work1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa77f4df438>"
      ]
     },
     "execution_count": 4,
     "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(20, 2, 100000)\n",
    "sns.distplot(normal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa77f383518>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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S3A4lpBYVZ9Pe6WPrkUa3QzFRyBKCiTot7Z1sO9YYVf0HXeYWZhEfJ2ywfgQzCCwhmKhTVtVIh1dZWBQ58ycHKzU5gRkF6ayvsH4EE3qWEEzU2VDRgCdOuDrCpssM1sKibHZWn7VpNU3IWUIwUWdjRT2zx2WQkhjU3d0jzqIim1bTDI6gEoKILBORAyJSLiKP9LB+sYhsFZFOEbk7oHyWiGwUkT0islNEPhuw7uciclhEtjuPWaF5SyaWnb3Qwa6asyyIwuaiLnPGZ5LoibN+BBNyfX6FEpF44AlgKVANbBaRVQEznwEcBR4EvtFt8xbgflU9JCJjgC0iskZVzzjrv6mqr1zumzCmy0eHT+NTouL+Rb1JToindHymXY9gQi6YM4S5QLmqVqpqO/AisCKwgqpWqepOwNet/KCqHnKWjwO1QORNbGsixoaKepI8ccwel+F2KINqUXEO+08203Cuze1QTBQJJiHkA8cCnlc7Zf0iInOBRKAioPgfnaakx0UkOm44Y1y1saKBqydkkeSJdzuUQdU1pHZjpTUbmdAZkk5lEckDfgn8map2nUV8C5gKXA1kAX/Xy7YPiUiZiJTV1dUNRbgmQjWca2P/yeaovP6guxn56YxI8lg/ggmpYBJCDTA24HmBUxYUEUkDXge+raqbuspV9YT6tQHP4W+a+gRVfVJVS1W1NDfXWptM7zZV+kfdRHP/QRdPfBzzCrPYYP0IJoSCSQibgUkiUigiicBKYFUwO3fqvwr8onvnsXPWgIgIcCewuz+BG9Pdhop6RiR5mJ6f7nYoQ2JhcQ5VDS3UnLngdigmSvSZEFS1E3gYWAPsA15S1T0i8piILAcQkatFpBq4B/iZiOxxNv8MsBh4sIfhpb8SkV3ALiAH+IeQvjMTczZWNDC3MAtPfGxcXtN1JmRnCSZUgrpyR1VXA6u7lT0asLwZf1NS9+3+A/iPXvZ5Q78iNeYSTpy9QGX9eT43b5zboQyZKaNSyR6eyMaKBu4pHdv3Bsb0ITa+Spmo1zVpTCx0KHeJixPmF2WzvqIeVXU7HBMFLCGYqLChooGMlASmjU5zO5Qhtagoh1NNbVTWn3c7FBMFLCGYiKeqbKxoYMHEbOLixO1whpT1I5hQsoRgIt7R0/6RNrEw3LS78dkp5GcMs+sRTEhYQjAR77/6D6L3hna9EREWFGWzsbIBn8/6EczlsYRgIt6GigZyU5Moyh3udiiuWFSczZmWDvaeaHI7FBPhLCGYiKaqbKhoYGFRNv5rHGNP18xwG2wWNXOZLCGYiFZee476c20x2X/QZVRaMkW5w60fwVw2SwgmonX9E4zG+ZP7Y2FRDh8dPk17p6/vysb0whKCiWgbKxooyBzG2KwUt0Nx1aLibFraveysPtN3ZWN6YQnBRCyfT9lY2RDTzUVd5k/MRgTWl1uzkRk4SwgmYu090cTZCx0xdbuK3mSkJHLFmDTrWDaXxRKCiVgXrz+YGNv9B10WFuWw7egZLrR73Q7FRChLCCZibaioZ2LucEanJ7sdSlhYWJRNu9dH2ZHTbodiIpQlBBOROrw+Pjp82voPAlw9IQtPnFg/ghkwSwgmIu2qOcv5dm/MDzcNNDzJw+xxGWy0fgQzQEElBBFZJiIHRKRcRB7pYf1iEdkqIp0icne3dQ+IyCHn8UBA+VUissvZ548kVi8zNQPS1X8wf6KdIQRaUJTDrpqznL3Q4XYoJgL1mRBEJB54ArgVKAHuFZGSbtWOAg8CL3TbNgv4LjAPmAt8V0QyndU/Ab4MTHIeywb8LkzM2VBRz7S8NLKGJ7odSlhZVJSNT+HDSms2Mv0XzBnCXKBcVStVtR14EVgRWEFVq1R1J9D9MslbgLdU9bSqNgJvActEJA9IU9VN6p/q6RfAnZf7ZkxsaO3wUlbVyAI7O/iE2eMySU6Is9tYmAEJJiHkA8cCnlc7ZcHobdt8Z3kg+zQxbtvRM7R1+qxDuQeJnjiunpBl1yOYAQn7TmUReUhEykSkrK6uzu1wTBjYWNlAnMDciVluhxKWFhXncPDUOWqbW90OxUSYYBJCDTA24HmBUxaM3ratcZb73KeqPqmqpapampubG+TLmmi2obye6fnppCUnuB1KWOo6c9pozUamn4JJCJuBSSJSKCKJwEpgVZD7XwPcLCKZTmfyzcAaVT0BNInIfGd00f3AawOI38SY5tYOth07wzWTbLhpb64Yk05asocNdj2C6ac+E4KqdgIP4//nvg94SVX3iMhjIrIcQESuFpFq4B7gZyKyx9n2NPD3+JPKZuAxpwzgq8DTQDlQAbwR0ndmotKmytN4fco1xXa22Jv4OGH+xGw2VFo/gukfTzCVVHU1sLpb2aMBy5v5eBNQYL1ngWd7KC8DruxPsMZ8cKiOYQnxzBmf4XYoYW1RcQ5v7j3FsdMtMX9rcBO8sO9UNibQuvJ65k3MIskT73YoYa2rH8FGG5n+sIRgIsbxMxeorDvPNcXWf9CX4pEjyE1NsvsamX6xhGAixgeH/N92r51k/Qd9EREWFmWzoaIB/7WfxvTNEoKJGOvK6xmZmsTkUSPcDiUiLCrKof5cG4dqz7kdiokQlhBMRPD5lPXl9VxTnIPdBzE4XTPJbSi3fgQTHEsIJiLsPdHE6fPtdv1BP4zNSmFcVgrr7QI1EyRLCCYirHP6D6xDuX8WFmWzqbIBr8/6EUzfLCGYiPBBeR1TRqUyMs2my+yPhcU5NLd2srvmrNuhmAhgCcGEvdYOL5urGq25aAC6bhFut8M2wbCEYMLeR4dP097ps4QwALmpSUwZlWoXqJmgWEIwYe+D8noS4oV5hXa764FYUJTN5qrTtHZ43Q7FhDlLCCbsrT1Qy9zCLFISg7r1lunmusm5tHb4+Ojw6b4rm5hmCcGEtZozFzh46hxLJo90O5SINX9iNomeONYesAmmzKVZQjBhbe2BWgCWTLHbVQzUsMR45k/MZu3BWrdDMWHOEoIJa2sP1JGfMYzikXa7istx/ZRcKuvOc7Shxe1QTBizhGDCVlunlw3l9SyZkmu3q7hMS6b4m9zsLMFcSlAJQUSWicgBESkXkUd6WJ8kIr9x1n8oIhOc8s+LyPaAh09EZjnr1jr77FpnjcTmY8qqGjnf7uX6KXZoXK7CnOFMyE7h3f2WEEzv+kwIIhIPPAHcCpQA94pISbdqXwIaVbUYeBz4AYCq/kpVZ6nqLOA+4LCqbg/Y7vNd61XVjlTzMWsP1JIYH8fC4my3Q4kKS6aMZGNlgw0/Nb0K5gxhLlCuqpWq2g68CKzoVmcF8Lyz/Apwo3zyHP9eZ1tjgrL2QJ0NNw2h66b4h59+aMNPTS+CSQj5wLGA59VOWY91VLUTOAt0/1r3WeDX3cqec5qLvtNDAjExrLqxhUO152x0UQgtmJhNkifOmo1Mr4akU1lE5gEtqro7oPjzqjoduNZ53NfLtg+JSJmIlNXV2TjqWNE1Zn6J9R+ETHJCPAuKsnnvoP0dmZ4FkxBqgLEBzwucsh7riIgHSAcC76a1km5nB6pa4/xsBl7A3zT1Car6pKqWqmppbq59W4wV7+6vpSBzGEW5w90OJapcP2Ukh+vPU1lns6iZTwomIWwGJolIoYgk4v/nvqpbnVXAA87y3cA76kzkKiJxwGcI6D8QEY+I5DjLCcAdwG6MAVraO/mgvJ6bpo2y4aYhduM0/xnXn/adcjkSE476TAhOn8DDwBpgH/CSqu4RkcdEZLlT7RkgW0TKga8DgUNTFwPHVLUyoCwJWCMiO4Ht+M8wnrrsd2OiwvsH62nr9HFzySi3Q4k6BZkpTMtL4629lhDMJwU1fENVVwOru5U9GrDcCtzTy7Zrgfndys4DV/UzVhMj3tp7irRkD1fb3U0HxdKSUfz4nUM0nGsje0SS2+GYMGJXKpuw4vUp7+w/xQ1TR5IQb4fnYLi5ZBQ+hXdstJHpxv7iTFjZcqSRxpYOlpaMdjuUqHXFmDTy0pOt2ch8giUEE1be2nuShHhh8WSbHW2wiAg3TRvFukP1dtWy+RhLCCZsqCpv7T3FgqIcUpMT3A4nqi0tGcWFDi8fHLKpNc1/sYRgwkZF3TmqGlpYaqOLBt38idmkJnms2ch8jCUEEzbedP453TTNrk4ebImeOK6bksvb+0/h86nb4ZgwYQnBhI01u08yoyCdvPRhbocSE5aWjKL+XDtlRxrdDsWECUsIJiwcO93Cjuqz3DY9z+1QYsaN00aR6Ilj9a4TbodiwoQlBBMWuv4p3W4JYciMSPJw/ZRcVu86Yc1GBrCEYMLE67tOMLMgnbFZKW6HElNunzGG2uY2azYygCUEEwaONrSws/ost8+ws4OhduPUkSR54nh953G3QzFhwBKCcd3rTnOR9R8MveFJHm6YOpLVu0/itWajmGcJwbhu9a4TzBybQUGmNRe54bbpedQ1t7G5yqbWjHWWEIyrjjScZ1fNWe6wswPX3DB1JMkJNtrIWEIwLutqLrp1ut3Mzi0Xm412WbNRrLOEYFy1avtxZo+z5iK33T59DPXn2thY0dB3ZRO1gkoIIrJMRA6ISLmIPNLD+iQR+Y2z/kMRmeCUTxCRCyKy3Xn8NGCbq0Rkl7PNj8TmSow5e483sf9kM3fNznc7lJh347SRpCZ7+N3WardDMS7qMyGISDzwBHArUALcKyIl3ap9CWhU1WLgceAHAesqVHWW8/hKQPlPgC8Dk5zHsoG/DROJXt1WTUK8cMeMMW6HEvOSE+K5Y8YY3th9knNtnW6HY1wSzBnCXKBcVStVtR14EVjRrc4K4Hln+RXgxkt94xeRPCBNVTepqgK/AO7sd/QmYnV6ffx++3GWTBlJ1vBEt8MxwN1X5XOhw8sb1rkcs4JJCPnAsYDn1U5Zj3VUtRM4C2Q76wpFZJuIvCci1wbUDzw37WmfAIjIQyJSJiJldXV1QYRrIsH6igbqmtv49BxrLgoXc8ZlMiE7hd9as1HMGuxO5RPAOFWdDXwdeEFE0vqzA1V9UlVLVbU0Nzd3UII0Q+93W6tJH5bA9VPtVtfhQkT41JwCNlWe5tjpFrfDMS4IJiHUAGMDnhc4ZT3WEREPkA40qGqbqjYAqOoWoAKY7NQv6GOfJko1tXawZs9Jbp+RR5In3u1wTICuDv5Xt9mfYywKJiFsBiaJSKGIJAIrgVXd6qwCHnCW7wbeUVUVkVynUxoRmYi/87hSVU8ATSIy3+lruB94LQTvx0SA17Yfp7XDx2dLx/Zd2QypsVkpzJ+Yxe+2VuPv3jOxpM+E4PQJPAysAfYBL6nqHhF5TESWO9WeAbJFpBx/01DX0NTFwE4R2Y6/s/krqtp1ffxXgaeBcvxnDm+E6D2ZMKaq/PrDo5TkpTGjIN3tcEwPPj2ngKqGFjZX2R1QY40nmEqquhpY3a3s0YDlVuCeHrb7LfDbXvZZBlzZn2BN5NtVc5a9J5r4+zuvxC49CU+3z8jjsT/u5VcfHmFuYZbb4ZghZFcqmyH164+OMiwhnhWz7NqDcJWS6OHTcwp4Y9dJGs61uR2OGUKWEMyQOdfWyWvbj3PHjDzSkhPcDsdcwufmjaPd6+PlLTYENZZYQjBD5tVtNbS0e7l33ji3QzF9mDwqlbmFWbzw4VGbXjOGWEIwQ8LnU36+/jAzCtKZPTbD7XBMEL4wfzxHT7fw/iG7IDRWWEIwQ+L9Q3VU1J3nzxZNsM7kCLHsitHkjEji5xuq3A7FDBFLCGZIPLe+itzUJG6fbp3JkSLRE8f9C8az9kAdh041ux2OGQKWEMygK689x3sH67hv/ngSPXbIRZIvzB9PckIcT6877HYoZgjYX6cZdM+tP0xifByfs87kiJM1PJFPzyng1W011DXbENRoZwnBDKraplZeLqvm01cVkDMiye1wzAB88ZpC2r0+frmxyu1QzCCzhGAG1VPrKvGq8hfXFbkdihmgotwR3DRtFM9vPGKT50Q5Swhm0DSeb+dXHx5l+cwxjMu2OZMj2V/dUMzZCx08byOOopolBDNonlt/mJZ2L3+xxM4OIt3MsRksmZLL0+sqOW9nCVHLEoIZFGda2nluQxW3XDGKyaNS3Q7HhMDXbpxEY0sHv9h4xO1QzCCxhGAGxU/WVnCurZO/WTrZkTs8AAAPiUlEQVTZ7VBMiMwel8niybk8ta7S+hKilCUEE3Inzl7g5xuquGt2PlNH92vGVBPmvr50MqfPt/PkexVuh2IGQVAJQUSWicgBESkXkUd6WJ8kIr9x1n8oIhOc8qUiskVEdjk/bwjYZq2zz+3OwybXjRI/evsQPlX+5iY7O4g2s8ZmcMeMPJ5ad5hTTa1uh2NCrM+E4EyB+QRwK1AC3CsiJd2qfQloVNVi4HHgB055PfDfVHU6/ik2f9ltu8+r6iznUXsZ78OEiUOnmnmprJrPzxvP2CwbWRSN/vaWqXT6fPzrmwfdDsWEWDBnCHOBclWtVNV24EVgRbc6K4DnneVXgBtFRFR1m6oed8r3AMNExK5OilKqyvf+sIfhifH81Q3FbodjBsm47BTuXzCBl7ccY//JJrfDMSEUTELIB44FPK92ynqs48zBfBbI7lbn08BWVQ28/v05p7noO2K3wIx4b+w+yfryBr55yxSy7arkqPZXNxSTPiyB7/x+t82XEEWGpFNZRK7A34z03wOKP+80JV3rPO7rZduHRKRMRMrq6uy+7OGqpb2Tf/jjXkry0vjcvPFuh2MGWUZKIt+6bRqbqxp5xWZVixrBJIQaYGzA8wKnrMc6IuIB0oEG53kB8Cpwv6peHJqgqjXOz2bgBfxNU5+gqk+qaqmqlubm5gbznowL/u1Phzh+tpXHVlxBfJyd7MWCe64qYO6ELP7PG/ts7uUoEUxC2AxMEpFCEUkEVgKrutVZhb/TGOBu4B1VVRHJAF4HHlHV9V2VRcQjIjnOcgJwB7D78t6KccuWI6d5al0ln5s3jtIJWW6HY4aIiPCPd13JudZO/v6Pe90Ox4RAnwnB6RN4GFgD7ANeUtU9IvKYiCx3qj0DZItIOfB1oGto6sNAMfBot+GlScAaEdkJbMd/hvFUKN+YGRoX2r184+WdjEkfxv+6bZrb4ZghNmlUKg/fUMzvtx/n9Z0n3A7HXCZRjZwOodLSUi0rK3M7DBPg+3/Yw3Prq3jhz+exsDjH7XCMCzq8Pu7+6Uaq6s+z5q8XMzo92e2QTDciskVVS/uqZ1cqmwH7z90neW59FQ8unGDJIIYlxMfx+Gdm0t7p4xsv78Bro44iliUEMyBHG1r45is7mFmQzrdum+p2OMZlE3NH8Oh/K+GD8nr+7U92wVqksoRg+u1Cu5e/+NUWBPjx5+aQ5Il3OyQTBlZePZbPlBbw/94pZ82ek26HYwbAEoLpF69P+dqL29h7oonHPzvLbk9hLhIRHltxJTML0vn6b7bbVcwRyBKC6Zd/eH0vb+49xXfvKOHGaaPcDseEmeSEeH5631WMSPZw/zMfcex0i9shmX6whGCC9sS75Ty3voovLirkwUWFbodjwlRe+jB+8cV5tHZ4eeDZj+yitQhiCcEE5Yl3y/nhmgPcOWsM377drjcwlzZldCrPPng1NWcu8LmnPqS22W6VHQksIZhLUlUef+vgxWTwL5+ZZbemMEEpnZDFsw9ezdHTLXz2Z5s4fuaC2yGZPlhCML3yjyvfyb+/fYi7ryqwZGD6bVFxDr/80lzqm9v49E82sLvmrNshmUuwhGB6VNvUyn3PfMhvt1bzNzdN5od3z7BkYAakdEIWL/73+Qhw90838Medx/vcxrjDEoL5hHWH6rjtR+vYUX2Gxz87k6/dNAmbrsJcjivGpPPaw9dwxZh0Hn5hG99+dRcX2r1uh2W6sYRgLmpq7eB//34X9z3zEZkpiax6+Bruml3gdlgmSuSmJvHCl+fx0OKJvPDRUW7/f+soqzrtdlgmgN3czuDzKa/tqOH/rt5P/bk2HlxYyDdvmcKwRLsC2QyODeX1fOPlHRw/28qn5uTzyK1TGZlqN8UbLMHe3M4SQgzz+ZS399fyL28eYP/JZqbnp/OPd13JjIIMt0MzMaClvZMfv1POU+sqiY8THlgwgYcWT7TpVweBJQTTq6bWDlZtP86z6w9TWXee8dkp/M+bp3DH9DzirOPYDLGq+vP8+9uH+P32GhLj41g+cwz3L5jA9IJ0t0OLGpYQzMe0dnhZX17Pq9tqeGvvKdo6fVyZn8aXr53IbdPzSIi37iTjrvLaZp75oIrXttfQ0u5len46t8/IY9kVo5mQM9zt8CJaSBOCiCwD/h2IB55W1X/qtj4J+AVwFf65lD+rqlXOum8BXwK8wP9Q1TXB7LMnlhCC19bpZf+JZrYcaeT9Q3VsrGigrdNHZkoCy2eO4a45BcwsSLfRQybsNLV28OrWGn67tZqd1f7rFqaMSmVhcTbzJ2YzrzCLjJREl6OMLCFLCCISDxwElgLV+OdYvldV9wbU+SowQ1W/IiIrgbtU9bMiUgL8GpgLjAH+BEx2NrvkPntiCeGTvD7l+JkLVNafp7LuHJV159lVc5a9x5to9/oAKMwZznWTc1kyJZeFRTkkeuxswESG6sYW/nP3Sd7ZX8uWI420dfqP6YLMYZTkpVEyJo0po1IZm5VCQeYw0ocl2JecHgSbEDxB7GsuUK6qlc6OXwRWAIH/vFcA33OWXwF+LP7fygrgRVVtAw47cy7Pder1tc+opqp0eJUOr48Or4/2Th/tXh8dXqWt08v5tk6aWzs519bJOedn04UO6s61U9vUSm1zG7XNrdSfa//YDFWpSR6mjUnjzxZNYObYDGaOzSA/Y5iL79SYgSvITOHPr53In187kbZOLzuOnWVz1Wn2nWhi74km3tp3isDvtKlJHvIzhzEyLZmslAQyhyeSlZJI5vBE0oclkJIYz7CEeIYlxpOS6Lm4nJQQhydOiI8TEuLiYrYvLZiEkA8cC3heDczrrY6qdorIWSDbKd/Ubdt8Z7mvfYbM/3p1Fx9WNnDxuFFQf6wXy1RBUf/PgAOsq05X2cU6BNYLLNOul3D2pR97XZ+TCLq+vfeHCGQPTyQ3NZmRqUlMHZ3KyLQk8jNSmJg7nIm5w8kdkWTfkExUSvLEM7cwi7mFWRfLWto7qaw7T3XjBaobWy7+rDvXTlX9eRrPt9Pc1tnv1xIBT5zgiXMSRbx/uStPiIAgdP2pCVz8u5M+6kjXk27b9eXZB65mXPbgzj8STEJwlYg8BDwEMG7cuAHtIz9jGFNHp4Fc/D1c/MVID2U4v0gC1kv3Mqfix3/ZfR8kcSIkxMeR6IkjMf6/lhPi40gMWB6R7GFEkofUgJ/DEz0x+83FmJ6kJHq4Mj+dK/N7H5HU3unjTEs7Zy90cKHDS0u7lwvt3oDlTto6fXh9SqdP6fQqXp+PDp/6y7xKp89/9k7Al8aur3qBXxAvfv3r7QtitzKUoA1FU28wCaEGGBvwvMAp66lOtYh4gHT8ncuX2ravfQKgqk8CT4K/DyGIeD/hL68vHshmxpgokOiJY2RaMiPT7MK3vgSTcjYDk0SkUEQSgZXAqm51VgEPOMt3A++oPw2uAlaKSJKIFAKTgI+C3Kcxxpgh1OcZgtMn8DCwBv8Q0WdVdY+IPAaUqeoq4Bngl06n8Wn8/+Bx6r2Ev7O4E/hLVfUC9LTP0L89Y4wxwbIL04wxJsoFO+zUBqQbY4wBLCEYY4xxWEIwxhgDWEIwxhjjsIRgjDEGiLBRRiJSBxwZ4OY5QH0IwwkVi6t/LK7+sbj6J1rjGq+quX1ViqiEcDlEpCyYYVdDzeLqH4urfyyu/on1uKzJyBhjDGAJwRhjjCOWEsKTbgfQC4urfyyu/rG4+iem44qZPgRjjDGXFktnCMYYYy4h4hOCiDwrIrUisjugLEtE3hKRQ87PzF62fcCpc0hEHuipTojj+qGI7BeRnSLyqohk9LJtlYjsEpHtIhLSu/n1Etf3RKTGeb3tInJbL9suE5EDIlIuIo8MQVy/CYipSkS297LtYH5eY0XkXRHZKyJ7RORrTrmrx9gl4nL1GLtEXK4eY5eIy9VjTESSReQjEdnhxPV9p7xQRD50PoffiH+agJ62/5ZT54CI3HLZAalqRD+AxcAcYHdA2T8DjzjLjwA/6GG7LKDS+ZnpLGcOclw3Ax5n+Qc9xeWsqwJyhvDz+h7wjT62iwcqgIlAIrADKBnMuLqt/xfgURc+rzxgjrOcChwEStw+xi4Rl6vH2CXicvUY6y0ut48x/BMrjnCWE4APgfnAS8BKp/ynwF/0sG2J8xklAYXOZxd/OfFE/BmCqr6Pfw6GQCuA553l54E7e9j0FuAtVT2tqo3AW8CywYxLVd9U1a4JXjfhnyluSPXyeQVjLlCuqpWq2g68iP9zHvS4RESAzwC/DtXrBUtVT6jqVme5GdiHf15wV4+x3uJy+xi7xOcVjEE7xvqKy61jTP3OOU8TnIcCNwCvOOW9HV8rgBdVtU1VDwPl+D/DAYv4hNCLUap6wlk+CYzqoU4+cCzgeTXBH7ih8EXgjV7WKfCmiGwR/5zSQ+Fhp5nh2V6aP9z8vK4FTqnqoV7WD8nnJSITgNn4v8WFzTHWLa5Arh5jPcQVFsdYL5+Xa8eYiMQ7TVW1+L80VABnAhJ7b59DyD+vaE0IF6n/3CqshlKJyLfxzyD3q16qXKOqc4Bbgb8UkcWDHNJPgCJgFnAC/6lzOLmXS39zG/TPS0RGAL8F/lpVmwLXuXmM9RaX28dYD3GFxTF2id+ja8eYqnpVdRb+s7m5wNRQ7bu/ojUhnBKRPADnZ20PdWqAsQHPC5yyQSUiDwJ3AJ93/pF8gqrWOD9rgVe5zNPAvqjqKeeg9AFP9fJ6bn1eHuBTwG96qzPYn5eIJOD/J/IrVf2dU+z6MdZLXK4fYz3FFQ7H2CU+L9ePMWffZ4B3gQVAhhMX9P45hPzzitaEsAroGtHxAPBaD3XWADeLSKZz+nqzUzZoRGQZ8LfAclVt6aXOcBFJ7Vp24trdU90QxpUX8PSuXl5vMzDJGf2QiH/e7FWDGZfjJmC/qlb3tHKwPy+nbfkZYJ+q/mvAKlePsd7icvsYu0Rcrh5jl/g9govHmIjkijMSTESGAUvx92+8C9ztVOvt+FoFrBSRJBEpBCYBH11WQKHuNR/qB/7TvBNAB/42tC8B2cDbwCHgT0CWU7cUeDpg2y/i74gpB/5sCOIqx9/mt915/NSpOwZY7SxPxD9yYAewB/j2EMT1S2AXsNM5yPK6x+U8vw3/6IyKoYjLKf858JVudYfy87oGf3PQzoDf221uH2OXiMvVY+wScbl6jPUWl9vHGDAD2ObEtRtnlJPzmh85v8+XgSSnfDnwWMD233Y+qwPArZcbj12pbIwxBojeJiNjjDH9ZAnBGGMMYAnBGGOMwxKCMcYYwBKCMcYYhyUEY4wxgCUEY4wxDksIxhhjAPj/+JO7sBmgU1MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "normal = stats.norm(20,2)\n",
    "xx = np.linspace(10, 30, 400)\n",
    "plt.plot(xx, normal.pdf(xx))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa77e390828>]"
      ]
     },
     "execution_count": 80,
     "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": [
    "normal = stats.norm(loc=20,scale=2) # loc: 평균, scale:표준편차\n",
    "xx = np.linspace(10, 30, 400) # np.linspace(start, end, num_points)\n",
    "plt.plot(xx, normal.pdf(xx)) # pdf: 확률밀도함수 , cdf, ppf: inverse cdf, rvs: random variable sampling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "135분이상 걸리는 AS작업비율 :  15.9%\n",
      "135분이상 걸리는 AS작업비율 :  15.9%\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "135.0"
      ]
     },
     "execution_count": 12,
     "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": [
    "normal3 = stats.norm(loc=115, scale=20)\n",
    "xx = np.linspace(60, 170, 400)\n",
    "plt.plot(xx,normal3.pdf(xx))\n",
    "x=135\n",
    "v2 = stats.norm.cdf(x, 115, 20)\n",
    "print(\"{0}분이상 걸리는 AS작업비율 : {1: .1f}%\".format(x,(1-v2)*100))\n",
    "v3 = stats.norm.sf(x, 115, 20)\n",
    "print(\"{0}분이상 걸리는 AS작업비율 : {1: .1f}%\".format(x,v3*100))\n",
    "v2\n",
    "inverse = stats.norm.ppf(v2, loc=115, scale=20)\n",
    "inverse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "180의 키는 상위  15.9%\n"
     ]
    },
    {
     "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": [
    "# 실습 1.\n",
    "\n",
    "normal4 = stats.norm(loc=175, scale=5)\n",
    "xx = np.linspace(160, 190, 400)\n",
    "plt.plot(xx,normal4.pdf(xx))\n",
    "x=180\n",
    "v3 = stats.norm.cdf(x, 175, 5)\n",
    "print(\"{0}의 키는 상위 {1: .1f}%\".format(x,(1-v3)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1리터를 가지고 12 이상 15 이하를 달릴 확률은 :  34.1\n",
      "1리터를 가지고 9 이상 15 이하를 달릴 확률은 :  68.3\n",
      "1리터를 가지고 15 이상 달릴 확률은 :  15.9\n"
     ]
    },
    {
     "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": [
    "# 실습 2.\n",
    "normal5 = stats.norm(loc = 12, scale = 3)\n",
    "xx = np.linspace(3, 21, 400)\n",
    "plt.plot(xx, normal5.pdf(xx))\n",
    "num1 = 12\n",
    "num2 = 15\n",
    "num3 = 9\n",
    "\n",
    "v1 = stats.norm.cdf(num1, 12, 3)\n",
    "v2 = stats.norm.cdf(num2, 12, 3)\n",
    "v3 = stats.norm.cdf(num3, 12, 3)\n",
    "\n",
    "print(\"1리터를 가지고 {0} 이상 {1} 이하를 달릴 확률은 : {2: .1f}\".format(num1, num2, (v2-v1)*100))\n",
    "print(\"1리터를 가지고 {0} 이상 {1} 이하를 달릴 확률은 : {2: .1f}\".format(num3, num2, (v2-v3)*100))\n",
    "print(\"1리터를 가지고 {0} 이상 달릴 확률은 : {1: .1f}\".format(num2, (1-v2)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa77f2e3978>]"
      ]
     },
     "execution_count": 30,
     "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": [
    "# 실습 3. 표준정규분포\n",
    "normal6 = stats.norm(0, 1)\n",
    "xx= np.linspace(-3,3,400)\n",
    "plt.plot(xx , normal6.pdf(xx))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa77eeea630>]"
      ]
     },
     "execution_count": 36,
     "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-분포\n",
    "np.random.seed(seed=1234)\n",
    "xx = np.linspace(-4, 4, 400)\n",
    "for df in [1, 5, 25]:\n",
    "    t_df = stats.t(df=df)\n",
    "    plt.plot(xx, t_df.pdf(xx))\n",
    "plt.plot(xx, stats.norm.pdf(xx), lw= 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(T<=1.53):  0.907\n"
     ]
    }
   ],
   "source": [
    "# 실습 4. 확률변수 t는 자유도가 5인 t-분포를 따른다. P(T <= t)를 계산하시오\n",
    "t = 1.53\n",
    "df = 5\n",
    "prop = stats.t.cdf(t, df)\n",
    "print(\"P(T<={0}): {1: .3f}\".format(t,prop))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(T<=2.0) :  0.963\n"
     ]
    }
   ],
   "source": [
    "# 실습 5.\n",
    "t = 2.0\n",
    "df = 10\n",
    "prop = stats.t.cdf(t, df)\n",
    "print(\"P(T<={0}) : {1: .3f}\".format(t, prop))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa77ece3278>"
      ]
     },
     "execution_count": 52,
     "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 = 29, size = 1000)\n",
    "sns.distplot(t_df29, fit = stats.norm, kde = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa77eb8c748>"
      ]
