Assignment_01.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.stats as sp\n",
    "from scipy import stats\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from statsmodels.stats.proportion import proportions_ztest\n",
    "\n",
    "#ignore warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 확률분포(Probability Distribution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 실습01 - p12\n",
    "- 정규분포 / 표준정규분포"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([31.179, 27.023, 33.582, ..., 32.576, 26.347, 31.613])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(seed=1234)\n",
    "normal = np.random.normal(30,2.5,1000000)\n",
    "sns.distplot(normal, fit=stats.norm, kde=False)\n",
    "\n",
    "normal.round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 실습02 - p14\n",
    "- 정규분포 / 표준정규분포"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "180내 키의 위치 : 84.1%\n"
     ]
    }
   ],
   "source": [
    "mu = 175\n",
    "sigma = 5\n",
    "x = 180\n",
    "prop = stats.norm.cdf(x, mu, sigma)\n",
    "print(\"{0}내 키의 위치 : {1:.1f}%\".format(x,(prop) * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 실습03 - p15\n",
    "- 정규분포 / 표준정규분포"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1리터를 가지고  12km 이상 15km 이하 달릴 확률 : 34.1%\n",
      "1리터를 가지고  9km 이상 15km 이하 달릴 확률 : 68.3%\n",
      "1리터를 가지고  15km 이상 달릴 확률 : 15.9%\n"
     ]
    }
   ],
   "source": [
    "#1, 12Km이상 15Km이하를 달릴 확률\n",
    "mu = 12\n",
    "sigma = 3\n",
    "x = 12\n",
    "y = 15\n",
    "z = 9\n",
    "prop = stats.norm.cdf(y, mu, sigma)\n",
    "\n",
    "#2\n",
    "#9km 이상 15km 이하를 달릴 확률은 얼마인가\n",
    "prop2 = stats.norm.cdf(x, mu, sigma)\n",
    "\n",
    "#3\n",
    "#15km 이상를 달릴 확률은 얼마인가\n",
    "prop3 = stats.norm.cdf(z, mu, sigma)\n",
    "\n",
    "print(\"1리터를 가지고  {0}km 이상 {1}km 이하 달릴 확률 : {2:.1f}%\".format(x,y,(prop - prop2) * 100))\n",
    "print(\"1리터를 가지고  {0}km 이상 {1}km 이하 달릴 확률 : {2:.1f}%\".format(z,y,(prop - prop3) * 100))\n",
    "print(\"1리터를 가지고  {0}km 이상 달릴 확률 : {1:.1f}%\".format(y,(1 - prop) * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 실습04 - p17\n",
    "- 정규분포 / 표준정규분포"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f375bc5e8d0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(seed = 1234)\n",
    "normal = np.random.normal(0,1,1000)\n",
    "sns.distplot(normal)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 실습05 - p19\n",
    "- t분포"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f375b1be898>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(seed=1234)\n",
    "t_df29 = np.random.standard_t(df = 10, size = 10000)\n",
    "sns.distplot(t_df29, fit = stats.norm, kde = False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f375991b550>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t_df29_2 = np.random.standard_t(df = 50, size = 10000)\n",
    "sns.distplot(t_df29_2, fit = stats.norm, kde = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 실습06 - p21\n",
    "- t분포"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(T<=2.0):0.963\n"
     ]
    }
   ],
   "source": [
    "t = 2.0\n",
    "df = 10\n",
    "\n",
    "prop = stats.t.cdf(t,df)\n",
    "print(\"P(T<={0}):{1:.3f}\".format(t,prop))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 실습07 - p23\n",
    "- x^2 분포(카이스퀘어)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f375983a208>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(seed = 1234)\n",
    "chisq_df10 = np.random.chisquare(df = 3, size = 1000)\n",
    "sns.distplot(chisq_df10, kde = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f375975f4e0>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(seed = 1234)\n",
    "chisq_df10 = np.random.chisquare(df = 50, size = 1000)\n",
    "sns.distplot(chisq_df10, kde = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 실습08 - p25\n",
    "- x^2 분포(카이스퀘어)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(X<=3.0):0.065642\n"
     ]
    }
   ],
   "source": [
    "chisq = 3.0\n",
    "df = 8\n",
    "\n",
    "prop = stats.chi2.cdf(chisq, df)\n",
    "print(\"P(X<={}):{:.6f}\".format(chisq, prop))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 실습09 - p27\n",
    "- F분포"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f375974f0f0>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(seed = 1234)\n",
    "F_df10_10 = np.random.f(dfnum = 10, dfden = 10, size = 1000)\n",
    "sns.distplot(F_df10_10, kde = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3759624550>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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C4dn7pItIvFC4R9mxriGy0/3kZybvJQQfuGo5redGaTh1zutSROQCFO5RdrxzkPKCzKSbKTPTTRsrycnw84OXW70uRUQuQOEeRc45mjoGk2ZNmQvJyUjjliuq+MnBdgITIa/LEZE5KNyjqGtwjIFAMOE36IjEb161nMGxoFaKFIlTCvcoOnZ+pkxyt9wBdq0uoaY4m0cbWrwuRUTmoHCPomOdQ0DyzpSZyeczfmv7Cp5v7qGld8TrckRkFoV7FB3vHGRZbgZ5STxTZqYPbqvBDL6r1rtI3FG4R1FT5yD15XlelxEz1UXZvK2+jO++1EpIc95F4orCPUqcczR3DnFZZb7XpcTUnVevoL0/wHPHu70uRURmSI3+gxho7w8wOBakviL5w33mZh7BcJjcDD9//1QT7X2TSwHfvXOlV6WJyBS13KNkeqbMuhTqlgFI8/nYtqqYI+0D9I9OeF2OiExRuEfJ8amZMutSoOU+2466EpyD/Sd7vS5FRKYo3KOkqXOQsvxMinMzvC4l5pblZrCuIp/9J3s1sCoSJxTuUXK8c5B1FanVJTPTzrplDAaCHG4f8LoUEUHhHhXhsON41xD15anXJTNtXWU+RTnp7DvR43UpIoLCPSrO9I0yMh5Kyf72aT4zdtYu48TZYZo6Br0uRyTlKdyjYHqmzGWVqdstA3B17TLS/cbDv3jD61JEUl5E4W5mN5tZk5k1m9lnLnLcB83Mmdn26JUY/6bXlFmbwt0yADmZaWxdWcz/O3CGs0NjXpcjktLmDXcz8wMPArcAG4C7zGzDHMflA/8B2BftIuPd8c5BKguyKMxO97oUz123ppTxYJhvvHjK61JEUlokLfcdQLNz7oRzbhz4DnDHHMf9D+BvgUAU60sIx7oGqU/hmTIzleVncuPl5XzjxVPayEPEQ5EsP7AcmLnsXyuwc+YBZrYVWOGc+7GZ/acLncjM7gPuA1i