Ch08.ipynb

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    "from itertools import product\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "import scipy.stats\n",
    "import statsmodels.api as sm"
   ]
  },
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   "source": [
    "### Глава 8\n",
    "#### Анализ зависимостей\n",
    "\n",
    "##### УРАВНЕНИЕ РЕГРЕССИИ\n",
    "\n",
    "x - независимая переменная, y - зависимая переменная.  Набор значений у, соответствующих определенному значению х, обозначается у|х. $\\mu_{y|x}$ - среднее y в точке х.\n",
    "\n",
    "$\\mu_{y|x}=\\alpha+\\beta x$ - формулся для прямой средних.\n",
    "\n",
    "для применения методов регрессий необходимо:\n",
    "\n",
    "* Среднее значение $\\mu_{y|x}$ линейно зависит от х.\n",
    "* Для любого значения х значения у|х распределены нормально.\n",
    "* Стандартное отклонение $\\sigma_{y|x}$ одинаково при всех значениях х.\n",
    "\n",
    "##### ОЦЕНКА ПАРАМЕТРОВ УРАВНЕНИЯ РЕГРЕСИИ ПО ВЫБОРКЕ\n",
    "\n",
    "**Метод наименьших квадратов**\n",
    "\n",
    "$\\hat{y}=a+bx$\n",
    "\n",
    "$a=\\cfrac{\\sum{Y}\\sum{X^2}-\\sum{X}\\sum{XY}}{n\\sum{X^2}-(\\sum{Y})^2}$\n",
    "\n",
    "$b=\\cfrac{n\\sum{XY}-\\sum{X}\\sum{Y}}{n\\sum{X^2}-(\\sum{Y})^2}$\n",
    "\n",
    "где X и Y — значения независимой и зависимой переменных у n членов выборки.\n",
    "\n",
    "$S_{y|x} = \\sqrt{\\cfrac{\\sum(Y-(a+bX))^2}{n-2}}$ - остаточное стандартное отклонение.\n",
    "\n",
    "$S_{y|x}=\\sqrt{\\cfrac{n-1}{n-2}(S_Y^2-b^2 S_X^2)}$ - Связь $S_{y|x}$ со стандартными отклонениями $S_{y}$ и $S_{x}$ зависимой и независимой переменных.\n",
    "\n",
    "**Стандартные ошибки коэффициентов регрессии**\n",
    "\n",
    "$S_a=S_{y|x}\\sqrt{\\cfrac{1}{n}+\\cfrac{\\overline{X}^2}{(n-1)S_X^2}}$\n",
    "\n",
    "$S_b=\\cfrac{1}{\\sqrt{n-1}}\\cfrac{S_{y|x}}{S_X}$\n",
    "\n",
    "**Есть ли зависимость?**\n",
    "\n",
    "!!! Речь идет исключительно о линейной зависимости.\n",
    "\n",
    "* Вариант 1\n",
    "\n",
    "Критерий стьюдента: \n",
    "$t=\\cfrac{Выборочная оценка - Истинная величина}{Стандартная ошибка выборочной оценки}=\\cfrac{b-\\beta}{S_b}$\n",
    "\n",
    "Нулевая гипотеза верна если наклон равен нулю, т.е. $\\beta=0$ => $t=\\cfrac{b}{S_b}$. t сравнивается с критическим значением $t_\\alpha$ для выбранного уровня значимости $\\alpha$ и числа степеней свободы $ν = n – 2$. Если $t > t_\\alpha$, то $P<\\alpha$, т.е. зависимость статистически значима.\n",
    "\n",
    "* Вариант 2\n",
    "\n",
    "На основе доверительных интервалов.\n",
    "\n",
    "$b - t_\\alpha S_b < \\beta < b + t_\\alpha S_b$ - попадает ли ноль, в полученный интервал.\n",
    "\n",
    "**Доверительная область для линии регрессии**\n",
    "\n",
    "Стандартная ошибка регрессии: $S_{\\hat{y}}=S_{y|x}\\sqrt{\\cfrac{1}{n}+\\cfrac{(x-\\overline{X})^2}{(n-1)S_X^2}}$\n",
    "\n",
    "доверительный интервал для значения уравнения регрессии в точке х: $\\hat{y} - t_\\alpha S_{\\hat{y}} < y <\\hat{y} - t_\\alpha S_{\\hat{y}}$, где $t_\\alpha$ — критическое значение с $ν = n – 2$ степенями свободы, а $\\hat{y} = a + b x$ — значение уравнения регрессии в точке х. Т.о. получается уравнение для кривых, ограничивающих доверительную область линии регрессии.\n",
    "\n",
    "**Доверительная область для значений**\n",
    "\n",
    "Разброс значений складывается из разброса значений вокруг линии регресии и неопределенности положения самой этой линии. Характеристикой разброса значений вокруг линии регрессии\n",
    "является остаточное стандартное отклонение $S_{y|x}$, а неопределенности положения линии регрессии — стандартная ошибка регрессии $S_{\\hat{y}}$ . Дисперсия суммы двух величин равна сумме дисперсий, поэтому:\n",
    "\n",
    "$S_Y=\\sqrt{S_{y|x}^2+S_{\\hat{y}^2}}=S_{y|x}\\sqrt{1 + \\cfrac{1}{n} + \\cfrac{(x-\\overline{X})^2}{(n-1)S_X^2}}$\n",
    "\n",
    "Тогда доверительный интервал для зависимой переменной: $\\hat{y} - t_\\alpha S_Y < y <\\hat{y} - t_\\alpha S_Y$\n",
    "\n",
    "##### СРАВНЕНИЕ ДВУХ ЛИНИЙ РЕГРЕССИИ\n",
    "\n",
    "Часто требуется сравнить линии регрессии, рассчитанные по двум выборкам. Это можно сделать тремя способами.\n",
    "1. Сравнить коэффициенты наклона b\n",
    "\n",
    "$t=\\cfrac{b_1-b_2}{S_{b_1-b_2}}$, где $S_{b_1-b_2}$ - стандартная ошибка разности коэффициентов наклона.\n",
    "\n",
    "Сравнивается с критическим значением $t_\\alpha$ c $n_1+n_2-4$ степенями свободы.\n",
    "\n",
    "$S_{b_1-b_2}=\\sqrt{\\cfrac{S_{y|x_{общ}}^{2}}{(n_1-1)S_{x_1}^{2}}+\\cfrac{S_{y|x_{общ}}^{2}}{(n_2-1)S_{x_2}^{2}}}$, где $S_{y|x_{общ}}^{2} = \\cfrac{(n_1-2)S_{y|x_1}^{2}+(n_2-2)S_{y|x_2}^{2}}{n_1+n_2-4}$\n",
    "\n",
    "Если обе регрессии оценены по одинаковому числу наблюдений, то стандартная ошибка разности: $S_{b_1-b_2}=\\sqrt{S_{b_1}^2+S_{b_2}^2}$\n",
    "\n",
    "2. Сравнить коэффициенты сдвига a\n",
    "\n",
    "*Точно так же как и с кэффициентами b*\n",
    "\n",
    "3. Сравнить линии в целом\n",
    "\n",
    "Сравнить две линии регрессии — значит оценить вероятность нулевой гипотезы о совпадении линий.\n",
    "\n",
    "Для этого объединяеются обе выборки в одну и для неё строится линия регрессии. Если линии регрессии для двух выборок близки, остаточная дисперсия ($S_{y|x}^2$) при этом существенно не изменится.\n",
    "\n",
    "И наоборот, если они различаются, то совпадение точек и линии ухудшится и остаточная дисперсия возрастет. Порядок действий таков:\n",
    "\n",
    "* Построить прямую регресии для каждой из выборок.\n",
    "* По остаточным дисперсиям каждой из регрессий ($S_{y|x_1}^{2}$ и $S_{y|x_2}^{2}$) вычислить объединенную оценку остаточной дисперсии ($S_{y|x_{общ}}^{2}$).\n",
    "* Объединить обе выборки. Построить прямую регрессии для получившейся выборки и вычислить остаточную дисперсию ($S_{y|x_{един}}^{2}$)\n",
    "* Вычислить «выигрыш» от использования двух раздельных регрессий. Мерой выигрыша служит величина:\n",
    "\n",
    "$S_{y|x_{в}}^{2} = \\cfrac{(n_1+n_2-2)S_{y|x_{един}}^{2}-(n_1+n_2-4)S_{y|x_{общ}}^{2}}{2}$\n",
    "* по $S_{y|x_{в}}^{2}$ и $S_{y|x_{общ}}^{2}$ вычислить критерий F: $F = \\cfrac{S_{y|x_{в}}^{2}}{S_{y|x_{общ}}^{2}}$\n",
    "* Сравнить вычисленное значение с критическим значением F для числа степеней свободы $\\nu_{меж} = 2$ и $ν_{вну} = n_1 + n_2 – 4$. Если полученное значение больше критического, то гипотеза о совпадении линий регрессии должна быть отклонена.\n",
    "\n",
    "##### КОРРЕЛЯЦИЯ\n",
    "\n",
    "**Коэффициент корреляции Пирсона**\n",
    "Предназначен для описания линейной связи количественных признаков (требует нормальности распределения).\n",
    "\n",
