test-half-semester.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from s import Sample, ZHyp, THyp, ChiHyp, Z2Hyp, T2Hyp, FHyp, AltHypKind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_dist(dist, label=''):\n",
    "    domain = np.linspace(dist.ppf(0.001), dist.ppf(0.999), 200)\n",
    "    values = dist.pdf(domain)\n",
    "    \n",
    "    plt.plot(domain, values, label=label)\n",
    "    plt.fill_between(domain, 0, values, alpha=.2)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample 1: m=8.906, s=0.9000518503582632, s^2=0.8100933333333333\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc3572b4e0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Crit values:\n",
      "(-1.2581418099469051, 1.2581418099469051)\n",
      "Criterion value: -0.3302633064278515\n",
      "P-value: 0.6256204826828377\n",
      "H0\n"
     ]
    }
   ],
   "source": [
    "def v1():\n",
    "    sample1 = Sample.from_data(\n",
    "        '1',\n",
    "        np.array([8.60, 9.70, 7.83, 8.77, 9.15, 9.40, 9.36, 8.90, 10.22, 7.13])\n",
    "    )\n",
    "    \n",
    "    sample1.describe()\n",
    "    \n",
    "    hyp = THyp(kind=AltHypKind.RIGHT, m=9, sample=sample1)\n",
    "    alpha = 0.12\n",
    "    \n",
    "    plot_dist(hyp.dist)\n",
    "    \n",
    "    print('Crit values:')\n",
    "    print(hyp.critical_values(alpha))\n",
    "    \n",
    "    criterion_value, _, p_value, result = hyp.full_test(sample1, alpha)\n",
    "    print('Criterion value: {}'.format(criterion_value))\n",
    "    print('P-value: {}'.format(p_value))\n",
    "    print('H0' if result else 'H1')\n",
    "\n",
    "v1()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample 1: m=10.4738, s=0.3382339098579234, s^2=0.11440217777777785\n",
      "Sample 2: m=10.5522, s=0.532955240980787, s^2=0.2840412888888887\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc37bd3cf8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Crit values:\n",
      "(-0.91536508784281401, 0.91536508784281434)\n",
      "Criterion value: -0.4409646897881731\n",
      "P-value: 0.32961928002396845\n",
      "H0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-1.2815515655446004"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def v2():\n",
    "    sample1 = Sample.from_data(\n",
    "        '1',\n",
    "        np.array([10.73, 9.878, 10.12, 10.58, 10.56, 10.50, 10.93, 10.32, 10.23, 10.89])\n",
    "    )\n",
    "    sample2 = Sample.from_data(\n",
    "        '2',\n",
    "        np.array([9.594, 11.37, 10.53, 11.04, 10.47, 10.30, 10.90, 9.878, 10.84, 10.60])\n",
    "    )\n",
    "    \n",
    "    sample1.describe()\n",
    "    sample2.describe()\n",
    "    \n",
    "    hyp = Z2Hyp(kind=AltHypKind.LEFT, sigma1=0.35, sigma2=0.44)\n",
    "    alpha = 0.18\n",
    "    \n",
    "    real_dist1 = stats.norm(10.5, 0.35)\n",
    "    real_dist2 = stats.norm(10.5, 0.44)\n",
    "    \n",
    "    plot_dist(hyp.dist)\n",
    "    \n",
    "    print('Crit values:')\n",
    "    print(hyp.critical_values(alpha))\n",
    "    \n",
    "    criterion_value, _, p_value, result = hyp.full_test(sample1, sample2, alpha)\n",
    "    print('Criterion value: {}'.format(criterion_value))\n",
    "    print('P-value: {}'.format(p_value))\n",
    "    print('H0' if result else 'H1')\n",
    "    \n",
    "v2()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample 1: m=27.893999999999995, s=1.0288488713120114, s^2=1.0585299999999997\n",