5MnEvUZ95+X14avelnXUlb3o8ld17fR0f+fI+Hjtwhg9fnbg/Z5FEFknLfa7NQM9fqWJmPuAfgE/PdyLn3EPOue3Oue1lZWWRVxnHzg2PMxFylOcn/+5Lkbp2TQkbqwv4l5+d0EVNIh6JJNxbgRUz7tcAbTPu5wNXAD81s5PALmB3qgyqdg5MDhymwgYdkTIz7n/nWt44O8xPXmv3uhyRlBRJuO8H6s2szswygDuB3dNPOuf6nXOlzrla51wt8CJwu3OuYUkqjjNdg5NDDGq5v9lNGytZW57Hg3uaCav1LhJz8/a5O+eCZnY/8BTgBx52zjWa2WeBBufc7oufIbl1DAQoykknM93vdSlxY3rs4aqVRTza0MoDjx1iQ3Xh+ee1JLDI0otoPXfn3OPA47Mee+ACx77j0stKHB39ASrVJTOnTcuL+LcjXext6mZ9VQFmcw3fiMhS0BWqlyAYDnN2aEz97Rfg9xlvX1fGmb5RjncNeV2OSEpRuF+Cs4PjhB1quV/E1pVFFGans/doF86p710kVhTul6BjYHIwtaJQ4X4haT4fN9SXcqp3hDd6hr0uRyRlKNwvQedAAL8ZpXmpt0HHQlxdu4zczDR+2qRNtEViReF+CTr6A5TmZ5Dm07fxYtL9Pm5YW0pz1xAtvSNelyOSEpRKl6BzIKDB1AjtrFtGdrqfvU1dXpcikhIU7osUmAjRNzqhwdQIZab7uW5tCUc7Bjl0pt/rckSSnsJ9kTqnBlMV7pG7ZnUpmWk+vrin2etSRJKewn2RNFNm4bIz/Fy7poQnGzu0FZ/IElO4L1LnQIDMNB9F2qBjQa5bU0puhp8v7lXrXWQpKdwXqaN/8spUXVK/MDmZaXz0mlX8+GAbr3frqlWRpaJwXwTnnGbKXIJP3rCazDQfD6r1LrJkFO6LMBAIMjoRorJAy/wuRmleJnfvWMVjB9o43aN57yJLQeG+CJ0aTL1kv/v21fh9xpd+qta7yFJQuC+CpkFeuoqCLD68fQXff7mV9v5Rr8sRSToK90Xo6A9QkJVGTkZEy+HLBdz3ttWEwo5Hnj/pdSkiSUfptAgaTL000zs1AWysLuSRF05SUZBF1tRuVtqpSeTSqeW+QMFQmK5BbdARLTfUlzIWDNNwstfrUkSSisJ9gU72jBAMO/W3R0lNcQ51pbk8/3oPIW2kLRI1CvcFmr5sXjNloueG+lL6Ryd47Uyf16WIJA2F+wI1dQxgQHm+5rhHy7qKfMryMnnh9R6vSxFJGgr3BWrqHKQkL5N0v7510eIz45o1JbSeG+W0NvMQiQol1AI1dQxSoStTo27ryiIy03y88PpZr0sRSQoK9wUYGQ9yqndEg6lLIDPNz/ZVxRw603/+IjERWTyF+wIc7xzCOTQNconsWl2Cc/DNF095XYpIwlO4L8CR9gEAqjRTZkmU5GVyWWU+39x3msBEyOtyRBKawn0BGtsGyM9Mozg3w+tSkta1a0rpGR7nxwfbvS5FJKEp3BfgUFs/66sL8GmDjiWzpiyX+vI8vvr8Gzini5pEFkvhHqFQ2HGkfYCN1QVel5LUzIx7rqulsW2Al06d87ockYSlcI/Qie4hAhNhrqgu9LqUpPf+rcspyErjqy+c9LoUkYSlcI9QY9vkYOrG5Wq5L7WcjDTu3LGSJw910Nantd5FFkPhHqHGtn4y03ysLcvzupSU8LFdq3DO8Q1NixRZFIV7hA6dGeDyynzStOxATKxYlsO7N1Tw7V9pWqTIYkSUVGZ2s5k1mVmzmX1mjuf/2MwOm9lBM3vWzFZFv1TvOOdobOtn43L1t8fSPdfWcW5kgt0H2rwuRSThzBvuZuYHHgRuATYAd5nZhlmHvQJsd85tBr4H/G20C/VS67lRBgJBzZSJsV2rl3F5ZT5ffeGkpkWKLFAkLfcdQLNz7oRzbhz4DnDHzAOcc3udc9PL+b0I1ES3TG81tvUDaKZMjJkZ91xby5H2Afa9oZ2aRBYiknBfDrTMuN869diF3As8cSlFxZvGtgH8PuOyynyvS0k5d1y5nKKcdG2iLbJAkYT7XJdjzvl/ZDP7KLAd+LsLPH+fmTWYWUN3d3fkVXrstTP9rC3LO7+Bs8ROdoafO69eydOHO2g9p7XeRSIVSbi3Aitm3K8B3jLCZWbvAv4bcLtzbmyuEznnHnLObXfObS8rK1tMvTHnnONASx9XrijyupSU9bFrVmFmfF3TIkUilhbBMfuBejOrA84AdwJ3zzzAzLYC/we42TnXFfUqPXSyZ4S+kQmuXKlwj5Vv7Tv9lsfWV+bztRdOUVWQzT3X1ca+KJEEM2/L3TkXBO4HngKOAI865xrN7LNmdvvUYX8H5AHfNbMDZrZ7ySqOsQMtk+ubbFW4e+qaNaWMToQ40KJNtEUiEUnLHefc48Djsx57YMbtd0W5rrjxyuk+cjP81JdrMNVLtSU5VBVm8cLrZ3HOYVqZU+SidLnlPF453cfmmiL8PoWJl8yM69aU0jU4xjOHO70uRyTuKdwvIjAR4kj7gLpk4sSWFUWU5Gbw+WeOEQ7roiaRi1G4X8ShM/0Ew04zZeKE32fceHk5RzsGebKxw+tyROKawv0iXjk9OXinmTLxY8uKItaW5/EPzxwjpNa7yAUp3C/iQEsfNcXZlOdrQ+x44TPjj95Vz/GuIR47cMbrckTilsL9Il45fU5dMnHo1iuq2FJTyOeeOEr/6ITX5YjEJYX7BbT3j9LWH2DrymKvS5FZfD7jL//dJnqGxvhfTzd5XY5IXFK4X8AvX+8BJpedlfizqaaQj+1axddfPMXBVl3YJDKbwv0CXni9h6KcdNZXag33ePXpmy6jNC+Tz3z/Ne3WJDKLwn0Ozjl++XoP16wuwaeLl+JWQVY6n3v/Jg63D/BnjzV6XY5IXIlo+YFU88U9zZzpG2XbquI5F7GS+PGuDRV86p1reHDv62xdWcSdO1Z6XZJIXFDLfQ6vdw8DsKYsz+NKJBJ//O7LuKG+lAcea+T55rNelyMSFxTuc3i9e4iCrDRK8zK8LkUi4PcZX7hzK3WluXz8kf08e0Rrz4go3GdxznGie4jVZXlaeTCBFOdm8J37dnF5ZT6/+/WX+G5DizbVlpSmcJ+lqXOQ4fEQa8pyvS5FFqg4N4NvfmIn22uL+ZPvHeRT33qZ3uFxr8sS8YQGVGd5vnlyfvtq9bfHrfkGub/5iV089NwJPv9ME/tO9PL771jDR3auIjtDe+BK6lDLfZanGjsoz8+kOEf97YnK7zN+/x1reOxT17O+qoC//MkRbvjbvXz+6SZO92iTbUkNarnP0DUYYP/JXt55WbnXpUgUbKgu4Buf2Mmv3ujlwb3N/NPeZr6wp5ldq5fxoW0ruGVTJTkZ+hWQ5KR/2TM83diJc3DF8kKvS5Eo2lG3jB11O2jrG+UHL7fy3Zda+fR3X+XPdjdy2+YqPrS9hqtWFmsAXZKKwn2GJw61s7osl4r8TK9LkSVQXZTN/TfW86l3rmX/yXM82tDC7lfb+M7+FtZV5HHPtXW8f+ty9c1LUjCvpott377dNTQ0ePLac+kZGmPH/3yW33/7GqqLsr0uR2JkLBjitdZ+fnmih/b+ADkZfv7gxnp++5pV5Gaq7SPxx8xecs5tn+84/eud8szhTkJhxy2bKnm1pd/rciRGMtP8bK9dxrZVxZzsGeFnx7r4myeP8k97jvP2dWXsrCshI+3N8w7u3qklDiT+KdynPH6og1UlOWyoKlC4pyAzo640l7rSOk73DPPs0S6eONTBc8fP8vb6UnbMEfIi8UzhDpzuGeHnx7v51DvWalBNWFmSy8evq+NUzzD/dqSTxw918LPjZ3lbfSk760q8Lk8kIgp34KsvvIHfjI/uWuV1KRJHVpXkcu/1qzl5dpg90y35Y90EgiE+umsVeeqTlziW8v86BwITPLq/hds2V1FZqI2w5a1qS3P599dPtuT3HO3ir584yheePc77NlfzwalplP4Yr/s/31W6GheQlA/3R/e3MDwe4t7rV3tdisS5VVPdNRuqC/jWvlP86GAb/9rQQnFOOm9fV8b22mVsqSnissp89c+L51I63IOhMF99/iQ7apexqUYXLklkrlxRxJUrinjgfRvZc7SLnx7t4mfHuvnhgTZgcvmDVctyWFOex9ryPNaUTX/OJT8r3ePqJVWkdLh//+VWzvSN8qe3bfC6FElAeZlp3L6lmtu3VOOco/XcKK+29nG0fZC9TV282tLHniNdhGZcS1KQlUZ5fhZl+ZnnP8rzM8nLTHvTYL66VeRSpWy4dw0G+KufHGFH3TLes6HC63IkwZkZK5blsGJZDrdt5vyFcKGwo3d4nO7BMboHA3QNjtE9NMbLp88xFgyf//qcDD/l+VlUFGRSUZBFVeHkH4BluRnkZPjJSveTmebTbC6JWMqG+1/sPkwgGOZzH9ikTbBlQRayr67fZ+db6FBw/nHnHAOBIN2DY3QNBugcCNA5MMaBlj7GgmF2v9r2lnP5DLLT/WRn+AmFHZlpfgqz0ynKSaeiIIua4mwqC7JI86u/X1I03J9u7OAnr7XzJzddpn1SxRNmRmF2OoXZ6awt//W/wenQ37l6Gd2DY5wbHmd0IsTIeIjARIjR8RAjEyGOtA0QmAjRNzrBqd5hAhOT/wtI8xm1pbkMBia4vr6U9ZUFarykqJQL95dO9fIf//UA66sKuO9tmiEj8WU69K9aWXzR42b+78E5R9/oBK3nRjnVM0xz1xCfe+IoPAGleRlcu6aU6+tLuaG+lKpCrZuUKlIq3F8+fY7feXg/5QVZPPLxq0nXf18lCZgZxTkZFOdksGlqueobLy/nF81n+cXxbn7R3HO+m2dNWS431JcxOhGiujCbkrwMfHP042tAN/FFFO5mdjPwj4Af+LJz7q9nPZ8JfA3YBvQAH3bOnYxuqYsXDIX5+oun+PunmijNz+Tbn9xFRYEuWJLkVVmYxQe31fDBbTU45zjaMcgvjp/l581n+favTp8fzM3w+6gsnBzArSjIojw/k/KCLJxzGrxNcPOGu5n5gQeBdwOtwH4z2+2cOzzjsHuBc865tWZ2J/A3wIeXouCFGBkPsudoF1/a+zqH2wd427oy/uY3N+lKVIl7Cxm0nY+Zsb6qgPVVBXzybasZD4b5wrPHae+l7bxOAAAE7ElEQVQfpa0vQHv/6PmB3Glf+mkz9VPz9NeW558frK0omJzFE+srcmXhImm57wCanXMnAMzsO8AdwMxwvwP486nb3wO+aGbmlnCx+GAozETIMR4MMxYKMRQI0js8TsdAgGOdQxxuG+D55rOMToRYXpTNlz5yFbdcUanWiKS8jDQf1UXZVBdls21qOaXpgdyugcnpmgXZ6bzeNcSThzo4N9Lypq/3GZTmZZKbmUZmmo+sdD9Z6ZOf03w+0v3Gmb5R/Gb4fHb+c4bfSPf7SPf7uHZtCVlpfrIy/JMzgNL9ZGdMnmN6RlB2+uQU0Onu0+nfXDPe8nvsnCMUdpOZEAozHgwzEZr8GA+GCUyECQR/PSj97NEuJoJhJsJu6nOYiaBjIhwmGApTX5FPhn/yvUzXnD2