    "*Зачем? Почему не достаточно регрессионного анализа, а в качестве меры тесноты использовать остаточное стандартное отклонение?* - Если поменять местами зависимую и независимую\n",
    "переменные, то остаточное стандартное отклонение, как и другие показатели регрессионного анализа, будет иным (линии регрессии тоже будут разными!!!). Асимметричность регрессионного анализа — вот что мешает непосредственно использовать его для характеристики силы связи. Коэффициент корреляции, хотя его идея вытекает из регрессионного анализа, свободен от этого недостатка.\n",
    "\n",
    "$r=\\cfrac{\\sum{(X-\\overline{X})(Y-\\overline{Y})}}{\\sqrt{\\sum{(X-\\overline{X})^2}\\sum{(Y-\\overline{Y})^2}}}$ - коэффициент корреляции.\n",
    "\n",
    "Коэффициент корреляции принимает значения от –1 до +1. Чем теснее связь, тем больше абсолютная величина коэффициента корреляции. Знак показывает направление связи. При r > 0 говорят о прямой корреляции (с увеличением одной переменной другая также возрастает), при r < 0 — об обратной (с увеличением одной переменной другая уменьшается).\n",
    "\n",
    "**Связь регрессии и корреляции**\n",
    "\n",
    "Уравнение регрессии строится так, чтобы минимизировать сумму квадратов отклонений от линии регрессии. $S_{ост}$ - остаточная сумма квадратов.\n",
    "\n",
    "Сумму квадратов отклонений значений зависимой переменной Y от ее среднего обозначается $S_{общ}$. Тогда: $r^2 = 1+\\cfrac{S_{ост}}{S_{общ}}$ - коэффициент детерминации (это просто квадрат коэффициента корреляции). \n",
    "\n",
    "Коэффициент детерминации показывает силу связи, но не ее направленность. Если значения зависимой переменной лежат на прямой регрессии, то $S_{ост} = 0$, и тем самым r = +1 или r = –1, то есть существует линейная связь зависимой и независимой переменной. По любому значению независимой переменной можно совершенно точно предсказать значение зависимой переменной. Напротив, если переменные вообще не связаны между собой, то $S_{ост} = S_{общ}$. Тогда r = 0.\n",
    "\n",
    "Видно также, что коэффициент детерминации равен той доле общей дисперсии $S_{общ}$, которая обусловлена или, как говорят, объясняется линейной регрессией. \n",
    "\n",
    "Остаточная сумма квадратов $S_{ост}$ связана с остаточной дисперсией $S_{y|x}^{2}$ соотношением $S_{ост} = (n – 2)S_{y|x}^{2}$, а общая сумма квадратов $S_{общ}$ с дисперсией $S_{y}^{2}$ соотношением $S_{общ}=(n-1)S_{y}^{2}$. В таком случае \n",
    "\n",
    "$r^2 = 1-\\cfrac{n-2}{n-1}\\cfrac{S_{y|x}^{2}}{S_{y}^{2}}$\n",
    "\n",
    "!!! Коэффициент корреляции отражает тесноту линейной связи переменных. Однако если речь идет о предсказании значения одной переменной по значению другой, на коэффициент корреляции не следует слишком полагаться. Поэтому даже при большом коэффициенте корреляции обязательно нужно вычислять доверительную область значений.\n",
    "\n",
    "**Статистическая значимость корреляции**\n",
    "\n",
    "Соотношение коэффициента корреляции и коэффициента наклона прямой регрессии b: $r = b\\cfrac{S_X}{S_Y}$, где b — коэффициент наклона прямой регрессии, $S_X$ и $S_Y$ — стандартные отклонения переменных.\n",
    "\n",
    "Поскольку из b = 0 следует r = 0, гипотеза об отсутствии корреляции равнозначна гипотезе о нулевом наклоне прямой регрессии. Поэтому для оценки статистической значимости корреляции можно воспользоваться формулой для оценки статистической значимости отличия b от нуля:\n",
    "\n",
    "$t=\\cfrac{b}{S_b}$, $\\nu=n-2$\n",
    "\n",
    "или $t=\\cfrac{r}{\\sqrt{\\cfrac{1-r^2}{n-2}}}$, $\\nu=n-2$\n",
    "\n",
    "Формулы тождественны хотя вывод не быстр.\n",
    "\n",
    "**Коэффициентранговой корреляции Спирмена**\n",
    "Можно использовать, когда связь нелинейна (не всегда \"U\" образную кривую нельзя) — и не только для количественных, но и для порядковых признаков. Это непараметрический метод, он не требует какого-либо определенного типа распределения. Обозначается $r_s$\n",
    "\n",
    "Идея проста:  Нужно упорядочить данные по возрастанию и заменить реальные значения их рангами. Затем, беря вместо самих значений их ранги, рассчитывают обычный коэффициент корреляции Пирсона. Это и будет коэффициент ранговой корреляции Спирмена. Его можно рассчитать и проще:\n",
    "\n",
    "$r_s = 1 - \\cfrac{6\\sum{d^2}}{n^3-n}$, где d — разность рангов для каждого члена выборки\n",
    "\n",
    "Как быть, если в ряду встретятся одинаковые значения? Им следует присвоить один и тот же ранг, равный среднему занимаемых ими мест.\n",
    "\n",
    "Уровень значимости коэффициента корреляции Спирмена для n < 50, можно найти в таблицах, а при n > 50 нужно воспользоваться критерием Стьюдента: $t=\\cfrac{r_s}{\\sqrt{\\cfrac{1-r_s^2}{n-2}}}$, $\\nu=n-2$\n",
    "\n",
    "##### ЧУВСТВИТЕЛЬНОСТЬ КОЭФФИЦИЕНТА КОРРЕЛЯЦИИ\n",
    "\n",
    "Величина $Z=\\cfrac{1}{2}ln(\\cfrac{1+r}{1-r})$ имеет нормальное распределение со стандартным отклонением $\\sigma_Z=\\sqrt{\\cfrac{1}{n-3}}$.\n",
    "\n",
    "Поэтому величина $z=\\cfrac{Z}{\\sigma_Z}$ в отсутствие корреляции имеет стандартное нормальное распределение со средним, равным нулю.\n",
    "\n",
    "Можно показать, что чувствительность $1 – \\beta$, необходимая для обнаружения корреляции, не меньшей $\\rho$, при уровне значимости $\\alpha$ и при объеме выборки n равна площади под кривой стандартного нормального распределения правее точки: $z_{1-\\beta}=z_{\\alpha}-\\cfrac{Z_{\\rho}}{\\sqrt{\\cfrac{1}{n-3}}}$ (формула для нахождения чувствительности по известному объему выборки)\n",
    "\n",
    "Если нужно найти объем выборки, при котором достигалась бы чувствительность $1 – \\rho$, то, разрешив это уравнение относительно n, получим: $n=(\\cfrac{z_{\\alpha}-z_{1-\\beta}}{Z_{\\rho}})^2+3$\n",
    "\n",
    "##### СРАВНЕНИЕ ДВУХ СПОСОБОВ ИЗМЕРЕНИЯ: МЕТОД БЛЭНДА—АЛТМАНА\n",
    "\n",
    "Пусть с помощью двух методов получены две серии измерений. Казалось бы, ничто не мешает применить регрессионный анализ или рассчитать коэффициент корреляции. Однако регрессионный анализ неприменим уже потому, что его результаты зависят от того, какую переменную считать независимой, а какую зависимой (т.к. сравнивается не с эталоном).\n",
    "\n",
    "Идея метода (описательный метод оценки согласованности измерений): Для каждой — выполненной одним и другим способами — пары измерений вычисляется их разность. Находится средняя величина и стандартное отклонение разности. Средняя разность характеризует систематическое расхождение, а стандартное отклонение — степень разброса результатов. Далее, если в качестве оценки измеряемого признака взять среднее значение пары измерений, то можно определить, зависит ли расхождение от величины признака."
