number-plate-study/модуль 1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       v131      v132      v133  v134\n",
      "0 -4.524858  0.579889  7.180352    15\n",
      "1  2.453333 -0.339670  6.720415     7\n",
      "2 -4.804977  3.298895  6.179200    11\n",
      "3 -0.664316  0.805015  7.302714     9\n",
      "4  7.296641  4.166122  7.505439     9\n"
     ]
    }
   ],
   "source": [
    "# Загрузка данных\n",
    "data = pd.read_excel('данные модуль 1.xlsx')\n",
    "\n",
    "# Предварительный просмотр данных\n",
    "print(data.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "V1 = data['v131']\n",
    "V2 = data['v132']\n",
    "V3 = data['v133']\n",
    "V4 = data['v134']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, axs = plt.subplots(2, 2, figsize=(12, 10))\n",
    "\n",
    "sns.histplot(V1, kde=True, ax=axs[0, 0])\n",
    "axs[0, 0].set_title('Гистограмма V1')\n",
    "\n",
    "sns.histplot(V2, kde=True, ax=axs[0, 1])\n",
    "axs[0, 1].set_title('Гистограмма V2')\n",
    "\n",
    "sns.histplot(V3, kde=True, ax=axs[1, 0])\n",
    "axs[1, 0].set_title('Гистограмма V3')\n",
    "\n",
    "sns.histplot(V4, kde=True, ax=axs[1, 1])\n",
    "axs[1, 1].set_title('Гистограмма V4')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def descriptive_stats(data):\n",
    "    stats_dict = {\n",
    "        'Объем выборки (n)': len(data),\n",
    "        'Среднее': np.mean(data),\n",
    "        'Медиана': np.median(data),\n",
    "        'Дисперсия': np.var(data, ddof=1),\n",
    "        'Стандартное отклонение': np.std(data, ddof=1),\n",
    "        'Коэффициент асимметрии': stats.skew(data),\n",
    "        'Коэффициент эксцесса': stats.kurtosis(data),\n",
    "        'Минимальное значение': np.min(data),\n",
    "        'Максимальное значение': np.max(data),\n",
    "        'Размах выборки': np.ptp(data),\n",
    "        'Нижняя квартиль (Q1)': np.percentile(data, 25),\n",
    "        'Верхняя квартиль (Q3)': np.percentile(data, 75),\n",
    "    }\n",
    "    return stats_dict\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Статистические характеристики V1:\n",
      "Объем выборки (n): 200\n",
      "Среднее: 3.042330651615597\n",
      "Медиана: 3.24236251866\n",
      "Дисперсия: 17.566849111278266\n",
      "Стандартное отклонение: 4.1912825138945555\n",
      "Коэффициент асимметрии: -0.10466907194323638\n",
      "Коэффициент эксцесса: -0.0038508159993266844\n",
      "Минимальное значение: -9.04243513703\n",
      "Максимальное значение: 13.9581583142\n",
      "Размах выборки: 23.00059345123\n",
      "Нижняя квартиль (Q1): 0.33781273369275\n",
      "Верхняя квартиль (Q3): 5.8620395763725\n",
      "\n",
      "Статистические характеристики V2:\n",
      "Объем выборки (n): 200\n",
      "Среднее: 1.0480258893092393\n",
      "Медиана: 1.12799311818\n",
      "Дисперсия: 3.933229643500426\n",
      "Стандартное отклонение: 1.9832371626964906\n",
      "Коэффициент асимметрии: -0.19789624679145457\n",
      "Коэффициент эксцесса: -0.18279648031150364\n",
      "Минимальное значение: -4.5922700602\n",
      "Максимальное значение: 5.57765925343\n",
      "Размах выборки: 10.16992931363\n",
      "Нижняя квартиль (Q1): -0.415424220853\n",