     },
     "execution_count": 55,
     "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_df10 = np.random.standard_t(df = 10, size = 1000)\n",
    "sns.distplot(t_df10, fit = stats.norm, kde = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa77e949780>"
      ]
     },
     "execution_count": 59,
     "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": [
    "# 그림 얹기\n",
    "np.random.seed(seed=1234)\n",
    "t_df29 = np.random.standard_t(df = 29, size = 1000)\n",
    "t_df10 = np.random.standard_t(df = 10, size = 1000)\n",
    "sns.distplot(t_df29, kde = True)\n",
    "sns.distplot(t_df10, kde = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa77ee6e940>]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oq2oz8BiwDNgNLFDVnSLyjIjM9mz2mIjsFJEC4LvAA559dwILgF3AUuBRVZuwPNAlV+UDsPZwM4NyJzic5uIqe04gLsxF4dqF1sVjuhSvZtlU1cXA4gvanm71+vEv2fdZ4NkrDWi6nuSqfKpd0ZTUR3DvNb45mKsswZ0rS0rYu3cvgwYNcjiRMe3D7sg1nUuV5Kp8NpWHkZUzivDIjn8k4pWoi0jmZGQfxiU3sGLFCqfjGNNurOibThVTW0xUQwXrSlwMutY3u3bOKU8az5hezaz+6EOnoxjTbqzom06V7LnTdVNFBANG+taonQuVxY8hMqiF0IrPKCkpcTqOMe3Cir7pVMlV+VQ2huLqPYLo2O5Ox/lS5fG5AIy2Lh7ThVjRN51HlaSKDWw8Hsygayc5naZNDeHxkJzDtMwQK/qmy7CibzpP9UG6NVWysTycgaPGO53GO5mTGBp7hh0FW6isrHQ6jTFXzYq+6TyH1gFwMLQ/cQnJDofxUuZEQmkip3sDq1atcjqNMVfNir7pNI37V1JZH0TYgKlOR/FehnuE0Yz+4dbFY7oEK/qm0zQXrWFTRRj9h491Oor3ohMgeQjTskLZsGEDdXV1Ticy5qpY0Tedo/owUY0n2HYqjt59Bzid5vJkTSJDjuFqqmfDhg1OpzHmqljRN53CdXANAOXxowkK8rOHp2VOJNjVQG5KEHl5eU6nMeaqeDX3jjFX62TBB0hDEBGDZzgd5bLM/7SYsMYM7gamD+7Brz/8iOxZX0NEALhvbB9nAxpzmexM33SK4JL15FeE03fYaKejXLbGsO5UxwxgTFIDp6pOUHbkgNORjLliVvRNxzt5hDjXSY6F9fWpxyJejrL4MfSlmNAgpXCbPRLC+C8r+qbDnfpsCQCROTc4nOTKlSfkEuJqYOqQnuy3om/8mFdFX0RmicheESkUkScusv67IrJLRLaLyEciktFqXYuIFHh+Fl24r+n6qjYv4mSDMOz6e52OcsXKe+SiCNdnd6OkaDe1p2ucjmTMFWmz6ItIMPACcBOQA8wVkZwLNtsK5KrqcOBt4LlW6+pUdYTnZzYm4ESfKGDHqRgGDhrsdJQr1hgWx8mYAYyIqwFVCnfkOx3JmCvizZn+GKBQVQ+oaiPuB5/f3noDVV2lqrWexQ24H4BuDE2Vh0kKPs3phGvOj3jxV2UJo0mt30/37nHsL/jU6TjGXBFvin4qcKTVcomn7VIeApa0Wo4QkXwR2SAid1xBRuPHitfMByBhlP//kVcWP5oQVz0zr83kwGebaWludjqSMZetXS/kisg8IBf4WavmDFXNBe4Dfiki/S6y3yOeL4b8ioqK9oxkHHZm5zJONQpDr5/jdJSrVtHjWhRhUp8Q6mvPUFK4y+lIxlw2b4p+KZDeajnN0/Y5IjID+AEwW1UbzrWraqnn9wFgNTDywn1V9WVVzVXV3KSkpMs6AOPbEs/soai5J9Ex/jlUs7XGsDiqYwYyOOwYQcEhNorH+CVviv4mIFtEskQkDJgDfG4UjoiMBH6Lu+CXt2rvISLhnteJwATATo8CxLH9BaRG1NGc6kcTrLWhPGE0yad20G9wDvu3Wb++8T9tTsOgqs0i8hiwDAgGXlXVnSLyDJCvqotwd+d0A97yXKwr9ozUGQz8VkRcuL9g/kNVreh3cfM/LQag+cMX+QegMGk6+z1t/q4sfjSDDv2JGUNTeOnN7Rw5coT09PS2dzTGR3g1946qLgYWX9D2dKvXF51QRVU/AYZdTUDjv5IqPuVMTBCaNdnpKO2mPH4UijC2VzMvAXl5ecybN8/pWMZ4ze7INR2iqbGBgSGlFLrSIKjrzOvXFBpHdewgMhr2Et8zlTVr1jgdyZjLYkXfdIgTOz+mb0wTFYldpz//nLL40SSe3Mbg4SPZuHGjPVjF+BUr+qZDhO939wY2DrjF4STtrzx+NCGuBiYNjKehoYGNG20Uj/EfVvRNu1NVUmoKqG0J4VTCNU7HaXfn+vWv6VZNZGSkdfEYv2JF37S7yuMlXBNTzeGQvmgX6s8/pyk0lurYQaTUbGHcuHHk5eWhqk7HMsYrVvRNuzu+bRX945qpTp3idJQOU5YwhsST25g6cTylpaUcPHjQ6UjGeMWKvml33Q5/BMDp9GkOJ+k4ZfGjCXY1Mi27G4B18Ri/YUXftKuzZ8/St2kf9RpKVaz/TqXclooeI3ERRNKZPfTv398emG78hhV9067Wr19PbmI9JVE5XbI//5xz/focWsvkyZPZvHkzZ8+edTqWMW2yom/a1eY1S+gf10xNWtft2jmnLGEMlGxk6sRxNDU1sX79eqcjGdMmK/qm3agqdbtXAHTJm7IuVB4/GloaGZnYRHR0tPXrG79gRd+0m7179zIwvJx6wt1dH11cRY+RIEGElGxgwoQJrFmzxoZuGp9nRd+0mzVr1jAmuZHy7iO7dH/+OU2hMZByzfl+/bKyMvbt2+d0LGO+lBV9024K1i6jX2wzlT2vczpK58mcBCWbmDR+NGBDN43vs6Jv2sXJkyeJLNsCeC5wBorMSdDSSHL9QQYPHmxDN43Ps6Jv2sW6desYnVRPS0g01TEDnY7TefqMAwk638VTUFBATU2N06mMuSSvir6IzBKRvSJSKCJPXGT9d0Vkl4hsF5GPRCSj1boHRGS/5+eB9gxvfEdeXh7jU5oJypoQEP3550XEQsoIOLSWKVOm0NLSwieffOJ0KmMuqc2iLyLBwAvATUAOMFdEci7YbCuQq6rDgbeB5zz7xgM/AsYCY4AfiUiP9otvfEFLSwu7N64iI7oRyZzkdJzOl+Xu1x8+uD9xcXHWr298mjdn+mOAQlU9oKqNwJvA7a03UNVVqlrrWdwApHlezwQ+VNUqVa0GPgRmtU904ys+++wzBoRVuBeyArDoZ04CVxPBRzczceJEPv74Y1wul9OpjLkob4p+KnCk1XKJp+1SHgKWXOG+xg/l5eUxpmcjGhYDvYY7HafzpY8FCT7fr19ZWcmuXbucTmXMRbXrhVwRmQfkAj+7zP0eEZF8EcmvqKhoz0imE6xZs4ZJ6SCZEyAo2Ok4nS8iFnq7+/UnTpyIiNgoHuOzvCn6pUB6q+U0T9vniMgM4AfAbFVtuJx9VfVlVc1V1dykpCRvsxsfUFFRQXnRdnqHnYXMiU7HcU7mJCjJJ75bBMOGDbN+feOzvCn6m4BsEckSkTBgDrCo9QYiMhL4Le6CX95q1TLgRhHp4bmAe6OnzXQRH3/8MeN6er7jA/Ei7jmefn2OfMrkyZPZsWMHVVVVTqcy5gvaLPqq2gw8hrtY7wYWqOpOEXlGRGZ7NvsZ0A14S0QKRGSRZ98q4Ce4vzg2Ac942kwXsWbNGqZnBqFRCYHZn39On7/360+ZMgVVZe3atU6nMuYLvBpQraqLgcUXtD3d6vWML9n3VeDVKw1ofFdjYyPr1q3lmZsbkKyZEBTA9/qFx0DvkXBoLTnTfkBCQgJr1qxh9uzZbe9rTCcK4P+l5mpt2bKFZDlJrJyFvlOdjuO8rElQupmg5jomTZrE2rVraWlpcTqVMZ9jRd9csdWrVzMp1VPU+k51MopvyJz4uX79mpoatm/f7nQqYz7Hir65YqtXr2bWgAjokQU9MtreoatLH3e+X/+6664jODjYhm4an2NF31yRgwcPUlp8iCHRVdCv6z8a0Svh3SB1FBxaS1xcHCNGjLChm8bnWNE3VyQvL4+h8U2Euuqta6e1THe/Pg1nmDJlCrt376a8vLzt/YzpJFb0zRXJy8vj1pxoQAJ7fP6FMieCq/l8vz6472UwxldY0TeX7fTp0+Tn5zMlXd3TD0TFOx3Jd6SPhaAQOLSWAQMG0LNnT+viMT7Fir65bOvWrSOMRlJdpda1c6HwbtB7FBzMQ0SYPHky69ato6mpyelkxgBW9M0VWL16NVMyQxBtgb52EfcL+l8PpVvgbCWTJ0/m7NmzbN261elUxgBe3pFrzDktLS2sWbOG/5zWHUJOuLszAtj8T4u/0JbQMIyZKBStZPz4mwkNDXVPPz0mgJ4dbHyWnemby7J9+3aqq6sZGVPpvmgZGuF0JJ9TGTeE+tAeULiC6Ohorr32WuvXNz7Dir65LHl5eWTGKt3qj0H2jU7H8U0SxPHE8VC4AlwupkyZQmFhIaWlX5hV3JhOZ0XfXJa8vDzmjk50L/S/5Dx7Ae9o0kSoPQHHtzF16lQAVq5c6WwoY7Ciby7DsWPH2LNnD9PSmiG+LyT0czqSzzqWeJ37xf4VZGZm0q9fPyv6xidY0TdeW716NWFBSmrTQeh/g9NxfFpDeIJ7quXCDwGYPn06mzZtoqamxuFkJtBZ0TdeW7FiBbcO605QSz1kW9FvU/8boGQT1FUzffr08yOfjHGSV0VfRGaJyF4RKRSRJy6yfrKIbBGRZhG5+4J1LZ6naZ1/opbxP6dOnWLjxo18ZVgMhEQE9vNwvdV/BqgLilYxfPhwEhMTrYvHOK7Noi8iwcALwE1ADjBXRHIu2KwYeBCYf5G3qFPVEZ4fe4yQn1qzZg3Nzc0MCT/mGaoZ6XQk35eWCxHdoXAFQUFBTJs2jTVr1tDY2Oh0MhPAvDnTHwMUquoBVW0E3gRub72Bqh5S1e2AqwMyGh+wYsUKhqXFEHHmiPXneysoGPpNdw/dVOX666+ntraWTz/91OlkJoB5U/RTgSOtlks8bd6KEJF8EdkgIndcVjrjExoaGvj44495cEKKu8H6872XfQOcKYNj2xg3bhxRUVF89NFHTqcyAawzLuRmqGoucB/wSxH5wjg/EXnE88WQX1FR0QmRzOVYv349tbW1XBdfDYkDbajm5cieCRIEexcTHh7OxIkTWblyJS6X/VFsnOFN0S8F0lstp3navKKqpZ7fB4DVwMiLbPOyquaqam5SUpK3b206yYoVK+jVPZK4kzth0M1Ox/Ev0QnuxyjuWQy4h25WVFSwY8cOh4OZQOVN0d8EZItIloiEAXMAr0bhiEgPEQn3vE4EJgC7rjSs6XwtLS2sWrWKh6f0QVzNMNCKvrfmf1rM/E+L2RI5Hsp28LdV6zkR05+g4BCWL1/udDwToNos+qraDDwGLAN2AwtUdaeIPCMiswFEZLSIlAD3AL8VkZ2e3QcD+SKyDVgF/IeqWtH3I1u3bqWqqorpvesgOhlSc52O5HdKe7qnn04tX01kdAx9h4xk+fLlqKrDyUwg8qpPX1UXq+oAVe2nqs962p5W1UWe15tUNU1Vo1U1QVWHeNo/UdVhqnqN5/crHXcopiMsX76c6IhQep35DAbOgiC7n+9ynY7OoCa6L2nlqwAYnDuJkpISdu2y8x/T+ex/sLkkl8vF8uXL+drU/kjjGRh4i9OR/FZJz2kkV+UT2lTDgFHjCQ4Oti4e4wgr+uaSCgoKKCsr45b+AqFR0HeK05H8VknyNIK0hdTyj4nqFsvYsWNZunSpdfGYTmdF31zS0qVLCQsLpU/dTvdNRnYX7hWr7D6MuvBEUj1dPDNnzqS4uJh9+/Y5nMwEGiv65qJcLhfLli1j3rQcgs4cg0HWtXNVJIiS5Kn0rlhLcEs9119/PUFBQSxbtszpZCbAWNE3F7V161bKy8v5ykCBoFAYeJPTkfzekV43ENpSS0rFOhISEhgzZoz165tOZ0XfXNSyZcsICwslq7YA+l8PkT2cjuT3yuLHUB/ag4zjSwG48cYbKSoqYv/+/Q4nM4HEir75ApfLxdKlS/mH6TkEnT4KQ+50OlKXoEEhHOk1g9TyPGis5YYbbiAoKIgPPvjA6WgmgFjRN1+wZcsWKioquHMAEBxmXTvtqDhlJiEtdbB/OYmJiYwfP54PPvjARvGYTmNF33zB4sWLiQgPI/NsgXsa5Yg4pyN1GeXxudSFxcPOhQDceuutlJSUUFBQ4HAyEyis6JvPaWxsZPHixTx041D3qB3r2mlXKsEc6XUD7FsODWeYMWMG4eHhvP/++05HMwHCir75nLVr11JTU8Md2ep+LOLAWU5H6nKKe82E5jrYv4xu3boxbdo0lixZQlNTk9PRTACwom8+Z9GiRSTG9yC1Jt/9AJDwGKcjdTkV8aOgWy/47O9dPNXV1axfv97hZCYQWNE35506dYpVq1bxrVn+mQLMAAAVNElEQVQ5yJkyGHp32zuZy6YSDEPvgn3LoLaKSZMmERsba108plNY0TfnLVu2jMbGRm7qfdL9QG8btdNxRtwHribY8TZhYWHMnDmTjz76iNraWqeTmS7Oin4AO/eQj3M/v3/jLdJTU+hW+rH7TDQk3OmIXVevodBrGBS8Abi7eGpra+35uabDWdE3AJw8UUbx3h3MG9eTEFc9XDPX6Uhd34ivwrECKNtFbm4uaWlpvPPOO06nMl1ciDcbicgs4L+BYOD3qvofF6yfDPwSGA7MUdW3W617APihZ/HfVfX19ghu2teOT9xnmNd3P8IpzSQ27dJPyJr/aXFnxeraht0Dy38I2+YTdOO/c+edd/LrX/+aI0eOkJ6e3vb+xlyBNs/0RSQYeAG4CcgB5opIzgWbFQMPAvMv2Dce+BEwFhgD/EhEbBIXH6MuFwUfL2Ps8P6kntnBwd63gYjTsbq+6ETIngnbF0BLM3feeSciwsKFC51OZrowb7p3xgCFqnpAVRuBN4HbW2+gqodUdTvgumDfmcCHqlqlqtXAh4AN/PYxB3cXcLLiOA+ODMdFEAdTZzsdKXCMuA/OlEHRSlJSUpg4cSLvvvsuLS0tTiczXZQ3RT8VONJqucTT5g2v9hWRR0QkX0TyKyoqvHxr0162rF5MTEwMY11bOJo8mdrIXk5HChzZN0JUAmxx93reddddHD9+nHXr1jkczHRVPnEhV1VfVtVcVc1NSkpyOk5AOVNTzd4tn/APU7KIbKykMP0epyMFhPOjpjYfZ1fP2bj2LuGveZso79afqJg46+IxHcabol8KtL6qlOZp88bV7Gs6wfZ1H+JqaWF2z+OcjUjhWNIEpyMFnP197kHURf8j7xAcEsqw665n5cqVVFVVOR3NdEHeFP1NQLaIZIlIGDAHWOTl+y8DbhSRHp4LuDd62owPUFW25i1h3NC+9DlTQFH6V9x3i5pOdTYqnWNJE+h35G3E1cSIyTNpamrir3/9q9PRTBfUZtFX1WbgMdzFejewQFV3isgzIjIbQERGi0gJcA/wWxHZ6dm3CvgJ7i+OTcAznjbjAw7v2UZV2VG+NjIUlwRTlPYVpyMFrP3p9xLVUEFa+WqSUzMZPXo08+fPtwu