   ]
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   "source": [
    "### ЗАДАЧИ"
   ]
  },
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   "source": [
    "**8.1.** \n",
    "\n",
    "Постройте графики для приведенных наборов данных. Найдите линии регрессии и коэффициенты корреляции. Нанесите данные и прямые регрессии на графики. Что в этих\n",
    "трех случаях общего, в чем различия?\n",
    "\n",
    "X1: 30, 30, 40, 40\n",
    "\n",
    "Y1: 37, 47, 50, 60\n",
    "\n",
    "X2: 30, 30, 40, 40, 20, 20, 50, 50\n",
    "\n",
    "Y2: 37, 47, 50, 60, 25, 35, 62, 72\n",
    "\n",
    "X3: 30, 30, 40, 40, 20, 20, 50, 50, 10, 10, 60, 60\n",
    "\n",
    "Y3: 37, 47, 50, 60, 25, 35, 62, 72, 13, 23, 74, 84"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = np.array([30, 30, 40, 40])\n",
    "y1 = np.array([37, 47, 50, 60])\n",
    "\n",
    "x2 = np.array([30, 30, 40, 40, 20, 20, 50, 50])\n",
    "y2 = np.array([37, 47, 50, 60, 25, 35, 62, 72])\n",
    "\n",
    "x3 = np.array([30, 30, 40, 40, 20, 20, 50, 50, 10, 10, 60, 60])\n",
    "y3 = np.array([37, 47, 50, 60, 25, 35, 62, 72, 13, 23, 74, 84])\n",
    "\n",
    "xmin, xmax = 10, 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize=(20, 6), sharex=True, sharey=True)\n",
    "c = [\"red\", \"green\", \"blue\"]\n",
    "\n",
    "for i, (x, y) in enumerate([(x1, y1), (x2, y2), (x3, y3)]):\n",
    "    reg = LinearRegression().fit(x.reshape(-1,1), y)\n",
    "    a, b, r = reg.intercept_, reg.coef_[0], np.corrcoef(x, y)[0, 1]\n",
    "\n",
    "    # print(f\"{i+1}: a = {a:.2f}, b = {b:.2f}, r = {r:.2f}\")\n",
    "\n",
    "    axs[i].scatter(x, y, c=c[i])\n",
    "    axs[i].set_title(f\"{i+1}: a = {a:.2f}, b = {b:.2f}, r = {r:.2f}\", c=c[i])\n",
    "    \n",
    "    # чтоб визуально показать различия в наклоне\n",
    "    for j in range(3):\n",
    "        axs[j].plot([xmin, xmax], np.array([xmin, xmax])*b + a, c=c[i], linestyle=\":\" if i!=j else \"-\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "С увеличением диапазона данных растет и коэффициент корреляции."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**8.2.**\n",
    "\n",
    "Постройте графики для двух наборов данных. Найдите для каждого линию регрессии и коэффициент корреляции.\n",
    "\n",
    "X1: 15, 15, 20, 20, 25, 25, 30, 30, 60\n",
    "\n",
    "Y1: 19, 29, 25, 35, 31, 41, 37, 47, 40\n",
    "\n",
    "X2: 20, 20, 30, 30, 40, 40, 40, 40, 50, 50, 60, 60\n",
    "\n",
    "Y2: 21, 31, 18, 28, 15, 25, 75, 85, 65, 75, 55, 65\n",
    "\n",
    "Нанесите полученные прямые регрессии на графики с исходными данными. Обсудите результаты."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = np.array([15, 15, 20, 20, 25, 25, 30, 30, 60])\n",
    "y1 = np.array([19, 29, 25, 35, 31, 41, 37, 47, 40])\n",
    "\n",
    "x2 = np.array([20, 20, 30, 30, 40, 40, 40, 40, 50, 50, 60, 60])\n",
    "y2 = np.array([21, 31, 18, 28, 15, 25, 75, 85, 65, 75, 55, 65])\n",
    "\n",
    "xmin, xmax = 15, 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(16, 7), sharex=True, sharey=True)\n",
    "c = [\"red\", \"green\", \"blue\"]\n",
    "\n",
    "for i, (x, y) in enumerate([(x1, y1), (x2, y2)]):\n",
    "    reg = LinearRegression().fit(x.reshape(-1,1), y)\n",
    "    a, b, r = reg.intercept_, reg.coef_[0], np.corrcoef(x, y)[0, 1]\n",
    "\n",
    "    # print(f\"{i+1}: a = {a:.2f}, b = {b:.2f}, r = {r:.2f}\")\n",
    "\n",
    "    axs[i].scatter(x, y, c=c[i])\n",
    "    axs[i].plot([xmin, xmax], np.array([xmin, xmax])*b + a, c=c[i])\n",
    "\n",
    "    axs[i].set_title(f\"{i+1}: a = {a:.2f}, b = {b:.2f}, r = {r:.2f}\", c=c[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Первый пример показывает, сколь большое влияние может иметь единственная точка.\n",
    "* Второй пример показывает, как важно нанести данные на график, прежде чем приступить в регрессионному анализу: здесь выборка явно разнородна и может быть описана двумя различными зависимостями.\n",
    "\n",
    "Условия применимости регрессионного анализа не соблюдены, и попытка выразить связь единственной линией регрессии несостоятельна."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**8.3.** \n",
    "\n",
    "В таблице представлены результаты четырех экспериментов. Вычислите для каждого эксперимента коэффициенты линейной регрессии и коэффициент корреляции. В чем сходство и различие результатов экспериментов? Проверьте, выполняются ли условия применимости регрессионного анализа.\n",
    "\n",
    "X1|Y1|X2|Y2|X3|Y3|X4|Y4\n",
    "-|-|-|-|-|-|-|-|\n",
    "10|8.04|10|9.14|10|7.46|8|6.58\n",
    "8|6.95|8|8.14|8|6.77|8|5.76\n",
    "13|7.58|13|8.74|13|12.74|8|7.71\n",
    "9|8.81|9|8.77|9|7.11|8|8.84\n",
    "11|8.33|11|9.26|11|7.81|8|8.47\n",
    "14|9.96|14|8.10|14|8.84|8|7.04\n",
    "6|7.24|6|6.13|6|6.08|8|5.25\n",
    "4|4.26|4|3.10|4|5.39|19|12.50\n",
    "12|10.84|12|9.13|12|8.15|8|5.56\n",
    "7|4.82|7|7.26|7|6.42|8|7.91\n",
    "5|5.68|5|4.74|5|5.73|8|6.89"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Эксперимент А</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Эксперимент Б</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Эксперимент В</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Эксперимент Г</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>8.04</td>\n",
       "      <td>10</td>\n",
       "      <td>9.14</td>\n",
       "      <td>10</td>\n",
       "      <td>7.46</td>\n",
       "      <td>8</td>\n",
       "      <td>6.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>6.95</td>\n",
       "      <td>8</td>\n",
       "      <td>8.14</td>\n",
       "      <td>8</td>\n",
       "      <td>6.77</td>\n",
       "      <td>8</td>\n",
       "      <td>5.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>7.58</td>\n",
       "      <td>13</td>\n",
       "      <td>8.74</td>\n",
       "      <td>13</td>\n",
       "      <td>12.74</td>\n",
       "      <td>8</td>\n",
       "      <td>7.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>8.81</td>\n",
       "      <td>9</td>\n",
       "      <td>8.77</td>\n",
       "      <td>9</td>\n",
       "      <td>7.11</td>\n",
       "      <td>8</td>\n",
       "      <td>8.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>8.33</td>\n",
       "      <td>11</td>\n",
       "      <td>9.26</td>\n",
       "      <td>11</td>\n",
       "      <td>7.81</td>\n",
       "      <td>8</td>\n",
       "      <td>8.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14</td>\n",
       "      <td>9.96</td>\n",
       "      <td>14</td>\n",
       "      <td>8.10</td>\n",
       "      <td>14</td>\n",
       "      <td>8.84</td>\n",
       "      <td>8</td>\n",
       "      <td>7.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>7.24</td>\n",
       "      <td>6</td>\n",
       "      <td>6.13</td>\n",
       "      <td>6</td>\n",
       "      <td>6.08</td>\n",
       "      <td>8</td>\n",
       "      <td>5.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>4.26</td>\n",
       "      <td>4</td>\n",
       "      <td>3.10</td>\n",
       "      <td>4</td>\n",
       "      <td>5.39</td>\n",
       "      <td>19</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12</td>\n",
       "      <td>10.84</td>\n",
       "      <td>12</td>\n",
       "      <td>9.13</td>\n",
       "      <td>12</td>\n",
       "      <td>8.15</td>\n",
       "      <td>8</td>\n",
       "      <td>5.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7</td>\n",
       "      <td>4.82</td>\n",
       "      <td>7</td>\n",
       "      <td>7.26</td>\n",
       "      <td>7</td>\n",
       "      <td>6.42</td>\n",
       "      <td>8</td>\n",
       "      <td>7.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5</td>\n",
       "      <td>5.68</td>\n",
       "      <td>5</td>\n",
       "      <td>4.74</td>\n",
       "      <td>5</td>\n",
       "      <td>5.73</td>\n",
       "      <td>8</td>\n",
       "      <td>6.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Эксперимент А        Эксперимент Б       Эксперимент В         \\\n",
       "               X      Y             X     Y             X      Y   \n",
       "0             10   8.04            10  9.14            10   7.46   \n",
       "1              8   6.95             8  8.14             8   6.77   \n",
       "2             13   7.58            13  8.74            13  12.74   \n",
       "3              9   8.81             9  8.77             9   7.11   \n",
       "4             11   8.33            11  9.26            11   7.81   \n",
       "5             14   9.96            14  8.10            14   8.84   \n",
       "6              6   7.24             6  6.13             6   6.08   \n",
       "7              4   4.26             4  3.10             4   5.39   \n",
       "8             12  10.84            12  9.13            12   8.15   \n",
       "9              7   4.82             7  7.26             7   6.42   \n",
       "10             5   5.68             5  4.74             5   5.73   \n",
       "\n",
       "   Эксперимент Г         \n",
       "               X      Y  \n",
       "0              8   6.58  \n",
       "1              8   5.76  \n",
       "2              8   7.71  \n",
       "3              8   8.84  \n",
       "4              8   8.47  \n",
       "5              8   7.04  \n",
       "6              8   5.25  \n",
       "7             19  12.50  \n",
       "8              8   5.56  \n",
       "9              8   7.91  \n",
       "10             8   6.89  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(data=[[10,8.04,10,9.14,10,7.46,8,6.58],\n",