      "Верхняя квартиль (Q3): 2.4777878204975\n",
      "\n",
      "Статистические характеристики V3:\n",
      "Объем выборки (n): 200\n",
      "Среднее: 6.5195654172197\n",
      "Медиана: 6.464160536565\n",
      "Дисперсия: 0.7546047389961228\n",
      "Стандартное отклонение: 0.8686798829235789\n",
      "Коэффициент асимметрии: 0.019544398868115844\n",
      "Коэффициент эксцесса: -1.2254735902540985\n",
      "Минимальное значение: 5.00216182551\n",
      "Максимальное значение: 7.99016901628\n",
      "Размах выборки: 2.9880071907700003\n",
      "Нижняя квартиль (Q1): 5.7534552626524995\n",
      "Верхняя квартиль (Q3): 7.271182201435\n",
      "\n",
      "Статистические характеристики V4:\n",
      "Объем выборки (n): 200\n",
      "Среднее: 10.525\n",
      "Медиана: 11.0\n",
      "Дисперсия: 9.949120603015075\n",
      "Стандартное отклонение: 3.1542226622442295\n",
      "Коэффициент асимметрии: 0.5104487794952168\n",
      "Коэффициент эксцесса: 1.9103281524427569\n",
      "Минимальное значение: 2\n",
      "Максимальное значение: 25\n",
      "Размах выборки: 23\n",
      "Нижняя квартиль (Q1): 8.75\n",
      "Верхняя квартиль (Q3): 12.0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "stats_V1 = descriptive_stats(V1)\n",
    "stats_V2 = descriptive_stats(V2)\n",
    "stats_V3 = descriptive_stats(V3)\n",
    "stats_V4 = descriptive_stats(V4)\n",
    "\n",
    "\n",
    "print(\"Статистические характеристики V1:\")\n",
    "for k, v in stats_V1.items():\n",
    "    print(f\"{k}: {v}\")\n",
    "\n",
    "print(\"\\nСтатистические характеристики V2:\")\n",
    "for k, v in stats_V2.items():\n",
    "    print(f\"{k}: {v}\")\n",
    "\n",
    "print(\"\\nСтатистические характеристики V3:\")\n",
    "for k, v in stats_V3.items():\n",
    "    print(f\"{k}: {v}\")\n",
    "\n",
    "print(\"\\nСтатистические характеристики V4:\")\n",
    "for k, v in stats_V4.items():\n",
    "    print(f\"{k}: {v}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Оценка μ для V2: 1.0480258893092393\n",
      "Оценка σ² для V2: 3.933229643500426\n"
     ]
    }
   ],
   "source": [
    "\n",
    "mu_V1 = np.mean(V2)\n",
    "sigma2_V1 = np.var(V2, ddof=1)\n",
    "print(f\"Оценка μ для V2: {mu_V1}\")\n",
    "print(f\"Оценка σ² для V2: {sigma2_V1}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Оценка a для V3: 5.00216182551\n",
      "Оценка b для V3: 7.99016901628\n"
     ]
    }
   ],
   "source": [
    "a_V3 = np.min(V3)\n",
    "b_V3 = np.max(V3)\n",
    "print(f\"Оценка a для V3: {a_V3}\")\n",
    "print(f\"Оценка b для V3: {b_V3}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Оценка λ для V4: 10.525\n"
     ]
    }
   ],
   "source": [
    "lambda_V3 = np.mean(V4)\n",
    "print(f\"Оценка λ для V4: {lambda_V3}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Доверительный интервал для μ (V1): (0.40023447725624794, 1.6958173013622306)\n",
      "Доверительный интервал для σ² (V1): (3.198187050029381, 4.947452667095656)\n",
      "Доверительный интервал для λ (V3): (10.027178240238207, 11.022821759761793)\n"
     ]
    }
   ],
   "source": [
    "n_V1 = len(V1)\n",
    "alpha = 0.03  # 1 - γ\n",
    "t_crit = stats.t.ppf(1 - alpha/2, n_V1 - 1)\n",