      "Sample 2: m=29.38888888888889, s=0.8676324746752574, s^2=0.7527861111111112\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc356a3f60>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Crit values:\n",
      "(-1.2815515655446004, 1.2815515655446004)\n",
      "Criterion value: -2.977889946293517\n",
      "P-value: 0.002902401802124835\n",
      "H1\n"
     ]
    }
   ],
   "source": [
    "def v3():\n",
    "    sample1 = Sample.from_data(\n",
    "        '1',\n",
    "        np.array([27.84, 27.65, 26.47, 28.18, 29.33])\n",
    "    )\n",
    "    sample2 = Sample.from_data(\n",
    "        '2',\n",
    "        np.array([29.28, 28.40, 28.90, 30.47, 30.48, 30.34, 29.44, 28.23, 28.96])\n",
    "    )\n",
    "    \n",
    "    sample1.describe()\n",
    "    sample2.describe()\n",
    "    \n",
    "    hyp = Z2Hyp(kind=AltHypKind.TWO_SIDED, sigma1=0.9, sigma2=0.9)\n",
    "    alpha = 0.20\n",
    "    \n",
    "    plot_dist(hyp.dist)\n",
    "    \n",
    "    print('Crit values:')\n",
    "    print(hyp.critical_values(alpha))\n",
    "    \n",
    "    criterion_value, _, p_value, result = hyp.full_test(sample1, sample2, alpha)\n",
    "    print('Criterion value: {}'.format(criterion_value))\n",
    "    print('P-value: {}'.format(p_value))\n",
    "    print('H0' if result else 'H1')\n",
    "    \n",
    "v3()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample 1: m=18.973, s=2.544597981781972, s^2=6.474978888888886\n",
      "Sample 2: m=19.572, s=1.662633787432191, s^2=2.7643511111111123\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc35abd048>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Crit values:\n",
      "(0.47298630971728645, 2.1142260979978902)\n",
      "Criterion value: 2.342314209965286\n",
      "P-value: 0.11041740287916424\n",
      "H1\n"
     ]
    }
   ],
   "source": [
    "def v4():\n",
    "    sample1 = Sample.from_data(\n",
    "        '1',\n",
    "        np.array([19.29, 20.04, 23.29, 16.00, 21.47, 16.05, 19.02, 15.34, 20.23, 19.00])\n",
    "    )\n",
    "    sample2 = Sample.from_data(\n",
    "        '2',\n",
    "        np.array([19.11, 17.81, 23.75, 20.70, 18.51, 19.72, 19.38, 18.49, 19.32, 18.93])\n",
    "    )\n",
    "    \n",
    "    sample1.describe()\n",
    "    sample2.describe()\n",
    "    \n",
    "    hyp = FHyp(kind=AltHypKind.RIGHT, sample1=sample1, sample2=sample2)\n",
    "    alpha = 0.14\n",
    "    \n",
    "    real_dist1 = stats.norm(18.5, 2.20)\n",
    "    real_dist2 = stats.norm(19.2, 1.65)\n",
    "    \n",
    "    plot_dist(hyp.dist)\n",
    "    \n",
    "    print('Crit values:')\n",
    "    print(hyp.critical_values(alpha))\n",
    "    \n",
    "    criterion_value, _, p_value, result = hyp.full_test(sample1, sample2, alpha)\n",
    "    print('Criterion value: {}'.format(criterion_value))\n",
    "    print('P-value: {}'.format(p_value))\n",
    "    print('H0' if result else 'H1')\n",
    "    \n",
    "v4()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample 1: m=8.906, s=0.9000518503582632, s^2=0.8100933333333333\n"
     ]
    },
    {
     "data": {
      "image/png": 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W+sZRL6yuJDo8lKvGpzsdJWDMmZRJp0tZsqG6751N0LHSN4453tTG29sOcfX4dGIibJ4db8lOimbayCReWldFZ5fL6TjGx1jpG8e8UlpNe5eLuXYB1+vmTsriSGMb7+866nQU42Os9I0julzdd49OyUlk5IgYp+MEnBkFI0iLi2TR2kqnoxgfY6VvHPHB7qMcbGi1s/whEhoiXD8xg0/KjlNR1+R0HONDrPSNI55fXUlqXASzRtk8O0PluuJMQkOEl9YdcDqK8SFW+mbYVdQ18XHZMWZP7C4lMzSSYyO4eHQKr5VW23KK5r9Z6Ztht2hNFWEhwnU2z86Qmzspi8bWTv641ebjMd08Kn0RmS0ie0SkTEQePMvz3xKRnSKyTUT+IiL5PZ7rEpEt7o+lvY81waW5vZPXN9Zw8ehUkmMinI4T8CZmJzByRAyLbMpl49Zn6YtIKPAoMAcoBu4QkeJeu20GSlR1CvA68Isez7Wo6lT3xzxMUHtr80FOtXXacojDRESYOymTT2tPsrW6wek4xgd4cqY/EyhT1QpVbad74fP5PXdQ1RWq2ux+uJbuBdCN+QxV5fk1lRSmxjIhM97pOEHjqvHpRIWH2Nm+ATwr/Ryg5/3cNe5t5/I1YFmPx1EiUioia0Xk5gFkNAGitKqePYdPccPkLJtnZxjFRIRx1bj07tlMm9udjmMc5tULuSLyZaAE+GWPzfmqWgJ8Cfi1iIw+y3H3uv8wlNbV1XkzkvEhz39SSVxkGFeMTXM6StCZMymLtk4Xr2+scTqKcZgnpV8L5PV4nOve9hkici3wA2Ceqrad2a6qte7/VgArgWm9j1XVJ1W1RFVL0tKsEALR0cZW/rTjMNeMTycqPNTpOEGnMDWW4qwEFq2twuWyBVaCmSelvwEoEpFCEYkAFgCfGYUjItOAJ+gu/KM9tieLSKT781TgEmCnt8Ib//Hy+mo6XWp34DpozqRMqo4383HZMaejGAf1Wfqq2gksBN4DdgGvquoOEXlIRM6MxvklEAe81mto5gSgVES2AiuAf1dVK/0g09Hl4qV1VVw4MonspGin4wStS8akkhgdbhd0g5xH89mq6rvAu722/ajH59ee47jVwOTBBDT+b/nOIxw91cbfXf5Xl3PMMAoPDeG64gze2FRDbUMLOfYHOCjZHblmyD2/upKMhEim5yc7HSXoXT8xE1VYst7m4wlWVvpmSO05fIp1+08wZ1KWzbPjAzISoigpSObl9Qdo77QFVoKRlb4ZUovWVhIeKlw7IcPpKMZt7uQsjjW1896Ow05HMQ6w0jdD5lRrB29uquWyojQSo8OdjmPcLhyZTGZClF3QDVJW+mbIvLmplub2Lm6wYZo+JUSEOZMyWb//BLsPNzodxwwzK30zJM7MszM2I46xGTbPjq+5ZkIG4aHCi3a2H3Ss9M2QWF1+nIq603aW76MSo8O5rCiNNzfVcqq1w+k4ZhhZ6Zsh8cKaShKjw7h0jE2r4atumJxFc3sXb276q1lVTACz0jded7ChheU7j3DthEwiwuxHzFeNzYinKD2ORWuqULX5eIKF/UYar1u87gCq3XO9GN82d3IWZXVNrKk47nQUM0ys9I1XtXV2sXj9AWYUjCAjIcrpOKYPlxWlEh8VxqI1dkE3WFjpG69a9ulhTpxut+UQ/URkWCifn5DBn3ce4dDJFqfjmGFgpW+86oU1lWQnRTM1L8npKMZDcyZl4XIpL6+z+XiCgZW+8ZrttSfZdKCBuZMyCbHlEP1GZmIU0/OTeXl9tc3HEwSs9I3XLFpTRVRYCNfYPDt+54bJWdQ1tdl8PEHASt94Rf3pdt7aUssV49KJi/RomQbjQy7M756P54U1lU5HMUPMo9IXkdkiskdEykTkwbM8/y0R2Ski20TkLyKS3+O5u0Vkn/vjbm+GN77jldJq2jpd3Gh34PqlM/PxbKist/l4AlyfpS8iocCjwBygGLhDRIp77bYZKFHVKcDrwC/cx44AfgxcBMwEfiwitpJGgOnscvHCmkom5yRSkBrrdBwzQNdOyCAiLMSGbwY4T870ZwJlqlqhqu3AEmB+zx1UdYWqNrsfrgVy3Z9fDyxX1ROqWg8sB2Z7J7rxFe/vOsrBhlZutGGafi0hOpzLxqTyh821NNp8PAHLk9LPAap7PK5xbzuXrwHLBnis8UPPr64kLT6SiwpTnI5iBum/5+PZWON0FDNEvHohV0S+DJQAv+zncfeKSKmIlNbV1Xkzkhliew6fYk3FceZMyrTlEANA0Zn5eNbafDyBypPSrwXyejzOdW/7DBG5FvgBME9V2/pzrKo+qaolqlqSlmazMvqT59d0L4d4XbHNsxMobpicRXndaVaX23w8gciT0t8AFIlIoYhEAAuApT13EJFpwBN0F/7RHk+9B1wnIsnuC7jXubeZAHCyuYM/bKrlyrHpthxiALmsKI2EqDCeW13pdBQzBPosfVXtBBbSXda7gFdVdYeIPCQi89y7/RKIA14TkS0istR97AngYbr/cGwAHnJvMwHgtY3VtHR02QXcABMRFsL1EzN5f+cRDhxv7vsA41c8uotGVd8F3u217Uc9Pr/2PMc+Azwz0IDGN3W5lBfWVFGclcCotDin4xgvu2FyFm9uruX5NZX88MbeI7SNP7M7cs2ArNxzlAMnmu0sP0ClxEVyyegUXtlQTVNbp9NxjBdZ6ZsBeW51JalxEXxulA3TDFQ3XZBNU1snb9jwzYBipW/6rexoEx/tO8bsiZmEhdqPUKAan5nA2Iw4nv1kPy6XDd8MFPYba/rthTXdwzSvn2jDNAPdTVOyqTzezId77f6ZQGGlb/rlZEsHr2+s4dIxqSTFRDgdxwyxS8akMiI2gmc+2e90FOMlVvqmX15ef4Dm9i5unmqzaQSD8NAQ5k7O4qN9xyg7esrpOMYLrPSNxzq6XDz7yX6m5CbaMM0gMntiJuGhwrOfVDodxXiBlb7x2DvbDnGksc3O8oNMYnQ4V45N581NtTQ0tzsdxwySlb7xiKry1EcV5CVHMz3flkQINvOnZtPS0cWLa22ufX9npW88srbiBNsPNjJ/ao4teh6E8lNimT4ymedWV9La0eV0HDMIVvrGI099VNH9z/xxNgtqsPrCtByONbXz1ua/mijX+BErfdOn8rom/rL7KHMnZRIZFup0HOOQKbmJjE6L5clVFXazlh+z0jd9evrj/YSHCnNt0fOgJiLcPDWHimOn+WD30b4PMD7JSt+c14nT7byxsYarxqXbzViGS8ekkh4fyROryp2OYgbISt+c1wtrKmnrdDHvgmynoxgfEBYawk0XZLOhsp7NB+qdjmMGwErfnNPptk6e+6SSmYUjyE+JdTqO8RHXFWcQGxnK7z+qcDqKGQCPSl9EZovIHhEpE5EHz/L85SKySUQ6ReSWXs91uVfT+u8VtYx/eHn9ARpaOrj1wlynoxgfEhMRxuyJWfxp+2Gqjp92Oo7ppz5LX0RCgUeBOUAxcIeI9F5K5wBwD7D4LF+iRVWnuj/mneV544PaOrv4/UcVTMpJYHxWgtNxjI+5aUoWISI89ZFNxOZvPDnTnwmUqWqFqrYDS4D5PXdQ1UpV3Qa4hiCjccBbm2s50tjGrdPznI5ifFBKXCRXjU/nldJqjja2Oh3H9IMnpZ8DVPd4XOPe5qkoESkVkbUicnO/0hlHdLmU360sZ3RaLNPykpyOY3zULRfm0tnl4qmP7WzfnwzHhdx8VS0BvgT8WkRG995BRO51/2EorauzxRqctmz7ISqPN3Pr9DzEplww55CdFM3lRWm8uLaKE6dtIjZ/4Unp1wI9/42f697mEVWtdf+