j1jfXPetzup+sDN/k+zabs95YiCTclwMzf7qtwM4LHeOcC5pZP1ACnI1GkTP93+dO8LknjhC+yJ8Nn0FdaS7vv2o5t22uYmddiVoaIhcxc/ZOfUX+m/rce4bGaOsL0DEw+dHZH6BrMDA1gyfMWDDE2ESY3uFxJkKOYGjydngqcEMOQuEwwZAjOPWL+2RjRxRrh2g0I/0+mwxzn483zo6c/+MwMdWQvNQafWb4poL+z9+3ccnHNSIJ97lScfY7jeQYzOw+4L6pu0Nm1hTB6y/KG8Ae4HO/fqiUJfhjkwD0vlPH+ff8kUs80aV+fYwl3M/6I391Sd/jiJavjSTcW4EVM+7XALOvsJg+ptXM0oBCoHf2iZxzDwEPRVJYtJlZQyRbUyUbve/UkYrvGVL3fc8nkrmA+4F6M6szswzgTmD3rGN2A78zdfuDwJ6l7G8XEZGLm7flPtWHfj/wFJNTIR92zjWa2WeBBufcbuArwNfNrJnJFvudS1m0iIhcXETz3J1zjwOPz3rsgRm3A8CHolta1HnSHRQH9L5TRyq+Z0jd931Rpt4TEZHko+vvRUSSUEqEu5ndbGZNZtZsZp/xup5YMLOHzazLzA55XUusmNkKM9trZkfMrNHM/tDrmmLBzLLM7Fdm9urU+/4Lr2uKFTPzm9krZvZjr2uJN0kf7jOWT7gF2ADcZWapsPXSI8DNXhcRY0Hg08659cAu4FMp8rMeA250zm0BrgRuNrNdHtcUK38IHPG6iHiU9OHOjOUTnHPjwPTyCUnNOfccc1xrkMycc+3OuZenbg8y+Uu/3Nuqlp6bNDR1N33qI+kH08ysBngv8GWva4lHqRDucy2fkPS/8KnOzGqBrcA+byuJjanuiQNAF/CMcy4V3vf/Bv4zEJ7vwFSUCuEe0dIIkjzMLA/4PvBHzrkBr+uJBedcyDl3JZNXkO8wsyu8rmkpmdltQJdz7iWva4lXqRDukSyfIEnCzNKZDPZvOud+4HU9seac6wN+SvKPt1wH3G5mJ5nsar3RzL7hbUnxJRXCPZLlEyQJ2OS6ql8BjjjnPu91PbFiZmVmVjR1Oxt4F3DU26qWlnPuvzjnapxztUz+Tu9xzn3U47LiStKHu3MuCEwvn3AEeNQ51+htVUvPzL4N/BK4zMxazexer2uKgeuAjzHZijsw9XGr10XFQBWw18wOMtmYecY5p6mBKU5XqIqIJKGkb7mLiKQihbuISBJSuIuIJCGFu4hIElK4i4gkIYW7iEgSUriLiCQhhbuISBL6//xq06DCLYD5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(seed = 1234)\n",
    "F_df10_10 = np.random.f(dfnum = 10, dfden = 50, size = 1000)\n",
    "sns.distplot(F_df10_10, kde = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 실습10 - p30\n",
    "- 와이블 분포"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(X>=2500): 0.14313\n"
     ]
    }
   ],
   "source": [
    "x = 2500\n",
    "alpha = 5.2\n",
    "beta = 2200\n",
    "\n",
    "prop = stats.weibull_min.cdf(x, alpha, scale = beta)\n",
    "print(\"P(X>={}): {:.5f}\".format(x,(1-prop)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 실습11 - p35\n",
    "- 이항 분포"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(X=0) = 0.168\n",
      "P(X=1) = 0.360\n",
      "P(X=2) = 0.309\n",
      "P(X=3) = 0.132\n",
      "P(X=4) = 0.028\n",
      "P(X=5) = 0.002\n"
     ]
    }
   ],
   "source": [
    "n = 5\n",
    "for i in range(n + 1):\n",
    "    prop = stats.binom.pmf(k= i, n = n, p = 0.3)\n",
    "    print(\"P(X={}) = {:.3f}\".format(i, prop))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 실습12 - p37\n",
    "- 포아송 분포"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2명의 손님이 도착할 확률:0.2707\n",
      "1명 이하의 손님이 도착할 확률:0.4060\n",
      "2명 이하의 손님이 도착할 확률:0.1429\n"
     ]
    }
   ],
   "source": [
    "mu = 2\n",
    "prop = stats.poisson.pmf(2,mu)\n",
    "cdf_prop = stats.poisson.cdf(1,mu)\n",
    "cdf_prop2 = stats.poisson.cdf(3,mu)\n",
    "\n",
    "print(\"{}명의 손님이 도착할 확률:{:.4f}\".format(2,prop))\n",
    "print(\"{}명 이하의 손님이 도착할 확률:{:.4f}\".format(1,cdf_prop))\n",
    "print(\"{}명 이하의 손님이 도착할 확률:{:.4f}\".format(2,(1-cdf_prop2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 기술통계(Descriptive Statistics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 중심\n",
    "    - 산술평균, 중앙값, 최빈값\n",
    "- 산포\n",
    "    - Range, 분산, 표준편차\n",
    "    \n",
    "- 범주형 데이터에 계산\n",
    "    - counts, percents, cumulative counts, cumulative percents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 실습 13 - p42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">mpg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>manufacturer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>audi</th>\n",
       "      <td>18.0</td>\n",
       "      <td>17.611111</td>\n",
       "      <td>1.974511</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>18.75</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chevrolet</th>\n",
       "      <td>19.0</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>2.924988</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.50</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dodge</th>\n",
       "      <td>37.0</td>\n",
       "      <td>13.135135</td>\n",
       "      <td>2.485091</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ford</th>\n",
       "      <td>25.0</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.914854</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>honda</th>\n",
       "      <td>9.0</td>\n",
       "      <td>24.444444</td>\n",
       "      <td>1.943651</td>\n",
       "      <td>21.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>25.00</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hyundai</th>\n",
       "      <td>14.0</td>\n",
       "      <td>18.642857</td>\n",
       "      <td>1.499084</td>\n",
       "      <td>16.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.5</td>\n",
       "      <td>19.75</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jeep</th>\n",
       "      <td>8.0</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>2.507133</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>land rover</th>\n",
       "      <td>4.0</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>12.00</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lincoln</th>\n",
       "      <td>3.0</td>\n",
       "      <td>11.333333</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.50</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mercury</th>\n",
       "      <td>4.0</td>\n",
       "      <td>13.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.25</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nissan</th>\n",
       "      <td>13.0</td>\n",
       "      <td>18.076923</td>\n",
       "      <td>3.426892</td>\n",
