Statistic_HW2_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import scipy\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Датасет об этих прекрасных морских обитателях \n",
    "<img src=\"1.jpg\" alt=\"drawing\" width=\"500\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Сохраним датасет\n",
    "df = pd.read_csv('abalone.csv', names = \n",
    "                 ['Sex', 'Length', 'Diameter', 'Height', 'Whole_weight', \n",
    "                  'Shucked_weight', 'Viscera_weight', 'Shell_weight', 'Rings'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сформулием первую гипотезу\n",
    "\n",
    "H0: a = b; H1: a != b\n",
    "\n",
    "a - средний диаметр раковины у infant (насколько я поняла, это что-то типа личинок, некая \"детская\" форма, но возраст у них может быть и достаточно большой), b - средний диаметр раковины у взрослых ушек"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Значение статистики: -42.45457241339881\n",
      "P-value:  0.0\n"
     ]
    }
   ],
   "source": [
    "# Применим z-test для двух независимых выборок\n",
    "X_1 = df[df.Sex == 'I'].Diameter\n",
    "X_2 = df[df.Sex != 'I'].Diameter\n",
    "# Посчитаем z-статистику:\n",
    "z = (X_1.mean() - X_2.mean())/(np.sqrt(X_1.std()**2/X_1.count() + X_2.std()**2/X_2.count()))\n",
    "print('Значение статистики:', z)\n",
    "# Доверительная область уровня значимости 0.05: (-1.96, 1.96) \n",
    "p_value = 1 - (scipy.stats.norm.cdf(abs(z)) - scipy.stats.norm.cdf(-abs(z)))\n",
    "print('P-value: ', p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eсть основания отвергнуть нулевую гипотезу"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": [
    "# нарисуем распределения, чтобы видеть, что средние действительно отличаются\n",
    "sns.distplot(df[df.Sex == 'I'].Diameter);\n",
    "sns.distplot(df[df.Sex != 'I'].Diameter);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сформулируем вторую гипотезу:\n",
    "\n",
    "H0: a = b; H1: a != b\n",
    "\n",
    "a - средний диаметр раковины у женских особей, b - средний диаметр раковины у мужских особей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Значение статистики: 5.293544096523467\n",
      "P-value:  1.199682966301907e-07\n"
     ]
    }
   ],
   "source": [
    "# Применим z-test для двух независимых выборок\n",
    "X_1 = df[df.Sex == 'F'].Diameter\n",
    "X_2 = df[df.Sex == 'M'].Diameter\n",
    "# Посчитаем z-статистику:\n",
    "z = (X_1.mean() - X_2.mean())/(np.sqrt(X_1.std()**2/X_1.count() + X_2.std()**2/X_2.count()))\n",
    "print('Значение статистики:', z)\n",
    "# Доверительная область уровня значимости 0.05: (-1.96, 1.96) \n",
    "p_value =  1 - (scipy.stats.norm.cdf(abs(z)) - scipy.stats.norm.cdf(-abs(z)))\n",