3AlgJTDvLPk+qaomqlqSl2W3+TlJVHltRTk5SNLNs/VvTh1tL8mhu7+JZW2TFb3hS+huAIhEpFJEIYAHg0SgcEUkWkUj356nAJcDOgYY1Q+/DvXXsPNTILRfmEhpiZ/nm/EaOiOHi0Sk8+0klJ1s6nI5jPNBn6atqJ7AQeA/YBbyqqjtE5CERmQcgIjNEpAa4FXhCRHa4D58AlIrIVmAF8O+qaqXvwx5dUUZqXARX2MRqxkO3leTR1NbJojWVTkcxHgjzZCdVfRd4t9e2H/X4fAPdb/v0Pm41MHmQGc0wWV1+jA2V9fztZaMID7X79oxnRqfFMaMgmac+3s9XLy0kJsKjWjEOsd9sA3S/l/+r5XtJiY3g+okZTscxfua26Xk0NHeweN0Bp6OYPljpGwA+KTvOhsp6bp2ea9Mnm34bn5XABbmJPPFhhS2y4uOs9A2qyiPL95AaF8F1EzOdjmP81O0ledQ1tdnZvo+z0jes2neMTQcauK0kz97LNwM2OTeJKbmJPLqijNNtnU7HMedgv+FBTlV55M97SI+P5NoJ9l6+GZy7ZuVz/HQ7z62udDqKOQcr/SC3Ys9Rttac5PYZdpZvBm98ZgIzC0fwxIflnGy2cfu+yH7Lg1j3Wf5eMhOjuHpcutNxTID48kX5NLZ28uRHtrqWL7LSD2LLdx5h+8FGFpTkEWZn+cZLClNjubwolWc/qaTuVJvTcUwv9psepLpcyi/f20N2UjRX2lm+8bIvzcynraOLx1aWOR3F9GKlH6Te2FjDvqNNfGVWvs2xY7wuJzmaqydk8OLaKg42tDgdx/RgpR+EWtq7+I/lexiXEc/Fo20mTTM0Fszonpz3P/+yz+Ekpicr/SD07Or9HGls456LC2y+fDNk0uOjmDMpi1dLq9l75JTTcYyblX6QqT/dzu9WljOjIJlJOYlOxzEB7vaSPGIiwvjXd3Y5HcW4WekHmd980H235N2fK3A6igkCCdHh3F6Sx8q9dazaa6vi+QIr/SBSXtfEC2squXZCBvkpsU7HMUHihilZZCZG8dN3dtLlUqfjBD0r/SDy07d3EhEWwpdn5TsdxQSR8NAQ7vlcAXuPNPFqabXTcYKeR6UvIrNFZI+IlInIg2d5/nIR2SQinSJyS6/n7haRfe6Pu70V3PTPit1HWbGnjgUz8kiOiXA6jgkyF49OoTgrgf/73h4aW216Bif1WfoiEgo8CswBioE7RKS4124HgHuAxb2OHQH8GLgImAn8WESSBx/b9Ed7p4uH395JdlI0N07JdjqOCUIiwtcvLeTE6XZ+vdyGcDrJkzP9mUCZqlaoajuwBJjfcwdVrVTVbYCr17HXA8tV9YSq1gPLgdleyG364YU1lVQcO83XLy20SdWMY4oy4rl+YibPr6lk9+FGp+MELU8aIAfo+UZcjXubJzw6VkTuFZFSESmtq7Mr/N50pLGVX72/l+kjkynJt39kGWfdNSufmIhQfvxfO1C1i7pO8InTPlV9UlVLVLUkLS3N6TgB5aG3d9Le6eLvrhhlN2IZxyVEh3PXrHzW7T/B0q0HnY4TlDwp/Vogr8fjXPc2TwzmWDNIK/cc5Z1th7itJI+sxGin4xgDwHXFmYxJj+Nn7+yyi7oO8KT0NwBFIlIoIhHAAmCph1//PeA6EUl2X8C9zr3NDLHWji5++F/byUmK5osX5jodx5j/FhoifOOK0RxrauPny3Y7HSfo9Fn6qtoJLKS7rHcBr6rqDhF5SETmAYjIDBGpAW4FnhCRHe5jTwAP0/2HYwPwkHubGWKPriij+kQL//vK0Xbx1vicoox4bpqSzUvrDrCh0iphOImvXUwpKSnR0tJSp2P4tZ0HG5n324+5tCiVb39+nNNxjDmr1o4uFr68ibjIMN594DIiw0KdjuTXRGSjqpb0tZ+dAgaYji4X33ltK/FRYfztpaOcjmPMOUWFh/KNK8ZQXneaR1fY0orDxUo/wDy+spydhxr531eMJiE63Ok4xpzXhfnJXDkujcdWlLHj4Emn4wQFK/0AsufwKf7fB/u4rCiVz41OdTqOMR7520tHkRAdzj++soW2zi6n4wQ8K/0A0d7p4tuvbSE2Ioy/u3y003GM8VhCdDh/f9UY9h5p4pHle52OE/Cs9APEr97fy/baRu6/cjSJ9raO8TMlBSO4fmImT35YYaN5hpiVfgBYXX6Mx1c1CF78AAAOjklEQVSWc31xhr2tY/zW1y4pJCMhim+9soVTdtPWkLHS93MNze384ytbyE6K5uuX2Wgd47+iI0L5x8+Ppbahhe+9+anNzTNErPT9mKryvTc/5XhTO9+5bhxR4TbO2fi34qwE7rwon7e3HWLJBltwZShY6fuxZz6pZNn2w9w1K58x6XFOxzHGK26Znsu0vCR+snQHuw7ZFMzeZqXvpzZUnuDf3t3FrFEj+MI0T2e6Nsb3hYjwj58fS0xEKAsXb6KprdPpSAHFSt8PHT3Vyv0vbSI9PpJvXjPWpkw2ASc5JoLvXDeO/cdO8+1Xt+CyBdW9xkrfz7R3uli4eDMnWzp4cM4EYiPDnI5kzJCYkpvE31xSyHs7jvCbD8qcjhMwrPT9iKryw7e2s37/CRZeNYbC1FinIxkzpOZfkM1V49L41ft7+fOOw07HCQhW+n7kqY/280ppNbeV5HHluHSn4xgz5ESE+68aQ1F6HN98ZYtd2PUCK30/sXznEf713V1cMjqFOy8a6XQcY4ZNZFgo3587gZiIUO55dj0HG1qcjuTXPCp9EZktIntEpExEHjzL85Ei8or7+XUiUuDeXiAiLSKyxf3xuHfjB4fNB+p5YMlmxqTH8c1rxxJiF25NkEmNi+RHN07kVGsn9zy7npMtdsfuQPVZ+iISCjwKzAGKgTtEpLjXbl8D6lV1DPAr4Oc9nitX1anuj/u8lDto7D1yir95dgMJUeH88IZiuwHLBK3C1Fi+P2cC5XWnuW/RRpuRc4A8OdOfCZSpaoWqtgNLgPm99pkPPO/+/HXgGrFxhINWfaKZu55aR0iI8PD8SSTHRjgdyRhHXZCXxAPXFLGm4jgLF2+mo8vldCS/40np5wA974eucW876z7uNXVPAinu5wpFZLOIfCgilw0yb9A40tjKl59ex+n2Lv7lpolkJkY5HckYn3DVuHTuu3wUy3ce4ZtLttBpxd8vQz3I+xAwUlWPi8h04C0Rmaiqn7kELyL3AvcCjBxpFykPNrRwx+/XUneqjX+5aSIFNjTTmM+4YUo2HV3K05/sJzxU+I/bphIaYm8ueMKTM/1aIK/H41z3trPuIyJhQCJwXFXbVPU4gKpuBMqBsb2/gao+qaolqlqSlpbW/1cRQGrqm7n9iTXdhT9vIuOzEpyOZIxPunlaDnfNyuetLQf55itb7K0eD3lypr8BKBKRQrrLfQHwpV77LAXuBtYAtwAfqKqKSBpwQlW7RGQUUARUeC19gKmoa+Kup7tHJjw8fxJjM+KdjmSMT7utJI/QEOG51ZU0tXbw2J3TiY6wwQ7n0+eZvvs9+oXAe8Au4FVV3SEiD4nIPPduTwMpIlIGfAs4M6zzcmCbiGyh+wLvfapqy+KcxaYD9fyv362mqa3TCt+Yfvjihbncf+UYVu6p4yvPrKPRFmA5L/G1hQpKSkq0tLTU6RjD6v2dR1j48iaSYyL4yU0TyU6KdjqSMX5n1d46Hnl/L6NSY3nmnhnkjYhxOtKwEpGNqlrS1352R66DVJXfr6rg3kWl5CXH8IsvTrHCN2aALh+bxkPzJnL4ZCvzfvsxpbbW7llZ6Tukpb2LB5Zs4Wfv7mLWqBT+9QuTSYqxcfjGDMaU3CR+ecsFREeEccfv17Jk/QFbdrEXK30H7D92mi/+bjV/3HqQr8zK58HZ4+1OW2O8JCc5mv97yxQmZify4Juf8u3XttLcbguxnGGTsQ8jVeW1jTX8ZOkOQkX40U3FlOSPcDqWMQEnPiqcn9w0kVdLq3l5/QE+rTnJb790IeMybYCEnekPk/rT7Sx8eTP/9Po2RqfF8Z93TLPCN2YIhYYId8wcycPzJ1HX1MaNv/mI360spyvIV+Gy0TtDTFX547ZD/GTpDk62dHDnRSP5X9Ny7e5BY4bRyZYOHltZxury40zLS+KXt05hTHpgnfV7OnrHSn8IHTjezE+WbueDPXUUpcfx91cX2WpXxjhEVVm17xhPfFhOS2cXX790FH9/9ZiAWXLU09IPjFfrY061dvDbFWU88/F+QkOEr19ayI1Tsu3s3hgHiQhXjE3jgtxEnl9TyeMflvPWllq+N2c8N03JJiRIfj/tTN+LWju6eHn9AX7zQRknTrdz9fh0vjIrn5S4SKejGWN62XWokcc/LKfi2GkmZifwT7PHc3lRKv46K7y9vTOMWju6WLL+AI+tLOfoqTYm5yTy1UsKGZMe53Q0Y8x5uFRZtbeOl9Yd4HBjKyUFydx/5RiuHJfmd+VvpT8Mjja2smhtFS+uraK+uYNJ2Ql8aeZIJucmOR3NGNMPHV0u/rzjMG9uruXoqTbGZ8Zz7+WjmDs5y2/uobHSHyIul/JJ+TFeLa1h2fZDdHUpFxWOYN7UHCbnJDodzxgzCJ1dLlbtq+ONTbUcONFMUkw4t8/I444ZI31+XQsrfS9SVXYcbGTZ9kO8tfkgtQ0txEeGceW4NG6ckm3z5RgTYFSVbTUneefTQ6zbfxyXwgW5icybmsNNU7JIT/C9leys9Aepy6VsrWngT9sPs+zTQ1TXtxAicEFuEtdOyGDWqBQiwuzeNmMC3fGmNj7cW8eqfXWU151GgFmjUvh8cQZXjEtjVGqsT7z/b6XfT6pKed1pVpcf45OyY6wpP05jaydhIcIFuUlcPCaFiwpTSIwOH/ZsxhjfUFPfzKq9dXxcdozq+hYAcpOjuWJsGpcVpTE9P5m0eGdG61np96H+dDtbaxrYVnOSrdUNbK1p4FhTOwDp8ZFckJvElNxESvJHEBdltzMYYz7rcGMrmw/Us7Gqnq01DbR2dC/XOHJEDCX5yVyYn8zknETGZsQPy2peXi19EZkN/D8gFHhKVf+91/ORwAvAdOA4cLuqVrqf+x7wNaAL+AdVfe9838ubpd/lUo40tlJ9opmKY6fZe+QUZUeb2HP4FEdPtXVnB3JHRFOUFs/4rHim5iWRmRDlE/9cM8b4h44uF/uONrH7UCO7D59i9+FG6pu7V/ASgYKUWCZkxTM2I56ClFjyRsSQnxJDSmyE17rGa3fkikgo8CjweaAG2CAiS1V1Z4/dvgbUq+oYEVkA/By4XUSK6V5TdyKQDbwvImNVtav/L+n8TrZ08PzqSmrqm6mpb6G6vplDDa109phcKSoshNwRMUzKTmRkSgxj0uMYkxYXMLdhG2OcER4aQnFWAsVZCUD328VHTrWxv66JyuPNVB4/zdbqkyz79DA9T7NjIkIZ6f4DkJUYzfjMeBbMHDmkWT1pu5lAmapWAIjIEmA+0LP05wM/cX/+OvBb6f7zNR9YoqptwH73Groz6V5A3eseWb6XEbERpMdHUpgSx6zCFNLjo0iPjyQnOZq0+EhC7AzeGDPERITMhCgyE6L43Oj/2d7e6eJIYyuHG1s5dLKVwydbONzYyq5Dp/ho3zEmZif4ROnnANU9HtcAF51rH1XtFJGTQIp7+9pex+YMOO15JEaHs/4H13DsVPtQfHljjBm06IhQClJjzznmf1Ta0K/r6xPva4jIvcC9ACNHDvyvXPdZve+NnzXGGF/hyUDzWiCvx+Nc97az7iMiYUAi3Rd0PTkWVX1SVUtUtSQtLc3z9MYYY/rFk9LfABSJSKGIRNB9YXZpr32WAne7P78F+EC7hwUtBRaISKSIFAJFwHrvRDfGGNNffb69436PfiHwHt1DNp9R1R0i8hBQqqpLgaeBRe4LtSfo/sOAe79X6b7o2wncPxQjd4wxxngmaG/OMsaYQOLpOH2bPMYYY4KIlb4xxgQRK31jjAkiVvrGGBNErPSNMSaI+NzoHRGpA6q8+CVTgWNe/HpOCITXAIHxOuw1+IZAeA3g3deRr6p93t3qc6XvbSJS6skwJl8WCK8BAuN12GvwDYHwGsCZ12Fv7xhjTBCx0jfGmCASDKX/pNMBvCAQXgMExuuw1+AbAuE1gAOvI+Df0zfGGPM/guFM3xhjjFtQlL6IPCwi20Rki4j8WUSync7UXyLySxHZ7X4dfxCRJKcz9ZeI3CoiO0TEJSJ+NfJCRGaLyB4RKRORB53OMxAi8oyIHBWR7U5nGSgRyRORFSKy0/2z9IDTmfpLRKJEZL2IbHW/hn8Z1u8fDG/viEiCqja6P/8HoFhV73M4Vr+IyHV0r1PQKSI/B1DV7zocq19EZALgAp4AvqOqfjGdqoiEAnuBz9O95OcG4A5V3XneA32MiFwONAEvqOokp/MMhIhkAVmquklE4oGNwM3+9P/CvX54rKo2iUg48DHwgKqu7eNQrwiKM/0zhe8WC/jdXzpV/bOqdrofrqV7FTK/oqq7VHWP0zkGYCZQpqoVqtoOLAHmO5yp31R1Fd3rXfgtVT2kqpvcn58CdjFE624PFe3W5H4Y7v4Ytk4KitIHEJGfiUg1cCfwI6fzDNJXgWVOhwgiOUB1j8c1+FnRBCIRKQCmAeucTdJ/IhIqIluAo8ByVR221xAwpS8i74vI9rN8zAdQ1R+oah7wErDQ2bRn19drcO/zA7pXIXvJuaTn5slrMGawRCQOeAP4Zq9/yfsFVe1S1al0/4t9pogM29ttfS6X6C9U9VoPd30JeBf48RDGGZC+XoOI3APcCFyjPnoxph//H/xJLZDX43Gue5txgPt98DeAl1T1TafzDIaqNojICmA2MCwX2APmTP98RKSox8P5wG6nsgyUiMwG/gmYp6rNTucJMhuAIhEpFJEIuteAXupwpqDkvgj6NLBLVR9xOs9AiEjamdF3IhJN9wCBYeukYBm98wYwju6RI1XAfarqV2dq7kXnI4Hj7k1r/XAE0heA3wBpQAOwRVWvdzaVZ0RkLvBrIBR4RlV/5nCkfhORl4Er6Z7Z8QjwY1V92tFQ/SQilwIfAZ/S/fsM8H1Vfde5VP0jIlOA5+n+WQoBXlXVh4bt+wdD6RtjjOkWFG/vGGOM6Walb4wxQcRK3xhjgoiVvjHGBBErfWOMCSJW+sYYE0Ss9I0xJohY6RtjTBD5//Z8F31WYrLXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc35af7780>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Crit values:\n",
