{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IntroStat Week 6\n",
    "\n",
    "Welcome to the 6th lecture in IntroStat\n",
    "\n",
    "During the lectures we will present both slides and notebooks. \n",
    "\n",
    "This is the notebook used in the lecture in week 6.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: Nutrition study 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First visualise the data :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Enter data\n",
    "A = np.array([7.53, 7.48, 8.08, 8.09, 10.15, 8.40, 10.88, 6.13, 7.90])\n",
    "B = np.array([9.21, 11.51, 12.79, 11.85, 9.97, 8.79, 9.69, 9.68, 9.19])\n",
    "\n",
    "# Plotting histograms together\n",
    "plt.hist(A, alpha=0.5)\n",
    "plt.hist(B, alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets calculate mean and sample standard deviation for each group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8.293333333333335, 10.297777777777776]\n",
      "[1.4280756282494287, 1.3978713976773558]\n"
     ]
    }
   ],
   "source": [
    "mean_A = A.mean()\n",
    "mean_B = B.mean()\n",
    "print([mean_A, mean_B])\n",
    "\n",
    "s_A = A.std(ddof=1)\n",
    "s_B = B.std(ddof=1)\n",
    "print([s_A, s_B])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean energy use in hospital B is higher than hospital. Is the mean differnce statistically significant?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the mean values as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(A, alpha=0.5)\n",
    "plt.hist(B, alpha=0.5)\n",
    "# Lines with means\n",
    "plt.axvline(mean_A, linestyle='-', color=\"blue\", ymin=0, ymax=1)\n",
    "plt.axvline(mean_B, linestyle='-', color=\"orange\", ymin=0, ymax=1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great!\n",
    "But are they the same?\n",
    "\n",
    "What else can we do?\n",
    "\n",
    "lets calculate standard error (se) for each group mean and add the confidence intervals "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.47602520941647625, 0.4659571325591186]\n"
     ]
    }
   ],
   "source": [
    "n_A = len(A) \n",
    "n_B = len(B)\n",
    "# Standard errors\n",
    "se_A = s_A/np.sqrt(n_A)\n",
    "se_B = s_B/np.sqrt(n_B)\n",
    "print([se_A, se_B])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7.195617231957511, 9.391049434709158]\n",
      "[9.223278703268573, 11.37227685228698]\n"
     ]
    }
   ],
   "source": [
    "# Need to choose alpha - we choose alpha = 0.05 \n",
    "t_0975 = stats.t.ppf(0.975, df=n_A-1) # same for B since n_A = n_B\n",
    "\n",
    "# Confidence intervals [lower, upper]\n",
    "CI_A = [mean_A - t_0975 * se_A, mean_A + t_0975 * se_A]\n",
    "CI_B = [mean_B - t_0975 * se_B, mean_B + t_0975 * se_B]\n",
    "print(CI_A)\n",
    "print(CI_B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(A, alpha=0.5)\n",
    "plt.hist(B, alpha=0.5)\n",
    "plt.axvline(mean_A, linestyle='-', color=\"blue\", ymin=0, ymax=1)\n",
    "plt.axvline(mean_B, linestyle='-', color=\"orange\", ymin=0, ymax=1)\n",
    "# Adding confidence intervals to the plot from earlier\n",
    "plt.axvline(CI_A[0], linestyle='--', color=\"blue\", ymin=0, ymax=1)\n",