6pt15NU5fVRcDiy9oe7rV6024u24utu+rwKtXkdF0kPyV7xMfE8W4lo0U97qRuojk8+tsLH7nOpo8ibMRKWQffpMjvW5g3rx5PP7446xatYoZM2Y4Hc90IT5xIdd0vtLSUvbkr+PR6amEtZxlb+b9TkcKaCrB7MuYS6+qjfSo2c306dNJSUnhjTfecDqa6WKs6AeoN954gyBRbonbR0X3EVR2H+Z0pIBXmH43TcHRDDr0OiEhIcydO5cNGzawb98+p6OZLsSKfgA6e/Ysb731FvdP7ktcwzH22Fm+T2gKjaEw/S4yji2FmhLuvvtuwsPDmT9/fts7G+MlK/oBaOHChZw5c5qvZpRzJrI3JT2nOx3JeOzNnOd+seElevTowa233srf/vY3ampqnA1mugwr+gGmpaWFP/3pT9w3vg9p9XvZnfUgGuTV9XzTCWojU9yPU9z8OtRVM2/ePOrr61mwYIHT0UwXYUU/wKxatYojR47wraFnqA1PsmGaPmhX369D42nY8BKDBg1iwoQJvP7669TX1zsdzXQBVvQDiKryyiuvcMOgGBJO7WR31oO4gu1BKb7mZOxAinveQOO6F3hr7Q76TbqDyspKnvj572worblqVvQDyCeffEJBQQFPjg+GqEQK+9g8O77qs/7fIKz5DIMO/S99Bg4jPXsI65e8RUtzk9PRjJ+zoh8gVJUXX3yRGwdGk1K7C677J1qCI52OZS7h3Nn+wEP/S1jzKSbeNpdTVRXs+GSl09GMn7OiHyDWr1/Pli2b+eG4ZojpDWO/4XQk04bP+n+D0OazDCl6hX7DcumV0Z91H/zFpmYwV8WKfhd2bvreNzYc5sf/+Ty3DQwjqf4gGzK+wfwt9twCX3cydiAHU29j4OE36FZ3lIm3zqGqrJSlS5c6Hc34MSv6AeDQ7m0c3f8Z3xtxlprovvZkLD+yLfufUIRr9v2KQddOIDkti1/96lc0NjY6Hc34KSv6XZyqsnrha3xtmJLMCQoGfsfG5fuRushe7Mn6BzKPLSbh1C6u/z8PUVxcbOP2zRWzot/F7dqYR33JZ3xr8EmOJk6gNHmq05HMZdrV9yHqwhLI3fVT+g8dybhx43jxxRc5ffq009GMH7Ki34U1Nzby0YJX+eF4F2HSwuacJ0HE6VjmMjWHRLN10PdIrNlB/5KFfO9736O6uppXXnnF6WjGD3lV9EVklojsFZFCEXniIuvDReQvnvWfikimpz1TROpEpMDz8z/tG998mQ3LF5IdVMzM5Ap2932Q09EZTkcyV+hQ71s4njCWEXt/yZA+idxyyy28/vrrlJWVOR3N+Jk2O3dFJBh4AbgBKAE2icgiVd3VarOHgGpV7S8ic4D/BO71rCtS1RHtnNt4XOoOzTMnq9iyZD4Lb6znTGQqO/v9YycnM+1KhE05P+DmtXfB0if4znd+wvLly/mv//ovnnvuOafTGT/izZn+GKBQVQ+oaiPwJnD7BdvcDrzuef02cL2I9SM4adU7r/HYwAp6h51lw7Cf2I1YXcDpblns7P8I7FxI2smNPPTQQ7z33nusW7fO6WjGj3hT9FOBI62WSzxtF93G80zdGiDBsy5LRLaKSJ6ITLrKvMYLB3cVELpnEfOyz7A346uUJ4x2OpJpJzv7Pgyp18L7/8w3v3oHGRkZPPPMMzYZm/FaR1/IPQb0UdWRwHeB+SISe+FGIvKIiOSLSH5Fhd00dDUaG+pZ86ef8dx1pzkVmU7BgG87Hcm0Iw0KgTtfhuYGwpf8Mz/+0dMUFxfz0ksvOR3N+Alvin4pkN5qOc3TdtFtRCQEiAMqVbVBVSsBVHUzUAQMuPADVPVlVc1V1dykpKTLPwpz3uq3X+Wp/ntJjHCxbuTPaAmJcjqSaW+J/WHms1D0EeOa13PHHXfw6quv2mMVjVe8KfqbgGwRyRKRMGAOsOiCbRYBD3he3w2sVFUVkSTPhWBEpC+QDRxon+jmQkf272RYyRtM6d3A1px/pTpuiNORTEfJ/ToMvRtW/jtPzbmOmJgYnnjiCbtT17SpzaLv6aN/DFgG7AYWqOpOEXlGRM7dz/8KkCAihbi7cc4N65wMbBeRAtwXeL+pqlXtfRAGGurOUrHw3/jOsFMcTJrB/j73tr2T8V8iMPtXkDiQmGWP8/Mffpvdu3fz85//3Olkxsd5dT++qi4GFl/Q9nSr1/XAFyZnV9V3gHeuMqNpg6qy/U8/4t8HF1IRnsnGEc/aTViBICwa7v1f+N10rjvwPP847x5+96c/MX78eKZNm+Z0OuOj7I7cLmDv8tf5fvwKmkK6sX7C760fP5Ak9oc5b0DVAb6Tks+wnIE89dRTdtOWuSSbecvPVe35hK/V/Dfdo4TVE35PXURPpyOZDvbFG/IyyBj2EyZse4I/3DqD6S808Pjjj/Paa68RERHhSEbju+xM348dO7CTmdsfp1eUi1WjfsPJuBynIxmHHO59C5sH/yvRh1fwtweS2bljG0888QQul8vpaMbHWNH3UyeLd9H4P9fTJ6qBxf1+TE3vCU5HMg7bmzkPbnyWXpXrWfRAEis/XMrzzz/vdCzjY6zo+6G6I9tp+u00EkLreSftKRoG3+l0JOMrrnsMZv6UrNpt/O2eYBb88Xf8+c9/djqV8SHWp+9nGnctwfXm/QS1tLBr/HPQa5bTkYyvGf8oRCeR+e63eHd2FF9/3j3Qbu7cuQ4HM77AzvT9hctF/UfPEfKXOZScUjZd81PG3P6I06mMrxr+f5B5b5MSE8Rfb6pm3StP8tprrzmdyvgAO9P3AZeaHhngvrF94NRRGhc8TETJOpaXRqG3v8Cs277SiQmNv/j8v6W+RI/5MxO2fpffTNzFH5Y9xW/P1vDIt76NTYIbuOxM35epwva3aPnNWFyH1/Pv25KIemABM63gGy+djUplxbg/sTftHr428Cw3FD3Dr7//VWpra52OZhxiRd9HxZ3ez/SND8HCh9l1vJ6HNg3gth+/w8RJNju1uTyu4DA2D3uaj3J/S1KPGB6L/oC8719L6b4Cp6MZB4iqOp3hc3JzczU/P9/pGJ2q9Z/kUXXHGVL0O/odeYfalmB+vjWS/IgJ3P6NJ4mO7e5gStMVhDTXkrH+B1x7agX1LcKayJmcmvb/4gqJcnclGr8lIptVNbet7axP30d0O3uEQYdep9+Rhai6eOdwLL/YGsGwWfcz5/b7CAoKdjqi6QKaQ6IomvQLCo9sImvdk9wcupSaJSvZ3ec+GP5jiOzhdETTwexM30muFihcwdEPf0VKxTpcBLO0MoXnP2mAXkO4+YFvk5LR3+mUpotSVapXvsTw4teZ3KuWRsLQoXcRPu4fIXWUTdrnZ7w907ei39lUoXQzfLYQdv4VTh/lTHAP3itL5qV1JzkT3J2pX3mAUdNutrN70ylqKss5+N4vGV/7EbPS64gMUZoTBhIy8j4YfBsk9HM6ovGCFX1fUlcNB9dA0UooXAk1xWhwGMeic/jLLuXVT8oJjYpl3Ky7GD1jNuGR0U4nNgFodHwDv/3Vz4g6sJR7+tUxLN79QBZNGowMuhmypkD6GAiNdDipuRgr+k5Rhcoi99l8aT6UbIJj20BdaFgM5dEDWXW8G79dfYTjJ+vo168ffcfMYOTkmVbsjaPOXcgtKipi/vz5bFj2FhPiq7mlnzIs7gxBKBochqSNgYzroPcISLkGYlOtK8gHtGvRF5FZwH8DwcDvVfU/LlgfDvwRuBaoBO5V1UOedU8CDwEtwLdVddmXfZYvF/3Wo2yCW+qJqj9O7JlDxJ49QOyZA8SdOUjs2QOENZ8BoCkokqOhGWw/G8/WsmDe3VRMXUMT3bt3Z/r06dx5551ce+21/HnjEacOyZhLqq89y578tezcmEf5vs1cm1DPdWlwXarSN/wkQbhrR31od6pjB3OqW19OR/fhdFQGp6L7UBvZG5VgGxXUSdqt6HuecbsPuAEowf3M3LmquqvVNt8ChqvqN0VkDnCnqt4rIjnAn4ExQG9gBTBAVVsu9XmOFv2WJndXTG0l1FZ5fldCXRWcLuPI4f1E1x0jqr6MiMbPP/XxdHB3ykjicF0UO08I6w/Vsr3kNC4VRILIyRnMmDFjmDp1KqNGjSIk5O8Dp77sjlxjfEHt6Rr2b9vIwV1bObSrgKbTFQyIa2ZoootRaeEMimskNfQ0EfL3Z/S2SAh1ET3plpgGMSnun9gU6NYLouIhMh4iu7tfh8dBkN02dDXas+iPB36sqjM9y08CqOr/12qbZZ5t1otICHAcSMLzrNxz27be7lKf1x5F3+Vy0dLS8rnfF2s797u5qYn0v0wjpOHkJd+zgXDKm6OoaIzgeH0ox84KJTUt7Cmrp6gmhNNN7n+wQcEhJKakk5yeRXJaJj3T+5LWP4eIKOu6MV2DqlJdfpSjB/dx7OA+jhcXUXm8lNPVFSRGuMjo1kxGTAtZcUpqXDB9YoTEiCbiguqIkKaLvqcLoShqJO+mPUlwSCjBISEEh4Zy9+hMQkNDCQ4OJigoiKCgIEQEEfnccuv2q51ioq2TMF/9y6U9x+mnAq37H0qAsZfaRlWbRaQGSPC0b7hg31QvPvOyVVVVMXnyZFpaLvlHxJf6zrBmGltiqG4M4mRDENUNQedfn2wMokmDiYiOISI6hsjoGKK6xRDZPY64/slMiU8mLiGJ2IRkeiT1IjgktJ2PzhjfISLE90wlvmcqQ8f9/Vm8jQ31VJWVUnW8lJrKcj6pqeZ0TRXdTtRSUVHBmTNnaKmtoZueIS7cRfcwF93DXcSFuV8fqy3irQOPf+6zXrjCfBd+CVzOF0FLGz3ePwuS859zOZm8MXToUF5//XWv3/dK+MTNWSLyCHBuysgzIrK3szN8cw8AicCJzv7sTmTH59/s+Pxbm8e3ZcsW/vjHP17p+2d4s5E3Rb8USG+1nOZpu9g2JZ7unTjcF3S92RdVfRl42ZvAHUlE8r3588hf2fH5Nzs+/+Yrx+fNlZNNQLaIZIlIGDAHWHTBNouABzyv7wZWqvtiwSJgjoiEi0gWkA1sbJ/oxhhjLlebZ/qePvrHgGW4h2y+qqo7ReQZIF9VFwGvAH8SkUKgCvcXA57tFgC7gGbg0S8buWOMMaZjedWnr6qLgcUXtD3d6nU9cM8l9n0WePYqMnYmx7uYOpgdn3+z4/NvPnF8PndHrjHGmI5jd0MYY0wAsaJ/CSLyLyKiIpLodJb2JCI/E5E9IrJdRP4qIn7/ZBYRmSUie0WkUESecDpPexKRdBFZJSK7RGSniDze9l7+R0SCRWSriLzvdJaOICLdReRtz/+93Z6bXh1hRf8iRCQduBHoivMjfAgMVdXhuKfXeNLhPFfFM03IC8BNQA4w1zP9R1fRDPyLquYA44BHu9jxnfM4sNvpEB3ov4GlqjoIuAYHj9WK/sX9Avh/gC53wUNVl6tqs2dxA+57J/zZGKBQVQ+oaiPwJnC7w5najaoeU9UtntencReLDrmr3SkikgbcAvze6SwdQUTigMm4Rzmiqo2qeuk5XzqYFf0LiMjtQKmqbnM6Syf4OrDE6RBX6WLThHSponiOiGQCI4FPnU3S7n6J+yTL5XSQDpIFVAB/8HRh/V5EHJuMyyemYehsIrIC6HWRVT8AnsLdteO3vuz4VPVvnm1+gLvr4I3OzGaujIh0A94BvqOqp5zO015E5FagXFU3i8hUp/N0kBBgFPBPqvqpiPw37sko/82pMAFHVWdcrF1EhuH+Vt7mmSApDdgiImNU9XgnRrwqlzq+c0TkQeBW4Hr1/zG7Xk314c9EJBR3wX9DVRc6naedTQBmi8jNQAQQKyL/q6rzHM7VnkqAElU99xfa23hmIHaCjdP/EiJyCMhV1S4zCZTngTjPA1NUtcLpPFfLM9fTPuB63MV+E3Cfqu50NFg7EffZx+tAlap+x+k8Hclzpv89Vb3V6SztTUQ+Bh5W1b0i8mMgWlW/70SWgDzTD3C/AcKBDz1/zWxQ1W86G+nKXWqaEIdjtacJwP3ADhEp8LQ95blL3viPfwLe8MxfdgD4mlNB7EzfGGMCiI3eMcaYAGJF3xhjAogVfWOMCSBW9I0xJoBY0TfGmABiRd8YYwKIFX1jjAkgVvSNMSaA/P8XM+LBr62hrgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# answer\n",
    "np.random.seed(seed=1234)\n",
    "t_df10 = np.random.standard_t(df = 10, size = 1000)\n",
    "sns.distplot(t_df10, fit =stats.norm, kde= False)\n",
    "\n",
    "t_df = stats.t(df = 10)\n",
    "xx = np.linspace(-4,4,400)\n",
    "plt.plot(xx, t_df.pdf(xx))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "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(1234)\n",
    "xx2 = np.linspace(0,100,400)\n",
    "for df in [1,3,5,7,9,11,15,50]:\n",
    "    chisq = stats.chi2(df = df)\n",
    "    plt.plot(xx2, chisq.pdf(xx2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa77e759438>"
      ]
     },
     "execution_count": 65,
     "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 = 10, size = 1000)\n",
    "sns.distplot(chisq_df10, fit = stats.norm, kde = True) # kde : kernel density estimate : 여러개의 정규분포를 가정해서 실질적 형상에 가깝게 그려줌"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa77e773d68>"
      ]
     },
     "execution_count": 64,
     "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_df40 = np.random.chisquare(df = 40, size = 1000)\n",
    "sns.distplot(chisq_df40, fit = stats.norm,kde = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(T<=10) :  0.0002\n",
      "P(T>=10) :  0.9998\n",
      "10.000000000000155\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa77e358fd0>]"
      ]
     },
     "execution_count": 84,
     "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": [
    "# 실습 6. Chisq\n",
    "np.random.seed(1234)\n",
    "chisq = 10\n",
    "df = 30\n",
    "\n",
    "prop = stats.chi2.cdf(chisq, df)\n",
    "print(\"P(T<={0}) : {1: .4f}\".format(chisq, prop))\n",
    "prop2 = stats.chi2.sf(chisq, df)\n",
    "print(\"P(T>={0}) : {1: .4f}\".format(chisq, prop2))\n",
    "ppf = stats.chi2(df = df).ppf(prop)\n",
    "print(ppf)\n",