      "(-1.2815515655446004, 1.2815515655446004)\n",
      "Criterion value: -0.3963388000744344\n",
      "P-value: 0.6918551075069946\n",
      "H0\n"
     ]
    }
   ],
   "source": [
    "def v5():\n",
    "    sample1 = Sample.from_data(\n",
    "        '1',\n",
    "        np.array([8.60, 9.70, 7.83, 8.77, 9.15, 9.40, 9.36, 8.90, 10.22, 7.13])\n",
    "    )\n",
    "    \n",
    "    sample1.describe()\n",
    "    \n",
    "    hyp = ZHyp(kind=AltHypKind.TWO_SIDED, m=9, sigma=np.sqrt(0.5625))\n",
    "    alpha = 0.2\n",
    "    \n",
    "    plot_dist(hyp.dist)\n",
    "    \n",
    "    print('Crit values:')\n",
    "    print(hyp.critical_values(alpha))\n",
    "    \n",
    "    criterion_value, _, p_value, result = hyp.full_test(sample1, alpha)\n",
    "    print('Criterion value: {}'.format(criterion_value))\n",
    "    print('P-value: {}'.format(p_value))\n",
    "    print('H0' if result else 'H1')\n",
    "\n",
    "v5()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample 1: m=34.403999999999996, s=2.3662027714368765, s^2=5.598915555555555\n",
      "Sample 2: m=35.797999999999995, s=4.047042267027997, s^2=16.37855111111111\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc358c7d68>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Crit values:\n",
      "(-1.0364333894937898, 1.0364333894937898)\n",
      "Criterion value: -0.9456554081394523\n",
      "P-value: 0.17216218672720102\n",
      "H0\n"
     ]
    }
   ],
   "source": [
    "def v6():\n",
    "    sample1 = Sample.from_data(\n",
    "        '1',\n",
    "        np.array([36.90, 34.47, 33.78, 30.72, 33.04, 37.09, 34.94, 36.73, 30.69, 35.68])\n",
    "    )\n",
    "    sample2 = Sample.from_data(\n",
    "        '2',\n",
    "        np.array([32.26, 29.95, 39.11, 40.90, 38.73, 34.21, 31.79, 37.27, 40.88, 32.88])\n",
    "    )\n",
    "    \n",
    "    sample1.describe()\n",
    "    sample2.describe()\n",
    "    \n",
    "    hyp = Z2Hyp(kind=AltHypKind.LEFT, sigma1=2.7, sigma2=3.8)\n",
    "    alpha = 0.15\n",
    "    \n",
    "    plot_dist(hyp.dist)\n",
    "    \n",
    "    print('Crit values:')\n",
    "    print(hyp.critical_values(alpha))\n",
    "    \n",
    "    criterion_value, _, p_value, result = hyp.full_test(sample1, sample2, alpha)\n",
    "    print('Criterion value: {}'.format(criterion_value))\n",
    "    print('P-value: {}'.format(p_value))\n",
    "    print('H0' if result else 'H1')\n",
    "    \n",
    "v6()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample 1: m=27.72857142857143, s=1.1686377906410037, s^2=1.3657142857142863\n",
      "Sample 2: m=29.199999999999992, s=0.9715966241192898, s^2=0.9440000000000005\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc3599aa58>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Crit values:\n",
      "(0.34049106026289289, 2.4605819674472413)\n",
      "Criterion value: 1.446731234866828\n",
      "P-value: 0.5769743476827027\n",
      "H0\n"
     ]
    }
   ],
   "source": [
    "def v7():\n",
    "    sample1 = Sample.from_data(\n",
    "        '1',\n",
    "        np.array([27.8, 27.6, 26.4, 28.1, 29.3, 26.1, 28.8])\n",
    "    )\n",
    "    sample2 = Sample.from_data(\n",