       "      <td>12.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.00</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pontiac</th>\n",
       "      <td>5.0</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18.00</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subaru</th>\n",
       "      <td>14.0</td>\n",
       "      <td>19.285714</td>\n",
       "      <td>0.913874</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>toyota</th>\n",
       "      <td>34.0</td>\n",
       "      <td>18.529412</td>\n",
       "      <td>4.046961</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>21.00</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volkswagen</th>\n",
       "      <td>27.0</td>\n",
       "      <td>20.925926</td>\n",
       "      <td>4.556702</td>\n",
       "      <td>16.0</td>\n",
       "      <td>18.5</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.00</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               mpg                                                    \n",
       "             count       mean       std   min   25%   50%    75%   max\n",
       "manufacturer                                                          \n",
       "audi          18.0  17.611111  1.974511  15.0  16.0  17.5  18.75  21.0\n",
       "chevrolet     19.0  15.000000  2.924988  11.0  13.5  15.0  16.50  22.0\n",
       "dodge         37.0  13.135135  2.485091   9.0  11.0  13.0  15.00  18.0\n",
       "ford          25.0  14.000000  1.914854  11.0  13.0  14.0  15.00  18.0\n",
       "honda          9.0  24.444444  1.943651  21.0  24.0  24.0  25.00  28.0\n",
       "hyundai       14.0  18.642857  1.499084  16.0  18.0  18.5  19.75  21.0\n",
       "jeep           8.0  13.500000  2.507133   9.0  12.5  14.0  15.00  17.0\n",
       "land rover     4.0  11.500000  0.577350  11.0  11.0  11.5  12.00  12.0\n",
       "lincoln        3.0  11.333333  0.577350  11.0  11.0  11.0  11.50  12.0\n",
       "mercury        4.0  13.250000  0.500000  13.0  13.0  13.0  13.25  14.0\n",
       "nissan        13.0  18.076923  3.426892  12.0  15.0  19.0  19.00  23.0\n",
       "pontiac        5.0  17.000000  1.000000  16.0  16.0  17.0  18.00  18.0\n",
       "subaru        14.0  19.285714  0.913874  18.0  19.0  19.0  20.00  21.0\n",
       "toyota        34.0  18.529412  4.046961  11.0  15.0  18.0  21.00  28.0\n",
       "volkswagen    27.0  20.925926  4.556702  16.0  18.5  21.0  21.00  35.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mycars = pd.read_csv(\"./data/pre/mycars.csv\")\n",
    "df = mycars[['manufacturer','mpg']]\n",
    "df.groupby('manufacturer').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">mpg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>automatic</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>auto</th>\n",
       "      <td>157.0</td>\n",
       "      <td>15.968153</td>\n",
       "      <td>3.848777</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>manual</th>\n",
       "      <td>77.0</td>\n",
       "      <td>18.675325</td>\n",
       "      <td>4.488118</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             mpg                                                  \n",
       "           count       mean       std  min   25%   50%   75%   max\n",
       "automatic                                                         \n",
       "auto       157.0  15.968153  3.848777  9.0  13.0  16.0  18.0  29.0\n",
       "manual      77.0  18.675325  4.488118  9.0  15.0  18.0  21.0  35.0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mycars = pd.read_csv(\"./data/pre/mycars.csv\")\n",
    "df = mycars[['automatic','mpg']]\n",
    "df.groupby('automatic').describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 실습14 - p45\n",
    "- mpg(연비)와 모델에 따른 counts, percents, cumulative counts, cumulative percents를 계산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>model</th>\n",
       "      <th>displacement</th>\n",
       "      <th>year</th>\n",
       "      <th>cylinder</th>\n",
       "      <th>automatic</th>\n",
       "      <th>driving</th>\n",
       "      <th>mpg</th>\n",
       "      <th>highway_mileage</th>\n",
       "      <th>fuel</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1999</td>\n",
       "      <td>4</td>\n",
       "      <td>auto</td>\n",
       "      <td>f</td>\n",
       "      <td>18</td>\n",
       "      <td>29</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1999</td>\n",
       "      <td>4</td>\n",
       "      <td>manual</td>\n",
       "      <td>f</td>\n",
       "      <td>21</td>\n",
       "      <td>29</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>4</td>\n",
       "      <td>manual</td>\n",
       "      <td>f</td>\n",
       "      <td>20</td>\n",
       "      <td>31</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>4</td>\n",
       "      <td>auto</td>\n",
       "      <td>f</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1999</td>\n",
       "      <td>6</td>\n",
       "      <td>auto</td>\n",
       "      <td>f</td>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  manufacturer model  displacement  year  cylinder automatic driving  mpg  \\\n",
       "0         audi    a4           1.8  1999         4      auto       f   18   \n",
       "1         audi    a4           1.8  1999         4    manual       f   21   \n",
       "2         audi    a4           2.0  2008         4    manual       f   20   \n",
       "3         audi    a4           2.0  2008         4      auto       f   21   \n",