    "print('P-value: ', p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Так как значение статистики попало в критическую область, есть основания отвергнуть нулевую гипотезу"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kC2REfR5wjVLqEPAXYIVS6s/HHqS1XqO1rtBaV+Tk5AQ5phCRxeMc4CPtv6HaWkLrjBvNjjMxWeUw0AYjvSybniV96igwbqHWWn9da12otS4BbgLWa60/HvJkQkSwzK0PMlV18M7Mr6ItUbYI5eE+dccBlk3Pkj51FIiCsx9CRBZ7fx0rux5ng+0CVMn5ZseZuNSpEJcInQdYXJwhfeooMKFCrbV+VWsdJWdNhAgBn5cFlV/Hg5VdZ/y72WlOjbJA1gzoOECC3Sp96iggI2ohJsD9+v2Uj7zPrxI+TebU6WbHOXXZ5TDcBV010qeOAlKohQhU42Ysr/2Qdd5z8M27yew0pyfL36eufUv61FFACrUQgRjqwvvE7bT50nkk415KcpLNTnR6kvOMVf5q35Y+dRSQQi3EeLweWHs7uq+Jz7g/z/lnzjA70elTCjLL4NCbJNitLCiUPnUkk0ItxHhe+Q5Uv8p/uO9g1uLl5KXGm50oODLLoKcWehukTx3houwCUCHCbMdaePuXvJF+LU91LOe1S2eyfm/bSb/lsU11lNVFweg0q8z4XPs255St4FcbDvJuTReXzMkzN5c4jhRqIY5V+bDxubcB3voFg6ll3NHyEe6c1U/+gcejowgHInUqONKg9i2WrLqO+DgLr+9vl0IdgaRQCzEW1yBU/gHsiXzFdy9Jdgt3zxoyO9Vp23TM+tOFqQtJ3vsqf8ttpDgzkdcPdJiUTJyM9KiFOJbWsO1RcPayueTT/K0jh3tmD5Jm12YnC7q2zCWkDR4i3tlBeW4KNR2D1HdF/y+kWCOFWohj1Fb+Hdp2U5VzKZ/dM5cpDhezrU1squk6bkQa7doyKwDI6dpMeZ5xyeFr+2X1y0gjhVqI0Rq3UNT2T7pSZvE/rlW0OO3cVtRKnCX2RtMAXamzcVsTyO3eTE6yg4L0BF6XQh1xpFALcZh7GJ76FG5bMpU5q3mqJZuz0vtZmDZodrKQ0ZY4OjIWkdtViVKKC2fm8HZVJ26v3CMkkkihFuKwDT+EzoNUT72G3zeVAPDJwlZzM4VBW8YSMvoPYHf1ctHMbAacHrbIdPKIIld9iMnl8KV3x+quhbd/CcXn8KZvHpt6Urlxajs5Dk9485ngSJ+6ezOLz/oYcVbF+r1tLJ2eZXIycZiMqIXweeD9xyE+Ddesa3i4Lo88h4sP5cXWicOxlNU9SVrfPnzKxszax0nZ+WeWZjl5Zev+E/9SE2EnI2oRcx7bVHfCfWV1XSwtzTx6Y80b0N8CZ93Jb6uzaHI6+NqMeuwxegLxWNpiYyChgJTBWgBWTHHyve0p1A5YmWZyNmGQEbWY3Eb64MCLkDuXPY4FPLA7iXMz+lgUwycQx9KXNI2kkRZwj3DJFBcArzTbTU4lDpNCLSa3vc+D14N7zmq+XJlKml1ze3Hsn0A8Vn/iNBQauquZluxlRoqH9c0Os2MJPynUYvLqroWGd2H6xTzUMI2dPXF8f3E/qTav2cnCbiCxEB8W6KwC4JIpTja1x8lqehFi3EKtlIpXSr2rlNqulNqllPpuOIIJEVJaw551YE9mf+4qHtidxNWFI6wqcJqdzBQ+SxyDCVOh63ChduHWSmYpRohARtROYIXWegGwEFillFoW2lhChFjbHuiqwlu+ii9uzSXVrvneon6zU5mqP2ka9NSBx8mSbDdZDh8v7Zp8baBING6h1oYB/5dx/o/JcTpcxCbtg73PQWI2vx1eYbQ8FvWT6ZjcP9Z9icXGe9NTi1XBpVOdrN/bhtMz+VpBkSagHrVSyqqU2ga0AS9rrTeNccxdSqlKpVRle7v8d0lEsMZK6G+msfhD3L8njasLR7iicHK2PEYbSCwGFHQeBODyqU4GnB7ePij3UjRbQIVaa+3VWi8ECoGzlVLzxjhmjda6QmtdkZOTE+ycQgSF0l7Y/w98qUXcXXMhqXbNdyd5y+Mwr9UBaYVHTiiem+sixWHjxZ0tJicTE7rqQ2vdA7wKrApJGiFCLKt3Jwx18kLih9nR6+D7i/rJmuQtj6Nk+e+j6HXjsMKKObm8vKcVjyzSZKpArvrIUUql+x8nACuBvaEOJkSwKZ+Hqe1vMJJUyBdql0nLYyyZZcaU+h5jdueqM/LpGnTx3iFZpMlMgUwhnwI8opSyYhT2J7TWz4c2lhDBN635RRJcXfyAj5FqR1oeY8mcbnzuqoKsMi6alUNCnJW/7WjinLKsk07PB7hlaXEYQk4+gVz18b7WepHWer7Wep7W+nvhCCZEMCnt5YyqNTRbp/C7vqXS8jgRexKkTDnSp06027hkTi4v7GiRNapNJDMTxaRQ1PIyaYM1/GDkBq4sdEnL42SyZkB3DfiMy/KuWTCVrkEXbx2UG9+aRQq1iH3ax7yDD1GjinhDLZ70E1vGlVkGXhf0Gm2Oi2blkBpvY932JpODTV5SqEXMK2p9hfSBKu53fpg7itul5TGe7BnG544DADhsVlbNy+elXa3S/jCJFGoR27Rm1v4HqfZNoTb/MpZlyGh6XPZkSC2Ejv1HNl2zoIABp4d9LfL+mUEKtYhpU1o3kDt4gP9hNVctLDI7TvTILjf61C5jXe5zyrLITnawvaHH5GCTkxRqEbu0pnTXr6n15WJZcCPJDrmhUcCyZxonE+veAcBqUVw9fwr7WvoZccvaH+EmhVrErKS69ZS49vNMyk2cUSg3ap2QzOlgsUL1a0c2fWjBVDw+ze6mPhODTU5SqEVM0j4f5Xt/Q6POJrHiY2bHiT42B2SUQvWrRzYtLk4nIzFO2h8mkEItYtLAnpeZ69vPm1M+SVJiotlxolP2TGh5HwaN1fOUUswvTKeqfYABp8fkcJOLFGoRcwZG3JxVu4ZWlY13/s1mx4le2TONzzUftD/mF6bh07CzsdekUJOTFGoRc+q3vMhitZ+dpXeAVW7QesrSisCRBlWvHNmUnxpPboqDbfXS/ggnOQ0uos7JFgY60NbPVd1/ojsui/YZN4YxVQyyWKFsORz4p3GPSaVQSrGoOIN/7Gqhc8BJVrL8IgwHKdQiulQ+TFld15i73D7F+n1Wlln28O6Mr+CT0fSEbao5+r31WSs4Z+AZ/v7yP+hOmwvAwqJ0XtrVwrb6Hi6Zk2dGzElHWh8iZrzYlsEn3U8wYMukZtr1ZseJCU0556NRFLS/fmRbWkIcpTlJbK3vQWuZjh8OUqhFTOhzW6lq6eQC6072z7gDrzXB7EgxwenIojPtTKa2vXHU9kVF6XQNuqjvGjIp2eQihVrEhLXN2Xza8ixDtnQOFN1gdpyY0pR7AVm9O3A4P7jJ7RlT07BZFFvlpGJYSKEWUa9h2E5rRycXW99n3/Tb8Nrkuulgasy5EIVmavubR7bFx1mZOzWV9xt68fhkRb1Qk0Itot6jjbl8Pu4pXJYEDky7yew4Mac7dQ5DjhymjupTg9H+GHZ72d8yYFKyyUMKtYhq7/cl4uxrZ7llG63ZS/HYksyOFHuUoinnQqa2v4XF+8GdcWbkppBkt7K1Xm58G2qB3IW8SCm1QSm1Rym1Syn1+XAEE2I8Pg1/asjly/a/4rE4aM082+xIMas+/1LivINM6Xj7yDarRTG/KJ29Lf0Mu2RFvVAKZETtAb6ktZ4DLAM+q5SaG9pYQozv7e5UEkfauFhtoSVrKV5rvNmRYlZL1tk449IobnnpqO2LitLx+rRMKQ+xcSe8aK2bgWb/436l1B6gANgd4mxCnJBXw9qmbH7g+BUei52WrKUBf++xkzrE+LQljvq8SyhueQmL13lkMlFBegI5yQ621ndzVmmmySlj14R61EqpEmARsGmMfXcppSqVUpXt7e3BSSfECbzZlUqyq43lbKY182y5bjoM6vMvw+4ZOKr9YUwpT+dQ5xDdgy4T08W2gKeQK6WSgb8C/6a1Pm7lcK31GmANQEVFhUxXEiHj0fDXpmx+6PgNPmWjJWvZkX1ldU+amCy2jW5/NOYtP7J9QVE6L+1ulWuqQyigEbVSKg6jSD+qtX4qtJGEOLnXOtNIdbexgvdozToLj1w3HRaH2x+FrRuOuvojI9FOaXYS2+q7ZUp5iARy1YcCfg/s0Vr/LPSRhDgxpxeeas7mm/FP4LPE0Zx1rtmRJpW6/MuJ8w5S0PbaUdsXFqXTMeDi/QY5qRgKgYyozwM+AaxQSm3zf1wZ4lxCjOmJQwlku5u5SFfSkrlURtNh1pq9lCFHLtMbnzlq+zz/lPKntzaalCy2jVuotdZvaq2V1nq+1nqh/+OFcIQTYrQRt5df703km47/w2Nx0Jy9bPxvEkGllZXqwg8zpf0tEkZaj2xPsFuZPSWV57Y34fbKlPJgk5mJImo8/m4dWSN1nM9WWrKWyZUeJqkuWI0FH6WN647avqgonc5BF6/vl6u+gk0KtYgKwy4vv3m1iu8mPonHEj+h66ZFcA0kFdGaeRZlDU8bd37xm5mXQkZiHE9J+yPopFCLqPDnjbVMGdhNhXcbzdnnyCxEk1UVriZlqJ6c7s1Htlktig8tmMo/d7fSN+I2MV3skUItIt6g08NDr1VxX9o6iEukRdb0MF19/kpctmTK6544avvqRQU4PT5e3NFiUrLYJIVaRLw/vn2IaUM7WTDyHpStkHshRgCvNYHqwtUUt7x81EnFhUXplGYn8dTWBhPTxR4p1CKi9Qy5eOi1g/xn6lpIyoWSC8yOJPz2TbsFtI+ZtY8f2aaUYvWiAjZWd9HYM2xiutgihVpEtAdfq+Ic90ZmOnfC8q+DTUbTkWIwsZCGvBXMqH8Sq+eDeydeu7AAgGfkpGLQSKEWEauld4Q/v3WQ7yc9CdmzYNGtZkcSx9hX8gkc7j5KG587sq04K5GKaRk8vbVRppQHScCLMgkRbg+sP8AN6hVyXQ1w2RNglR/XcBtvkauqouvpTDuD2Yf+RFXx9Ue2r15cwDee3smupj7mFaSFOmbMkxG1iEg1HYO8/N4uvmJfC6UXQvllZkcSY1GK3aV3kDpUS3Hzi0c2X33mVOxWC09tkfZHMMgQRUSkn728n6/bHiNBj8CVPwGlzI4kxlBW9yRozZAjl8V7fgKFdlAW0oAV+amsqzzI/7tyNjarjAlPh7x7IuLsbOyl7f1X+IjlNdS5n4OcWWZHEiejFI05F5Lg6oCmrUc2ry4eocNp5Y2DHSaGiw1SqEVE0Vrzw3Xb+KHjYXxpxXDhl82OJALQlTqHIUcOHHgJtLEo0/IpLtLtPrn6Iwik9SEiyrrtTSxvfIgyWwNcvRbssoxpVFCKxpyLKG9YS9W21+lInw/AWalxvLDDysLCdBxxVm5ZWmxy0OgkI2oRMQacHl55/nE+ZXsBX8WdUH6p2ZHEBHSlzmEgfgqFbetRPmOtjwuy+nB7Nbuajrt7n5gAKdQiYvz2hU180/1LhtPLsVz+fbPjiIlSirr8S3G4+8jvfBeAmUnDZCbZ2VrfbXK46CaFWkSErdXNXLj1C2RYhki46WGIk7Wmo1F/UgndyeVM7XgTm2cIpYz1P6rbB+kdlhX1TpX0qIXpnG433Y99ihWWfQxf8z88WZsGtXVjHltW1xXmdGKi6vNWcmbVQxS0v0btlCtYVJTO+r1tbJe7lJ8yGVELc2nN9t/fywrPG1Qt+DIJi240O5E4TcPxObRmVpDXVUnicAtZyQ6KMhLYJoX6lI07olZK/QG4GmjTWs8LfSRhisqHT76/4vbgv6bXQ8ujn+bslrW8nXUd5177jeC/hjBFQ+7FZPXuoqTl7+yYeQ+LijNYt72J3U19zJ2aana8qBPIiPqPwKoQ5xCTzVAXzj9/lPzqtfzJcROL7lojsw9jiNeaQH3eJaQM1VPa9BzzC9KwKsXTsk71KRl3RK21fl0pVRL6KCKqjTcihw9G5fteRD93L9aBDr7tu5OP3fEdEhxyuiTWtKcvJLd7Cwv3/pTGCy5kZn4Kz25r4qurZEr5RAXt3VJK3aWUqlRKVba3y12IxRheuQ8ePA8e/yiNQ1aucd5HytTZdL++hk1P/pTHNtXx2KaxTyKKKKQUNVOvwuHuY8meH7OkOJ22fifr97aZnSzqBK1Qa63XaK0rtNYVOTk5wXpaEe28bmjaApsegjd+Al1VrE+8ghVDP6I8L4ULsmQiRCwbimdQUuMAABDuSURBVM9n1/R/obTpOZZbt5OX6uBR+WU8YfL/TRFc2gcDbdBVBe37oGMfeJwQnw4zr+BvcZdyz+Y8lqQNcNNU+Z/XZLCr7C6KWv/Jst3f49ZFf+EnrzdT1zlEcZYsDxAoKdTi1I30QfteaNsNO5+C/mborQfPiLE/IQOmLoapiyBrBhta4vn822nMTh7m89ObsBxz7nC8RepFdPJZ7Ww683tc+s4nuH3oYX6mruCxd+v42hWzzY4WNQK5PO9x4GIgWynVAHxba/37UAcTEUT7oG0PtOyEtl3Qutsozr31HxxjtUNKvlGUM0qMj6TcI1dyrG+2c/c7acxO8/ClkgbsFrlF02TSmT6ffaWfYM6OR/jXafN5rDKOL1xajsNmNTtaVAjkqo+bwxFERJjhHmjdAW17obsG3P6bl1pskD0TipbCktsg7wzInQMHXwE19imPfzTauWdjGrPTPfzpgh72NfrC9+cQEeP98s8yp/cN/rX/AX43+B2e3drEjWcVmR0rKkjrQ3zAM2Is/F63EXpqjW2J2ZA/H5Z8EqYsgKxysNmP/94TFOnHquP55pYU5md6eOT8HtLsMpKerLzWBLjmlyT88Sr+M+1pHng9i+uXFGI5tgcmjiOFWkBvI+x+BmrfAa8TkvNh9tWQfyYk5xnHLLxlQk/p03D/riR+uTeJi/Od/HpZH0k2KdKTXsn5sPRuPrzpIZ7un8P6vXNYOTfP7FQRTwr1ZNbfAq/9GLb8L2gvTFkEpRdA+rTTmiXY61Lc8Vo8m3uTWJ7Vw51TW9hZP/73iUli5XfRNa/zs7bf8pUNC1k59yqzE0U8KdST0UgvvPUL2PggeF2w+FZILYDErNN+6n29Vu5+J426ASu3F7VweU6PzAwXR4uLR133e1J/exEfa/kvNladzbIymXtxMjKPczJxj8Dbv4RfLIA3fgqzroR73oOr7w9KkX6+3sHq9RkMeBTfmlXHqlwp0uIE8uaiL/0By63bOfjX+9Ba2mInIyPqycDrge2Pw6s/gr5GKLsEVn7bODkYqJOs5dHvVnx3WwpraxNYkuXiN8v6ONQyHITgIpbFLfsUtTs2cHPj/1K5/iLOuuQ6syNFLCnUsczjMgr0m/cbl9gVLIHVD0HphUF5eq3h5WY7392WQvOQhXtmD3Lv3EHsFjgUlFcQseCoiUzWzKP2Fcy7iPqWHZS/8W94FlVgy5wW5nTRQQp1LHIPGycI33oA+hpgykL46KMw+6qgLSVa3W/lu9uSea3VwcxUD08u72ZJlicozy0mD5vdQeNla8j8+0fo+f11ZN+7ARwpZseKOFKoY0n3Idj8CGz9Ewy2Q/E5cM0vjFZHkAp0n1vx4N5Efrc/kXir5j8W9HNr2TBxcrZDnKJzl57DA9u/zWeb/h+Dj32SpE8+CRaZsTiaFOpIN946z85+40awu56B2reMglx+OZz7OSg5L2gxel2Khw8m8IcDifS5LVyU1cstBW2k27xsqQ3ay4hJSCnFzbfczn/99CDfqP0dvhe+guWqn8iNJEaRQh2NnAPQsh2atkHnQUBDci7MvNyY2p2QAR37jQ84pdtobaoxbiI74LHw97ZMXmjLYMhr5az0fq6b0kFpojOIfyAx2eWmxDPnmi/w26ea+XTl7yA+BVZ+x+xYEUMKdbRwDUDzDmjeCh0HAA1JOVB+qTFRJSX/xCOQQO6+cox2p42/tWWyviMdp88iBVqEjv/nc7WGrxWs5s+NI3z8zfuNZXLLLwvN/TqjjBTqSOYahIZKaKw0RsfaZ6y9MWOlsUpdypSg//dwd4+NNfsTWVeXgwLOzezjQ3ldTJMCLUJMKbhvyQC3DnyM5D4n1+57wbj2f8ltk74NIoU60ng9UPMqvP8E7Hke3INGK2P6cqM4pxYE/YdWa3i7LY6H9ifyRquDJJuPK3K7uTKvi2y7XMkhgudwS+1ElpZm8uC5/dz86p0MjCTw8eqX4Zl/hWseAGtcmFJGHinUkUBraN4G7z8JO9fCQCvEp8H8G8CeApmlJ1yd7lRtqunC6VNs6k7hhbZMaobiSbd5uLmgjUuze0iyyVKkwhwZDs1flvfyL2/eQltvGl/c/hi+zoNYbvgjpBWYHc8UKhRTNysqKnRlZWXQnzemaA2Nm41V63Y/Cz11YIkzTgjOv9G4ciMu/pT6y+O97M4eGw9sV7zZlcqwz8rUeCcfyuvigsw+4mRBfxEhnD7Fmtp80nr28GP7GhzxidhW/wZmXWF2tJBQSm3WWleMtU9G1OGiNfQ2QP0mOPQGHPinMRnFEgfTL4YLv2wsLZqYOd4zTdiIF7Z3xfFqi51/Njs40GcjTvlYltHPiuwe5iQPT/YWoIhADovmc6XNPGu9kRu3T+cnQz9lzuM30V50OZnX3Y81ffKMrid3oT7ZaFX74MwbjDubuAaND/cQ+LzGPn34sw98vqO3uYaMFeoG243i3FMLrbtgpMd4bkcqlFwAl/wHzFwFCelB+eN4fNA8bOFgn42D/VYO9tnY32djV48Nl09hU5qzst3cuqifAl+ztDdEVJhXkEZ57sW87jqHV974OXfWrcX984VszPkII2d/lkVzZ5OZNMbNLGLI5G19aA1v/tzoBw+0wlCnUUiHe4wi6+wziu7pUBZImQpphcbtqvLOMNbbmLLgg5lXE2htaA1dLkXdgJUNh4Zpc8bR5rLT5oyj3RlHhysOLx8MjbMdXmakepmf4easbDdnZ7uP3GFlvJM6QkSKquIbjjx2e3201u5lcc1DLHe+igcbf/edxXrHStLPWMmcggzmTEllVl4KCfbomt14stZHQIVaKbUK+AVgBX6ntf7Pkx0fUYXa2Q9dNdBVZUwO6TjgnwxywLg2+TCrHeLTjdFtfJrxOC7RuO2U1WHst8b5C6wyirBS/iswRn2NMo63J4AtYcJTYYc8UD9o5ZXqYVqdcbS5jCLc6i/KTt/RJxXTbB5yHG5y7W7yHC5yHG4K4l1cMzORDMeJ/26lUItoMbpQj5bYX0vR/oeZ3fEPknwD9OhkXvedyZu+eezQZbgzZjItJ5WS7CRKspMozUqiNCeJKanxEXn7r9Mq1EopK7AfuBRoAN4DbtZa7z7R9wStUPv87QSfF3yeDx57Xf52xIAxS881AIMdMNACA23GnUv6W6CrGgbbjn7OtCLILjdu0DrYYdxqKjnPWAjmFBq1WoMP8Grj9lNerfBqY7tHw7BHMXjUh4UBt6LbpWgfsdA2YqVt2EK700LbsIVe99GF2GHxkWt3ketwGx92/2eHi1y7m3jr2H9/S0tP3uuWQi1ihfJ5cDoyOd9Xie/gK1iH2gFwKTstKpdD3izqvdk06BxadAbDliSsiWnYE9NJSEknNTWFjORkslITSEqIJ8ERT2JCAokOG8kOG4kOK3arBatFffChjM8qiCd3TrdQnwN8R2t9uf/rrwNorX90ou855UL94+nGCNjnNYryqXCkGtOpk/MhswQyyyBzOmSVQUYpOJI/OPYUrqj4v5p4vrU1xV+Uwcep/0U5LJrcBB85Dh+5CV5y4n3kx/soSvbS19tNrt1Nqs0rJ/qEGMeRUbf2kTJYS2bfbjL79pA01EjScBOJQ00keHom9JxerfBiQR/5N67QwL3ue3jJdxYAFsWRgq2AnBQHb351xSn9GU63UF8PrNJa3+n/+hPAUq31Pcccdxdwl//LWcC+U0obOtlAh9khTlG0Zo/W3CDZzTKZs0/TWo95T7JArvoYazx3XHXXWq8B1kwwWNgopSpP9Nsq0kVr9mjNDZLdLJJ9bIFMd2sAikZ9XQg0hSKMEEKI4wVSqN8DypVSpUopO3ATsC60sYQQQhw2butDa+1RSt0D/APj8rw/aK13hTxZ8EVsWyYA0Zo9WnODZDeLZB9DSCa8CCGECB65050QQkQ4KdRCCBHhYq5QK6VWKaX2KaUOKqW+Nsb+C5VSW5RSHv814hEhgNxfVErtVkq9r5R6RSk1zYycYwkg+91KqR1KqW1KqTeVUnPNyDmW8bKPOu56pZRWSkXMpWMBvO+3KaXa/e/7NqXUnWbkHEsg77tS6kb/z/wupdRj4c44lgDe8/tHvd/7lVITm2VzIlrrmPnAONlZBUwH7MB2YO4xx5QA84H/Ba43O/MEci8HEv2PPwP8n9m5J5A9ddTja4AXzc4daHb/cSnA68BGoMLs3BN4328DfmV21lPMXg5sBTL8X+dGQ+5jjv8cxsUXp/3asTaiPhs4qLWu1lq7gL8AHx59gNb6kNb6fYwlOiJFILk3aK2H/F9uxLiePRIEkr1v1JdJjDFhyiTjZve7D/gxMBLOcOMINHskCiT7p4Bfa627AbTWxyzaY4qJvuc3A48H44VjrVAXAPWjvm7wb4t0E839L8DfQ5oocAFlV0p9VilVhVHw7g1TtvGMm10ptQgo0lo/H85gAQj0Z+Y6f7tsrVKqaIz9Zggk+0xgplLqLaXURv8KnmYL+N+pvzVZCqwPxgvHWqEOaLp7BAo4t1Lq40AF8N8hTRS4QJcY+LXWugz4KvDNkKcKzEmzK6UswP3Al8KWKHCBvO/PASVa6/nAP4FHQp4qMIFkt2G0Py7GGJn+TikVnDtsnLqJ1JebgLVan+rqckeLtUIdrdPdA8qtlFoJfAO4RmvtDFO28Uz0Pf8LcG1IEwVuvOwpwDzgVaXUIWAZsC5CTiiO+75rrTtH/Zz8D7AkTNnGE8jPTAPwrNbarbWuwVjkrTxM+U5kIj/rNxGktgcQcycTbUA1xn85Djf7zzjBsX8kck4mjpsbWIRxIqPc7LynkL181OMPAZVm557oz4v/+FeJnJOJgbzvU0Y9Xg1sNDv3BLKvAh7xP87GaDlkRXpu/3GzgEP4JxQG5bXN/ksLwZt5JcaNDqqAb/i3fQ9jFApwFsZvxkGgE9hlduYAc/8TaAW2+T/WmZ15Atl/Aezy595wsmIYadmPOTZiCnWA7/uP/O/7dv/7PtvszBPIroCfAbuBHcBNZmcO9OcF+A7wn8F8XZlCLoQQES7WetRCCBFzpFALIUSEk0IthBARTgq1EEJEOCnUQggR4aRQi4iklPL6VyDbpZTa7l890OLfV6GUeiDEr39tJK3yJyY3uTxPRCSl1IDWOtn/OBd4DHhLa/3tML3+H4HntdZrJ/A9Nq21J3SpxGQlhVpEpNGF2v/1dIwbLWcDFwH/rrW+Wil1NvBzIAEYBm7XWu9TSt2GMVXdijEN/KcYs8k+ATiBK7XWXUqpMuDXQA4whLFqWybwPNDr/7jOH+Oo47TWe/0FvQtj5ugWrXUkrgsioty4N7cVIhJorav9rY/cY3btBS7Uxk2YVwI/5IPCOg+jgMYDB4Gvaq0XKaXuB27FKPBrgLu11geUUkuB32itVyil1jFqRK2UeuXY44AV/teZCazUQVqAR4hjSaEW0WSs1cvSgEeUUuUYK5nFjdq3QWvdD/QrpXoxVpMDY0ryfKVUMnAu8KRSR57acdyLjn/ck1KkRShJoRZRwd/68AJtwJxRu+7DKMirlVIlGOtxHDZ6hUHfqK99GD/7FqBHa71wnJcf77jBAP4IQpwyuepDRDylVA7wEMZtpY49qZIGNPof3zaR59XGnWdqlFI3+F9HKaUW+Hf3YyxzOt5xQoScFGoRqRIOX56HsXLgS8B3xzjux8CPlFJvYZw4nKiPAf+ilNqOsdLc4Vsr/QX4slJqq/+E44mOEyLk5KoPIYSIcDKiFkKICCeFWgghIpwUaiGEiHBSqIUQIsJJoRZCiAgnhVoIISKcFGohhIhw/x/ITYrkSgoXmQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# нарисуем графики, чтобы посмотреть, насколько отличаются средние\n",
    "sns.distplot(df[df.Sex == 'F'].Diameter);\n",
    "sns.distplot(df[df.Sex == 'M'].Diameter);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Согласно википедии, средний размер морского ушка равен 6 см, в нашем датасете размеры указаны, видимо, в дециметрах (что странно, ведь в описании к датасету указано, что величины в мм, но даже по картинке к датасету видно, что это не микробы какие-то, а вполне себе большие существа, поэтому я решила считать, что здесь все в дециметрах)\n",
    "\n",
    "Проверим гипотезу о том, что среднее действительно 6, применяя z-test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Значение статистики: -2643.6614219735197\n",
      "P-value:  0.0\n"
     ]
    }
   ],
   "source": [
    "X = df.Length\n",
    "# Посчитаем z-статистику:\n",
    "z = X.count()*(X.mean() - 0.6)/(X.std())\n",
    "print('Значение статистики:', z)\n",
    "# Доверительная область уровня значимости 0.05: (-1.96, 1.96) \n",
    "p_value =  1 - (scipy.stats.norm.cdf(abs(z)) - scipy.stats.norm.cdf(-abs(z)))\n",
    "print('P-value: ', p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Нулевую гипотезу можно отклонить. \n",
    "\n",
    "Возможно, если мы уберем из выборки \"детей\", что-то изменится:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Значение статистики: -900.8490496917295\n",
      "P-value:  0.0\n"
     ]
    }
   ],
   "source": [
    "X = df[df.Sex != 'I'].Length\n",
    "# Посчитаем z-статистику:\n",
    "z = X.count()*(X.mean() - 0.6)/(X.std())\n",
    "print('Значение статистики:', z)\n",
    "# Доверительная область уровня значимости 0.05: (-1.96, 1.96) \n",
    "p_value =  1 - (scipy.stats.norm.cdf(abs(z)) - scipy.stats.norm.cdf(-abs(z)))\n",
    "print('P-value: ', p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Но все же гипотеза отклоняется"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверим, что диаметр ушка в подгруппах для разного количества колец распределен нормально с помощью Шапиро теста на нормальность."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Диаметр ушек со следуюим количество колец не распределен нормально: \n",
      "Количество колец: 9; P-value: 1.3939747967040006e-10\n",
      "Количество колец: 10; P-value: 1.5274552456201462e-11\n",
      "Количество колец: 8; P-value: 0.007633548695594072\n",
      "Количество колец: 11; P-value: 1.4213797916440285e-09\n",
      "Количество колец: 12; P-value: 0.019435716792941093\n",
