statistics_t_test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Сравнение средних значений: t-тест\n",
    "\n",
    "Научимся сравнивать две группы, применяя для этого t-критерий."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/tljh/user/lib/python3.7/site-packages/outdated/utils.py:18: OutdatedCacheFailedWarning: Failed to use cache while checking for outdated package.\n",
      "Set the environment variable OUTDATED_RAISE_EXCEPTION=1 for a full traceback.\n",
      "Set the environment variable OUTDATED_IGNORE=1 to disable these warnings.\n",
      "  **kwargs\n",
      "/opt/tljh/user/lib/python3.7/site-packages/outdated/utils.py:18: OutdatedPackageWarning: The package pingouin is out of date. Your version is 0.4.0, the latest is 0.5.3.\n",
      "Set the environment variable OUTDATED_IGNORE=1 to disable these warnings.\n",
      "  **kwargs\n",
      "/opt/tljh/user/lib/python3.7/site-packages/outdated/utils.py:18: OutdatedPackageWarning: The package outdated is out of date. Your version is 0.2.1, the latest is 0.2.2.\n",
      "Set the environment variable OUTDATED_IGNORE=1 to disable these warnings.\n",
      "  **kwargs\n"
     ]
    }
   ],
   "source": [
    "#загузка используемых библиотек\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as st\n",
    "import pingouin as pg\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "sns.set(rc={'figure.figsize':(12,6)}, style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#сгененируем распределение\n",
    "mu = 100\n",
    "sigma = 10\n",
    "population = np.round(np.random.normal(mu, sigma, 1000), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f1e0eb96f28>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Визуализируем \n",
    "sns.displot(population, kde = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.116798923691228"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Средене значние и стандартное отклонение\n",
    "population.mean()\n",
    "population.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98.305"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Получение выборочного среднего\n",
    "sample_size = 30 #можно менять размер выбороки  и из-за этого будет меняться стандартная ошибка среднего\n",
    "sample = np.random.choice(population, sample_size, False)\n",
    "sample.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 10000\n",
    "sample_means = []\n",
    "for i in range(n):\n",
    "    sample = np.random.choice(population, sample_size, False)\n",
    "    sample_means.append(sample.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f1e50728278>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(sample_means, kde = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "правило трех сигм - 100% в этом диапазоне\n",
    "\n",
    "правило 1.96 сигм ~ 95% в этом диапазоне\n",
    "\n",
    "se = sigma / sqrt(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.8470663267498904"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Стандартная ошибка\n",
    "se = population.std() / sample_size ** 0.5\n",
    "se"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.7893159994451893"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(sample_means)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95.70598999957019"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Диапазон, в котором лежит 95% наблюдений\n",
    "population.mean() + 1.96 * se\n",
    "population.mean() - 1.96 * se"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 93.88,  90.16,  97.38, 103.07,  90.28,  79.21,  94.91,  98.64,\n",
       "        90.36,  80.13, 101.42,  81.32, 100.78,  98.18,  97.56,  85.94,\n",
       "       105.77,  93.78,  92.37,  97.16, 105.38, 120.69,  94.85,  89.81,\n",
       "       110.99, 102.55, 107.14,  99.42,  97.34, 103.2 ])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_real = np.random.choice(population, sample_size, False)\n",
    "sample_real"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6205397831861117"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_real.mean()\n",
    "sample_real.std()\n",
    "se = sample_real.std() / sample_size ** 0.5\n",
    "se"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.4683133616970996"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#как вычислить z значение\n",
    "(104 - 100)/se # значение лежит правее на более чем 2 сигмы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "z_scores = []\n",
    "n = 10000\n",
    "\n",
    "mu = population.mean()\n",
    "sigma = population.std()\n",
    "\n",
    "se = sigma / sample_size ** 0.5\n",
    "\n",
    "for i in range(n):\n",
    "    sample = np.random.choice(population, sample_size, False)\n",
    "    sample_mean = sample.mean()\n",
    "    \n",
    "    z_score = (sample_mean - mu) / se\n",
    "    z_scores.append(z_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f1e4b0639b0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(z_scores, kde=False) # Z-распределение"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Рассчет z-значения для конкретной выборки\n",
