math-statistics/task2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c8c02636",
   "metadata": {
    "id": "c8c02636"
   },
   "source": [
    "# 2.2 Шаблон задания для СР (Занятие 2)\n",
    "\n",
    "1) Сначала скачайте файл `variant_*.csv` из генератора (HTML-страница).  \n",
    "2) Загрузите CSV рядом с ноутбуком (в Colab: через кнопку «📁 Файлы» в левой боковом меню. Затем нажмите на кнопку \"📤 Загрузить в сессионное хранилище\").  \n",
    "3) В коде ниже замените имя файла `variant_3.csv` на свой вариант и дополняйте строки с `...`.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a5a140f",
   "metadata": {
    "id": "9a5a140f"
   },
   "outputs": [],
   "source": [
    "# (Необязательно) Colab: загрузка файла variant_*.csv\n",
    "# from google.colab import files\n",
    "# files.upload()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4ef80898",
   "metadata": {
    "id": "4ef80898"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Объём выборки: n = 100\n",
      "\n",
      "первые 5: [44.6 47.1 50.3 50.6 51.8]\n",
      "последние 5: [89.5 90.5 90.7 91.7 94.5]\n",
      "Выборочные оценки:\n",
      "  Среднее:      x̄ = 69.76100000000001\n",
      "  Дисперсия:    s² = 129.99088787878787\n",
      "  Ст. откл.:    s = 11.401354651039844\n",
      "  Медиана:      x̃ = 68.15\n",
      "  Размах:       [44.6, 94.5]\n",
      "\n",
      "Правило Скотта:\n",
      "  Ширина интервала: h = 8.60\n",
      "  Число интервалов: k = 6\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 7 16 30 21 16 10]\n",
      "[44.6        52.91666667 61.23333333 69.55       77.86666667 86.18333333\n",
      " 94.5       ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Сравнение с истинными параметрами:\n",
      "  Истинное μ = 70, выборочное x̄ = 69.76100000000001\n",
      "  Истинное σ² = 121, выборочное s² = 129.99088787878787\n",
      "\n",
      "Вопрос: Почему выборочные оценки отличаются от истинных параметров?\n",
      "Ответ: Мы наблюдаем лишь часть генеральной совокупности, а не всю её.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "# -------------------------------------------------\n",
    "# ЗАДАНИЕ: Первичная обработка выборки\n",
    "# -------------------------------------------------\n",
    "# ШАГ 0: Загрузите свою выборку (замените номер файла на ваш вариант)\n",
    "data = np.loadtxt('variant_5.csv', delimiter=',')\n",
    "\n",
    "print(f\"Объём выборки: n = {len(data)}\")\n",
    "print()\n",
    "\n",
    "# -------------------------------------------------\n",
    "# ШАГ 1: Вариационный ряд\n",
    "# -------------------------------------------------\n",
    "# ДОПОЛНИТЕ КОД:\n",
    "sorted_data = np.sort(data)\n",
    "# Выведите первые 5 и последние 5 значений вариационного ряда\n",
    "print(f\"первые 5: {sorted_data[:5]}\")\n",
    "print(f\"последние 5: {sorted_data[-5:]}\")\n",
    "\n",
    "# -------------------------------------------------\n",
    "# ШАГ 2: Выборочные оценки\n",
    "# -------------------------------------------------\n",
    "# ДОПОЛНИТЕ КОД:\n",
    "x_bar = data.mean()       # среднее\n",
    "s2 = np.var(data, ddof=1) # дисперсия (несмещённая!)\n",
    "s = np.std(data, ddof=1)  # стандартное отклонение\n",
    "median = np.median(data)  # медиана\n",
    "x_min = np.min(data)      # минимум\n",
    "x_max = np.max(data)      # максимум\n",
    "\n",
    "print(\"Выборочные оценки:\")\n",
    "print(f\"  Среднее:      x̄ = {x_bar}\")\n",
    "print(f\"  Дисперсия:    s² = {s2}\")\n",
    "print(f\"  Ст. откл.:    s = {s}\")\n",
    "print(f\"  Медиана:      x̃ = {median}\")\n",
    "print(f\"  Размах:       [{x_min:.1f}, {x_max:.1f}]\")\n",
    "print()\n",
    "\n",
    "# -------------------------------------------------\n",
    "# ШАГ 3: Правило Скотта и гистограмма\n",
    "# -------------------------------------------------\n",