    "plt.axvline(CI_A[1], linestyle='--', color=\"blue\", ymin=0, ymax=1)\n",
    "plt.axvline(CI_B[0], linestyle='--', color=\"orange\", ymin=0, ymax=1)\n",
    "plt.axvline(CI_B[1], linestyle='--', color=\"orange\", ymin=0, ymax=1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another typical way to plot (only mean and CI):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1], mean_A, 'o', color=\"blue\")\n",
    "plt.plot([2], mean_B, 'o', color=\"orange\")\n",
    "plt.plot([1,1], CI_A, linestyle='-', color=\"blue\")\n",
    "plt.plot([2,2], CI_B, linestyle='-', color=\"orange\")\n",
    "plt.xlim([0,3])\n",
    "plt.xticks([1,2], [\"group A\", \"group B\"])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: Nutrition study 2: (Welch) t-test with two independent and unpaired samples\n",
    "Step by step (Welch) t-test with two independent and unpaired samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0044444444444416\n"
     ]
    }
   ],
   "source": [
    "# Difference in means\n",
    "diff = mean_B - mean_A\n",
    "print(diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6661201463570338\n"
     ]
    }
   ],
   "source": [
    "# Standard error of the difference\n",
    "se_diff = np.sqrt(se_A**2 + se_B**2)\n",
    "print(se_diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the null hypothesis\n",
    "mean_null_hyp = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.009133495521211\n"
     ]
    }
   ],
   "source": [
    "# Compute the \"test statistic\" from the oberserved data\n",
    "tobs = (diff - mean_null_hyp) / se_diff\n",
    "print(tobs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Standard Error\n",
    "$ SE = \\frac{s}{\\sqrt{n}}$ <br>\n",
    "$ SE^2 = \\frac{s^2}{n}$\n",
    "\n",
    "#### v degrees of freedom for Welch t-test (with two normally distributed population or sufficiently large sample sizes)\n",
    "\n",
    "$ v = \\frac{\\left( \\frac{s_1^2}{n_1} + \\frac{s_2^2}{n_2} \\right)^2}{\\frac{\\left( \\frac{s_1^2}{n_1} \\right)^2}{n_1 - 1} + \\frac{\\left( \\frac{s_2^2}{n_2} \\right)^2}{n_2 - 1}}$<br>\n",
    "$ v = \\frac{\\left( \\frac{s_A^2}{n_A} + \\frac{s_B^2}{n_B} \\right)^2}{\\frac{\\left( \\frac{s_A^2}{n_A} \\right)^2}{n_A - 1} + \\frac{\\left( \\frac{s_B^2}{n_B} \\right)^2}{n_B - 1}}$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.992693827602638\n"
     ]
    }
   ],
   "source": [
    "# compute v (degrees of freedom for difference-test)\n",
    "v = (se_A**2 + se_B**2)**2 / (se_A**4/(n_A-1) + se_B**4/(n_B-1))\n",
    "print(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.119984011855833"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute t_0.975 from t-distribution with df = v\n",
    "stats.t.ppf(0.975, df=v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.008322739957316144\n",
      "0.008322739957316028\n"
     ]
    }
   ],
   "source": [
    "# P-value\n",
    "print(2*stats.t.cdf(-tobs, df=v))\n",
    "# or\n",
    "print(2*(1 - stats.t.cdf(tobs, df=v)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ttest_ind function from scipy.stats for two independent samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TtestResult(statistic=3.009133495521211, pvalue=0.00832273995731614, df=15.992693827602634)\n"