    "\n",
    "t_scores = []\n",
    "\n",
    "n = 10000\n",
    "for i in range(n):\n",
    "    sample = np.random.choice(population, sample_size, False)\n",
    "    sample_mean = sample.mean()\n",
    "    sample_std = sample.std()\n",
    "    \n",
    "    se = sample_std / sample_size ** 0.5\n",
    "    t_score = (sample_mean - mu) / se\n",
    "    t_scores.append(t_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f1e2ae3fa20>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(t_scores, kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f1e2acdf550>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Что будет если сделать sample size поменьше - для Z-score ничего не изменилось\n",
    "\n",
    "sample_size_1 = 10\n",
    "z_scores_1 = []\n",
    "n = 10000\n",
    "\n",
    "for i in range(n):\n",
    "    sample_1 = np.random.choice(population, sample_size_1, False)\n",
    "    sample_mean_1 = sample_1.mean()\n",
    "    \n",
    "    \n",
    "    z_score_1 = (sample_mean_1 - mu) / se\n",
    "    z_scores_1.append(z_score_1)\n",
    "    \n",
    "sns.displot(z_scores_1, kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f1e2ab55c50>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Что будет если сделать sample size поменьше - а вот для t-scores\n",
    "t_scores_1 = []\n",
    "sample_size_1 = 10 #можно менять размер выборки, что бы посмотреть как изменяется распределение\n",
    "n = 10000\n",
    "for i in range(n):\n",
    "    sample_12 = np.random.choice(population, sample_size_1, False)\n",
    "    sample_mean_12 = sample_12.mean()\n",
    "    sample_std_12 = sample_12.std()\n",
    "    \n",
    "    se_ = sample_std_12 / sample_size_1 ** 0.5\n",
    "    t_score_1 = (sample_mean_12 - mu) / se_\n",
    "    t_scores_1.append(t_score_1)\n",
    "\n",
    "sns.displot(t_scores_1, kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.57490927675926"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_real.mean() + 2 * se"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93.00309072324072"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_real.mean() - 2 * se"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = sample_size - 1 #число степеней свободы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "91.24264290954767"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_real.mean() + 2.93 * se\n",
    "sample_real.mean() - 2.93 * se"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# T-test\n",
    "\n",
    "    stats.ttest_ind(a, b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Тесты для проверки нормальности распределения:\n",
    "\n",
    "- Тест Колмогорова-Смирнова\n",
    "- Тест Шапиро-Уилка \n",
    "\n",
    "        from scipy import stats\n",
    "        stats.shapiro(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### U-критерий Манна-Уитни\n",
    "\n",
    "Переводит все данные в ранговую шкалу, после этого считает сумму рангов в первой группе и во второй. \n",
    "Этот критерий менее чувствителен к экстремальным отклонениям от нормальности и наличию выбросов. \n",
    "\n",
    "Этот критерий не сравнивает средние. \n",
    "Наиболее частая его интерпретация - он проверяет, что вероятность случайно взять из первой выборки более высокое значение, \n",
    "чем из второй, равна 50%. \n",
    "Соответственно, если вероятность не равна 50%, то в какой-то из выборок более высокие значения встречаются чаще."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "males = [19, 22, 16, 29, 24]\n",
    "females = [20, 11, 17, 12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17.0\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import mannwhitneyu\n",
    "U1, p = mannwhitneyu(males, females, method=\"exact\")\n",
    "print(U1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "mannwhitneyu всегда сообщает статистику, связанную с первой выборкой, которой в данном случае являются мужчины."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.0\n"
     ]
    }
   ],
   "source": [
    "nx, ny = len(males), len(females)\n",
    "U2 = nx*ny - U1\n",
    "print(U2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1111111111111111"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Точное распределение тестовой статистики асимптотически нормально, поэтому пример продолжается сравнением точного значения p со значением p, полученным с использованием нормального приближения."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11134688653314041\n"
     ]
    }
   ],
   "source": [
    "_, pnorm = mannwhitneyu(males, females, method=\"asymptotic\")\n",
    "print(pnorm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сообщаемое здесь значение p противоречит значению mnw. Причина в том, что не применяется поправка на непрерывность, выполняемая mannwhitneyu; \n",
    "\n",