    "\n",
    "plt.plot(xx2, stats.chi2(df=df).pdf(xx2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(T<=3.0) :  0.0656\n",
      "P(T>=3.0) :  0.9344\n"
     ]
    }
   ],
   "source": [
    "# 실습 7. Chisq\n",
    "np.random.seed(1234)\n",
    "chisq = 3.0\n",
    "df = 8\n",
    "\n",
    "prop = stats.chi2.cdf(chisq, df)\n",
    "print(\"P(T<={0}) : {1: .4f}\".format(chisq, prop))\n",
    "prop2 = stats.chi2.sf(chisq, df)\n",
    "print(\"P(T>={0}) : {1: .4f}\".format(chisq, prop2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/scipy/stats/_continuous_distns.py:1679: RuntimeWarning: divide by zero encountered in log\n",
      "  lPx = m/2 * np.log(m) + n/2 * np.log(n) + (n/2 - 1) * np.log(x)\n"
     ]
    },
    {
     "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(1234)\n",
    "xx = np.linspace(0, 10, 400)\n",
    "for df in [1,3,5,7,9,30]:\n",
    "    f_df = stats.f(dfn=df, dfd =10)\n",
    "    plt.plot(xx, f_df.pdf(xx))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(F<=2) :  0.904\n",
      "P(F>=2) :  0.096\n",
      "1.999999999999999\n"
     ]
    }
   ],
   "source": [
    "f =2 \n",
    "dfnum = 15\n",
    "dfden = 15\n",
    "\n",
    "prop = stats.f.cdf(x=f,dfn = dfnum, dfd = dfden)\n",
    "print(\"P(F<={0}) : {1: .3f}\".format(f, prop))\n",
    "\n",
    "prop2 = stats.f.sf(x=f,dfn = dfnum, dfd = dfden)\n",
    "print(\"P(F>={0}) : {1: .3f}\".format(f, prop2))\n",
    "\n",
    "ppf = stats.f(dfn = dfnum, dfd=dfden).ppf(prop)\n",
    "print(ppf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:2316: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  Lhat = muhat - Shat*mu\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/scipy/stats/_continuous_distns.py:1679: RuntimeWarning: divide by zero encountered in log\n",
      "  lPx = m/2 * np.log(m) + n/2 * np.log(n) + (n/2 - 1) * np.log(x)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa77e518c88>]"
      ]
     },
     "execution_count": 97,
     "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(1234)\n",
    "f_df10 = np.random.f(dfnum = 10, dfden=10, size=1000)\n",
    "sns.distplot(f_df10, fit = stats.f ,kde = True)\n",
    "\n",
    "f_dist = stats.f(dfn = 10, dfd = 10)\n",
    "\n",
    "xx = np.linspace(0,4,400)\n",
    "plt.plot(xx,f_dist.pdf(xx))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "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(1234)\n",
    "xx = np.linspace(0, 3000, 400)\n",
    "for c in [1,3,5,25,50]:\n",
    "    weibull_df = stats.weibull_min(c=c, scale=1200)\n",
    "    plt.plot(xx, weibull_df.pdf(xx))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(x>=1500) :  0.19518\n",
      "0.19518415119971322\n"
     ]
    }
   ],
   "source": [
    "# 실습 와이블\n",
    "np.random.seed(1234)\n",
    "x = 1500\n",
    "alpha = 2.2\n",
    "beta = 1200\n",
    "\n",
    "prop = stats.weibull_min.cdf(x, c = alpha, scale = beta)\n",
    "print(\"P(x>={0}) : {1: .5f}\".format(x, (1-prop)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(X=0) :  0.216\n",
      "P(X=1) :  0.432\n",
      "P(X=2) :  0.288\n",
      "P(X=3) :  0.064\n"
     ]
    }
   ],
   "source": [
    "n = 3\n",
    "for i in range(n+1):\n",
    "    prop = stats.binom.pmf(k = i, n=n, p = 0.4)\n",
    "    print(\"P(X={0}) : {1: .3f}\".format(i, prop))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "for n in [3, 5, 25, 50]:\n",
    "    binorm2 = stats.binom(n=n, p=0.4)\n",
    "    xx=np.linspace(0, n, n+1)\n",
    "    plt.plot(xx, binorm2.pmf(xx))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "for mu in [2,4,6,10]:\n",
    "    poisson_df = stats.poisson(mu = mu)\n",
    "    xx = np.linspace(0, 3*mu, 3*mu+1)\n",
    "    plt.plot(xx, poisson_df.pmf(xx))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1분당 2번의 전화가 걸려올 확률:  0.1804\n",
      "1분당 최대 2번 이하의 전화가 걸려올 확률:  0.6767\n"
     ]
    }
   ],
   "source": [
    "mu = 2\n",
    "prop = stats.poisson.pmf(3, mu)\n",
    "\n",
    "cdf_prop = stats.poisson.cdf(2, mu)\n",
    "print(\"1분당 {0}번의 전화가 걸려올 확률: {1: .4f}\".format(mu, prop))\n",
    "print(\"1분당 최대 {0}번 이하의 전화가 걸려올 확률: {1: .4f}\".format(mu, cdf_prop))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>model</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>4runner 4wd</th>\n",
       "      <td>6.0</td>\n",
       "      <td>15.166667</td>\n",
       "      <td>0.752773</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.75</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a4</th>\n",
       "      <td>7.0</td>\n",
       "      <td>18.857143</td>\n",
       "      <td>1.864454</td>\n",
       "      <td>16.0</td>\n",
       "      <td>18.00</td>\n",
       "      <td>18.0</td>\n",
       "      <td>20.50</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a4 quattro</th>\n",
       "      <td>8.0</td>\n",
       "      <td>17.125000</td>\n",
       "      <td>1.807722</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.75</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18.25</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a6 quattro</th>\n",
       "      <td>3.0</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.50</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.50</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>altima</th>\n",
       "      <td>6.0</td>\n",
       "      <td>20.666667</td>\n",
       "      <td>1.966384</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.00</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.50</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c1500 suburban 2wd</th>\n",
       "      <td>5.0</td>\n",
       "      <td>12.800000</td>\n",
       "      <td>1.303840</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.00</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>camry</th>\n",
       "      <td>7.0</td>\n",
       "      <td>19.857143</td>\n",
       "      <td>1.463850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.50</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.00</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>camry solara</th>\n",
       "      <td>7.0</td>\n",
       "      <td>19.857143</td>\n",
       "      <td>1.772811</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.00</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.00</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caravan 2wd</th>\n",
       "      <td>11.0</td>\n",
       "      <td>15.818182</td>\n",
       "      <td>1.834022</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.50</td>\n",
       "      <td>16.0</td>\n",
       "      <td>17.00</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>civic</th>\n",
       "      <td>9.0</td>\n",
       "      <td>24.444444</td>\n",
       "      <td>1.943651</td>\n",
       "      <td>21.0</td>\n",
       "      <td>24.00</td>\n",
       "      <td>24.0</td>\n",
       "      <td>25.00</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>corolla</th>\n",
       "      <td>5.0</td>\n",
       "      <td>25.600000</td>\n",
       "      <td>1.673320</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.00</td>\n",
       "      <td>26.0</td>\n",
       "      <td>26.00</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>corvette</th>\n",
       "      <td>5.0</td>\n",
       "      <td>15.400000</td>\n",
       "      <td>0.547723</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.00</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dakota pickup 4wd</th>\n",
       "      <td>9.0</td>\n",
       "      <td>12.777778</td>\n",
       "      <td>1.986063</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.00</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.00</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>durango 4wd</th>\n",
       "      <td>7.0</td>\n",
       "      <td>11.857143</td>\n",
       "      <td>1.573592</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.00</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>expedition 2wd</th>\n",