    "        '2',\n",
    "        np.array([29.2, 28.4, 28.9, 30.4, 30.4, 30.3, 29.4, 28.2, 28.9, 27.4, 29.7])\n",
    "    )\n",
    "    \n",
    "    sample1.describe()\n",
    "    sample2.describe()\n",
    "    \n",
    "    hyp = FHyp(kind=AltHypKind.TWO_SIDED, sample1=sample1, sample2=sample2)\n",
    "    alpha = 0.20\n",
    "    \n",
    "    real_dist1 = stats.norm(27.8, 0.9)\n",
    "    real_dist2 = stats.norm(29.3, 0.9)\n",
    "    \n",
    "    plot_dist(hyp.dist)\n",
    "    \n",
    "    print('Crit values:')\n",
    "    print(hyp.critical_values(alpha))\n",
    "    \n",
    "    criterion_value, _, p_value, result = hyp.full_test(sample1, sample2, alpha)\n",
    "    print('Criterion value: {}'.format(criterion_value))\n",
    "    print('P-value: {}'.format(p_value))\n",
    "    print('H0' if result else 'H1')\n",
    "    \n",
    "v7()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample 1: m=28.973000000000003, s=2.5445979817819726, s^2=6.474978888888888\n",
      "Sample 2: m=34.372, s=2.4337250826208323, s^2=5.923017777777777\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc37babb70>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Crit values:\n",
      "(-0.67448975019608171, 0.67448975019608171)\n",
      "Criterion value: -6.20841348627239\n",
      "P-value: 2.676108686802941e-10\n",
      "H1\n"
     ]
    }
   ],
   "source": [
    "def v8():\n",
    "    sample1 = Sample.from_data(\n",
    "        '1',\n",
    "        np.array([29.29, 30.04, 33.29, 26.00, 31.47, 26.05, 29.02, 25.34, 30.23, 29.00])\n",
    "    )\n",
    "    sample2 = Sample.from_data(\n",
    "        '2',\n",
    "        np.array([34.11, 32.81, 38.75, 35.70, 31.51, 36.72, 34.38, 30.49, 35.32, 33.93])\n",
    "    )\n",
    "    \n",
    "    sample1.describe()\n",
    "    sample2.describe()\n",
    "    \n",
    "    hyp = Z2Hyp(kind=AltHypKind.LEFT, sigma1=2.2, sigma2=1.65)\n",
    "    alpha = 0.25\n",
    "    \n",
    "    plot_dist(hyp.dist)\n",
    "    \n",
    "    print('Crit values:')\n",
    "    print(hyp.critical_values(alpha))\n",
    "    \n",
    "    criterion_value, _, p_value, result = hyp.full_test(sample1, sample2, alpha)\n",
    "    print('Criterion value: {}'.format(criterion_value))\n",
    "    print('P-value: {}'.format(p_value))\n",
    "    print('H0' if result else 'H1')\n",
    "\n",
    "v8()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample 1: m=34.403999999999996, s=2.3662027714368765, s^2=5.598915555555555\n",
      "Sample 2: m=35.797999999999995, s=4.047042267027997, s^2=16.37855111111111\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc35651d30>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Crit values:\n",
      "(0.53131475620518898, 1.882123521549266)\n",
      "Criterion value: 2.9253077580102818\n",
      "P-value: 0.9372099085495443\n",
      "H0\n"
     ]
    }
   ],
   "source": [
    "def v9():\n",
    "    sample1 = Sample.from_data(\n",
    "        '1',\n",
    "        np.array([36.90, 34.47, 33.78, 30.72, 33.04, 37.09, 34.94, 36.73, 30.69, 35.68])\n",
    "    )\n",
    "    sample2 = Sample.from_data(\n",
    "        '2',\n",
    "        np.array([32.26, 29.95, 39.11, 40.90, 38.73, 34.21, 31.79, 37.27, 40.88, 32.88])\n",
    "    )\n",
    "    \n",
    "    sample1.describe()\n",
    "    sample2.describe()\n",
    "    \n",
    "    hyp = FHyp(kind=AltHypKind.LEFT, sample1=sample1, sample2=sample2)\n",
    "    alpha = 0.18\n",