    "mannwhitneyu уменьшает расстояние между тестовой статистикой и средним значением на 0.5, чтобы исправить тот факт, что дискретная статистика сравнивается с непрерывным распределением. \n",
    "\n",
    "Используемая здесь статистика меньше среднего значения, поэтому мы уменьшаем расстояние, добавляя 0.5 в числитель."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11134688653314041\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import norm\n",
    "U = min(U1, U2)\n",
    "N = nx + ny\n",
    "z = (U - nx*ny/2 + 0.5) / np.sqrt(nx*ny * (N + 1)/ 12)\n",
    "p = 2 * norm.cdf(z)  # use CDF to get p-value from smaller statistic\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "При желании мы можем отключить коррекцию непрерывности"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0864107329737\n"
     ]
    }
   ],
   "source": [
    "_, pnorm = mannwhitneyu(males, females, use_continuity=False,\n",
    "                        method=\"asymptotic\")\n",
    "print(pnorm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Независимо от того, выполняем ли мы точный или асимптотический тест, вероятность того, что статистика теста случайно окажется экстремальной или более экстремальной, превышает 5%, поэтому мы не считаем результаты статистически значимыми."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Предположим, что еще до просмотра данных мы предположили, что у женщин, как правило, диагноз диагностируется в более молодом возрасте, чем у мужчин. В этом случае было бы естественно указать возраст женщин в качестве первых входных данных, и мы бы провели односторонний тест, используя : женщинам диагностируется в возрасте, который стохастически меньше, чем у мужчин (alternative = 'less')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MannwhitneyuResult(statistic=3.0, pvalue=0.05555555555555555)\n"
     ]
    }
   ],
   "source": [
    "res = mannwhitneyu(females, males, alternative=\"less\", method=\"exact\")\n",
    "print(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Опять же, вероятность случайного получения достаточно низкого значения тестовой статистики при нулевой гипотезе превышает 5%, поэтому мы не отвергаем нулевую гипотезу в пользу нашей альтернативы.\n",
    "\n",
    "Если разумно предположить, что средние значения выборок из популяций нормально распределены, мы могли бы использовать **t-критерий** для выполнения анализа."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ttest_indResult(statistic=-2.239334696520584, pvalue=0.030068441095757924)\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import ttest_ind\n",
    "res = ttest_ind(females, males, alternative=\"less\")\n",
    "print(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "При этом предположении значение p будет достаточно низким, чтобы отвергнуть нулевую гипотезу в пользу альтернативы."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Первые премии Оскар за лучшую мужскую и женскую роль были вручены в 1929. Данные гистограммы демонстрируют распределение возраста победителей с 1929 по 2014 год (100 мужчин, 100 женщин). \n",
    "\n",
    "Используя t-критерий, проверьте, можно ли считать наблюдаемые различия в возрасте между лучшими актрисами и актерами статистически достоверными.\n",
    "\n",
    "Средний возраст мужчин равен 45, sd = 9.\n",
    "\n",
    "Средний возраст женщин равен 34, sd = 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=8.176235608718828, pvalue=3.4555440142902684e-14)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "stats.ttest_ind_from_stats(45, 9, 100, 34, 10, 100)\n",
    "# mean1, std1, nobs1, mean2, std2, nobs2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-statistic = 8.176235608718828 p-value = 3.4555440142902684e-14\n"
     ]
    }
   ],
   "source": [
    "# средние значения выборки\n",
    "x1 = 45\n",
    "x2 = 34\n",
    "\n",
    "# стандартные отклонения выборки\n",
    "sd1 = 9\n",
    "sd2 = 10\n",
    "\n",
    "# количество наблюдений\n",
    "n1 = 100\n",
    "n2 = 100\n",
    "\n",
    "# кол-во степеней свободы\n",
    "df = n1 + n2 - 2\n",
    "\n",
    "# непосредственно расчёт t-значения\n",
    "t = (x1-x2)/(sd1**2/n1 + sd2**2/n2) ** 0.5\n",
    "\n",
    "# подсчёт p-значения\n",
    "p = stats.t.sf(np.abs(t), df)*2\n",
    "\n",
    "# вывод результата\n",
    "print('t-statistic = {} p-value = {}'.format(t,p))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H0 rejected\n"
     ]
    }
   ],
   "source": [
    "# подтверждение или опровержение нулевой гипотезы\n",
    "if p < 0.05:\n",
    "    print('H0 rejected')\n",
    "else:\n",
    "    print('H0 confirmed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ПРОЕКТ\n",
    "\n",
    "Представьте, что вы работаете аналитиком в очень крупной компании по доставке пиццы над приложением для курьеров.\n",
    "\n",