    "                        [8,6.95,8,8.14,8,6.77,8,5.76],\n",
    "                        [13,7.58,13,8.74,13,12.74,8,7.71],\n",
    "                        [9,8.81,9,8.77,9,7.11,8,8.84],\n",
    "                        [11,8.33,11,9.26,11,7.81,8,8.47],\n",
    "                        [14,9.96,14,8.10,14,8.84,8,7.04],\n",
    "                        [6,7.24,6,6.13,6,6.08,8,5.25],\n",
    "                        [4,4.26,4,3.10,4,5.39,19,12.50],\n",
    "                        [12,10.84,12,9.13,12,8.15,8,5.56],\n",
    "                        [7,4.82,7,7.26,7,6.42,8,7.91],\n",
    "                        [5,5.68,5,4.74,5,5.73,8,6.89]],\n",
    "                  columns=pd.MultiIndex.from_tuples(product([f\"Эксперимент {x}\" for x in \"АБВГ\"], \"XY\")))\n",
    "xmin, xmax = df.iloc[:,::2].min(), df.iloc[:,::2].max()\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 2, figsize=(12, 12), sharex=True, sharey=True)\n",
    "c = [\"red\", \"green\", \"blue\", \"brown\"]\n",
    "\n",
    "for i, exp in enumerate(\"АБВГ\"):\n",
    "    x = df[f\"Эксперимент {exp}\"][\"X\"].values\n",
    "    y = df[f\"Эксперимент {exp}\"][\"Y\"].values\n",
    "    reg = LinearRegression().fit(x.reshape(-1,1), y)  # type: ignore\n",
    "    a, b, r = reg.intercept_, reg.coef_[0], np.corrcoef(x, y)[0, 1]\n",
    "\n",
    "    # print(f\"{i+1}: a = {a:.2f}, b = {b:.2f}, r = {r:.2f}\")\n",
    "\n",
    "    axs[i%2][i//2].scatter(x, y, c=c[i])\n",
    "    axs[i%2][i//2].plot([xmin, xmax], np.array([xmin, xmax])*b + a, c=c[i])\n",
    "\n",
    "    axs[i%2][i//2].set_title(f\"{i+1}: a = {a:.2f}, b = {b:.2f}, r = {r:.2f}\", c=c[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Во всех четырех экспериментах а = 3,0; b = 0,5; r = 0,82.\n",
    "Условия применимости регрессионного анализа соблюдены только в первом эксперименте."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**8.4.**\n",
    "\n",
    "Исследуя проницаемость сосудов сетчатки, Дж. Фишман и соавт. (G. A. Fishman et al. Blood-retinal barrier function in patients with cone or cone-rod dystrophy. Arch. Ophthalmoi, 104:545—548, 1986) решили выяснить, связан ли этот показатель с электрической активностью сетчатки. Позволяют ли полученные данные говорить о существовании связи?\n",
    "\n",
    "Проницаемость сосудов сетчатки|Электрическая активность сетчатки\n",
    "-|-\n",
    "19.5|0.0\n",
    "15.0|38.5\n",
    "13.5|59.0\n",
    "23.3|97.4\n",
    "6.3|119.2\n",
    "2.5|129.5\n",
    "13.0|198.7\n",
    "1.8|248.7\n",
    "6.5|318.0\n",
    "1.8|438.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data=[[19.5,0.0],\n",
    "                        [15.0,38.5],\n",
    "                        [13.5,59.0],\n",
    "                        [23.3,97.4],\n",
    "                        [6.3,119.2],\n",
    "                        [2.5,129.5],\n",
    "                        [13.0,198.7],\n",
    "                        [1.8,248.7],\n",
    "                        [6.5,318.0],\n",
    "                        [1.8,438.5]],\n",
    "                  columns=[\"Проницаемость сосудов сетчатки\", \"Электрическая активность сетчатки\"])\n",
    "\n",
    "# display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"Проницаемость сосудов сетчатки\", y=\"Электрическая активность сетчатки\", data=df, ci=None);\n",
    "ax = plt.gca()\n",
    "\n",
    "x = df[\"Проницаемость сосудов сетчатки\"].values\n",
    "y = df[\"Электрическая активность сетчатки\"].values\n",
    "\n",
    "reg = LinearRegression().fit(x.reshape(-1,1), y)  # type: ignore\n",
    "a, b = reg.intercept_, reg.coef_[0]\n",
    "r, p = scipy.stats.pearsonr(x, y)\n",
    "\n",
    "ax.set_title(f\"a = {a:.2f}, b = {b:.2f}, r = {r:.2f}, p = {p:.3f}\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Да, т.к. P = 0.032 < 0.05"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**8.5.**\n",
    "\n",
    "Наиболее точную оценку объема левого желудочка дает рентгеноконтрастная вентрикулография — метод, требующий катетеризации сердца, а потому дорогой и небезопасный. Продолжается поиск методов, не требующих катетеризации. Р. Слуцкий и соавт. (R. Slutsky et al. Left ventricular volumes by gated equilibriumradionuclide angiography method. Circulation, 60:556—564, 1979) исследовали метод оценкиобъема левогожелудочка по данным изотопной вентрикулографии с внутривенным введением изотопа.\n",
    "* Конечно-диастолический объем\n",
    "\n",
    "Изотопная вентрикулография|Рентгеноконтрастная вентрикулография\n",
    "-|-\n",
    "75|101\n",
    "48|75\n",
    "126|126\n",
    "93|106\n",
    "201|195\n",
    "260|265\n",
    "40|60\n",
    "293|288\n",
    "95|94\n",
    "58|67\n",
    "91|81\n",
    "182|168\n",
    "91|89\n",
    "88|102\n",
    "161|150\n",
    "118|94\n",
    "120|129\n",
    "* Конечно-систолический объем\n",
    "\n",
    "Изотопная вентрикулография|Рентгеноконтрастная вентрикулография\n",
    "-|-\n",
    "35|47\n",
    "30|35\n",
    "52|49\n",
    "23|23\n",
    "103|88\n",
    "182|173\n",
    "14|12\n",
    "166|163\n",
    "27|29\n",
    "24|25\n",
    "50|25\n",
    "139|131\n",
    "50|49\n",
    "40|44\n",
    "57|60\n",
    "41|18\n",
    "48|40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Конечно-диастолический объем</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Конечно-систолический объем</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Изотопная вентрикулография</th>\n",
       "      <th>Рентгеноконтрастная вентрикулография</th>\n",
       "      <th>Изотопная вентрикулография</th>\n",
       "      <th>Рентгеноконтрастная вентрикулография</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75</td>\n",
       "      <td>101</td>\n",
       "      <td>35</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48</td>\n",
       "      <td>75</td>\n",
       "      <td>30</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>126</td>\n",
       "      <td>126</td>\n",
       "      <td>52</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>93</td>\n",
       "      <td>106</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>201</td>\n",
       "      <td>195</td>\n",
       "      <td>103</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>260</td>\n",
       "      <td>265</td>\n",
       "      <td>182</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>40</td>\n",
       "      <td>60</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>293</td>\n",
       "      <td>288</td>\n",
       "      <td>166</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>95</td>\n",
       "      <td>94</td>\n",
       "      <td>27</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>58</td>\n",
       "      <td>67</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>91</td>\n",
       "      <td>81</td>\n",
       "      <td>50</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>182</td>\n",
       "      <td>168</td>\n",
       "      <td>139</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>91</td>\n",
       "      <td>89</td>\n",
       "      <td>50</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>88</td>\n",
       "      <td>102</td>\n",
       "      <td>40</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>161</td>\n",
       "      <td>150</td>\n",
       "      <td>57</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>118</td>\n",
       "      <td>94</td>\n",
       "      <td>41</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>120</td>\n",
       "      <td>129</td>\n",
       "      <td>48</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Конечно-диастолический объем                                       \\\n",
       "     Изотопная вентрикулография Рентгеноконтрастная вентрикулография   \n",
       "0                            75                                  101   \n",
       "1                            48                                   75   \n",
       "2                           126                                  126   \n",
       "3                            93                                  106   \n",
       "4                           201                                  195   \n",
       "5                           260                                  265   \n",
       "6                            40                                   60   \n",
       "7                           293                                  288   \n",
       "8                            95                                   94   \n",
       "9                            58                                   67   \n",
       "10                           91                                   81   \n",
       "11                          182                                  168   \n",
       "12                           91                                   89   \n",
       "13                           88                                  102   \n",
       "14                          161                                  150   \n",
       "15                          118                                   94   \n",
       "16                          120                                  129   \n",
       "\n",
       "   Конечно-систолический объем                                       \n",
       "    Изотопная вентрикулография Рентгеноконтрастная вентрикулография  \n",
       "0                           35                                   47  \n",
       "1                           30                                   35  \n",
       "2                           52                                   49  \n",
       "3                           23                                   23  \n",
