw', u'cmlstyr', u'screentime', u'intvlngth'],\n",
       "      dtype='object', length=3087)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import chap01soln\n",
    "resp = chap01soln.ReadFemResp()\n",
    "resp.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "응답자 가족에 대한 총소득 <tt>totincr</tt> 히스토그램을 생성하시오. 코드를 해석하기 위해서, [codebook](http://www.icpsr.umich.edu/nsfg6/Controller?displayPage=labelDetails&fileCode=MALE&section=R&subSec=7958&srtLabel=609776)을 살펴보시오."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import thinkstats2\n",
    "hist = thinkstats2.Hist(resp.totincr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "히스토그램을 화면에 표시하시오."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JfpjkB0k+3LWfmGRTkieSPJTk+J4xK5M8meTxJG+fqi9AknRo+p3x7wY+UlVnAGcDf5jk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaf13ddcc>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaee60aac>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import thinkplot\n",
    "thinkplot.Hist(hist, label='totincr')\n",
    "thinkplot.Show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "인터뷰 당시 응답자 나이 변수, <tt>age_r</tt>에 대한 히스토그램을 생성하시오."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5OIckaZrmYgb/3SQ/TfKhrm9pVY107RFg6SzPIUmagVmtwQNnVtULSd4K3Jvksd6NVVVJ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\nvCQ1yoCXpEYZ8JLUqP8P+GwpEmilI9sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaead778c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaeacbd2c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist = thinkstats2.Hist(resp.age_r)\n",
    "thinkplot.Hist(hist, label='age_r')\n",
    "thinkplot.Show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "응답자 가구의 가구원수, <tt>numfmhh</tt>에 대한 히스토그램을 생성하시오."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xae236acc>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaeac32ec>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist = thinkstats2.Hist(resp.numfmhh)\n",
    "thinkplot.Hist(hist, label='numfmhh')\n",
    "thinkplot.Show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "응답자가 낳은 자녀수, <tt>parity</tt>에 대한 히스토그램을 생성하시오. 이 분포를 어떻게 기술할까요?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xaeb58e2c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaebe3d4c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist = thinkstats2.Hist(resp.parity)\n",
    "thinkplot.Hist(hist, label='parity')\n",
    "thinkplot.Show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hist.Largest를 사용해서 <tt>parity</tt>의 가장 큰 수를 찾으시오."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('The largest parity is ...', [(22, 1), (16, 1), (10, 3), (9, 2), (8, 8), (7, 15), (6, 29), (5, 95), (4, 309), (3, 828)])\n"
     ]
    }
   ],
   "source": [
    "print('The largest parity is ...', hist.Largest(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<tt>totincr</tt>를 사용해서 가장 높은 임금을 갖는 응답자를 고르시오. 고임금 응답자에 대해서만 <tt>parity</tt> 분포를 계산하시오."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XWEuRpCmhp6eHnp6eMY0dU5gneQsDQX57Vd3bdPcnObKqNic5CtjS9G8C5g/ZfV7T9yZD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\ntYBhLkktYJhLUgsY5pLUAv8fEgWEd/DmKNEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaee6090c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaebbc08c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resp.totincr.value_counts() ## 총임금 빈도수 계산\n",
    "rich = resp[resp.totincr == 14]\n",
    "hist = thinkstats2.Hist(rich.parity)\n",
    "thinkplot.Hist(hist, label='rich parity')\n",
    "thinkplot.Show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "고임금 응답자에 대한 가장 큰 <tt>parity</tt>를 구하시오."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(8, 1), (7, 1), (5, 5), (4, 19), (3, 123), (2, 267), (1, 229), (0, 515)]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist.Largest(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "고임금과 고임금이 아닌 집단에 대한 평균 <tt>parity</tt>를 비교하시오."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Rich mean value is: ', 1.0758620689655172)\n",
      "('Poor mean value is: ', 1.2495758136665125)\n"
     ]
    }
   ],
   "source": [
    "rich = resp[resp.totincr == 14]\n",
    "poor = resp[resp.totincr < 14]\n",
    "print('Rich mean value is: ', rich.parity.mean())\n",
    "print('Poor mean value is: ', poor.parity.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "다른 흥미로워 보이는 변수도 조사하시오."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xaee6070c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaf124f8c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist = thinkstats2.Hist(resp.fmarno)\n",
    "thinkplot.Hist(hist, label='famrno')\n",
    "thinkplot.Show()"
   ]
  },
  {
   "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
}