    "У вас есть несколько ресторанов в разных частях города и целый штат курьеров. Но есть одна проблема — к вечеру скорость доставки падает из-за того, что курьеры уходят домой после рабочего дня, а количество заказов лишь растет. Это приводит к тому, что в момент пересмены наша доставка очень сильно проседает в эффективности. \n",
    "\n",
    "Наши data scientist-ы придумали новый алгоритм, который позволяет курьерам запланировать свои последние заказы перед окончанием рабочего дня так, чтобы их маршрут доставки совпадал с маршрутом до дома. То есть, чтобы курьеры доставляли последние свои заказы за день как бы \"по пути\" домой. \n",
    "\n",
    "Вы вместе с командой решили раскатить A/B тест на две равные группы курьеров. Часть курьеров использует старый алгоритм без опции \"по пути\", другие видят в своем приложении эту опцию и могут ее выбрать. Ваша задача – проанализировать данные эксперимента и помочь бизнесу принять решение о раскатке новой фичи на всех курьеров.\n",
    "\n",
    "### Описание данных\n",
    "- order_id - id заказа\n",
    "- delivery_time - время доставки в минутах\n",
    "- district - район доставки\n",
    "- experiment_group - экспериментальная группа"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment = pd.read_csv('/mnt/HC_Volume_18315164/home-jupyter/jupyter-a-eremina/statistics/experiment_lesson_4.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>order_id</th>\n",
       "      <th>delivery_time</th>\n",
       "      <th>district</th>\n",
       "      <th>experiment_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3159</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Purlieus</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10426</td>\n",
       "      <td>51.0</td>\n",
       "      <td>Purlieus</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9155</td>\n",
       "      <td>46.0</td>\n",
       "      <td>Purlieus</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10699</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Purlieus</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10061</td>\n",
       "      <td>43.0</td>\n",
       "      <td>Purlieus</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  delivery_time  district experiment_group\n",
       "0      3159           44.0  Purlieus          control\n",
       "1     10426           51.0  Purlieus          control\n",
       "2      9155           46.0  Purlieus          control\n",
       "3     10699           34.0  Purlieus          control\n",
       "4     10061           43.0  Purlieus          control"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiment.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20196 entries, 0 to 20195\n",
      "Data columns (total 4 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   order_id          20196 non-null  int64  \n",
      " 1   delivery_time     20196 non-null  float64\n",
      " 2   district          20196 non-null  object \n",
      " 3   experiment_group  20196 non-null  object \n",
      "dtypes: float64(1), int64(1), object(2)\n",
      "memory usage: 631.2+ KB\n"
     ]
    }
   ],
   "source": [
    "experiment.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для начала давайте сформулируем гипотезы для проверки. Выберите статистические гипотезы, которые мы сможем проверить в этом эксперименте:\n",
    "\n",
    "- **Альтернативная гипотеза (H1): Разница между средним временем доставки в тестовой и контрольной группе есть**\n",
    "- Альтернативная гипотеза (H1): Изменение алгоритма влияет на скорость доставки\n",
    "- **Нулевая гипотеза (H0): Разницы между средним временем доставки в тестовой и контрольной группе нет**\n",
    "- Нулевая гипотеза (H0): Разница между средним временем доставки в тестовой и контрольной группе есть\n",
    "- Нулевая гипотеза (H0): Изменение алгоритма не влияет на скорость доставки\n",
    "- Альтернативная гипотеза (H1): Разницы между средним временем доставки в тестовой и контрольной нет"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сопоставьте графики распределения и  экспериментальные группы:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1e2a9d3c88>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data = experiment.query('experiment_group == \"control\"'), hue = 'district', bins = 25, x = 'delivery_time')\n",
    "\n",
    "# другой способ\n",
    "# experiment.query('experiment_group == \"control\"').groupby('district').delivery_time.