       "4         audi    a4           2.8  1999         6      auto       f   16   \n",
       "\n",
       "   highway_mileage fuel    class  \n",
       "0               29    p  compact  \n",
       "1               29    p  compact  \n",
       "2               31    p  compact  \n",
       "3               30    p  compact  \n",
       "4               26    p  compact  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mycars = pd.read_csv(\"./data/pre/mycars.csv\")\n",
    "mycars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>mpg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a4</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a4</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a4</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a4</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a4</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  model  mpg\n",
       "0    a4   18\n",
       "1    a4   21\n",
       "2    a4   20\n",
       "3    a4   21\n",
       "4    a4   16"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = mycars[['model','mpg']]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4runner 4wd    6\n",
       "a4             7\n",
       "a4 quattro     8\n",
       "a6 quattro     3\n",
       "altima         6\n",
       "Name: model, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count = df['model'].value_counts().sort_index()\n",
    "count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4runner 4wd     6\n",
       "a4             13\n",
       "a4 quattro     21\n",
       "a6 quattro     24\n",
       "altima         30\n",
       "Name: model, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cumcnt = np.cumsum(count)\n",
    "cumcnt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4runner 4wd    2.564103\n",
       "a4             2.991453\n",
       "a4 quattro     3.418803\n",
       "a6 quattro     1.282051\n",
       "altima         2.564103\n",
       "Name: model, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent = count/sum(count)*100\n",
    "percent.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4runner 4wd     2.564103\n",
       "a4              5.555556\n",
       "a4 quattro      8.974359\n",
       "a6 quattro     10.256410\n",
       "altima         12.820513\n",
       "Name: model, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cumpct = np.cumsum(percent)\n",
    "cumpct.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "      <th>CumCnt</th>\n",
       "      <th>Percent</th>\n",
       "      <th>CumPct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4runner 4wd</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2.564103</td>\n",
       "      <td>2.564103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a4</th>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>2.991453</td>\n",
       "      <td>5.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a4 quattro</th>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>3.418803</td>\n",
       "      <td>8.974359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a6 quattro</th>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>1.282051</td>\n",
       "      <td>10.256410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>altima</th>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>2.564103</td>\n",
       "      <td>12.820513</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Count  CumCnt   Percent     CumPct\n",
       "4runner 4wd      6       6  2.564103   2.564103\n",
       "a4               7      13  2.991453   5.555556\n",
       "a4 quattro       8      21  3.418803   8.974359\n",
       "a6 quattro       3      24  1.282051  10.256410\n",
       "altima           6      30  2.564103  12.820513"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_data = pd.DataFrame({'Count':count, 'CumCnt':cumcnt, 'Percent':percent, 'CumPct':cumpct})\n",
    "count_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Model</th>\n",
       "      <th>Count</th>\n",
       "      <th>CumCnt</th>\n",
       "      <th>Percent</th>\n",
       "      <th>CumPct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4runner 4wd</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2.564103</td>\n",
       "      <td>2.564103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a4</th>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>2.991453</td>\n",
       "      <td>5.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a4 quattro</th>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>3.418803</td>\n",
       "      <td>8.974359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a6 quattro</th>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>1.282051</td>\n",
       "      <td>10.256410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>altima</th>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>2.564103</td>\n",
       "      <td>12.820513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c1500 suburban 2wd</th>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>2.136752</td>\n",
       "      <td>14.957265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>camry</th>\n",
       "      <td>7</td>\n",
       "      <td>42</td>\n",
       "      <td>2.991453</td>\n",
       "      <td>17.948718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>camry solara</th>\n",
       "      <td>7</td>\n",
       "      <td>49</td>\n",
       "      <td>2.991453</td>\n",
       "      <td>20.940171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caravan 2wd</th>\n",