    "SE_mu = stats.sem(V1)\n",
    "CI_mu_V1 = (mu_V1 - t_crit * SE_mu, mu_V1 + t_crit * SE_mu)\n",
    "print(f\"Доверительный интервал для μ (V1): {CI_mu_V1}\")\n",
    "chi2_lower = stats.chi2.ppf(alpha/2, n_V1 - 1)\n",
    "chi2_upper = stats.chi2.ppf(1 - alpha/2, n_V1 - 1)\n",
    "CI_sigma2_V1 = ((n_V1 - 1) * sigma2_V1 / chi2_upper, (n_V1 - 1) * sigma2_V1 / chi2_lower)\n",
    "print(f\"Доверительный интервал для σ² (V1): {CI_sigma2_V1}\")\n",
    "z_crit = stats.norm.ppf(1 - alpha/2)\n",
    "SE_lambda = np.sqrt(lambda_V3 / n_V1)\n",
    "CI_lambda_V3 = (lambda_V3 - z_crit * SE_lambda, lambda_V3 + z_crit * SE_lambda)\n",
    "print(f\"Доверительный интервал для λ (V3): {CI_lambda_V3}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Статистика χ²: 1418738.7096036077\n",
      "Критическое значение χ²: 15.509089702796674\n",
      "Гипотеза о нормальном распределении для V1 отвергается.\n"
     ]
    }
   ],
   "source": [
    "observed_freq, bins = np.histogram(V1, bins='sturges')\n",
    "expected_freq = [n_V1 * (stats.norm.cdf(bins[i+1], mu_V1, np.sqrt(sigma2_V1)) - stats.norm.cdf(bins[i], mu_V1, np.sqrt(sigma2_V1))) for i in range(len(bins)-1)]\n",
    "\n",
    "chi_squared_stat = ((observed_freq - expected_freq) ** 2 / expected_freq).sum()\n",
    "df = len(bins) - 1 - 2  # число интервалов - 1 - число параметров\n",
    "chi_crit = stats.chi2.ppf(1 - alpha, df)\n",
    "\n",
    "print(f\"Статистика χ²: {chi_squared_stat}\")\n",
    "print(f\"Критическое значение χ²: {chi_crit}\")\n",
    "\n",
    "if chi_squared_stat < chi_crit:\n",
    "    print(\"Нет оснований отвергать гипотезу о нормальном распределении для V1.\")\n",
    "else:\n",
    "    print(\"Гипотеза о нормальном распределении для V1 отвергается.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Статистика K-S: 0.38088487239010693\n",
      "P-значение: 1.354956591550158e-26\n",
      "Гипотеза о нормальном распределении для V1 отвергается.\n"
     ]
    }
   ],
   "source": [
    "ks_stat, p_value = stats.kstest(V1, 'norm', args=(mu_V1, np.sqrt(sigma2_V1)))\n",
    "print(f\"Статистика K-S: {ks_stat}\")\n",
    "print(f\"P-значение: {p_value}\")\n",
    "\n",
    "if p_value > alpha:\n",
    "    print(\"Нет оснований отвергать гипотезу о нормальном распределении для V1.\")\n",
    "else:\n",
    "    print(\"Гипотеза о нормальном распределении для V1 отвергается.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Статистика t-теста: 6.082561360944136\n",
      "P-значение: 3.813205394708216e-09\n",
      "Гипотеза о равенстве средних X и Y отвергается.\n",
      "Статистика F-теста: 4.46626581804271\n",
      "Критические значения F: 0.7344284998995741, 1.3616029336235456\n",
      "Гипотеза о равенстве дисперсий X и Y отвергается.\n",
      "Статистика U-теста: 27125.0\n",
      "P-значение: 7.169589050726635e-10\n",