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1e28743588>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+r+2uu+7SXXfddd72Bx98sM7XvXr10oYNG85p3759+zmv//M//1MFBQW1jufll19ucMxNxdIUAAAAeNUXlquqqvxSt70iiAMAAAAGEMQBAAAAAwjiAAAAgAEEcQAAAMAAgjgAAABgAEEcAAAAMIAgDgAAAC+3x11nW2RkpF/q/tCbb76pUaNGKSMjQyNGjNCvf/3revc/ePCgkpKSmjy2s2VnZ+uVV15pkVoN4YE+AAAA8LLb7Hq1KK/WtprqaoWFhzep7h0DUn3ar6ysTHPnztW6desUGxsry7K0c+fOJvXZWG637/9ZaAkBuSKem5ur5ORkXXHFFdq9e7d3+969ezV27FilpKRo7Nix2rdvX7PbAADBw+32tOr6AALv8OHDCg0NVXR0tCQpJCRE/fv3P++qd21XwRcuXKi0tDSlpaXpww8/lHT6KZq33nqrd5+zX2/fvl1paWl69NFHlZGRoa1bt0qSdu3apczMTKWkpOixxx5TdXW1X841IFfEhw0bpv/6r//Sz3/+83O2z549W1lZWcrIyNCGDRuUk5Ojl156qVltAIDgYbfbtCp/l9/qZ6X081ttAGb069dPP/7xj3XjjTcqKSlJV199tTIyMho8rrKyUv369VN2dra2b9+uRx55RH/7298aPO7rr7/WvHnzNHjwYElSfn6+Pv30U61Zs0YOh0OTJk3Sq6++qnHjxjX73H4oIFfEExMTFRsbe8628vJyFRcXKzX19K8pUlNTVVxcrIqKiia3AQAAoHWz2Wx67rnn9PLLLyspKUl///vflZ6eriNHjtR7XFhYmNLT0yVJSUlJioiI0DfffNNgf7179/aG8DNGjhypyMhIhYaGatSoUXr//febfkL1MLZGvLS0VN27d5fdbpck2e12devWTaWlpbIsq0ltMTExpk4HAAAALahv377q27evfv7zn2vkyJH66quvZFmWt93lcvlUx26313tcx44dW2bATdCuv6xZVFRkpN/CwkIj/bZ3zHvgtfU5T0hIkNPp9GMP/ZpU3/dj+vn9zygQcxQs77NgGUd7wpy3jNDQUFVVVXlfR0ZGqqaeNdH1tTXk7H7qUlZWptLSUg0cOFCSdOjQIZWXlysuLk41NTXauXOnLrnkEq1bt85b88SJE6qpqdFf/vIX3XLLLfroo4904sQJXXzxxaqsrNSBAwdUWlqqCy64QBs2bJDH41FVVZVOnjzp/fmMU6dO6Y033tDtt9+usLAwrV27Vtddd51PY6+urm7U+9JYEI+NjdWhQ4fkdrtlt9vldrtVVlbm/XZsU9oaa8CAAXI4HH44u7oVFhYqISEhoH2CeTehvcx5XFxcUNV3Op2NOiYQf0b+nqNgeJ+1l/d7MGHOW87OnTvPuy1hXXdGac5dUyTfbn8YHh6uF198Ud9++60iIiLk8Xj08MMPa8iQIZo1a5amTJmimJgY3Xjjjd6aHTp0UHR0tPbu3avMzExJ0lNPPaXo6GhFR0fr7rvv1rhx49S1a1ddc8012rt3ryIjIxURESGbzXbOuEJDQzVw4EA98MADqqio0JAhQzR+/HiF+3De4eHh3v9ASKevvtd34ddYEO/SpYvi4+OVl5enjIwM5eXlKT4+3ru8pKltAAAAaDq3x+3zrQYbW9dusze4X48ePfS///u/tbbddtttuu2227yvH3jgAUlSz549tX37dknSjBkzzjtuypQpmjJlynnbk5KStHbt2nO2LVy4sMExtpSABPHHH39cBQUFOnz4sCZMmKDo6Ght2rRJc+bMUXZ2tp577jl16tRJubm53mOa2gYAAICmqy8sV1VVNfmhPr6E8PYmIEH8scce02OPPXbe9ssvv1yvvfZarcc0tQ0AAABoDXjEPQAAAGAAQRwAAKCdO/v2fmiapswhQRwAAKAdi4iIUHl5OWG8GSzLUnl5uSIiIhp1XLu+jzgAAEB717NnTx08eFD/+te/Gty3urrap9v4tUcRERHq2bNno44hiAMAALRjYWFhuvTSS33at7Cw8Jz7ZKN5WJoCAAAAGEAQBwAAAAwgiAMAAAAGEMQBAAAAAwjiAAAAgAEEcQBGuN2eVl0fAIDm4vaFAIyw221alb/Lb/WzUvr5rTYAAC2BK+IAAACAAQRxAAAAwACCOAAAAGAAQRwAAAAwgCAOAAAAGEAQBwAAAAwgiAMAAAAGEMQBAAAAAwjiAAAAgAEEcQAAAMAAgjgAAABgAEEcAAAAMIAgDgAAABhAEAcAAAAMIIgDAAAABhDEAQAAAAMI4gAAAIABBHEAAADAAII4AAAAYABBHAAAADCAIA4AAAAYQBAHAAAADCCIAwAAAAYQxAEAAAADCOIAAACAAQRxAAAAwACCOAAAAGAAQRwAAAAwgCAOAAAAGEAQBwAAAAwgiAMAAAAGBEUQf/vttzVq1ChlZGQoPT1dBQUFkqS9e/dq7NixSklJ0dixY7Vv3z7vMfW1AQAAAMHOeBC3LEu/+c1vtGjRIm3YsEGLFi3SjBkz5PF4NHv2bGVlZSk/P19ZWVnKycnxHldfGwAAABDsjAdxSbLZbDp69Kgk6ejRo+rWrZu+//57FRcXKzU1VZKUmpqq4uJiVVRUqLy8vM42AAAAoDUINT2AkJAQPf3007r//vvVsWNHVVVV6YUXXlBpaam6d+8uu90uSbLb7erWrZtKS0tlWVadbTExMSZPBwAAAPCJ8SB+6tQpPf/883ruueeUkJCgwsJCPfTQQ1q0aJHf+y4qKvJ7H7UpLCw00m97x7wHXn1znpCQIKfT6cfe+/n9zzwQ59CU+r4f0zbmKFj+bgfLONoT5twM5r3lGA/iO3fuVFlZmRISEiSd/tDu0KGDHA6HDh06JLfbLbvdLrfbrbKyMsXGxsqyrDrbGmPAgAFyOBz+OK06FRYWes8VgcO8B54vcx4XF+fXMQTiz9zf59DY+k6ns1HHtIU5Coa/23zGBB5zbgbz3jgul6veC7/G14hffPHF+u677/TNN99Ikvbs2aPy8nL17t1b8fHxysvLkyTl5eUpPj5eMTEx6tKlS51tAAAAQGtg/Ir4RRddpDlz5mjq1KkKCQmRJC1YsEDR0dGaM2eOsrOz9dxzz6lTp07Kzc31HldfGwAAABDsjAdxSUpPT1d6evp52y+//HK99tprtR5TXxsAAAAQ7IwvTQEAAADaI4I4AAAAYABBHAAAADCAIA4AAAAYQBAHAAAADCCIA2iT3G6P6SEAAFCvoLh9IQC0NLvdplX5u/zaR1ZKP7/WBwC0bVwRBwAAAAwgiAMAAAAGEMQBAAAAAwjiAAAAgAEEcQAAAMAAgjgAtFPc4hEAzOL2hQDQTnGLRwAwiyviAAAAgAEEcQAAAMAAgjgAAABgAEEcAAAAMIAgDgAAABhAEAcAAAAMIIgDAAAABhDEAQAAAAMI4gAAAIABBHEAAADAAJ+D+Jtvvlnr9s2bN7fYYAAAAID2wucgPmvWrFq35+TktNhgAAAAgPYitKEdSkpKJEmWZXl/PrstPDzcPyMDAAAA2rAGg/jw4cMVEhIiy7I0fPjwc9q6du2qBx980G+DAwAAANqqBoP4rl27JEnjxo3TK6+84vcBAQAQLNxuj+z2hldxJiQk+L0PAG1Pg0H8DEI4AKC9sdttWpW/q8H9nE6n4uLimtRHVkq/Jh0HoPXzOYiXlJTo6aef1s6dO3X8+PFz2t55552WHhcAAADQpvkcxKdNm6ZevXppxowZ6tChgz/HBAAAALR5Pgfxr776SqtXr5bNxjo2AAAAoLl8TtXXXHONiouL/TkWAAAAoN3w+Yp4jx49NHHiRA0fPlxdu3Y9p23q1KktPjAAAACgLfM5iJ84cUI//elPderUKX333Xf+HBMAAADQ5vkcxP/nf/7Hn+MAAAAA2pVG3b6wLr169WqRwQAAAADthc9B/OxH3Z8REhIiSdq5c2fLjwwAAABow3wO4mcedX/Gv/71Ly1btkyJiYktPigAAACgrWvyTcEvuugizZo1S08++WRLjgcAAABoF5r1dJ5vvvlGJ06caKmxAAAAAO2Gz0tTsrKyvGvCpdO3M/z66681ZcoUvwwMAAAAaMt8DuK33377Oa87dOigfv36qU+fPs0ehMvl0oIFC/TPf/5TDodDgwYN0vz587V3715lZ2ersrJS0dHRys3N9fZXXxsAAAAQ7HwO4qNHj/bbIBYvXiyHw6H8/HyFhITo8OHDkqTZs2crKytLGRkZ2rBhg3JycvTSSy812AYAAAAEO5/XiNfU1OjZZ5/VsGHDdNVVV2nYsGF69tlnVV1d3awBVFVVaf369Zo6dap36UvXrl1VXl6u4uJipaamSpJSU1NVXFysioqKetsAAACA1sDnK+KLFy/WZ599prlz5youLk5Op1PPPfecjh07ppkzZzZ5ACUlJYqOjtayZcu0fft2RUZGaurUqYqIiFD37t1lt9slSXa7Xd26dVNpaaksy6qzLSYmpsljAQAAAALF5yC+efNmbdiwQZ07d5YkXXbZZerfv78yMjKaFcTdbrdKSkrUv39/zZgxQ59++qkmT56sZ555psk1fVVUVOT3PmpTWFhopN/2jnkPvPrmPCEhQU6n04+99/Nz/UD00bT6vh/TNubIn3+3G/M+bfp5+vcc2jLmzQzmveX4HMTPfqKmL9t9FRsbq9DQUO8yk4EDB6pz586KiIjQoUOH5Ha7Zbfb5Xa7VVZWptjYWFmWVWdbYwwYMEAOh6NZ42+swsJCJSQkBLRPMO8m+DLncXFxfh2Dv+sHoo/G1nc6nY06pi3Mkb//bvsy/sbO+w/x+dR4fK6bwbw3jsvlqvfCr89rxEeMGKH77rtP//jHP7Rnzx5t3bpVU6ZM0YgRI5o1wJiYGCUlJendd9+VdPpuKOXl5erTp4/i4+OVl5cnScrLy1N8fLxiYmLUpUuXOtsAAACA1sDnK+LTp0/X7373O82bN09lZWXq3r27brnlFt13333NHsTcuXM1c+ZM5ebmKjQ0VIsWLVKnTp00Z84cZWdn67nnnlOnTp2Um5vrPaa+NgAAACDYNRjECwsLtWXLFk2fPl1Tp07V1KlTvW2LFy9WcXGxBg0a1KxB9OrVSy+//PJ52y+//HK99tprtR5TXxsAAAAQ7BpcmvL888/rmmuuqbUtKSlJK1asaPFBAaif5XG3iT4AAGjPGrwivnPnTl133XW1tl177bXNumMKgKYJsdlVsfXPfu0j5vqxfq0PAEB71+AV8WPHjqmmpqbWtlOnTqmqqqrFBwUAAAC0dQ0G8csuu0zbtm2rtW3btm267LLLWnxQAAAAQFvXYBC/6667NHv2bBUUFMjj8UiSPB6PCgoKNGfOHE2YMMHvgwQAAADamgbXiKelpenw4cOaMWOGampqFB0drcrKSoWFhelXv/qV90E8AAAAAHzn033EJ0yYoNtvv10ff/yxKisrFR0drcGDBysqKsrf4wMAAADaJJ8f6BMVFVXn3VMAAAAANI7Pj7gHAAAA0HII4gAAAIABBHEAAADAAII4AAAAYABBHADQarndHtNDAIAm8/muKQAABBu73aZV+bv8Vj8rpZ/fagMAV8QBAAAAAwjiAAAAgAEEcQAAAMAAgjgAAABgAEEcAAAAMIAgDgAAABhAEAcAAAAMIIgDAAAABhDEAQAAAAMI4gAAAIABBHEAAADAAII4AAAAYABBHAAAADCAIA4AAAAYQBAHAAAADCCIAwAAAAYQxAEAAAADCOIAAACAAQRxAAAAwACCOAAAAGAAQRwAAAAwgCAOAAAAGEAQBwAAAAwgiAMAAAAGEMQBAAAAAwjiAAAY5HZ7WnV9AE0XanoAAAC0Z3a7Tavyd/mtflZKP7/VBtA8XBEHAAAADCCIAwAAAAYEVRBftmyZrrjiCu3evVuS9Mknnyg9PV0pKSm6++67VV5e7t23vjYAAAAg2AVNEP/iiy/0ySefqEePHpIkj8ej6dOnKycnR/n5+UpMTNSSJUsabAMAAABag6AI4tXV1Zo3b57mzJnj3VZUVCSHw6HExERJUmZmpjZv3txgGwAAANAaBEUQf+aZZ5Senq6ePXt6t5WWliouLs77OiYmRh6PR5WVlfW2AQAAAK2B8dsXfvzxxyoqKrLt3ywAABSlSURBVNK0adMC3ndRUVHA+5SkwsJCI/22d21p3hMSElTqdPq1jxg1f87qOz4hIUFOv55DPz/XD0QfTavv+zHtd478Ub/p4/D/ObSlz7+ztdXzCnbMe8sxHsR37NihPXv2aNiwYZKk7777Tvfcc4/Gjx9/zgdTRUWFbDaboqOjFRsbW2dbYwwYMEAOh6NlTsRHhYWFSkhICGifaJvzHnvWb4X8pTlz5sucx/n5HPxdPxB9NLa+0+ls1DHtcY78Ub+x896UPpqjrX3+SW3zc701YN4bx+Vy1Xvh1/jSlEmTJmnbtm3asmWLtmzZoosvvlgrV67UxIkTdfLkSX344YeSpDVr1mjEiBGSTgfoutoAAACA1sD4FfG62Gw2LVq0SLNnz5bL5VKPHj20ePHiBtsAAACA1iDogviWLVu8P1999dXauHFjrfvV1wYAAAAEO+NLU4C2yPK4TQ8BAAAEuaC7Ig60BSE2uyq2/tlv9WOuH+u32gAAIDC4Ig4AAAAYQBAHAAAADCCIAwAAAAYQxAEAAAADCOIAAACAAQRxAAAAwACCOAAAAGAAQRwAAAAwgCAOAAAAGEAQBwAAAAwgiAMAAAAGEMQBAAAAAwjiAAAAgAEEcQAAAMAAgjgAAABgAEEcAAAAMIAgDgAAABhAEAcAAAAMIIgDAAAABhDEAQAAAAMI4gAAAIABBHEAAADAAII4AAAAYABBHAAAADCAIA4AAAAYQBAHAAAADCCIAwAAAAYQxAHUyvK4m3V8QkJCC40EAIC2KdT0AAAEpxCbXRVb/9zk40udTsXGxdXZHnP92CbXBgCgLeCKOAAAAGAAQRwAAAAwgCAOAAAAGEAQBwAAAAwgiAMAAAAGEMQBAAAAAwjiAAAAgAHcRxxArdxuT7Pu9R3TgmMBAKAtIogDqJXdbtPKF15v8vFHjx7VBRdcUGf7PZPGNLk2AABtAUtTAAAAAAMI4gAAtGFut6dV1wfaMpamAADQhtntNq3K3+W3+lkp/fxWG2jrjF8R//7773XvvfcqJSVFaWlpeuCBB1RRUSFJ+uSTT5Senq6UlBTdfffdKi8v9x5XXxsAAAAQ7IwH8ZCQEE2cOFH5+fnauHGjevXqpSVLlsjj8Wj69OnKyclRfn6+EhMTtWTJEkmqtw0AAABoDYwH8ejoaCUlJXlfDxo0SE6nU0VFRXI4HEpMTJQkZWZmavPmzZJUbxsAAADQGhgP4mfzeDxavXq1kpOTVVpaqri4OG9bTEyMPB6PKisr620DAAAAWoOg+rLm/Pnz1bFjR40bN05vvfWW3/srKiryex+1KSwsNNJvexfIeU9ISFCp0+m3+jGSX+uf6ePo0aPNqtHQ8U6/nkM/P9cPRB9Nq+/7Me13jvxRv+njCJ5zaGp9U/+u8e+pGcx7ywmaIJ6bm6v9+/drxYoVstlsio2NPeeDo6KiQjabTdHR0fW2NcaAAQPkcDha7Bx8UVhYqISEhID2CTPzHnvWb21aY31J9T6QpyENPdBH0jm/2fIHf9cPRB+Nre90Oht1THucI3/Ub+y8N6WP5vB3fRP/rvHvqRnMe+O4XK56L/wGxdKUJ598UkVFRVq+fLnCw8MlnQ7JJ0+e1IcffihJWrNmjUaMGNFgGwAAANAaGL8i/tVXX+n5559Xnz59lJmZKUnq2bOnli9frkWLFmn27NlyuVzq0aOHFi9eLEmy2Wx1tgEAgMBxuz2y2/17XS8QfQAmGA/iP/rRj/Tll1/W2nb11Vdr48aNjW4DAACB4e8HBkk8NAhtF/+9BAAAAAwgiAMAAAAGEMQRdCyPu8Vr/vAb3v7oAwAAoDGMrxEHfijEZlfF1j+3aM1Sp/Oc2/3FXD+2ResDAAA0FlfEAQAAAAMI4gAAAIABBHG0S6wRBwAAprFGHO2SP9ahn4016AAAoCFcEQcAAAAMIIgDAAAABhDEAQAAAAMI4gAAAIABBHEAAADAAII4AAAAYABBHAAAADCAIA4AAAAYQBAHAAAADCCIAwAAAAYQxAEAAAADCOIAAACAAQRxAAAAwACCOAAAAGAAQRwAAAAwgCAOAAAAGEAQBwAAAAwgiAMAAAAGEMQBAAAAAwjiAAAAgAEEcQAA0K65Pe5WXR+tV6jpAQAAAJhkt9n1alGe3+rfMSDVb7XRunFFHAAAADCAII5Gs/gVGwAAQLOxNAWNFmKzq2Lrn/1WP+b6sX6rDQAAECy4Ig4AAAAYQBBvY1g2AgAA0DqwNKWN8feyEYmlIwAAAC2BK+IAACCoud2e87YlJCT4tT4QCFwRBwAAQc1ut2lV/i6/1c9K6ee32kB9COIAACDofVG2+5zXR48e1QUXXNBC1QniMIOlKQAAAIABBHEAAADAAIJ4gA24sr/pIQAAgAByB+DWwoHoAy2PNeIB5ojowFMpAQBoR+w2u14tyvNrH3cMSPVrffhHq74ivnfvXo0dO1YpKSkaO3as9u3bZ3pIAAAAgE9adRCfPXu2srKylJ+fr6ysLOXk5JgeEgAAQJtzZulLS96/va4+2pNWuzSlvLxcxcXF+sMf/iBJSk1N1fz581VRUaGYmBjDowMAM+Li4kwPAYABbo9bdpvdb/XPLK8pdToV66fPmfa4vKbVBvHS0lJ1795ddvvpN53dble3bt1UWlraYBC3LEuSVF1d7fdx1qYmxH/T7nK5/Fo/EH34o74n1HFOTX+fw/HjJxSZNMqv9QPx5xwe3vQ+HBFh9R7vcrkUavPf0+z8XT8QfbhcLrlLv2zUMceqjikqMsrH+pe2iTkKhvoRYSFNHkewnEOw1j/TR8ewc0OmJzzsvG3NqR9q+S/E+ru+JJ2qOaUNuwv8Vv+WvskKtexy2ML9di4ul8svdU06kzXPZM8fCrHqaglyRUVFmjFjhjZt2uTdNnLkSC1evFhXXnllvccePXpUu3fvrncfAAAAoCX07du31gdQtdor4rGxsTp06JDcbrfsdrvcbrfKysoUGxvb4LGRkZHq27evwsLCFBISEoDRAgAAoL2xLEs1NTWKjIystb3VBvEuXbooPj5eeXl5ysjIUF5enuLj431aH26z2VrwsbgAAABA7SIiIupsa7VLUyRpz549ys7O1r///W916tRJubm5uuyyy0wPCwAAAGhQqw7iAAAAQGvVqu8jDgAAALRWBHEAAADAAII4AAAAYABBHAAAADCAIA4AAAAY0GrvIx7scnNzlZ+fr2+//VYbN25U3759JUl79+5Vdna2KisrFR0drdzcXPXp08fsYNuQ77//Xr/5zW904MABhYeHq3fv3po3b55iYmL0ySefKCcnRy6XSz169NDixYvVpUsX00NuE+6//34dPHhQNptNHTt21H//938rPj6e93uALFu2TEuXLvV+1vBe96/k5GSFh4fL4XBIkqZNm6brrruOefcjl8ulBQsW6J///KccDocGDRqk+fPn8xnjRwcPHtSUKVO8r48ePapjx47pgw8+YN5bkgW/2LFjh+V0Oq2f/vSn1pdffundPn78eGv9+vWWZVnW+vXrrfHjx5saYpv0/fffW++//7739cKFC61HH33Ucrvd1k033WTt2LHDsizLWr58uZWdnW1qmG3Ov//9b+/Pb731ljVq1CjLsni/B0JRUZF1zz33eD9reK/73w8/1y3LYt79bP78+dYTTzxheTwey7Is61//+pdlWXzGBNLjjz9uzZ0717Is5r0lsTTFTxITExUbG3vOtvLychUXFys1NVWSlJqaquLiYlVUVJgYYpsUHR2tpKQk7+tBgwbJ6XSqqKhIDodDiYmJkqTMzExt3rzZ1DDbnLOfVHvs2DGFhITwfg+A6upqzZs3T3PmzPFu471uBvPuP1VVVVq/fr2mTp2qkJAQSVLXrl35jAmg6upqbdy4UWPGjGHeWxhLUwKotLRU3bt3l91ulyTZ7XZ169ZNpaWliomJMTy6tsfj8Wj16tVKTk5WaWmp4uLivG0xMTHyeDzeX6uh+WbNmqV3331XlmXpxRdf5P0eAM8884zS09PVs2dP7zbe64Exbdo0WZalhIQEPfLII8y7H5WUlCg6OlrLli3T9u3bFRkZqalTpyoiIoLPmADZsmWLunfvriuvvFJFRUXMewviijjarPnz56tjx44aN26c6aG0C0888YTeeecdPfzww1q0aJHp4bR5H3/8sYqKipSVlWV6KO3On/70J/31r3/V66+/LsuyNG/ePNNDatPcbrdKSkrUv39/rV27VtOmTdODDz6o48ePmx5au/H6669rzJgxpofRJhHEAyg2NlaHDh2S2+2WdPrDpays7LwlLGi+3Nxc7d+/X08//bRsNptiY2PldDq97RUVFbLZbFyp8oNRo0Zp+/btuvjii3m/+9GOHTu0Z88eDRs2