       "      <td>11</td>\n",
       "      <td>60</td>\n",
       "      <td>4.700855</td>\n",
       "      <td>25.641026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>civic</th>\n",
       "      <td>9</td>\n",
       "      <td>69</td>\n",
       "      <td>3.846154</td>\n",
       "      <td>29.487179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>corolla</th>\n",
       "      <td>5</td>\n",
       "      <td>74</td>\n",
       "      <td>2.136752</td>\n",
       "      <td>31.623932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>corvette</th>\n",
       "      <td>5</td>\n",
       "      <td>79</td>\n",
       "      <td>2.136752</td>\n",
       "      <td>33.760684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dakota pickup 4wd</th>\n",
       "      <td>9</td>\n",
       "      <td>88</td>\n",
       "      <td>3.846154</td>\n",
       "      <td>37.606838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>durango 4wd</th>\n",
       "      <td>7</td>\n",
       "      <td>95</td>\n",
       "      <td>2.991453</td>\n",
       "      <td>40.598291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>expedition 2wd</th>\n",
       "      <td>3</td>\n",
       "      <td>98</td>\n",
       "      <td>1.282051</td>\n",
       "      <td>41.880342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>explorer 4wd</th>\n",
       "      <td>6</td>\n",
       "      <td>104</td>\n",
       "      <td>2.564103</td>\n",
       "      <td>44.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f150 pickup 4wd</th>\n",
       "      <td>7</td>\n",
       "      <td>111</td>\n",
       "      <td>2.991453</td>\n",
       "      <td>47.435897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forester awd</th>\n",
       "      <td>6</td>\n",
       "      <td>117</td>\n",
       "      <td>2.564103</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grand cherokee 4wd</th>\n",
       "      <td>8</td>\n",
       "      <td>125</td>\n",
       "      <td>3.418803</td>\n",
       "      <td>53.418803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grand prix</th>\n",
       "      <td>5</td>\n",
       "      <td>130</td>\n",
       "      <td>2.136752</td>\n",
       "      <td>55.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gti</th>\n",
       "      <td>5</td>\n",
       "      <td>135</td>\n",
       "      <td>2.136752</td>\n",
       "      <td>57.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>impreza awd</th>\n",
       "      <td>8</td>\n",
       "      <td>143</td>\n",
       "      <td>3.418803</td>\n",
       "      <td>61.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jetta</th>\n",
       "      <td>9</td>\n",
       "      <td>152</td>\n",
       "      <td>3.846154</td>\n",
       "      <td>64.957265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k1500 tahoe 4wd</th>\n",
       "      <td>4</td>\n",
       "      <td>156</td>\n",
       "      <td>1.709402</td>\n",
       "      <td>66.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>land cruiser wagon 4wd</th>\n",
       "      <td>2</td>\n",
       "      <td>158</td>\n",
       "      <td>0.854701</td>\n",
       "      <td>67.521368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>malibu</th>\n",
       "      <td>5</td>\n",
       "      <td>163</td>\n",
       "      <td>2.136752</td>\n",
       "      <td>69.658120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maxima</th>\n",
       "      <td>3</td>\n",
       "      <td>166</td>\n",
       "      <td>1.282051</td>\n",
       "      <td>70.940171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mountaineer 4wd</th>\n",
       "      <td>4</td>\n",
       "      <td>170</td>\n",
       "      <td>1.709402</td>\n",
       "      <td>72.649573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mustang</th>\n",
       "      <td>9</td>\n",
       "      <td>179</td>\n",
       "      <td>3.846154</td>\n",
       "      <td>76.495726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>navigator 2wd</th>\n",
       "      <td>3</td>\n",
       "      <td>182</td>\n",
       "      <td>1.282051</td>\n",
       "      <td>77.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new beetle</th>\n",
       "      <td>6</td>\n",
       "      <td>188</td>\n",
       "      <td>2.564103</td>\n",
       "      <td>80.341880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>passat</th>\n",
       "      <td>7</td>\n",
       "      <td>195</td>\n",
       "      <td>2.991453</td>\n",
       "      <td>83.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pathfinder 4wd</th>\n",
       "      <td>4</td>\n",
       "      <td>199</td>\n",
       "      <td>1.709402</td>\n",
       "      <td>85.042735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ram 1500 pickup 4wd</th>\n",
       "      <td>10</td>\n",
       "      <td>209</td>\n",
       "      <td>4.273504</td>\n",
       "      <td>89.316239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>range rover</th>\n",
       "      <td>4</td>\n",
       "      <td>213</td>\n",
       "      <td>1.709402</td>\n",
       "      <td>91.025641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sonata</th>\n",
       "      <td>7</td>\n",
       "      <td>220</td>\n",
       "      <td>2.991453</td>\n",
       "      <td>94.017094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tiburon</th>\n",
       "      <td>7</td>\n",
       "      <td>227</td>\n",
       "      <td>2.991453</td>\n",