      "Количество колец: 6; P-value: 0.011130288243293762\n"
     ]
    }
   ],
   "source": [
    "# Для каждого уникального значения количества колец проверяется нормальность диаметра\n",
    "print('Диаметр ушек со следуюим количество колец не распределен нормально: ')\n",
    "for n_rings in df.Rings.unique():\n",
    "    if df[df.Rings == n_rings].shape[0] > 10:\n",
    "        X = df[df.Rings == n_rings].Diameter\n",
    "        if scipy.stats.shapiro(X)[1] < 0.05:\n",
    "            print('Количество колец: ', n_rings, '; P-value: ', scipy.stats.shapiro(X)[1], sep = '')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь с поправкой Бонферрони для множественной проверки гипотез:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Диаметр ушек со следуюим количество колец не распределен нормально: \n",
      "Количество колец: 9; P-value: 1.3939747967040006e-10\n",
      "Количество колец: 10; P-value: 1.5274552456201462e-11\n",
      "Количество колец: 11; P-value: 1.4213797916440285e-09\n"
     ]
    }
   ],
   "source": [
    "# Для каждого уникального значения количества колец проверяется нормальность диаметра с поправкой\n",
    "print('Диаметр ушек со следуюим количество колец не распределен нормально: ')\n",
    "for n_rings in df.Rings.unique():\n",
    "    if df[df.Rings == n_rings].shape[0] > 20:\n",
    "        X = df[df.Rings == n_rings].Diameter\n",
    "        if scipy.stats.shapiro(X)[1] < 0.05/17:\n",
    "            print('Количество колец: ', n_rings, '; P-value: ', scipy.stats.shapiro(X)[1], sep = '')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Количество отклоненных гипотез уменьшилось в 2 раза"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверим еще одну гипотезу\n",
    "\n",
    "H0: med = выборочное среднее; H1: med < выборочное среднее\n",
    "\n",
    "в случае верности нулевой гипотезы S = #{x_i < выборочное среднее} распределена биномиально с параметрами n, p, \n",
    "где p = P(x_i < выборочное среднее) = 1/2\n",
    "\n",
    "Посчитаем S и y = S/n:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "S = 0\n",
    "for i in range(len(df)):\n",
    "    if df.Whole_weight.iloc[i] < df.Whole_weight.mean():\n",
    "        S += 1\n",
    "y = S/len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь посчитаем следующую статистику:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "stat = (y - 0.5)/np.sqrt(0.5*0.5 / len(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "т.к. количество наблюдей достаточно велико (> 6000), то по ЦПТ эта статистика должна стремиться к стандартному нормальному распределению (по распределению) в случае верности нулевой гипотезы\n",
    "\n",
    "Тогда p-value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.002806053002654796\n"
     ]
    }
   ],
   "source": [
    "p_value = 1 - scipy.stats.norm.cdf(stat)\n",