TMnJyfruu+90zz33aP/+/bzX/ezMezg8PFxZWVn66KOP+Izxo9jYWIWGhnqXQgwcOFCdO3dWREQEnzEBcOjQIe3YsUNpaWmSyDItjSAeQF26dFF8fLzy8vIkSXl5eYqPj+dXOS3sySefVFFRkZYvX67w8HBJ0oABA3Ty5El9+OGHkqQ1a9ZoxIgRJofZZlRVVam0tNT7esuWLbrwwgt5v/vZpEmTtG3bNm3ZskVbtmzRxRdfrJUrV2rixIm81/3o+PHjOnr0qCTJsiy98cYbio+P5zPGj2JiYpSUlKR3331X0um7j5WXl6tPnz58xgTAunXrdMMNN6hz586SyDItLcSyLMv0INqixx9/XAUFBTp8+LA6d+6s6Ohobdq0SXv27FF2drb+/e9/q1OnTsrNzdVll11merhtxldffaXU1FT16dNHERERkqSePXtq+fLl+uijjzR79uxzbi3WtWtXwyNu/Q4fPqz7779fJ06ckM1m04UXXqgZM2boyiuv5P0eQMnJyVqxYoX69u3Le92PSkpK9OCDD8rtdsvj8ejyyy/XY489pm7dujHvflRSUqKZM2eqsrJSoaGheuihh3TDDTfwGRMAKSkpmjVrlq6//nrvNua95RDEAQAAAANYmgIAAAAYQBAHAAAADCCIAwAAAAYQxAEAAAADCOIAAACAAQRxAAhi2dnZeuqppxrcLzk5We+9954kacWKFZo1a5a/h9YsEydO1Lp160wPAwCMCjU9AABAy5o8ebLpIZxj6dKl2r9/v5YsWeLd9uKLLxocEQAEB66IAwB8cuaR1gCAlkEQB4AgUlxcrNGjR2vw4MF66KGH5HK5vG1vv/22MjIylJiYqMzMTO3atavWGkuXLtW0adMknV4C8sorr5zTnp6eroKCAkmnn5A3YcIEDRkyRCkpKXrjjTe8+2VnZ2v27Nm69957NWjQIP3hD3/Qtddee04gLygoUHp6ep3ns3XrVj3//PN68803NXjwYO++48eP12uvvSZJWrt2rTIzM7VgwQIlJiZq2LBh+uijj7R27VrdcMMN+slPfnLOMpbq6mrl5ubqxhtv1LXXXqucnBydPHnSp/kFgGBCEAeAIFFdXa0pU6YoIyNDH3zwgUaMGOENzMXFxZo5c6bmzZun7du3a+zYsbr//vtVXV1db83U1FTl5eV5X3/99ddyOp268cYbdfz4cd19991KTU3Ve++9p6eeekpz587V119/7d0/Ly9PkydP1kcffaTx48crOjpa27Zt87Zv2LBBo0aNqrP/66+/Xr/85S9188036+OPP9Zf//rXWvf77LPPdMUVV2j79u1KTU3VI488os8//1xvvfWWFi9erHnz5qmqqkqStGTJEu3du1fr169XQUGBysrKtHz58oYnGACCDEEcAILEp59+qpqaGv3iF79QWFiYRowYoauuukqS9Oc//1ljx47VwIEDZbfbNXr0aIWFhemTTz6pt+ZNN92kXbt26dtvv5Ukbdy4UcOHD1d4eLjeeecd9ejRQ2PGjFFoaKj69++vlJQUbd682Xv8sGHDlJCQIJvNJofDoVGjRnnDdGVlpbZt26bU1NRmn3vPnj01ZswY2e12jRw5UqWlpZoyZYrCw8M1dOhQhYeH68CBA7IsS6+++qpmzpyp6OhoRUVF6Ze//KU2bdrU7DEAQKDxZU0ACBJlZWXq3r27QkJCvNvi4uIkSU6nU+vXrz9nmUlNTY3KysrqrRkVFaUbbrhBmzZt0qRJk5SXl6fHH39ckvTtt9/qs88+U2Jiond/t9t9zlKT2NjYc+plZGTo5ptv1vHjx/Xmm28qMTFR3bp1a/pJ/z9dunTx/hwRESFJ6tq1q3ebw+FQVVWVKioqdOLECd16663eNsuy5PF4mj0GAAg0gjgABImLLrpIhw4dkmVZ3jDudDrVq1cvxcbGavLkybrvvvsaXTc1NVXLli3TNddcI5fLpaSkJEmnQ/Y111yjP/zhDz7X6t69uwYPHqyCggJt2LBBd955Z4PHnP0fi+bq3LmzIiIitGnTJnXv3r3F6gKACSxNAYAgMWjQIIWGhuqll15STU2NCgoK9Pnnn0uSbr/9dq1Zs0affvqpLMvS8ePH9c477+jYsWMN1r3hhhvkdDr17LPPauTIkbLZTn/033jjjdq3b5/Wr1+vmpoa1dTU6LPPPtOePXvqrZeRkaGVK1dq9+7d+tnPftZg/126dNG3337bIletbTabbr/9di1YsEDl5eWSpEOHDukf//hHs2sDQKARxAEgSISHh2vp0qVat26dhgwZojfeeEPDhw+XJF111VWaP3++5s2bp2uuuUY/+9nPtHbtWp/rDh8+XO+9994567mjoqK0cuVKvfHGG7ruuus0dOhQLVmypMEvgA4fPlzffvuthg8frg4dOjTY/4gRIyRJSUlJGj16tE9jrs/06dPVu3dv3XHHHbr66qt11113ae/evc2uCwCBFmJZlmV6EACA1uWmm27SvHnzdO2115oeCgC0WlwRBwA0Sn5+vkJCQvQf//EfpocCAK0aX9YEAPhs/Pjx+vrrr7Vo0SLvWnPp9IODCgsLz9v/l7/8pSZPnhzIIQJAq8HSFAAAAMAAlqYAAAAABhDEAQAAAAMI4gAAAIABBHEAAADAAII4AAAAYABBHAAAADDg/wMzNmGer89uqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data = experiment.query('experiment_group == \"test\"'), hue = 'district', bins = 25, x = 'delivery_time')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посчитайте количество наблюдений в тестовой и контрольной группе. \n",
    "На сколько по модулю отличается количество наблюдений в тесте и контроле?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs(experiment.query('experiment_group == \"control\"').order_id.count() - experiment.query('experiment_group == \"test\"').order_id.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>experiment_group</th>\n",
       "      <th>order_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>control</td>\n",
       "      <td>10092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test</td>\n",
       "      <td>10104</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  experiment_group  order_id\n",
       "0          control     10092\n",
       "1             test     10104"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiment.groupby('experiment_group', as_index = False).order_id.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "experiment_group\n",
       "control    10092\n",
       "test       10104\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Другой способ:\n",
    "experiment.groupby('experiment_group').size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь давайте изучим наши распределения. \n",
    "\n",
    "Проверим выполнение условий на нормальность и посчитаем стандартное отклонение в тесте и в контроле.\n",
    "Вообще для выборок таких колоссальных размеров мы не обязаны это делать - в отсутствие слишком уж выраженной асимметрии и выбросов в данных t-тест и так работать будет нормально, но посмотреть лишний раз всё равно можно "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Обычно нормальность тестируют с помощью теста Шапиро-Уилка (scipy.stats.shapiro()), однако на больших выборках этот тест слишком рьяно находит отклонения от нормальности! \n",
    "\n",
    "Поэтому используйте функцию scipy.stats.normaltest() - она больше адаптирована к большим выборкам:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "experiment_group\n",
       "control    9.990017\n",
       "test       9.883308\n",