       "      <td>3.0</td>\n",
       "      <td>11.333333</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.00</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.50</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>explorer 4wd</th>\n",
       "      <td>6.0</td>\n",
       "      <td>13.666667</td>\n",
       "      <td>0.816497</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>13.5</td>\n",
       "      <td>14.00</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f150 pickup 4wd</th>\n",
       "      <td>7.0</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.50</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forester awd</th>\n",
       "      <td>6.0</td>\n",
       "      <td>18.833333</td>\n",
       "      <td>0.983192</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.00</td>\n",
       "      <td>18.5</td>\n",
       "      <td>19.75</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grand cherokee 4wd</th>\n",
       "      <td>8.0</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>2.507133</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grand prix</th>\n",
       "      <td>5.0</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.00</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18.00</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gti</th>\n",
       "      <td>5.0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>17.0</td>\n",
       "      <td>19.00</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.00</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>impreza awd</th>\n",
       "      <td>8.0</td>\n",
       "      <td>19.625000</td>\n",
       "      <td>0.744024</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.00</td>\n",
       "      <td>19.5</td>\n",
       "      <td>20.00</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jetta</th>\n",
       "      <td>9.0</td>\n",
       "      <td>21.222222</td>\n",
       "      <td>4.867694</td>\n",
       "      <td>16.0</td>\n",
       "      <td>19.00</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.00</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k1500 tahoe 4wd</th>\n",
       "      <td>4.0</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>1.732051</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.00</td>\n",
       "      <td>12.5</td>\n",
       "      <td>14.00</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>land cruiser wagon 4wd</th>\n",
       "      <td>2.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.414214</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.50</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>malibu</th>\n",
       "      <td>5.0</td>\n",
       "      <td>18.800000</td>\n",
       "      <td>1.923538</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18.00</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.00</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maxima</th>\n",
       "      <td>3.0</td>\n",
       "      <td>18.666667</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.50</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.00</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mountaineer 4wd</th>\n",
       "      <td>4.0</td>\n",
       "      <td>13.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.25</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mustang</th>\n",
       "      <td>9.0</td>\n",
       "      <td>15.888889</td>\n",
       "      <td>1.452966</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>15.0</td>\n",
       "      <td>17.00</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>navigator 2wd</th>\n",
       "      <td>3.0</td>\n",
       "      <td>11.333333</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.00</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.50</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new beetle</th>\n",
       "      <td>6.0</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>6.511528</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>20.5</td>\n",
       "      <td>27.00</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>passat</th>\n",
       "      <td>7.0</td>\n",
       "      <td>18.571429</td>\n",
       "      <td>1.902379</td>\n",
       "      <td>16.0</td>\n",
       "      <td>17.50</td>\n",
       "      <td>18.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pathfinder 4wd</th>\n",
       "      <td>4.0</td>\n",
       "      <td>13.750000</td>\n",
       "      <td>1.258306</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.50</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.25</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ram 1500 pickup 4wd</th>\n",
       "      <td>10.0</td>\n",
       "      <td>11.400000</td>\n",
       "      <td>1.505545</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.00</td>\n",
       "      <td>11.5</td>\n",
       "      <td>12.75</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>range rover</th>\n",
       "      <td>4.0</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.00</td>\n",
       "      <td>11.5</td>\n",
       "      <td>12.00</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sonata</th>\n",
       "      <td>7.0</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.414214</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.00</td>\n",
       "      <td>18.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tiburon</th>\n",
       "      <td>7.0</td>\n",
       "      <td>18.285714</td>\n",
       "      <td>1.603567</td>\n",
       "      <td>16.0</td>\n",
       "      <td>17.00</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.50</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>toyota tacoma 4wd</th>\n",
       "      <td>7.0</td>\n",
       "      <td>15.571429</td>\n",
       "      <td>0.786796</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.00</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         mpg                                                 \\\n",
       "                       count       mean       std   min    25%   50%    75%   \n",
       "model                                                                         \n",
       "4runner 4wd              6.0  15.166667  0.752773  14.0  15.00  15.0  15.75   \n",
       "a4                       7.0  18.857143  1.864454  16.0  18.00  18.0  20.50   \n",
       "a4 quattro               8.0  17.125000  1.807722  15.0  15.75  17.0  18.25   \n",
       "a6 quattro               3.0  16.000000  1.000000  15.0  15.50  16.0  16.50   \n",
       "altima                   6.0  20.666667  1.966384  19.0  19.00  20.0  22.50   \n",
       "c1500 suburban 2wd       5.0  12.800000  1.303840  11.0  12.00  13.0  14.00   \n",
       "camry                    7.0  19.857143  1.463850  18.0  18.50  21.0  21.00   \n",
       "camry solara             7.0  19.857143  1.772811  18.0  18.00  21.0  21.00   \n",
       "caravan 2wd             11.0  15.818182  1.834022  11.0  15.50  16.0  17.00   \n",
       "civic                    9.0  24.444444  1.943651  21.0  24.00  24.0  25.00   \n",
       "corolla                  5.0  25.600000  1.673320  24.0  24.00  26.0  26.00   \n",
       "corvette                 5.0  15.400000  0.547723  15.0  15.00  15.0  16.00   \n",
       "dakota pickup 4wd        9.0  12.777778  1.986063   9.0  11.00  14.0  14.00   \n",
       "durango 4wd              7.0  11.857143  1.573592   9.0  11.00  13.0  13.00   \n",
       "expedition 2wd           3.0  11.333333  0.577350  11.0  11.00  11.0  11.50   \n",
       "explorer 4wd             6.0  13.666667  0.816497  13.0  13.00  13.5  14.00   \n",
       "f150 pickup 4wd          7.0  13.000000  1.000000  11.0  13.00  13.0  13.50   \n",
       "forester awd             6.0  18.833333  0.983192  18.0  18.00  18.5  19.75   \n",
       "grand cherokee 4wd       8.0  13.500000  2.507133   9.0  12.50  14.0  15.00   \n",