      "Гипотеза о равенстве распределений X и Y отвергается.\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "module 'scipy.stats' has no attribute 'binom_test'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[30], line 42\u001b[0m\n\u001b[0;32m     39\u001b[0m n \u001b[38;5;241m=\u001b[39m n_positive \u001b[38;5;241m+\u001b[39m n_negative\n\u001b[0;32m     41\u001b[0m \u001b[38;5;66;03m# Используем биномиальный тест\u001b[39;00m\n\u001b[1;32m---> 42\u001b[0m p_value \u001b[38;5;241m=\u001b[39m \u001b[43mstats\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbinom_test\u001b[49m(n_positive, n, \u001b[38;5;241m0.5\u001b[39m)\n\u001b[0;32m     43\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mP-значение критерия знаков: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mp_value\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     45\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m p_value \u001b[38;5;241m>\u001b[39m alpha:\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'scipy.stats' has no attribute 'binom_test'"
     ]
    }
   ],
   "source": [
    "t_stat, p_value = stats.ttest_ind(V1, V2, equal_var=False)\n",
    "print(f\"Статистика t-теста: {t_stat}\")\n",
    "print(f\"P-значение: {p_value}\")\n",
    "\n",
    "if p_value > alpha:\n",
    "    print(\"Нет оснований отвергать гипотезу о равенстве средних X и Y.\")\n",
    "else:\n",
    "    print(\"Гипотеза о равенстве средних X и Y отвергается.\")\n",
    "\n",
    "f_stat = np.var(V1, ddof=1) / np.var(V2, ddof=1)\n",
    "df1 = len(V1) - 1\n",
    "df2 = len(V2) - 1\n",
    "f_crit_lower = stats.f.ppf(alpha/2, df1, df2)\n",
    "f_crit_upper = stats.f.ppf(1 - alpha/2, df1, df2)\n",
    "\n",
    "print(f\"Статистика F-теста: {f_stat}\")\n",
    "print(f\"Критические значения F: {f_crit_lower}, {f_crit_upper}\")\n",
    "\n",
    "if f_crit_lower < f_stat < f_crit_upper:\n",
    "    print(\"Нет оснований отвергать гипотезу о равенстве дисперсий X и Y.\")\n",
    "else:\n",
    "    print(\"Гипотеза о равенстве дисперсий X и Y отвергается.\")\n",
    "\n",
    "# Критерий Манна-Уитни\n",
    "u_stat, p_value = stats.mannwhitneyu(V1, V2, alternative='two-sided')\n",
    "print(f\"Статистика U-теста: {u_stat}\")\n",
    "print(f\"P-значение: {p_value}\")\n",
    "\n",
    "if p_value > alpha:\n",
    "    print(\"Нет оснований отвергать гипотезу о равенстве распределений X и Y.\")\n",
    "else:\n",
    "    print(\"Гипотеза о равенстве распределений X и Y отвергается.\")\n",
    "\n",
    "differences = V1[:len(V2)] - V2[:len(V1)]\n",
    "signs = np.sign(differences)\n",
    "n_positive = np.sum(signs > 0)\n",
    "n_negative = np.sum(signs < 0)\n",
    "n = n_positive + n_negative\n",
    "\n",
    "# Используем биномиальный тест\n",
    "p_value = stats.binom_test(n_positive, n, 0.5)\n",
    "print(f\"P-значение критерия знаков: {p_value}\")\n",
    "\n",
    "if p_value > alpha:\n",
    "    print(\"Нет оснований отвергать гипотезу о равенстве медиан X и Y.\")\n",
    "else:\n",
    "    print(\"Гипотеза о равенстве медиан X и Y отвергается.\")\n",
    "\n",
    "# Критерий Уилкоксона\n",
    "w_stat, p_value = stats.wilcoxon(V1[:len(V2)], V2)\n",
    "print(f\"Статистика W-теста: {w_stat}\")\n",
    "print(f\"P-значение: {p_value}\")\n",
    "\n",
    "if p_value > alpha:\n",
    "    print(\"Нет оснований отвергать гипотезу об однородности выборок X и Y.\")\n",
    "else:\n",
    "    print(\"Гипотеза об однородности выборок X и Y отвергается.\")\n"
   ]
  }
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