       "Name: delivery_time, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# для начала стандартное отклонение\n",
    "\n",
    "experiment.groupby(\"experiment_group\").delivery_time.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NormaltestResult(statistic=0.25349755437872584, pvalue=0.8809549607809654)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiment_control = experiment.query('experiment_group == \"control\"').delivery_time\n",
    "\n",
    "st.normaltest(experiment_control)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NormaltestResult(statistic=7.024157092641915, pvalue=0.02983483685333173)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiment_test = experiment.query('experiment_group == \"test\"').delivery_time\n",
    "\n",
    "st.normaltest(experiment_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>W</th>\n",
       "      <th>pval</th>\n",
       "      <th>normal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>control</th>\n",
       "      <td>0.253498</td>\n",
       "      <td>0.880955</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test</th>\n",
       "      <td>7.024157</td>\n",
       "      <td>0.029835</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                W      pval  normal\n",
       "control  0.253498  0.880955    True\n",
       "test     7.024157  0.029835   False"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# через пакет pingouin \n",
    "pg.normality(data=experiment, dv=\"delivery_time\", \n",
    "                   group=\"experiment_group\", method=\"normaltest\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ShapiroResult(statistic=0.9974725842475891, pvalue=0.12411931157112122)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# вариант с сэмплированием\n",
    "\n",
    "st.shapiro(experiment.query(\"experiment_group == 'control'\").delivery_time.sample(1000, random_state=17))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ShapiroResult(statistic=0.9975479245185852, pvalue=0.14013493061065674)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.shapiro(experiment.query(\"experiment_group == 'test'\").delivery_time.sample(1000, random_state=17))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1e286b1278>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# вариант с qq-плотами\n",
    "\n",
    "pg.qqplot(experiment.query(\"experiment_group == 'control'\").delivery_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1e2aab8240>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pg.qqplot(experiment.query(\"experiment_group == 'test'\").delivery_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь пришло время сравнить средние в тесте и контроле. \n",
    "\n",
    "Здесь уже не нужно пользоваться методом .sample() — он нам нужен был только для теста Шапиро-Уилка, остальные тесты надо проводить на полных данных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-43.03582663183714, pvalue=0.0)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(experiment_test, experiment_control)\n",
    "\n",
    "# через пингвин:\n",
    "# pg.ttest(x=experiment_test, y=experiment_control)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=33985060.0, pvalue=0.0)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#попробуем для сравнения Манна-Уитни\n",
    "\n",
    "st.mannwhitneyu(x=experiment_test, y=experiment_control)\n",
    "# pg.mwu(x=experiment_test, y=experiment_control)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-42.16055367343425, pvalue=0.0)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.ttest_ind(a=experiment_test, b=experiment_control, trim=0.2)\n",
    "# ттест Юэна, убирает выбросы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-43.03582663183714, pvalue=0.0)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# permutation t-test (перестановочный тест)\n",
    "st.ttest_ind(a=experiment_test, b=experiment_control, permutations = 1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На сколько процентов изменилось среднее время доставки в тестовой группе по сравнению с контрольной (округлите до сотых)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.35"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs(((experiment_test.mean() - experiment_control.mean()) / experiment_control.mean() * 100).round(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Что в итоге? Катим? \n",
    "\n",
    "- Не катим, так как среднее время доставки статистически значимо не изменилось.\n",
    "- Да, раскатываем новый алгоритм на всех, так как среднее время доставки статистически значимо изменилось. Среднее время доставки в тесте больше, чем в контроле.\n",
    "- **Да, раскатываем новый алгоритм на всех, так как среднее время доставки статистически значимо изменилось. Среднее время доставки в тесте меньше, чем в контроле.**\n",
    "- Да, раскатываем на всех, новый алгоритм напрямую влияет на скорость доставки."
   ]
  }
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