     ]
    }
   ],
   "source": [
    "# You can also use the ttest_ind function from scipy.stats:\n",
    "# \"_ind\" is for independent - i.e., two independent samples\n",
    "test = stats.ttest_ind(B,A,equal_var=False) \n",
    "# \"equal_var=False\" means we are NOT assuming equal variances - this is a \"Welch t-test\"\n",
    "print(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since 0.001 < p < 0.01 we conclude there is \"Strong evidence against H_0\"\n",
    "\n",
    "We reject the nullhypothesis\n",
    "\n",
    "And we conclude that the average energy usage of nurses in Hospital B is significantly larger than in Hospital A. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: Nutrition study 3:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confidence interval for the difference in means:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.5922803841924627, 3.4166085046964207]\n"
     ]
    }
   ],
   "source": [
    "# [lower, upper] confidence interval for the difference\n",
    "diff_lower = diff - stats.t.ppf(0.975, df=v)*se_diff\n",
    "diff_upper = diff + stats.t.ppf(0.975, df=v)*se_diff\n",
    "print([diff_lower,diff_upper])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ConfidenceInterval(low=0.5922803841924627, high=3.4166085046964207)\n"
     ]
    }
   ],
   "source": [
    "# You can also retrieve the confidence interval from the \"test\" calculated with stats.ttest_ind:\n",
    "test = stats.ttest_ind(B,A,equal_var=False) \n",
    "print(test.confidence_interval(0.95))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The confidence interval does not include the value 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: Sleep medicine (paired t-test):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = np.array([.7,-1.6,-.2,-1.2,-1,3.4,3.7,.8,0,2])\n",
    "x2 = np.array([1.9,.8,1.1,.1,-.1,4.4,5.5,1.6,4.6,3.4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x1, alpha=0.5)\n",
    "plt.hist(x2, alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HOXv37lV+fn7E2OzZs9XQ0KCzZ892uyYUCikYDEYcAAAgPkUdRowxKi4u1t13362JEyf2OK+trU2pqakRY6mpqTp37pxOnTrV7ZqKigolJyeHj4yMjGjLBAAAV7iow8gTTzyhTz75RO+++26fc10uV8S5Mabb8YtKS0vV0dERPlpaWqItEwAAXOEcPTNy0ZNPPqlt27Zp9+7dGjNmTK9z09LS1NbWFjHW3t6uhIQEjRo1qts1brdbbrc7mtIAAECMcXRnxBijJ554Qlu3btVHH32kzMzMPtd4vV7V1tZGjO3cuVO5ubkaOnSos2oBAEDccRRGFi9erE2bNumdd97R8OHD1dbWpra2Nv33v/8NzyktLdX8+fPD50VFRTpx4oSKi4t19OhRrVu3TmvXrlVJScnAdQEAAGKWozBSVVWljo4OzZw5Ux6PJ3xs2bIlPCcQCKi5uTl8npmZqZqaGtXV1enOO+/U888/r1WrVvG2XgAAIMnhMyMXHzztzYYNG7qMzZgxQwcPHnRyKQAAcJXgs2kAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVY7DyO7duzVnzhylp6fL5XLp/fff73V+XV2dXC5Xl+PYsWPR1gwAAOJIgtMFZ86c0R133KGHH35Yc+fO7fe6xsZGJSUlhc9vuOEGp5cGAABxyHEYKSgoUEFBgeMLpaSkaMSIEY7XAQCA+HbZnhmZPHmyPB6P8vLytGvXrl7nhkIhBYPBiAMAAMSnQQ8jHo9Ha9askc/n09atW5WVlaW8vDzt3r27xzUVFRVKTk4OHxkZGYNdJgAAsMTxyzROZWVlKSsrK3zu9XrV0tKilStXavr06d2uKS0tVXFxcfg8GAwSSAAAiFNW3to7ZcoUHT9+vMevu91uJSUlRRwAACA+WQkjfr9fHo/HxqUBAMAVxvHLNF9//bX++c9/hs+bmpp06NAhjRw5UmPHjlVpaalaW1u1ceNGSVJlZaXGjRun7OxsdXZ2atOmTfL5fPL5fAPXBQAAiFmOw0hDQ4NmzZoVPr/4bMeCBQu0YcMGBQIBNTc3h7/e2dmpkpIStba2atiwYcrOztb27dtVWFg4AOUDAIBY5zLGGNtF9CUYDCo5OVkdHR08PwIAV5HyunLbJVwVymeWD8r37e/vbz6bBgAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFjlOIzs3r1bc+bMUXp6ulwul95///0+19TX1ysnJ0eJiYkaP368qquro6kVAADEIcdh5MyZM7rjjju0evXqfs1vampSYWGhpk2bJr/fr7KyMi1ZskQ+n89xsQAAIP4kOF1QUFCggoKCfs+vrq7W2LFjVVlZKUmaMGGCGhoatHLlSs2dO9fp5QEAQJwZ9GdG9u7dq/z8/Iix2bNnq6GhQWfPnu12TSgUUjAYjDgAAEB8cnxnxKm2tjalpqZGjKWmpurcuXM6deqUPB5PlzUVFRVavnz5YJcmSSqvK78s1xlI5TPLbZcADCh+Di+PWPzvjKvDZXk3jcvlijg3xnQ7flFpaak6OjrCR0tLy6DXCAAA7Bj0OyNpaWlqa2uLGGtvb1dCQoJGjRrV7Rq32y232z3YpQEAgCvAoN8Z8Xq9qq2tjRjbuXOncnNzNXTo0MG+PAAAuMI5DiNff/21Dh06pEOHDkn69q27hw4dUnNzs6RvX2KZP39+eH5RUZFOnDih4uJiHT16VOvWrdPatWtVUlIyMB0AAICY5vhlmoaGBs2aNSt8XlxcLElasGCBNmzYoEAgEA4mkpSZmamamhotXbpUr732mtLT07Vq1Sre1gsAACRFEUZmzpwZfgC1Oxs2bOgyNmPGDB08eNDppQAAwFWAz6YBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVkUVRl5//XVlZmYqMTFROTk5+vjjj3ucW1dXJ5fL1eU4duxY1EUDAID44TiMbNmyRU899ZSeffZZ+f1+TZs2TQUFBWpubu51XWNjowKBQPi45ZZboi4aAADED8dh5OWXX9bChQu1aNEiTZgwQZWVlcrIyFBVVVWv61JSUpSWlhY+hgwZEnXRAAAgfjgKI52dnTpw4IDy8/MjxvPz87Vnz55e106ePFkej0d5eXnatWtXr3NDoZCCwWDEAQAA4pOjMHLq1CmdP39eqampEeOpqalqa2vrdo3H49GaNWvk8/m0detWZWVlKS8vT7t37+7xOhUVFUpOTg4fGRkZTsoEAAAxJCGaRS6XK+LcGNNl7KKsrCxlZWWFz71er1paWrRy5UpNnz692zWlpaUqLi4OnweDQQIJAABxytGdkdGjR2vIkCFd7oK0t7d3uVvSmylTpuj48eM9ft3tdispKSniAAAA8clRGLnmmmuUk5Oj2traiPHa2lpNnTq139/H7/fL4/E4uTQAAIhTjl+mKS4u1rx585Sbmyuv16s1a9aoublZRUVFkr59iaW1tVUbN26UJFVWVmrcuHHKzs5WZ2enNm3aJJ/PJ5/PN7CdAACAmOQ4jDzwwAP68ssv9dxzzykQCGjixImqqanRjTfeKEkKBAIRf3Oks7NTJSUlam1t1bBhw5Sdna3t27ersLBw4LoAAAAxK6oHWB9//HE9/vjj3X5tw4YNEedPP/20nn766WguAwAArgJ8Ng0AALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwKqow8vrrryszM1OJiYnKycnRxx9/3Ov8+vp65eTkKDExUePHj1d1dXVUxQIAgPjjOIxs2bJFTz31lJ599ln5/X5NmzZNBQUFam5u7nZ+U1OTCgsLNW3aNPn9fpWVlWnJkiXy+XyXXDwAAIh9jsPIyy+/rIULF2rRokWaMGGCKisrlZGRoaqqqm7nV1dXa+zYsaqsrNSECRO0aNEiPfLII1q5cuUlFw8AAGJfgpPJnZ2dOnDggH77299GjOfn52vPnj3drtm7d6/y8/MjxmbPnq21a9fq7NmzGjp0aJc1oVBIoVAofN7R0SFJCgaDTsrtl9CZUN+TrjCD8d8BsImfw8sjFv874/IYrH/PF7+vMabXeY7CyKlTp3T+/HmlpqZGjKempqqtra3bNW1tbd3OP3funE6dOiWPx9NlTUVFhZYvX95lPCMjw0m5cWuFVtguAbjq8XOIeDLY/55Pnz6t5OTkHr/uKIxc5HK5Is6NMV3G+prf3fhFpaWlKi4uDp9fuHBBX331lUaNGtXrdQZbMBhURkaGWlpalJSUZK2Oy+Vq6pde49fV1C+9xq9Y7dcYo9OnTys9Pb3XeY7CyOjRozVkyJAud0Ha29u73P24KC0trdv5CQkJGjVqVLdr3G633G53xNiIESOclDqokpKSYuofw6W6mvql1/h1NfVLr/ErFvvt7Y7IRY4eYL3mmmuUk5Oj2traiPHa2lpNnTq12zVer7fL/J07dyo3N7fb50UAAMDVxfG7aYqLi/Xmm29q3bp1Onr0qJYuXarm5mYVFRVJ+vYllvnz54fnFxUV6cSJEyouLtbRo0e1bt06rV27ViUlJQPXBQAAiFmOnxl54IEH9OWXX+q5555TIBDQxIkTVVNToxtvvFGSFAgEIv7mSGZmpmpqarR06VK99tprSk9P16pVqzR37tyB6+IycbvdWrZsWZeXkOLV1dQvvcavq6lfeo1f8d6vy/T1fhsAAIBBxGfTAAAAqwgjAADAKsIIAACwijACAACsIoz0UygU0p133imXy6VDhw71Ovehhx6Sy+WKOKZMmXJ5Ch0ATno1xqi8vFzp6ekaNmyYZs6cqSNHjlyeQi/Rz372M40dO1aJiYnyeDyaN2+eTp482euaWN3baHqNxb397LPPtHDhQmVmZmrYsGG66aabtGzZMnV2dva6Llb3Ndp+Y3FvJenFF1/U1KlTde211/b7D2HG6t5G02us7qtEGOm3p59+us8/Z/tdP/nJTxQIBMJHTU3NIFY3sJz0+tJLL+nll1/W6tWrtX//fqWlpenee+/V6dOnB7nKSzdr1iz9+c9/VmNjo3w+nz799FP9/Oc/73NdLO5tNL3G4t4eO3ZMFy5c0BtvvKEjR47oD3/4g6qrq1VWVtbn2ljc12j7jcW9lb79sNZf/OIXeuyxxxyti8W9jabXWN1XSZJBn2pqasxtt91mjhw5YiQZv9/f6/wFCxaY++6777LUNtCc9HrhwgWTlpZmVqxYER775ptvTHJysqmurr4M1Q6sDz74wLhcLtPZ2dnjnFje2+/qq9d42tuXXnrJZGZm9jonXvbVmL77jYe9Xb9+vUlOTu7X3Fjf2/72Guv7yp2RPnzxxRd69NFH9ac//UnXXnttv9fV1dUpJSVFt956qx599FG1t7cPYpUDw2mvTU1NamtrU35+fnjM7XZrxowZ2rNnz2CWOuC++uorvf3225o6dWqfH1MQi3v7Xf3pNZ72tqOjQyNHjuxzXqzv60V99RtPe9tf8bK3vYn1fSWM9MIYo4ceekhFRUXKzc3t97qCggK9/fbb+uijj/T73/9e+/fv1z333KNQKDSI1V6aaHq9+AGI//shiampqV0+HPFK9cwzz+i6667TqFGj1NzcrA8++KDX+bG4txc56TUe9laSPv30U7366qvhj6voSSzv63f1p9942dv+ipe97UvM76vtWzM2LFu2zEjq9di/f7955ZVXzNSpU825c+eMMcY0NTX162Wa/3Xy5EkzdOhQ4/P5BqGb3g1mr3/729+MJHPy5MmI8UWLFpnZs2cPZls96m+/F/3rX/8yjY2NZufOneZHP/qRKSwsNBcuXOj39WJhby9y0uuVtrdOezXGmNbWVnPzzTebhQsXOr6ezX01ZnD7jYe9dfIyzf+KpZ9ZY/rf65W2r045/myaePDEE0/ol7/8Za9zxo0bpxdeeEH79u3r8lkAubm5evDBB/XWW2/163oej0c33nijjh8/HnXN0RrMXtPS0iR9m8g9Hk94vL29vUs6v1z62+9Fo0eP1ujRo3XrrbdqwoQJysjI0L59++T1evt1vVjY24uc9Hql7a3TXk+ePKlZs2bJ6/VqzZo1jq9nc1+lwe031vf2UsXSz6wTV9q+OnVVhpGL/1Puy6pVq/TCCy+Ez0+ePKnZs2dry5Yt+uEPf9jv63355ZdqaWmJ+AdyuQxmr5mZmUpLS1Ntba0mT54s6dsnwOvr6/W73/1uYBpwqL/9dsf8/8c0Obl9Gwt7252+er3S9tZJr62trZo1a5ZycnK0fv16fe97zl+Ntrmv0uD2G8t7OxBi9We2L1favjpm+9ZMLOnppYusrCyzdetWY4wxp0+fNr/5zW/Mnj17TFNTk9m1a5fxer3mBz/4gQkGgxaqjk5/ejXGmBUrVpjk5GSzdetWc/jwYfOrX/3KeDyeK77Xv//97+bVV181fr/ffPbZZ+ajjz4yd999t7npppvMN998E54XD3sbTa/GxObeXnyp4p577jGff/65CQQC4eO74mFfjYmuX2Nic2+NMebEiRPG7/eb5cuXm+uvv974/X7j9/vN6dOnw3PiZW+d9mpM7O6rMcYQRhzo6Re0JLN+/XpjjDH/+c9/TH5+vrnhhhvM0KFDzdixY82CBQtMc3Pz5S/4EvSnV2O+fTvZsmXLTFpamnG73Wb69Onm8OHDl7fYKHzyySdm1qxZZuTIkcbtdptx48aZoqIi8/nnn0fMi4e9jaZXY2Jzb9evX9/ja/HfFQ/7akx0/RoTm3trzLdv0+2u1127doXnxMveOu3VmNjdV2OMcRnz//drAQAALOCtvQAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKv+D4f5zmnLb2QWAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "differences = x1-x2\n",
    "plt.hist(differences, alpha=0.5, color = \"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### \"one sample t-test\" for the differences: stats.ttest_1samp()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TtestResult(statistic=-4.671645978656774, pvalue=0.0011658764685528319, df=9)\n",
      "ConfidenceInterval(low=-2.4786662557963277, high=-0.8613337442036719)\n"
     ]
    }
   ],
   "source": [
    "# make a \"one sample t-test\" for the differences (this time we only use the built-in python function):\n",
    "test1 = stats.ttest_1samp(differences, popmean=0)\n",
    "print(test1)\n",
    "print(test1.confidence_interval(0.95))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Paired t-test for two related samples: stats.ttest_rel(x1, x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TtestResult(statistic=-4.671645978656774, pvalue=0.0011658764685528319, df=9)\n",
      "ConfidenceInterval(low=-2.4786662557963277, high=-0.8613337442036719)\n"
     ]
    }
   ],
   "source": [
    "# equal to a paired t-test for two samples:\n",
    "test2 = stats.ttest_rel(x1, x2) # \"_rel\" is for related samples (= paired samples)\n",
    "print(test2)\n",
    "print(test2.confidence_interval(0.95))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### t-test for two independent samples: stats.ttest_ind(x1,x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TtestResult(statistic=-1.9334408348617205, pvalue=0.06906688754589858, df=18.0)\n",
      "ConfidenceInterval(low=-3.484661066395306, high=0.1446610663953074)\n"