       "grand prix               5.0  17.000000  1.000000  16.0  16.00  17.0  18.00   \n",
       "gti                      5.0  20.000000  2.000000  17.0  19.00  21.0  21.00   \n",
       "impreza awd              8.0  19.625000  0.744024  19.0  19.00  19.5  20.00   \n",
       "jetta                    9.0  21.222222  4.867694  16.0  19.00  21.0  21.00   \n",
       "k1500 tahoe 4wd          4.0  12.500000  1.732051  11.0  11.00  12.5  14.00   \n",
       "land cruiser wagon 4wd   2.0  12.000000  1.414214  11.0  11.50  12.0  12.50   \n",
       "malibu                   5.0  18.800000  1.923538  17.0  18.00  18.0  19.00   \n",
       "maxima                   3.0  18.666667  0.577350  18.0  18.50  19.0  19.00   \n",
       "mountaineer 4wd          4.0  13.250000  0.500000  13.0  13.00  13.0  13.25   \n",
       "mustang                  9.0  15.888889  1.452966  14.0  15.00  15.0  17.00   \n",
       "navigator 2wd            3.0  11.333333  0.577350  11.0  11.00  11.0  11.50   \n",
       "new beetle               6.0  24.000000  6.511528  19.0  20.00  20.5  27.00   \n",
       "passat                   7.0  18.571429  1.902379  16.0  17.50  18.0  20.00   \n",
       "pathfinder 4wd           4.0  13.750000  1.258306  12.0  13.50  14.0  14.25   \n",
       "ram 1500 pickup 4wd     10.0  11.400000  1.505545   9.0  11.00  11.5  12.75   \n",
       "range rover              4.0  11.500000  0.577350  11.0  11.00  11.5  12.00   \n",
       "sonata                   7.0  19.000000  1.414214  18.0  18.00  18.0  20.00   \n",
       "tiburon                  7.0  18.285714  1.603567  16.0  17.00  19.0  19.50   \n",
       "toyota tacoma 4wd        7.0  15.571429  0.786796  15.0  15.00  15.0  16.00   \n",
       "\n",
       "                              \n",
       "                         max  \n",
       "model                         \n",
       "4runner 4wd             16.0  \n",
       "a4                      21.0  \n",
       "a4 quattro              20.0  \n",
       "a6 quattro              17.0  \n",
       "altima                  23.0  \n",
       "c1500 suburban 2wd      14.0  \n",
       "camry                   21.0  \n",
       "camry solara            22.0  \n",
       "caravan 2wd             18.0  \n",
       "civic                   28.0  \n",
       "corolla                 28.0  \n",
       "corvette                16.0  \n",
       "dakota pickup 4wd       15.0  \n",
       "durango 4wd             13.0  \n",
       "expedition 2wd          12.0  \n",
       "explorer 4wd            15.0  \n",
       "f150 pickup 4wd         14.0  \n",
       "forester awd            20.0  \n",
       "grand cherokee 4wd      17.0  \n",
       "grand prix              18.0  \n",
       "gti                     22.0  \n",
       "impreza awd             21.0  \n",
       "jetta                   33.0  \n",
       "k1500 tahoe 4wd         14.0  \n",
       "land cruiser wagon 4wd  13.0  \n",
       "malibu                  22.0  \n",
       "maxima                  19.0  \n",
       "mountaineer 4wd         14.0  \n",
       "mustang                 18.0  \n",
       "navigator 2wd           12.0  \n",
       "new beetle              35.0  \n",
       "passat                  21.0  \n",
       "pathfinder 4wd          15.0  \n",
       "ram 1500 pickup 4wd     13.0  \n",
       "range rover             12.0  \n",
       "sonata                  21.0  \n",
       "tiburon                 20.0  \n",
       "toyota tacoma 4wd       17.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_mycars = pd.read_csv(\"실습파일/mycars.csv\", engine = \"python\")\n",
    "df = ds_mycars[['model', 'mpg']]\n",
    "df.groupby('model').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_mycars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.858974358974358"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_mycars['mpg'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.255945678889395"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_mycars['mpg'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg             16.858974\n",
       "displacement     3.471795\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_mycars[['mpg', 'displacement']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "displacement          3.471795\n",
       "year               2003.500000\n",
       "cylinder              5.888889\n",
       "mpg                  16.858974\n",
       "highway_mileage      23.440171\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_mycars.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>displacement</th>\n",
       "      <th>year</th>\n",
       "      <th>cylinder</th>\n",
       "      <th>highway_mileage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mpg</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.700000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.150000</td>\n",
       "      <td>2000.800000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>15.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5.050000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>17.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.833333</td>\n",
       "      <td>2005.000000</td>\n",
       "      <td>7.619048</td>\n",
       "      <td>17.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.557895</td>\n",
       "      <td>2003.736842</td>\n",
       "      <td>7.052632</td>\n",
       "      <td>18.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3.970833</td>\n",
       "      <td>2002.000000</td>\n",
       "      <td>6.416667</td>\n",
       "      <td>20.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3.678947</td>\n",
       "      <td>2003.263158</td>\n",
       "      <td>6.105263</td>\n",
       "      <td>23.368421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3.143750</td>\n",
       "      <td>2005.187500</td>\n",
       "      <td>5.875000</td>\n",
       "      <td>24.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.815385</td>\n",
       "      <td>2000.730769</td>\n",
       "      <td>5.307692</td>\n",
       "      <td>26.192308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2.565000</td>\n",
       "      <td>2003.050000</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>26.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.318182</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>4.181818</td>\n",
       "      <td>27.545455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2.173913</td>\n",
       "      <td>2003.695652</td>\n",
       "      <td>4.086957</td>\n",
       "      <td>29.130435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2.200000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>29.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2.200000</td>\n",
       "      <td>2005.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>30.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.720000</td>\n",
       "      <td>2000.800000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>32.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.700000</td>\n",
       "      <td>2003.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.800000</td>\n",
       "      <td>2005.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>34.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1.700000</td>\n",
       "      <td>2003.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.900000</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>41.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1.900000</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1.900000</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     displacement         year  cylinder  highway_mileage\n",
       "mpg                                                      \n",
       "9        4.700000  2008.000000  8.000000        12.000000\n",
       "11       5.150000  2000.800000  7.900000        15.550000\n",
       "12       5.050000  2008.000000  8.000000        17.375000\n",