       "4                          103                                   88  \n",
       "5                          182                                  173  \n",
       "6                           14                                   12  \n",
       "7                          166                                  163  \n",
       "8                           27                                   29  \n",
       "9                           24                                   25  \n",
       "10                          50                                   25  \n",
       "11                         139                                  131  \n",
       "12                          50                                   49  \n",
       "13                          40                                   44  \n",
       "14                          57                                   60  \n",
       "15                          41                                   18  \n",
       "16                          48                                   40  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(data=[[75, 101, 35, 47],[48, 75, 30, 35],[126, 126, 52, 49],[93, 106, 23, 23],[201, 195, 103, 88],\n",
    "                        [260, 265, 182, 173],[40, 60, 14, 12],[293, 288, 166, 163],[95, 94, 27, 29],[58, 67, 24, 25],\n",
    "                        [91, 81, 50, 25],[182, 168, 139, 131],[91, 89, 50, 49],[88, 102, 40, 44],[161, 150, 57, 60],\n",
    "                        [118, 94, 41, 18],[120, 129, 48, 40]],\n",
    "                  columns=pd.MultiIndex.from_tuples(product([\"Конечно-диастолический объем\", \"Конечно-систолический объем\"],\n",
    "                                                            [\"Изотопная вентрикулография\", \"Рентгеноконтрастная вентрикулография\"]))\n",
    "                  )\n",
    "\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Конечно-диастолический объем: a = 15.01, b = 0.90, r = -0.68, p = 0.032\n",
      "Конечно-систолический объем: a = -0.87, b = 0.95, r = -0.68, p = 0.032\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 566.25x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"Изотопная вентрикулография\", \n",
    "           y=\"Рентгеноконтрастная вентрикулография\", \n",
    "           data=df.stack(0).reset_index().rename(columns={\"level_1\": \"method\"}), \n",
    "           hue=\"method\",\n",
    "           ci=None)\n",
    "\n",
    "for i, method in enumerate(df.columns.levels[0]):\n",
    "    reg = LinearRegression().fit(df[(method, \"Изотопная вентрикулография\")].values.reshape(-1,1), df[(method, \"Рентгеноконтрастная вентрикулография\")].values)  # type: ignore\n",
    "    a, b = reg.intercept_, reg.coef_[0]\n",
    "    r, p = scipy.stats.pearsonr(x, y)\n",
    "\n",
    "    print(f\"{method}: a = {a:.2f}, b = {b:.2f}, r = {r:.2f}, p = {p:.3f}\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Применим метод Блэнда–Алтмана:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Конечно-диастолический объем: a = -13.54, b = 0.08, r = -0.68, p = 0.032\n",
      "Конечно-систолический объем: a = 1.99, b = 0.03, r = -0.68, p = 0.032\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(1, 2, figsize = (16, 6))\n",
    "\n",
    "for i, method in enumerate(df.columns.levels[0]):\n",
    "    diffs = df[(method, \"Изотопная вентрикулография\")] - df[(method, \"Рентгеноконтрастная вентрикулография\")]\n",
    "    means = (df[(method, \"Изотопная вентрикулография\")] + df[(method, \"Рентгеноконтрастная вентрикулография\")])/2\n",
    "\n",
    "    n, mean, std = diffs.agg([\"count\", \"mean\", \"std\"]).values.tolist()\n",
    "    # print(f\"{method}: mean = {mean:.3f}, std = {std:.3f}\")\n",
    "    sm.graphics.mean_diff_plot(df[(method, \"Изотопная вентрикулография\")], df[(method, \"Рентгеноконтрастная вентрикулография\")], ax = ax[i]);\n",
    "    ax[i].set_title(f\"{method}: mean = {mean:.3f}, std = {std:.3f}\");\n",
    "\n",
    "    # добавим moe чтоб видеть есть ли bias\n",
    "    moe = scipy.stats.t.ppf(.975, df=n-1, scale=std/n**.5)\n",
    "    ax[i].axhline(mean+moe, linestyle=\"--\")\n",
    "    ax[i].axhline(mean-moe, linestyle=\"--\")\n",
    "\n",
    "    # добавим линию регрессии\n",
    "    reg = LinearRegression().fit(means.values.reshape(-1,1), \n",
    "                                 diffs.values)  # type: ignore\n",
    "    a, b = reg.intercept_, reg.coef_[0]\n",
    "    r, p = scipy.stats.pearsonr(x, y)\n",
    "    xmin, xmax = means.min(), means.max()\n",
    "\n",
    "    ax[i].plot([xmin, xmax], np.array([xmin, xmax])*b + a, linestyle=\":\", c=\"grey\")\n",
    "    print(f\"{method}: a = {a:.2f}, b = {b:.2f}, r = {r:.2f}, p = {p:.3f}\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "средняя разность и стандартное отклонение говорит о хорошей согласованности по обоим показателям. При графическом анализе видно, что в обоих случаях разность увеличивается с ростом среднего показателя."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**8.6.**\n",
    "\n",
    "Азотистый баланс — разность между количеством азота, который попадает в организм с пищей, и количеством азота, выводимого из организма, — важный показатель полноценности питания. Отрицательный азотистый баланс свидетельствует о том, что организм не получает достаточно белка. Нормы суточного потребления белка, рекомендуемые Всемирной организацией здравоохранения и Японским комитетом питания, рассчитаны главным образом на мужчин. Целью исследования К. Канеко и Г. Койке (К. Kaneko, G. Koike. Utilization and requirement of egg protein in Japanese women. J. Nutr. Sci. Vitaminol. (Tokyo), 31:43—52, 1985) было определить количество белка в рационе, необходимое для поддержания нулевого азотистого баланса у японских женщин. Связь суточного потребления азота и азотистого баланса определили при калорийности суточного рациона 37 и 33 ккал/кг. Были получены следующие данные.\n",
    "\n",
    "* Калорийность суточного рациона 37 ккал/кг:\n",
    "\n",
    "Потребление азота, мг/кг|Азотистый баланс, мг/кг\n",
    "-|-\n",
    "49|–30\n",
    "47|–22\n",
    "50|–29\n",
    "76|–22\n",
    "77|–15\n",
    "99|–10\n",
    "98|–11\n",
    "103|–10\n",
    "118|–1\n",
    "105|–4\n",
    "100|–13\n",
    "98|–14\n",
    "\n",
    "* Калорийность суточного рациона 33 ккал/кг:\n",
    "\n",
    "Потребление азота, мг/кг|Азотистый баланс, мг/кг\n",
    "-|-\n",
    "32|–32\n",
    "32|–20\n",
    "32|–17\n",
    "51|–10\n",
    "53|–20\n",
    "51|–18\n",
    "52|–21\n",
    "74|4\n",
    "72|–16\n",
    "74|–14\n",
    "98|6\n",
    "97|–7\n",
    "\n",
    "Найдите уравнения регрессии для обеих групп. Изобразите на одном рисунке результаты наблюдений и линии регрессии. Является ли различие межцу линиями регрессии статистически значимым? Для группы 37 ккал/кг найдите величину потребления азота, обеспечивающую нулевой азотистый баланс."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Калорийность суточного рациона 37 ккал/кг</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Калорийность суточного рациона 33 ккал/кг</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Потребление азота, мг/кг</th>\n",
       "      <th>Азотистый баланс, мг/кг</th>\n",
       "      <th>Потребление азота, мг/кг</th>\n",
       "      <th>Азотистый баланс, мг/кг</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49</td>\n",
       "      <td>-30</td>\n",
       "      <td>32</td>\n",
       "      <td>-32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47</td>\n",
       "      <td>-22</td>\n",
       "      <td>32</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50</td>\n",
       "      <td>-29</td>\n",
       "      <td>32</td>\n",
       "      <td>-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>76</td>\n",
       "      <td>-22</td>\n",
       "      <td>51</td>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>77</td>\n",
       "      <td>-15</td>\n",
       "      <td>53</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>99</td>\n",
       "      <td>-10</td>\n",
       "      <td>51</td>\n",
       "      <td>-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>98</td>\n",
       "      <td>-11</td>\n",
       "      <td>52</td>\n",
       "      <td>-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>103</td>\n",
       "      <td>-10</td>\n",
       "      <td>74</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>118</td>\n",
       "      <td>-1</td>\n",
       "      <td>72</td>\n",
       "      <td>-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>105</td>\n",
       "      <td>-4</td>\n",
       "      <td>74</td>\n",
       "      <td>-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>100</td>\n",
       "      <td>-13</td>\n",
       "      <td>98</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>98</td>\n",
       "      <td>-14</td>\n",
       "      <td>97</td>\n",
       "      <td>-7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Калорийность суточного рациона 37 ккал/кг                          \\\n",
       "                    Потребление азота, мг/кг Азотистый баланс, мг/кг   \n",
       "0                                         49                     -30   \n",
       "1                                         47                     -22   \n",
       "2                                         50                     -29   \n",
       "3                                         76                     -22   \n",
       "4                                         77                     -15   \n",
       "5                                         99                     -10   \n",