    "    \n",
    "    plot_dist(hyp.dist)\n",
    "    \n",
    "    print('Crit values:')\n",
    "    print(hyp.critical_values(alpha))\n",
    "    \n",
    "    criterion_value, _, p_value, result = hyp.full_test(sample1, sample2, alpha)\n",
    "    print('Criterion value: {}'.format(criterion_value))\n",
    "    print('P-value: {}'.format(p_value))\n",
    "    print('H0' if result else 'H1')\n",
    "    \n",
    "v9()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample 1: m=10.4738, s=0.3382339098579234, s^2=0.11440217777777785\n",
      "Sample 2: m=10.155, s=0.7901933659273253, s^2=0.6244055555555558\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc35a0b668>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Crit values:\n",
      "(0.40977889172652099, 2.4403404377094704)\n",
      "Criterion value: 5.457986619524335\n",
      "P-value: 0.018710368845190173\n",
      "H1\n"
     ]
    }
   ],
   "source": [
    "def v10():\n",
    "    sample1 = Sample.from_data(\n",
    "        '1',\n",
    "        np.array([10.73, 9.878, 10.12, 10.58, 10.56, 10.50, 10.93, 10.32, 10.23, 10.89])\n",
    "    )\n",
    "    sample2 = Sample.from_data(\n",
    "        '2',\n",
    "        np.array([ 9.60, 11.37,  9.77,  9.20, 10.70,  9.28, 10.44, 10.26, 11.31,  9.62])\n",
    "    )\n",
    "    \n",
    "    sample1.describe()\n",
    "    sample2.describe()\n",
    "    \n",
    "    hyp = FHyp(kind=AltHypKind.TWO_SIDED, sample1=sample1, sample2=sample2)\n",
    "    alpha = 0.20\n",
    "    \n",
    "    plot_dist(hyp.dist)\n",
    "    \n",
    "    print('Crit values:')\n",
    "    print(hyp.critical_values(alpha))\n",
    "    \n",
    "    criterion_value, _, p_value, result = hyp.full_test(sample1, sample2, alpha)\n",
    "    print('Criterion value: {}'.format(criterion_value))\n",
    "    print('P-value: {}'.format(p_value))\n",
    "    print('H0' if result else 'H1')\n",
    "    \n",
    "v10()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample 1: m=2.671, s=0.5064571935404697, s^2=0.2564988888888888\n",
      "Sample 2: m=3.0410000000000004, s=0.197678189658512, s^2=0.03907666666666664\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc35746be0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Crit values:\n",
      "(-1.0364333894937898, 1.0364333894937898)\n",
      "Criterion value: -1.8500000000000028\n",
      "P-value: 0.0321567747956135\n",
      "H1\n"
     ]
    }
   ],
   "source": [
    "def v11():\n",
    "    sample1 = Sample.from_data(\n",
    "        '1',\n",
    "        np.array([2.89, 3.26, 2.52, 2.41, 3.28, 2.17, 2.57, 1.67, 3.04, 2.90])\n",
    "    )\n",
    "    sample2 = Sample.from_data(\n",
    "        '2',\n",
    "        np.array([3.34, 2.86, 3.26, 3.14, 2.97, 3.23, 2.97, 3.04, 2.87, 2.73])\n",
    "    )\n",
    "    \n",
    "    sample1.describe()\n",
    "    sample2.describe()\n",
    "    \n",
    "    hyp = Z2Hyp(kind=AltHypKind.LEFT, sigma1=0.6, sigma2=0.2)\n",
    "    alpha = 0.15\n",
    "    \n",
    "    plot_dist(hyp.dist)\n",
    "    \n",
    "    print('Crit values:')\n",
    "    print(hyp.critical_values(alpha))\n",
    "    \n",
    "    criterion_value, _, p_value, result = hyp.full_test(sample1, sample2, alpha)\n",
    "    print('Criterion value: {}'.format(criterion_value))\n",
    "    print('P-value: {}'.format(p_value))\n",
    "    print('H0' if result else 'H1')\n",
    "\n",
    "v11()\n"
   ]
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}