     ]
    }
   ],
   "source": [
    "# WRONG ANALYSIS: In the case of Sleep medication data\n",
    "test3 = stats.ttest_ind(x1, x2) # making independent 2 sample test - WRONG!\n",
    "print(test3)\n",
    "print(test3.confidence_interval(0.95))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A paired experiment is \"stronger\" than an unpaired experiment. \n",
    "\n",
    "In a paired experiment each person is its owm \"negative control\" and we are sure to meassure a difference that is not due to inter-personal variation. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### QQ-plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets re-visit the example with energy usage amongst nurses in hospital A and hospital B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "A = np.array([7.53, 7.48, 8.08, 8.09, 10.15, 8.40, 10.88, 6.13, 7.90])\n",
    "B = np.array([9.21, 11.51, 12.79, 11.85, 9.97, 8.79, 9.69, 9.68, 9.19])\n",
    "\n",
    "plt.hist(A, alpha=0.5)\n",
    "plt.hist(B, alpha=0.5)\n",
    "plt.title(\"Hospital A (blue) and Hospital B (orange)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Could the data come from underlying normal distributions?\n",
    "\n",
    "Do we need to assume that the distributions are normal?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets make the QQ-plots:\n",
    "fig, axs = plt.subplots(1, 2, figsize=(10,4))\n",
    "sm.qqplot(A,line=\"q\",a=3/8,ax=axs[0]) \n",
    "sm.qqplot(B,line=\"q\",a=3/8,ax=axs[1]) \n",
    "# OBS: \"a = 3/8\" is preferred for n <= 10 \n",
    "#     (\"a = 1/2\" is preferred for n >  10)  \n",
    "axs[0].set_title(\"Hospital A\")\n",
    "axs[1].set_title(\"Hospital B\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Its difficult to tell whether it looks normal\n",
    "\n",
    "We can try to compare with simulated data from normal distributions\n",
    "- This is a so-called \"Wally-plot\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for Hospital A\n",
    "fig, axs = plt.subplots(3, 3, figsize=(8,8))\n",
    "# data in first subplot:\n",
    "sm.qqplot(A,line=\"q\",a=3/8,ax=axs[0,0]) \n",
    "# simulated data in all other subplots:\n",
    "sm.qqplot(stats.norm.rvs(size=len(A)),line=\"q\",a=3/8,ax=axs[0,1]) \n",
    "sm.qqplot(stats.norm.rvs(size=len(A)),line=\"q\",a=3/8,ax=axs[0,2]) \n",
    "sm.qqplot(stats.norm.rvs(size=len(A)),line=\"q\",a=3/8,ax=axs[1,0]) \n",
    "sm.qqplot(stats.norm.rvs(size=len(A)),line=\"q\",a=3/8,ax=axs[1,1]) \n",
    "sm.qqplot(stats.norm.rvs(size=len(A)),line=\"q\",a=3/8,ax=axs[1,2]) \n",
    "sm.qqplot(stats.norm.rvs(size=len(A)),line=\"q\",a=3/8,ax=axs[2,0]) \n",
    "sm.qqplot(stats.norm.rvs(size=len(A)),line=\"q\",a=3/8,ax=axs[2,1]) \n",
    "sm.qqplot(stats.norm.rvs(size=len(A)),line=\"q\",a=3/8,ax=axs[2,2]) \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for Hospital B\n",
    "fig, axs = plt.subplots(3, 3, figsize=(8,8))\n",
    "# data in first subplot:\n",
    "sm.qqplot(B,line=\"q\",a=3/8,ax=axs[0,0]) \n",
    "# simulated data in all other subplots:\n",
    "sm.qqplot(stats.norm.rvs(size=len(B)),line=\"q\",a=3/8,ax=axs[0,1]) \n",
    "sm.qqplot(stats.norm.rvs(size=len(B)),line=\"q\",a=3/8,ax=axs[0,2]) \n",