       "      <td>97.008547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>toyota tacoma 4wd</th>\n",
       "      <td>7</td>\n",
       "      <td>234</td>\n",
       "      <td>2.991453</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Model                   Count  CumCnt   Percent      CumPct\n",
       "4runner 4wd                 6       6  2.564103    2.564103\n",
       "a4                          7      13  2.991453    5.555556\n",
       "a4 quattro                  8      21  3.418803    8.974359\n",
       "a6 quattro                  3      24  1.282051   10.256410\n",
       "altima                      6      30  2.564103   12.820513\n",
       "c1500 suburban 2wd          5      35  2.136752   14.957265\n",
       "camry                       7      42  2.991453   17.948718\n",
       "camry solara                7      49  2.991453   20.940171\n",
       "caravan 2wd                11      60  4.700855   25.641026\n",
       "civic                       9      69  3.846154   29.487179\n",
       "corolla                     5      74  2.136752   31.623932\n",
       "corvette                    5      79  2.136752   33.760684\n",
       "dakota pickup 4wd           9      88  3.846154   37.606838\n",
       "durango 4wd                 7      95  2.991453   40.598291\n",
       "expedition 2wd              3      98  1.282051   41.880342\n",
       "explorer 4wd                6     104  2.564103   44.444444\n",
       "f150 pickup 4wd             7     111  2.991453   47.435897\n",
       "forester awd                6     117  2.564103   50.000000\n",
       "grand cherokee 4wd          8     125  3.418803   53.418803\n",
       "grand prix                  5     130  2.136752   55.555556\n",
       "gti                         5     135  2.136752   57.692308\n",
       "impreza awd                 8     143  3.418803   61.111111\n",
       "jetta                       9     152  3.846154   64.957265\n",
       "k1500 tahoe 4wd             4     156  1.709402   66.666667\n",
       "land cruiser wagon 4wd      2     158  0.854701   67.521368\n",
       "malibu                      5     163  2.136752   69.658120\n",
       "maxima                      3     166  1.282051   70.940171\n",
       "mountaineer 4wd             4     170  1.709402   72.649573\n",
       "mustang                     9     179  3.846154   76.495726\n",
       "navigator 2wd               3     182  1.282051   77.777778\n",
       "new beetle                  6     188  2.564103   80.341880\n",
       "passat                      7     195  2.991453   83.333333\n",
       "pathfinder 4wd              4     199  1.709402   85.042735\n",
       "ram 1500 pickup 4wd        10     209  4.273504   89.316239\n",
       "range rover                 4     213  1.709402   91.025641\n",
       "sonata                      7     220  2.991453   94.017094\n",
       "tiburon                     7     227  2.991453   97.008547\n",
       "toyota tacoma 4wd           7     234  2.991453  100.000000"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_data.columns.name= \"Model\"\n",
    "count_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 추론통계 (Inferencial Statistics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 실습15 - p60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "신뢰구간 : ([18.8],[20.2])\n"
     ]
    }
   ],
   "source": [
    "df2 = pd.DataFrame({'감자무게':[18,18,20,21,20,23,19,18,17,21,22,20,20,21,20,19,19,18,17,19]})\n",
    "lower, upper = stats.norm.interval(0.95, loc = df2.mean(), scale = stats.sem(df2))\n",
    "print(\"신뢰구간 : ({},{})\".format(lower.round(2), upper.round(2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 실습16 - p62\n",
    "- 모평균 신뢰구간 추정 - 모표준편차를 모르는 경우"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "신뢰구간 : ([70.6], [72.4])\n"
     ]
    }
   ],
   "source": [
    "df4 = pd.DataFrame({'product' : [73,71,74,69,70,73,70,68,75,72,70,72,73,70,70,72,71,70,75,72]})\n",
    "lower, upper = stats.t.interval(0.95, len(df4)-1, loc = np.mean(df4), scale = stats.sem(df4))\n",
    "print(\"신뢰구간 : ({}, {})\".format(lower.round(2), upper.round(2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 실습17 - p64\n",
    "- 1-sample t-test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1-Sample t-test\n",
      "t검정통계량 : [4.8786]\n",
      "p-value : [0.0009]\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame({'sample':[85.0, 79.0, 79.1, 79.9, 81.6, 78.6, 85.4, 83.4, 78.1, 79.2]})\n",
    "t_result = stats.ttest_1samp(df, 76.7)\n",
    "t,p = t_result.statistic.round(4), t_result.pvalue.round(4)\n",
    "# t,p = t_result.statistic, t_result.pvalue\n",
    "print(\"1-Sample t-test\")\n",
    "print(\"t검정통계량 : {}\".format(t))\n",
    "print(\"p-value : {}\".format(p))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 유의수준 5%에서 검정결과 `P값이 0.0009`임으로 개선활동이 만족도를 `변화 시켰다`고 볼 수 있다"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 실습18 - p68\n",
    "- Paired t-test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Paired t-test\n",
      "t검정통계량 : [-2.266]\n",
      "P-value : [0.05]\n"
     ]
    }
   ],
   "source": [
    "df1 = pd.DataFrame({'A':[720, 589, 780, 648, 720, 589, 780, 648, 780, 648]})\n",
    "df2 = pd.DataFrame({'B':[710, 580, 787, 712, 750, 600, 782, 670, 790, 680]})\n",
    "\n",
    "t_result = stats.ttest_rel(df1, df2)\n",
    "t,p = t_result.statistic.round(3), t_result.pvalue.round(3)\n",
    "\n",
    "print(\"Paired t-test\")\n",
    "print(\"t검정통계량 : {}\".format(t))\n",