       "6                                         98                     -11   \n",
       "7                                        103                     -10   \n",
       "8                                        118                      -1   \n",
       "9                                        105                      -4   \n",
       "10                                       100                     -13   \n",
       "11                                        98                     -14   \n",
       "\n",
       "   Калорийность суточного рациона 33 ккал/кг                          \n",
       "                    Потребление азота, мг/кг Азотистый баланс, мг/кг  \n",
       "0                                         32                     -32  \n",
       "1                                         32                     -20  \n",
       "2                                         32                     -17  \n",
       "3                                         51                     -10  \n",
       "4                                         53                     -20  \n",
       "5                                         51                     -18  \n",
       "6                                         52                     -21  \n",
       "7                                         74                       4  \n",
       "8                                         72                     -16  \n",
       "9                                         74                     -14  \n",
       "10                                        98                       6  \n",
       "11                                        97                      -7  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(data=[[49,-30,32,-32],[47,-22,32,-20],[50,-29,32,-17],[76,-22,51,-10],\n",
    "                        [77,-15,53,-20],[99,-10,51,-18],[98,-11,52,-21],[103,-10,74,4],\n",
    "                        [118,-1,72,-16],[105,-4,74,-14],[100,-13,98,6],[98,-14,97,-7]],\n",
    "                  columns=pd.MultiIndex.from_tuples(product([\"Калорийность суточного рациона 37 ккал/кг\", \"Калорийность суточного рациона 33 ккал/кг\"],\n",
    "                                                            [\"Потребление азота, мг/кг\",\"Азотистый баланс, мг/кг\"]))\n",
    "                  )\n",
    "\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Калорийность суточного рациона 33 ккал/кг: a = -34.792, b = 0.352, r = 0.765, p = 0.004\n",
      "Калорийность суточного рациона 37 ккал/кг: a = -44.290, b = 0.344, r = 0.933, p = 0.000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 632.25x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"Потребление азота, мг/кг\", \n",
    "           y=\"Азотистый баланс, мг/кг\", \n",
    "           data=df.stack(0).reset_index().rename(columns={\"level_1\": \"method\"}), \n",
    "           hue=\"method\",\n",
    "           ci=None)\n",
    "\n",
    "coefs = []  # параметры которые необходимо сохранить для сравнения линий регрессии\n",
    "\n",
    "for i, method in enumerate(df.columns.levels[0]):\n",
    "    x = df[(method, \"Потребление азота, мг/кг\")].values\n",
    "    y = df[(method, \"Азотистый баланс, мг/кг\")].values\n",
    "    n = len(df)\n",
    "    \n",
    "    reg = LinearRegression().fit(x.reshape(-1,1), y)  # type: ignore\n",
    "    a, b = reg.intercept_, reg.coef_[0]\n",
    "    r, p = scipy.stats.pearsonr(x, y)\n",
    "\n",
    "    Sx, Sy = x.std(ddof=1), y.std(ddof=1)\n",
    "    Syx = ((n-1)/(n-2)*(Sy**2 - b**2 * Sx**2))**.5  # остаточное стандартное отклонение\n",
    "    Sa = Syx*(1/n + x.mean()**2/(n-1)/Sx**2)**.5\n",
    "    Sb = Syx/(n-1)**.5/Sx\n",
    "\n",
    "    coefs.append((Sa, Sb, a, b))  # можно было замарочиться с именнованным кортежом\n",
    "    \n",
    "    print(f\"{method}: a = {a:.3f}, b = {b:.3f}, r = {r:.3f}, p = {p:.3f}\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t05 = 2.0859634472658364\n",
      "Для коэффициента сдвига: ta=1.351, pa=0.158\n",
      "Для коэффициента наклона: tb=0.079, pb=0.393\n"
     ]
    }
   ],
   "source": [
    "Sa1, Sb1, a1, b1 = coefs[0]\n",
    "Sa2, Sb2, a2, b2 = coefs[1]\n",
    "\n",
    "tb = abs(b1-b2)/(Sb1**2+Sb2**2)**.5\n",
    "ta = abs(a1-a2)/(Sa1**2+Sa2**2)**.5\n",
    "t05 = scipy.stats.t.ppf(.975, df=20)\n",
    "pa = scipy.stats.t.pdf(ta, df=20)\n",
    "pb = scipy.stats.t.pdf(tb, df=20)\n",
    "\n",
    "print(f\"t05 = {t05}\")\n",
    "print(f\"Для коэффициента сдвига: ta={ta:.3f}, pa={pa:.3f}\")\n",
    "print(f\"Для коэффициента наклона: tb={tb:.3f}, pb={pb:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для обоих коэффициентов различия статистически не значимы.\n",
    "\n",
    "При калорийности 37 ккал/кг нулевой азотистый баланс достигается при поступлении азота 128 мг/кг (легко найти по формуле x = -a/b).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**8.7.**\n",
    "\n",
    "В. Ернайчик (W. Jernajczyk. Latency of eye movement and other REM sleep parameters in bipolar depression. Biol. Psychiatry, 21:465—472, 1986), изучая физиологию сна при депрессии, столкнулся с необходимостью оценки тяжести этого заболевания. Шкала депрессии Бека основана на опроснике, заполняемом самим больным. Она проста в применении, однако специфичность ее недостаточна. Применение шкалы депрессии Гамильтона более сложно, поскольку требует участия врача, но именно эта шкала дает наиболее точные результаты. Тем не менее автор был склонен использовать шкалу Бека. В самом деле, если ее специфичность недостаточна для диагностики, то это еще не говорит о том, что ее нельзя использовать для оценки тяжести депрессии у больных с уже установленным диагнозом. Сравнив оценки по обеим шкалам у 10 больных, В. Ернайчик получил следующие результаты.\n",
    "\n",
    "Оценка по шкале депрессии Бека|Оценка по шкале депрессии Гамильтона\n",
    "-|-\n",
    "20|22\n",
    "11|14\n",
    "13|10\n",
    "22|17\n",
    "37|31\n",
    "27|22\n",
    "14|12\n",
    "20|19\n",
    "37|29\n",
    "20|15\n",
    "\n",
    "Насколько согласованы оценки?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data=[[20, 22], [11, 14], [13, 10], [22, 17], [37, 31], [27, 22], [14, 12], [20, 19], [37, 29], [20, 15]],\n",
    "                  columns=[\"Оценка по шкале депрессии Бека\", \"Оценка по шкале депрессии Гамильтона\"])\n",
    "\n",
    "# display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"Оценка по шкале депрессии Бека\", y=\"Оценка по шкале депрессии Гамильтона\", data=df, ci=None);\n",
    "ax = plt.gca()\n",
    "\n",
    "x = df[\"Оценка по шкале депрессии Бека\"].values\n",
    "y = df[\"Оценка по шкале депрессии Гамильтона\"].values\n",
    "\n",
    "reg = LinearRegression().fit(x.reshape(-1,1), y)  # type: ignore\n",
    "a, b = reg.intercept_, reg.coef_[0]\n",
    "r, p = scipy.stats.pearsonr(x, y)\n",
    "rs, ps = scipy.stats.spearmanr(x, y)\n",
    "\n",
    "ax.set_title(f\"a = {a:.2f}, b = {b:.2f}, \\nr = {r:.2f}, p = {p:.3f}\\nrs = {rs:.2f}, ps = {ps:.3f}\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Оценки согласованы достаточно хорошо. В данной задаче можно применять как корреляцию Спирмана, так и корреляцию Пирсона."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**8.8.**\n",
    "\n",
    "Полоскание с хлоргексидином предотвращает образование зубного налета, но имеет вкус, который трудно назвать приятным, кроме того, оно окрашивает зубы. Полоскание на основе хлорида аммония приятнее на вкус, не окрашивает зубы; считается, однако, что оно менее эффективно. Ф. Эшли и соавт. (F. P. Ashley et al. Effect of a 0,1% cetylpyridinium chloride mouthrinse on the accumulation and biochemical composition of dental plaque in young adults. Caries Res., 18:465—471, 1984) сравнили эффективность двух видов полоскания. Участники исследования полоскали рот одним из растворов, после чего зубной налет отделяли и взвешивали. Опыт проводился 48 часов: за меньший срок налет не успевал накопиться в количестве, достаточном для точного взвешивания. Исследователей больше интересовало образование налета за 24 часа, поэтому в середине опыта налет оценивали визуально по специально разработанной шкале. Чтобы оценить точность визуальных оценок, их проводили и на 48-м часу и сравнивали с результатами взвешивания. Результаты сопоставления двух способов оценки зубного налета представлены в таблице:\n",
    "\n",
    "Визуальная оценка зубного налета, баллы|Сухой вес зубного налета, мг\n",
    "-|-\n",
    "25|2.7\n",
    "32|1.2\n",
    "45|2.7\n",
    "60|2.1\n",
    "60|3.5\n",
    "65|2.8\n",
    "65|3.7\n",
    "78|8.9\n",
    "80|5.8\n",
    "83|4.0\n",
    "100|5.1\n",
    "110|5.1\n",
    "120|4.8\n",
    "125|5.8\n",
    "140|11.7\n",
    "143|8.5\n",
    "143|11.1\n",
    "145|7.1\n",
    "148|14.2\n",
    "153|12.2\n",
    "\n",
    "Насколько, судя по этим данным, можно полагаться на визуальный способ оценки?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data=[[25,2.7], [32,1.2], [45,2.7], [60,2.1], [60,3.5], \n",
    "                        [65,2.8], [65,3.7], [78,8.9], [80,5.8], [83,4.0], \n",
    "                        [100,5.1], [110,5.1], [120,4.8], [125,5.8], [140,11.7], \n",
    "                        [143,8.5], [143,11.1], [145,7.1], [148,14.2], [153,12.2]],\n",
    "                  columns=[\"Визуальная оценка зубного налета, баллы\", \"Сухой вес зубного налета, мг\"])\n",
    "\n",
    "# display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"Визуальная оценка зубного налета, баллы\", y=\"Сухой вес зубного налета, мг\", data=df, ci=None);\n",
    "ax = plt.gca()\n",
    "\n",
    "x = df[\"Визуальная оценка зубного налета, баллы\"].values\n",