       "13       4.833333  2005.000000  7.619048        17.428571\n",
       "14       4.557895  2003.736842  7.052632        18.105263\n",
       "15       3.970833  2002.000000  6.416667        20.875000\n",
       "16       3.678947  2003.263158  6.105263        23.368421\n",
       "17       3.143750  2005.187500  5.875000        24.500000\n",
       "18       2.815385  2000.730769  5.307692        26.192308\n",
       "19       2.565000  2003.050000  4.600000        26.450000\n",
       "20       2.318182  2008.000000  4.181818        27.545455\n",
       "21       2.173913  2003.695652  4.086957        29.130435\n",
       "22       2.200000  2008.000000  4.000000        29.750000\n",
       "23       2.200000  2005.000000  4.000000        30.666667\n",
       "24       1.720000  2000.800000  4.000000        32.600000\n",
       "25       1.700000  2003.500000  4.000000        34.000000\n",
       "26       1.800000  2005.000000  4.000000        34.666667\n",
       "28       1.700000  2003.500000  4.000000        35.000000\n",
       "29       1.900000  1999.000000  4.000000        41.000000\n",
       "33       1.900000  1999.000000  4.000000        44.000000\n",
       "35       1.900000  1999.000000  4.000000        44.000000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_mycars.groupby('mpg').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cylinder</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21.012346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16.215190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12.571429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                mpg\n",
       "cylinder           \n",
       "4         21.012346\n",
       "5         20.500000\n",
       "6         16.215190\n",
       "8         12.571429"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_mycars[['mpg', 'cylinder']].groupby('cylinder').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.858974358974358"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_mycars.mpg.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    234.000000\n",
       "mean      16.858974\n",
       "std        4.255946\n",
       "min        9.000000\n",
       "25%       14.000000\n",
       "50%       17.000000\n",
       "75%       19.000000\n",
       "max       35.000000\n",
       "Name: mpg, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_mycars.mpg.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_EXH_QC1 = pd.read_csv(\"실습파일/EXH_QC1.csv\", engine = \"python\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Flaws</th>\n",
       "      <th>Period</th>\n",
       "      <th>Strength</th>\n",
       "      <th>Baking Time</th>\n",
       "      <th>Cabbage weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Scratch</td>\n",
       "      <td>Day</td>\n",
       "      <td>37.6</td>\n",
       "      <td>0.798922</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Scratch</td>\n",
       "      <td>Day</td>\n",
       "      <td>38.6</td>\n",
       "      <td>0.849020</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Peel</td>\n",
       "      <td>Day</td>\n",
       "      <td>37.2</td>\n",
       "      <td>0.816163</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Peel</td>\n",
       "      <td>Day</td>\n",
       "      <td>36.4</td>\n",
       "      <td>0.758266</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Smudge</td>\n",
       "      <td>Day</td>\n",
       "      <td>38.6</td>\n",
       "      <td>0.815894</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Flaws Period  Strength  Baking Time  Cabbage weight\n",
       "0  Scratch    Day      37.6     0.798922             3.6\n",
       "1  Scratch    Day      38.6     0.849020             2.9\n",
       "2     Peel    Day      37.2     0.816163             2.8\n",
       "3     Peel    Day      36.4     0.758266             2.6\n",
       "4   Smudge    Day      38.6     0.815894             3.4"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_EXH_QC1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Other       6\n",
       "Peel       15\n",
       "Scratch    13\n",
       "Smudge      6\n",
       "Name: Flaws, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = ds_EXH_QC1[['Flaws', 'Period']]\n",
    "count = df['Flaws'].value_counts().sort_index()\n",
    "count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Other       6\n",
      "Peel       21\n",
      "Scratch    34\n",
      "Smudge     40\n",
      "Name: Flaws, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "cumcnt= np.cumsum(count)\n",
    "print(cumcnt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Other      15.0\n",
      "Peel       37.5\n",
      "Scratch    32.5\n",
      "Smudge     15.0\n",
      "Name: Flaws, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "percent = count/sum(count)*100\n",
    "print(percent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Other       15.0\n",
      "Peel        52.5\n",
      "Scratch     85.0\n",
      "Smudge     100.0\n",
      "Name: Flaws, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "cumpct = np.cumsum(percent)\n",
    "print(cumpct)"
   ]
  },
  {
   "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 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>CumPct</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peel</th>\n",
       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "      <td>52.5</td>\n",
       "      <td>37.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scratch</th>\n",
       "      <td>13</td>\n",
       "      <td>34</td>\n",
       "      <td>85.0</td>\n",
       "      <td>32.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Smudge</th>\n",
       "      <td>6</td>\n",
       "      <td>40</td>\n",
       "      <td>100.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Count  CumCnt  CumPct  Percent\n",
       "Other        6       6    15.0     15.0\n",
       "Peel        15      21    52.5     37.5\n",
       "Scratch     13      34    85.0     32.5\n",
       "Smudge       6      40   100.0     15.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_data = pd.DataFrame({'Count': count, 'CumCnt': cumcnt, 'Percent': percent, 'CumPct': cumpct})\n",
    "count_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.stats.proportion import proportions_ztest\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "신뢰구간: (2.58, 2.82)\n"
     ]
    }
   ],
   "source": [
    "ds_exm = pd.read_csv(\"실습파일/EXH_QC.csv\", engine = \"python\")\n",
    "df = ds_exm[['Cabbage weight']]\n",
    "lower, upper = stats.norm.interval(0.95, loc=2.7, scale = 0.397/np.sqrt(40))\n",
    "print(\"신뢰구간: ({0}, {1})\".format(lower.round(2), upper.round(2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1-sample t-test\n",
      "t검정통계량: [0.864]\n",
      "p-value : [0.41]\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame({'sample' :[74.5, 81.2,73.8,82.0,76.3,75.7,80.2,72.6,77.9,82.8]})\n",
    "t_result = stats.ttest_1samp(df, 76.7)\n",
    "t, p = t_result.statistic.round(3), t_result.pvalue.round(3)\n",
    "print(\"1-sample t-test\")\n",
    "print(\"t검정통계량: {}\".format(t))\n",
    "print(\"p-value : {}\".format(p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "one way ANOVA\n",
      "F: [4.263]\n",
      "p: [0.025]\n"
     ]
    }
   ],
   "source": [
    "df1 =pd.DataFrame({'A': [892,623,721,678,723,790,720,670,690,771]})\n",
    "df2 =pd.DataFrame({'B': [721,821,910,678,723,790,711,790,745,891]})\n",
    "df3 =pd.DataFrame({'C': [621,915,888,721,894,834,841,912,845,889]})\n",
    "f_test=stats.f_oneway(df1, df2, df3)\n",
    "f , p = f_test.statistic.round(3), f_test.pvalue.round(3)\n",
    "print(\"one way ANOVA\")\n",
    "print(\"F: {}\".format(f))\n",
    "print(\"p: {}\".format(p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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