    "# ДОПОЛНИТЕ КОД:\n",
    "n = len(data)\n",
    "s = s  # уже посчитано в Шаге 2\n",
    "h = 3.5 * s * n**(-1/3)\n",
    "k = math.ceil((x_max - x_min) / h)  # число интервалов (округлить вверх)\n",
    "\n",
    "print(f\"Правило Скотта:\")\n",
    "print(f\"  Ширина интервала: h = {h:.2f}\")\n",
    "print(f\"  Число интервалов: k = {k}\")\n",
    "print()\n",
    "\n",
    "# Постройте ДВЕ гистограммы рядом:\n",
    "\n",
    "def draw_hist(title: str, bins: int) -> None:\n",
    "    plt.figure()\n",
    "    plt.hist(data, bins=bins, color='blue', edgecolor='black')\n",
    "    plt.axvline(x_bar, color='red', linestyle='dashed', linewidth=2, label=f'Среднее: {x_bar:.2f}')\n",
    "    plt.title(title)\n",
    "    plt.xlabel('Значения')\n",
    "    plt.ylabel('Частота')\n",
    "    plt.show()    \n",
    "\n",
    "#  а) с числом интервалов по Скотту\n",
    "draw_hist('Гистограмма с числом интервалов по Скотту', k)\n",
    "\n",
    "#  б) с фиксированным числом интервалов = 5\n",
    "draw_hist('Гистограмма с числом интервалов = 5', 5)\n",
    "\n",
    "# Добавьте на каждую вертикальную линию со средним значением\n",
    "\n",
    "# -------------------------------------------------\n",
    "# ШАГ 4: Полигон частот\n",
    "# -------------------------------------------------\n",
    "# ДОПОЛНИТЕ КОД:\n",
    "# Постройте полигон частот для интервального ряда\n",
    "# (используйте те же интервалы, что и в гистограмме по Скотту)\n",
    "\n",
    "counts, bin_edges = np.histogram(data, bins=k)\n",
    "bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.plot(bin_centers, counts, marker='o', linestyle='-', color='b')\n",
    "plt.title('Полигон частот')\n",
    "plt.xlabel('Значения')\n",
    "plt.ylabel('Частота')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# -------------------------------------------------\n",
    "# ШАГ 5: Эмпирическая функция распределения\n",
    "# -------------------------------------------------\n",
    "# ДОПОЛНИТЕ КОД:\n",
    "# Постройте график ЭФР с пунктирными вертикалями в точках скачков\n",
    "# и точками на горизонтальных участках\n",
    "\n",
    "cumulative = np.cumsum(counts) / len(data)\n",
    "\n",
    "x_ecdf = np.concatenate([[bin_edges[0]], bin_edges[1:]])\n",
    "y_ecdf = np.concatenate([[0], cumulative])\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.step(x_ecdf, y_ecdf, where='post', color='blue', linewidth=2)\n",
    "plt.plot(x_ecdf, y_ecdf, 'o', markersize=4, color='blue')\n",
    "for xi, yi_prev, yi in zip(bin_edges[1:], y_ecdf[:-1], y_ecdf[1:]):\n",
    "    plt.vlines(xi, yi_prev, yi, linestyles='dashed', colors='gray', linewidth=0.8)\n",
    "plt.title('Эмпирическая функция распределения')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('F(x)')\n",
    "plt.ylim(-0.05, 1.05)\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# -------------------------------------------------\n",
    "# ШАГ 6: Сравнение с истинными параметрами\n",
    "# -------------------------------------------------\n",
    "# Истинные параметры вашего варианта:\n",
    "mu_true = 70   # подставьте μ для вышего варианта\n",
    "sigma2_true = 121  # подставьте σ² для вышего варианта\n",
    "\n",
    "print(\"Сравнение с истинными параметрами:\")\n",
    "print(f\"  Истинное μ = {mu_true}, выборочное x̄ = {x_bar}\")\n",
    "print(f\"  Истинное σ² = {sigma2_true}, выборочное s² = {s2}\")\n",
    "print()\n",
    "print(\"Вопрос: Почему выборочные оценки отличаются от истинных параметров?\")\n",
    "print(\"Ответ: Мы наблюдаем лишь часть генеральной совокупности, а не всю её.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6362abbc-58b4-468d-81af-22fb9c0bda39",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "file_extension": ".py",
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