    "print(p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Значит есть основания отвергнуть нулевую гипотезу"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Покажем, что shapiro тест работает корректно, для этого сгенерируем несколько нормальных и ненормальных выборок и покажем, что в случае верности нулевой гипотезы значения p-value распределены равномерно, а значит вероятность отвергнуть нулевую гипотезу, в случае ее верности, действительно равна заранее установленному уровню значимости"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Фактическая частота ошибок первого рода: 0.047\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": [
    "samples_1, p_value = [], []\n",
    "errors = 0\n",
    "for i in range(1000):\n",
    "    samples_1.append(np.random.normal(0, 1, 100))\n",
    "for sample_1 in samples_1:\n",
    "    if scipy.stats.shapiro(sample_1)[1] < 0.05:\n",
    "        errors += 1\n",
    "    p_value.append(scipy.stats.shapiro(sample_1)[1])\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(x=[i for i in range(len(p_value))], y=p_value, c='r');\n",
    "ax.set_xlabel('Номер испытания');\n",
    "ax.set_ylabel('P-value');\n",
    "\n",
    "print('Фактическая частота ошибок первого рода:', errors/1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На следующем графике отражена зависимость уровня значимости от ошибки первого рода (черная прямая просто прояма y = x, чтобы видеть, что нет системных сдвигов)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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": [
    "samples_1, p_value = [], []\n",
    "errors = []\n",
    "for alpha in range(1, 100, 5):\n",
    "    samples_1 = []\n",
    "    for i in range(1000):\n",
    "        samples_1.append(np.random.normal(0, 1, 100))\n",
    "    error = 0\n",
    "    for sample_1 in samples_1:\n",
    "        if scipy.stats.shapiro(sample_1)[1] < alpha/100:\n",
    "            error += 1\n",
    "    errors.append(error/1000)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot([i/100 for i in range(1, 100, 5)], errors, c='turquoise', lw=5);\n",
    "plt.plot([i/100 for i in range(1, 100, 5)], [i/100 for i in range(1, 100, 5)], c='k', lw=1);\n",
    "ax.set_xlabel('Уровень значимости');\n",
    "ax.set_ylabel('Частота ошибок первого рода');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": [
    "# в случае верности альтернативной гипотезы видно, что p-value смещен\n",
    "samples_1, p_value = [], []\n",
    "for i in range(500):\n",
    "    samples_1.append(np.random.uniform(-1, 1, 100))\n",
    "for sample_1 in samples_1:\n",
    "    p_value.append(scipy.stats.shapiro(sample_1)[1])\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(x=[i for i in range(len(p_value))], y=p_value, c='turquoise');\n",
    "plt.axhline(y=0.05, color='r')\n",
    "ax.set_xlabel('Номер испытания');\n",
    "ax.set_ylabel('P-value');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}