    "y = df[\"Сухой вес зубного налета, мг\"].values\n",
    "\n",
    "reg = LinearRegression().fit(x.reshape(-1,1), y)  # type: ignore\n",
    "a, b = reg.intercept_, reg.coef_[0]\n",
    "r, p = scipy.stats.pearsonr(x, y)\n",
    "rs, ps = scipy.stats.spearmanr(x, y)\n",
    "\n",
    "ax.set_title(f\"a = {a:.2f}, b = {b:.2f}, \\nr = {r:.2f}, p = {p:.3f}\\nrs = {rs:.2f}, ps = {ps:.3f}\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Визуальная оценка достаточно хорошо соответствует результатам взвешивания. Однако, на графике легко заметить, что возможно имеет место нелинейная связь между результатами измерений и визуальной оценкой (при большом налете визуальная оценка занижает результат)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Прямое применение метода Блэнда—Алтмана в данном случае не возможно (!!!)** из-за того, что сравниваемые методы измерения имеют различные шкалы (между ними нет прямой конвертации). \n",
    "\n",
    "*Применение z стандартизация (для обезразмеривания) исключает возможность определения наличия смещения, но возможно позволит обратить внимание на нелинейность связи между сравниваемыми методами.*\n",
    "\n",
    "*В данном случае, чтоб визуально показать нелинейность связи, разницы в выборе между StandardScaler и MinMaxScaler нет.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = MinMaxScaler().fit_transform(df[\"Визуальная оценка зубного налета, баллы\"].values.reshape(-1,1)).flatten()\n",
    "y = MinMaxScaler().fit_transform(df[\"Сухой вес зубного налета, мг\"].values.reshape(-1,1)).flatten()\n",
    "\n",
    "f, ax = plt.subplots(1, 1, figsize = (6, 6))\n",
    "\n",
    "sm.graphics.mean_diff_plot(x, y, ax = ax);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**8.9.**\n",
    "\n",
    "Нормальный эритроцит легко меняет форму и проходит через мельчайшие сосуды. При генетическом дефекте β-цепи гемоглобин полимеризуется, в результате форма части эритроцитов меняется, они становятся ригидными, закупоривают сосуды и разрушаются. Такова в сильно упрощенном виде сущность серповидноклеточной анемии — тяжелого заболевания с многообразными проявлениями. Наиболее мучительны болевые кризы. Они развиваются, когда под влиянием гипоксии происходит массовая полимеризация гемоглобина, деформация эритроцитов и закупорка сосудов. Существует предположение, что дело не только в деформации и ригидности эритроцитов — определенную роль играет также повышенная склонность эритроцитов к адгезии — прилипанию к эндотелию (внутренней выстилке сосудов). Р. Хебелл и соавт. (R. Hebbel et al. Erythrocyte adherence to endothelium in sickle-cell anemia: a possible determinant of disease severity. N. Engl. J. Med., 302:992—995, 1980) решили выяснить, есть ли связь между тяжестью заболевания и адгезивностью эритроцитов. Прежде всего необходимо было\n",
    "разработать способы оценки этих признаков. \n",
    "\n",
    "Для оценки тяжести серповидноклеточной анемии была построена специальная шкала.\n",
    "\n",
    "* Ежегодное число болевых кризов, требующих госпитализации или применения наркотических анальгетиков:\n",
    "    * 1-5 - 1 балл\n",
    "    * 6-10 - 2 балла\n",
    "    * 10+ - 3 балла\n",
    "* Язвы на коже - 2 балла\n",
    "* Поражение сетчатки - 1 балл\n",
    "* Поражение ЦНС (судороги, инсульт) - 2 балла\n",
    "* Поражение костей (инфаркты, асептический некроз) - 2 балла    \n",
    "\n",
    "Баллы за отдельные признаки суммировали, таким образом казедый больной получал от 0 до 13 баллов и более (по 2 балла дается за каждый инфаркт или асептический некроз кости). Для оценки адгезивности эритроцитов известное их количество наносили на культуру эндотелия, инкубировали и затем смывали. Подсчитав число смытых эритроцитов, определяли число прилипших. Одновременно такой же опыт делали с нормальными эритроцитами. Результат выражали в виде коэффициента адгезии: отношения числа прилипших эритроцитов больного к числу прилипших эритроцитов здорового. Было обследовано 20 больных. У каждого оценили тяжесть заболевания и коэффициент адгезии. Подтверждают ли эти дан ные гипотезу о связи между адгезивностью эритроцитов и тяжестью серповидноклеточной анемии?\n",
    "\n",
    "Тяжесть заболевания, баллы|Коэффициент адгезии\n",
    "-|-\n",
    "0|1.0\n",
    "0|1.4\n",
    "1|1.0\n",
    "1|1.0\n",
    "1|1.9\n",
    "1|2.0\n",
    "1|2.5\n",
    "1|3.0\n",
    "2|2.0\n",
    "2|3.2\n",
    "3|3.0\n",
    "3|3.2\n",
    "3|6.3\n",
    "4|2.7\n",
    "5|3.0\n",
    "5|5.0\n",
    "5|17.0\n",
    "6|5.2\n",
    "9|19.8\n",
    "11|25.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data=[[0, 1.0], [0, 1.4], [1, 1.0], [1, 1.0], [1, 1.9], \n",
    "                        [1, 2.0], [1, 2.5], [1, 3.0], [2, 2.0], [2, 3.2], \n",
    "                        [3, 3.0], [3, 3.2], [3, 6.3], [4, 2.7], [5, 3.0], \n",
    "                        [5, 5.0], [5, 17.0], [6, 5.2], [9, 19.8], [11, 25.0]],\n",
    "                  columns=[\"Тяжесть заболевания, баллы\", \"Коэффициент адгезии\"])\n",
    "\n",
    "# display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rs = 0.861, P = 0.000\n"
     ]
    }
   ],
   "source": [
    "rs, ps = scipy.stats.spearmanr(df[\"Тяжесть заболевания, баллы\"], df[\"Коэффициент адгезии\"])\n",
    "\n",
    "print(f\"rs = {rs:.3f}, P = {ps:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Данные подтверждают гипотезу о связи между адгезивностью эритроцитов и тяжестью серповидноклеточной анемии."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**8.10.**\n",
    "\n",
    "Какова вероятность выявить коэффициент корреляции не меньше 0.6 при объеме выборки 39 и уровне значимости 5%?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P = 0.986\n"
     ]
    }
   ],
   "source": [
    "ro = 0.6\n",
    "n = 39\n",
    "alpha = 0.05\n",
    "\n",
    "Zp = np.log((1 + ro)/(1 - ro)) / 2\n",
    "sgmz = 1 / (n - 3)**.5\n",
    "\n",
    "p = 1 - scipy.stats.norm.cdf(scipy.stats.norm.ppf(1.-alpha/2) - Zp/sgmz)\n",
    "\n",
    "print(f\"P = {p:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**8.11.**\n",
    "\n",
    "Каков должен быть объем выборки, чтобы с вероятностью 80% выявить коэффициент корреляции не меньше 0,6 при уровне значимости 5%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n = 19.33641481471773\n"
     ]
    }
   ],
   "source": [
    "ro = 0.6\n",
    "p = 0.8\n",
    "alpha = 0.05\n",
    "\n",
    "Zp = np.log((1 + ro) / (1 - ro)) / 2\n",
    "\n",
    "n = ((scipy.stats.norm.ppf(1.-alpha/2) - scipy.stats.norm.ppf(1-p))/Zp)**2 + 3\n",
    "print(f\"n = {n}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "N = 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**8.12.**\n",
    "\n",
    "Ожирение предрасполагает к развитию инсулинонезависимого сахарного диабета. При этом играет роль тип ожирения: наиболее опасным считается так называемое ожирение по мужскому типу, когда жир откладывается преимущественно на туловище (при ожирении по женскому типу жир откладывается главным образом на бедрах и ягодицах). Однако далеко не у всех людей с ожирением по мужскому типу развивается инсулине независимый сахарный диабет. Необходимо действие дополнительного фактора, предположительно генетического. Т. Эндр и соавт. (Т. Endre et al. Insulin resistance is coupled to low physical fitness in normotensive men with a family history of hypertension. J. Hypertension, 12:81—88, 1994) исследовали связь чувствительности к инсулину (ее снижение лежит в основе инсулинонезависимого сахарного диабета) и отношения объема талии к объему бедра (показатель типа ожирения). Индекс чувствительности к инсулину рассчитывали как логарифм снижения уровня глюкозы плазмы после введения инсулина. В исследование вошло 15 мужчин, у которых не было родственников первой степени с артериальной гипертонией (1 -я группа) и 15 мужчин, у которых такие родственники были (2-я группа).\n",
    "\n",
    "* 1-я группа\n",
    "\n",
    "Отношение объема талии к объему бедра|Индекс чувствительности к инсулину\n",
    "-|-\n",
    "0.775|1.322\n",
    "0.800|1.301\n",
    "0.810|1.130\n",
    "0.800|0.929\n",
    "0.850|1.021\n",
    "0.860|1.000\n",
    "0.925|1.106\n",
    "0.900|0.954\n",
    "0.925|0.813\n",
    "0.945|1.041\n",
    "0.945|1.021\n",
    "0.950|0.978\n",
    "0.975|0.740\n",
    "1.050|0.778\n",
    "1.075|0.574\n",
    "\n",
    "\n",
    "* 2-я группа\n",
    "\n",
    "Отношение объема талии к объему бедра|Индекс чувствительности к инсулину\n",
    "-|-\n",
    "0.800|1.000\n",
    "0.810|0.699\n",
    "0.850|0.978\n",
    "0.875|0.398\n",
    "0.850|0.602\n",
    "0.870|0.760\n",
    "0.910|0.989\n",
    "0.925|0.903\n",
    "0.925|0.778\n",
    "0.940|0.628\n",
    "0.945|0.929\n",
    "0.960|0.954\n",
    "1.100|0.929\n",
    "1.100|0.653\n",
    "0.990|0.352\n",
    "\n",
    "Одинакова ли связь показателей в обеих группах?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">1-я группа</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2-я группа</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Отношение объема талии к объему бедра</th>\n",
       "      <th>Индекс чувствительности к инсулину</th>\n",
       "      <th>Отношение объема талии к объему бедра</th>\n",
       "      <th>Индекс чувствительности к инсулину</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.775</td>\n",
       "      <td>1.322</td>\n",
       "      <td>0.800</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.800</td>\n",
       "      <td>1.301</td>\n",
       "      <td>0.810</td>\n",
       "      <td>0.699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.810</td>\n",
       "      <td>1.130</td>\n",
       "      <td>0.850</td>\n",