    "sm.qqplot(stats.norm.rvs(size=len(B)),line=\"q\",a=3/8,ax=axs[1,0]) \n",
    "sm.qqplot(stats.norm.rvs(size=len(B)),line=\"q\",a=3/8,ax=axs[1,1]) \n",
    "sm.qqplot(stats.norm.rvs(size=len(B)),line=\"q\",a=3/8,ax=axs[1,2]) \n",
    "sm.qqplot(stats.norm.rvs(size=len(B)),line=\"q\",a=3/8,ax=axs[2,0]) \n",
    "sm.qqplot(stats.norm.rvs(size=len(B)),line=\"q\",a=3/8,ax=axs[2,1]) \n",
    "sm.qqplot(stats.norm.rvs(size=len(B)),line=\"q\",a=3/8,ax=axs[2,2]) \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When comparing to simulated data, it looks like our data could very well come from an underlying normal distribution.\n",
    "\n",
    "This is no proof of normality(!), but it can give us an idea whether the assumption could be true."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: Power calculations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import package for power calculations:\n",
    "import statsmodels.stats.power as smp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "82.394399899303\n"
     ]
    }
   ],
   "source": [
    "# We want to calculate the sample size needed in a new experiment\n",
    "\n",
    "# from previous experiments we have a good guess for the sample variation:\n",
    "sd = 1.62\n",
    "\n",
    "# we want to be able to detect a voltage drop of down to 0.5 volts\n",
    "delta = 0.5\n",
    "\n",
    "# we need some fractals from the normal distribution (assuming the samples will be big enough for normal distribution assumption to apply)\n",
    "z_power = stats.norm.ppf(0.80, loc=0, scale = 1)\n",
    "z_signif = stats.norm.ppf(0.975, loc=0, scale = 1)\n",
    "\n",
    "# use formula to calculate n_obs (needed number of observations):\n",
    "n_obs = (sd/delta*(z_power+z_signif))**2\n",
    "print(n_obs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "84.33509229986839\n"
     ]
    }
   ],
   "source": [
    "# we can also use the python function TTestPower().solve_power (for one sample power calculations): \n",
    "print(smp.TTestPower().solve_power(effect_size=delta/sd, alpha=0.05, power=0.80))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results is almost the same (due to assumption anout normal distribution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.43076647608180796\n",
      "0.6666809112171673\n"
     ]
    }
   ],
   "source": [
    "# What if we can only make 60 observations?\n",
    "n = 60\n",
    "\n",
    "# calculate the power:\n",
    "z_power_new = np.sqrt(n*(delta/sd)**2)-z_signif\n",
    "print(z_power_new)\n",
    "\n",
    "power = stats.norm.cdf(z_power_new)\n",
    "print(power)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6523725330696484\n"
     ]
    }
   ],
   "source": [
    "# we can also use the python function TTestPower().solve_power (for one sample power calculations): \n",
    "print(smp.TTestPower().solve_power(effect_size=delta/sd, alpha=0.05, nobs=60))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is almost the same (due to assumption about normaldistribution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: Power calculations, 2 samples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1)\n",
    "\n",