    "print(\"P-value : {}\".format(p))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 유의수준 5%에서 검정결과 `P값이 0.05`임으로 회사원들의 영어성적이 학원 전후로 `차이가 있다`고 볼 수 있다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 실습19 - p71\n",
    " - 1 Propotion test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Proportion test\n",
      "p 검정통계량 : -3.333\n",
      "p value : 0.001\n"
     ]
    }
   ],
   "source": [
    "count = 10\n",
    "nobs = 100\n",
    "value = .2\n",
    "\n",
    "stat, pval = proportions_ztest(count,nobs,value)\n",
    "\n",
    "print(\"1 Proportion test\")\n",
    "print('p 검정통계량 : {:0.3f}'.format(stat))\n",
    "print('p value : {:0.3f}'.format(pval))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 결과적으로 품질개선을 한결과 차이가 있다고 할 수 있다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 실습20 - p73\n",
    "- 2 Propotion test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 Proportion test\n",
      "p 검정통계량 : 2.073\n",
      "p value : 0.038\n"
     ]
    }
   ],
   "source": [
    "count = np.array([14,5])\n",
    "nobs = np.array([1200,1200])\n",
    "stat, pval = proportions_ztest(count, nobs)\n",
    "\n",
    "print(\"2 Proportion test\")\n",
    "print('p 검정통계량 : {:0.3f}'.format(stat))\n",
    "print('p value : {:0.3f}'.format(pval))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 실습21 - p75\n",
    "- 카이제곱 검정(chi-square test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi-square test\n",
      "chisq:87.785\n",
      "p:0.000\n",
      "degree pf freedom: 30\n",
      "expected value: \n",
      "[[10.633  7.932  5.063  3.713  1.857 10.802]\n",
      " [ 8.241  6.148  3.924  2.878  1.439  8.371]\n",
      " [10.101  7.536  4.81   3.527  1.764 10.262]\n",
      " [ 7.975  5.949  3.797  2.785  1.392  8.101]\n",
      " [ 9.835  7.338  4.684  3.435  1.717  9.992]\n",
      " [ 8.506  6.346  4.051  2.97   1.485  8.641]\n",
      " [ 7.709  5.751  3.671  2.692  1.346  7.831]]\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame({'A/S':[18,8,4,4,3,3], '가격':[1,2,1,1,1,25], \n",
    "                   '성능':[8,14,3,2,3,8], '확장성':[7,5,4,3,1,10],\n",
    "                  '디자인':[10,5,9,2,1,10], '안정성':[9,9,5,7,1,1],\n",
    "                  '기능성':[10,4,4,3,1,7]})\n",
    "chi, pval, dof, expected = stats.chi2_contingency(df.T)\n",
    "\n",
    "print(\"chi-square test\")\n",
    "print('chisq:{:0.3f}'.format(chi))\n",
    "print('p:{:0.03f}'.format(pval))\n",
    "print('degree pf freedom: {}'.format(dof))\n",
    "print('expected value: \\n{}'.format(expected.round(3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 차이가 있다고 할 수 있다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 상관, 회귀분석(Correlation, Regression)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 상관분석\n",
    "    - 산점도, 상관관계 vs 카이제곱(차이는 데이터유형임)\n",
    "2. 회귀분석\n",
    "    - 단순선형회귀분석(Simple Linear Regression)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 실습22 - p 80"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "correlation Analysis\n",
      "crr:-0.723\n",
      "p:0.000\n"
     ]
    }
   ],
   "source": [
    "Strength = [37.6, 38.6, 37.2, 36.34, 38.6, 39,37.2, 36.1, 35.9, 37.1, 36.9, 37.5, 36.3, 38.1, 39, 36.9, 36.8, 37.6, 33, 33.5]\n",
    "Temperature = [14,15,14,16,17,14,17,16,15,16,14,16,18,13,15,17,14,16,20,21]\n",
    "\n",
    "corr, pval = sp.stats.pearsonr(Strength, Temperature)\n",
    "\n",
    "print(\"correlation Analysis\")\n",
    "print('crr:{:0.3f}'.format(corr))\n",
    "print('p:{:0.3f}'.format(pval))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 실습 23 - p84\n",
    "- Simple Liner Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.647\n",
      "Model:                            OLS   Adj. R-squared:                  0.627\n",
      "Method:                 Least Squares   F-statistic:                     32.96\n",
      "Date:                Thu, 02 May 2019   Prob (F-statistic):           1.92e-05\n",
      "Time:                        16:33:48   Log-Likelihood:                 46.542\n",
      "No. Observations:                  20   AIC:                            -89.08\n",
      "Df Residuals:                      18   BIC:                            -87.09\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.0027      0.135      0.020      0.984      -0.281       0.286\n",
      "x1             0.0209      0.004      5.741      0.000       0.013       0.029\n",
      "==============================================================================\n",
      "Omnibus:                        2.608   Durbin-Watson:                   1.118\n",
      "Prob(Omnibus):                  0.271   Jarque-Bera (JB):                1.158\n",
      "Skew:                           0.040   Prob(JB):                        0.560\n",
      "Kurtosis:                       1.824   Cond. No.                         897.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "Strength = [37.6, 38.6, 37.2, 36.34, 38.6, 39,37.2, 36.1, 35.9, 37.1, \n",
    "            36.9, 37.5, 36.3, 38.1, 39, 36.9, 36.8, 37.6, 33, 33.5]\n",
    "\n",
    "BackingTime = [0.798922, 0.84902, 0.816163, 0.758266, 0.815894, 0.807477, 0.809068, \n",
    "               0.7664, 0.742243, 0.751118, 0.740629, 0.751495, 0.738863, 0.827428, \n",
    "               0.818172, 0.740939, 0.757128, 0.804695, 0.695121, 0.735377]\n",
    "\n",
    "Strength = sm.add_constant(Strength)\n",
    "model = sm.OLS(BackingTime, Strength)\n",
    "result = model.fit()\n",
    "\n",
    "print(result.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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