       "      <td>0.978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.800</td>\n",
       "      <td>0.929</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.850</td>\n",
       "      <td>1.021</td>\n",
       "      <td>0.850</td>\n",
       "      <td>0.602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.860</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.870</td>\n",
       "      <td>0.760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.925</td>\n",
       "      <td>1.106</td>\n",
       "      <td>0.910</td>\n",
       "      <td>0.989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.900</td>\n",
       "      <td>0.954</td>\n",
       "      <td>0.925</td>\n",
       "      <td>0.903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.925</td>\n",
       "      <td>0.813</td>\n",
       "      <td>0.925</td>\n",
       "      <td>0.778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.945</td>\n",
       "      <td>1.041</td>\n",
       "      <td>0.940</td>\n",
       "      <td>0.628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.945</td>\n",
       "      <td>1.021</td>\n",
       "      <td>0.945</td>\n",
       "      <td>0.929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.950</td>\n",
       "      <td>0.978</td>\n",
       "      <td>0.960</td>\n",
       "      <td>0.954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.975</td>\n",
       "      <td>0.740</td>\n",
       "      <td>1.100</td>\n",
       "      <td>0.929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.050</td>\n",
       "      <td>0.778</td>\n",
       "      <td>1.100</td>\n",
       "      <td>0.653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.075</td>\n",
       "      <td>0.574</td>\n",
       "      <td>0.990</td>\n",
       "      <td>0.352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              1-я группа                                     \\\n",
       "   Отношение объема талии к объему бедра Индекс чувствительности к инсулину   \n",
       "0                                  0.775                              1.322   \n",
       "1                                  0.800                              1.301   \n",
       "2                                  0.810                              1.130   \n",
       "3                                  0.800                              0.929   \n",
       "4                                  0.850                              1.021   \n",
       "5                                  0.860                              1.000   \n",
       "6                                  0.925                              1.106   \n",
       "7                                  0.900                              0.954   \n",
       "8                                  0.925                              0.813   \n",
       "9                                  0.945                              1.041   \n",
       "10                                 0.945                              1.021   \n",
       "11                                 0.950                              0.978   \n",
       "12                                 0.975                              0.740   \n",
       "13                                 1.050                              0.778   \n",
       "14                                 1.075                              0.574   \n",
       "\n",
       "                              2-я группа                                     \n",
       "   Отношение объема талии к объему бедра Индекс чувствительности к инсулину  \n",
       "0                                  0.800                              1.000  \n",
       "1                                  0.810                              0.699  \n",
       "2                                  0.850                              0.978  \n",
       "3                                  0.875                              0.398  \n",
       "4                                  0.850                              0.602  \n",
       "5                                  0.870                              0.760  \n",
       "6                                  0.910                              0.989  \n",
       "7                                  0.925                              0.903  \n",
       "8                                  0.925                              0.778  \n",
       "9                                  0.940                              0.628  \n",
       "10                                 0.945                              0.929  \n",
       "11                                 0.960                              0.954  \n",
       "12                                 1.100                              0.929  \n",
       "13                                 1.100                              0.653  \n",
       "14                                 0.990                              0.352  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(data=[[0.775, 1.322, 0.800, 1.000], [0.800, 1.301, 0.810, 0.699], [0.810, 1.130, 0.850, 0.978], \n",
    "                        [0.800, 0.929, 0.875, 0.398], [0.850, 1.021, 0.850, 0.602], [0.860, 1.000, 0.870, 0.760], \n",
    "                        [0.925, 1.106, 0.910, 0.989], [0.900, 0.954, 0.925, 0.903], [0.925, 0.813, 0.925, 0.778], \n",
    "                        [0.945, 1.041, 0.940, 0.628], [0.945, 1.021, 0.945, 0.929], [0.950, 0.978, 0.960, 0.954], \n",
    "                        [0.975, 0.740, 1.100, 0.929], [1.050, 0.778, 1.100, 0.653], [1.075, 0.574, 0.990, 0.352]],\n",
    "                  columns=pd.MultiIndex.from_tuples(product([\"1-я группа\", \"2-я группа\"], [\"Отношение объема талии к объему бедра\", \"Индекс чувствительности к инсулину\"])))\n",
    "\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1-я группа: a = 2.587, b = -1.774, r = -0.800, p = 0.000\n",
      "2-я группа: a = 0.934, b = -0.177, r = -0.075, p = 0.790\n",
      "Объединенная группа: a = 1.872, b = -1.090, r = -0.423, p = 0.020\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"Отношение объема талии к объему бедра\", \n",
    "           y=\"Индекс чувствительности к инсулину\", \n",
    "           data=df.stack(0).reset_index().rename(columns={\"level_1\": \"группа\"}), \n",
    "           hue=\"группа\",\n",
    "           ci=None,\n",
    "           facet_kws={'legend_out': False})\n",
    "\n",
    "coefs = []  # параметры которые необходимо сохранить для сравнения линий регрессии\n",
    "\n",
    "for i, method in enumerate(df.columns.levels[0]):\n",
    "    x = df[(method, \"Отношение объема талии к объему бедра\")].values\n",
    "    y = df[(method, \"Индекс чувствительности к инсулину\")].values\n",
    "    n = len(df)\n",
    "    \n",
    "    reg = LinearRegression().fit(x.reshape(-1,1), y)  # type: ignore\n",
    "    a, b = reg.intercept_, reg.coef_[0]\n",
    "    r, p = scipy.stats.pearsonr(x, y)\n",
    "\n",
    "    Sx, Sy = x.std(ddof=1), y.std(ddof=1)\n",
    "    Syx = ((n-1)/(n-2)*(Sy**2 - b**2 * Sx**2))**.5  # остаточное стандартное отклонение\n",
    "    Sa = Syx*(1/n + x.mean()**2/(n-1)/Sx**2)**.5\n",
    "    Sb = Syx/(n-1)**.5/Sx\n",
    "\n",
    "    coefs.append((Syx, Sa, Sb, a, b))  # можно было замарочиться с именнованным кортежом\n",
    "    \n",
    "    print(f\"{method}: a = {a:.3f}, b = {b:.3f}, r = {r:.3f}, p = {p:.3f}\");\n",
    "\n",
    "# Объединение выборок\n",
    "x = np.concatenate([df.iloc[:, 0].values, df.iloc[:,2].values])\n",
    "y = np.concatenate([df.iloc[:, 1].values, df.iloc[:,3].values])\n",
    "reg = LinearRegression().fit(x.reshape(-1,1), y)\n",
    "a0, b0 = reg.intercept_, reg.coef_[0]\n",
    "r0, p0 = scipy.stats.pearsonr(x, y)\n",
    "Sx0, Sy0 = x.std(ddof=1), y.std(ddof=1)\n",
    "n0 = len(x)\n",
    "\n",
    "xmin, xmax = x.min(), x.max()\n",
    "plt.gca().plot([xmin, xmax], np.array([xmin, xmax])*b0 + a0, linestyle=\"--\", c=\"grey\", label=\"Объединенная группа\")\n",
    "plt.gca().legend()\n",
    "print(f\"Объединенная группа: a = {a0:.3f}, b = {b0:.3f}, r = {r0:.3f}, p = {p0:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1) Для коэффициента сдвига: ta=2.393, pa=0.028\n",
      "2) Для коэффициента наклона: tb=2.134, pb=0.046\n",
      "3) При сравнении линий регрессии в целом: F=6.643, p=0.005\n"
     ]
    }
   ],
   "source": [
    "Syx1, Sa1, Sb1, a1, b1 = coefs[0]\n",
    "Syx2, Sa2, Sb2, a2, b2 = coefs[1]\n",
    "\n",
    "tb = abs(b1-b2)/(Sb1**2+Sb2**2)**.5\n",
    "ta = abs(a1-a2)/(Sa1**2+Sa2**2)**.5\n",
    "t05 = scipy.stats.t.ppf(.975, df=20)\n",
    "pa = scipy.stats.t.pdf(ta, df=20)\n",
    "pb = scipy.stats.t.pdf(tb, df=20)\n",
    "\n",
    "# print(f\"t05 = {t05}\")\n",
    "print(f\"1) Для коэффициента сдвига: ta={ta:.3f}, pa={pa:.3f}\")\n",
    "print(f\"2) Для коэффициента наклона: tb={tb:.3f}, pb={pb:.3f}\")\n",
    "\n",
    "Syx_ = ((Syx1**2 + Syx2**2)/2)**.5\n",
    "Syx0 = ((n0-1)/(n0-2)*(Sy0**2 - b0**2 * Sx0**2))**.5\n",
    "\n",
    "F = ((n0-2)*Syx0**2 - (n0-4)*Syx_**2)/2/Syx_**2\n",
    "p = 1-scipy.stats.f.cdf(F, 2, n0-4)\n",
    "\n",
    "print(f\"3) При сравнении линий регрессии в целом: F={F:.3f}, p={p:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Различия линий регрессии статистически значимы"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.7 ('kaggle')",
   "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": "3.9.7"
  },
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  "vscode": {
   "interpreter": {
    "hash": "c87114ee6b6cf384294f90c5f2b9fb4cb1520b5534c8c2c109b7c3f59f5d4f57"
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