    "Find the sample size in a test where:\n",
    "\n",
    "power = 0.90 (beta = 0.10)\n",
    "\n",
    "n1 = n2 (k = 1)\n",
    "\n",
    "We want to be able to detect a difference of 2\n",
    "\n",
    "using alpha = 0.05\n",
    "\n",
    "and assuming sigma = 1 in both populations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.2537115307203095\n"
     ]
    }
   ],
   "source": [
    "power = 0.90\n",
    "k = 1\n",
    "delta = 2\n",
    "sd = 1\n",
    "\n",
    "z_power = stats.norm.ppf(0.90, loc=0, scale = 1)\n",
    "z_signif = stats.norm.ppf(0.975, loc=0, scale = 1)\n",
    "\n",
    "n1 = (k+1) * (sd/delta*(z_signif+z_power))**2\n",
    "\n",
    "print(n1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.38675538417503\n"
     ]
    }
   ],
   "source": [
    "# we can also use the python function TTestIndPower().solve_power (for one sample power calculations): \n",
    "print(smp.TTestIndPower().solve_power(effect_size=delta/sd, alpha=0.05, power=0.90, ratio=k))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is almost the same (due to assumption anout normaldistribution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2)\n",
    "\n",
    "Find the power of an experiment where: \n",
    "\n",
    "n1 = n2 = 10  (k = 1)\n",
    "\n",
    "We want to be able to detect a difference of 2\n",
    "\n",
    "using alpha = 0.05\n",
    "\n",
    "and assuming sigma = 1 in both populations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n1 = 10\n",
    "n2 = 10\n",
    "k = 1\n",
    "delta = 2\n",
    "sd = 1\n",
    "z_signif = stats.norm.ppf(0.975, loc=0, scale = 1)\n",
    "\n",
    "z = np.sqrt(n1/(k+1)*delta**2/sd**2) - z_signif\n",
    "power = stats.norm.cdf(z)\n",
    "\n",
    "print(power)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can also use the python function TTestIndPower().solve_power (for one sample power calculations): \n",
    "print(smp.TTestIndPower().solve_power(effect_size=delta/sd, alpha=0.05, nobs1=10, ratio=k))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is almost the same (due to assumption anout normaldistribution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3)\n",
    "\n",
    "What effectsize (delta) can be detected when:\n",
    "\n",
    "n1 = n2 = 10  (k = 1)\n",
    "\n",
    "alpha = 0.05\n",
    "\n",
    "power = 0.90 (beta = 0.10)\n",
    "\n",
    "and assuming sigma = 1 in both populations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.4496498240223823\n"
     ]
    }
   ],
   "source": [
    "n1 = 10\n",
    "n2 = 10\n",
    "k = 1\n",
    "sd = 1\n",
    "\n",
    "z_power = stats.norm.ppf(0.90, loc=0, scale = 1)\n",
    "z_signif = stats.norm.ppf(0.975, loc=0, scale = 1)\n",
    "\n",
    "delta = sd*(z_signif+z_power)/np.sqrt(n1/(k+1))\n",
    "\n",
    "print(delta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.533693123772208\n"
     ]
    }
   ],
   "source": [
    "# we can also use the python function TTestIndPower().solve_power (for one sample power calculations): \n",
    "print(smp.TTestIndPower().solve_power(alpha=0.05, power=0.90, nobs1=10, ratio=k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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