From a8ff27bc2cac39d3d0b738870171f4ef5565da9e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Fran=C3=A7ois=20Laurent?= <francois.laurent@posteo.net>
Date: Sun, 26 Sep 2021 16:13:47 +0200
Subject: [PATCH] SciPy course material complete

---
 notebooks/scipy_TP_solutions.ipynb | 865 ++++++++++++++++++++++++++---
 notebooks/scipy_cours.ipynb        | 688 +++++++++++++++++------
 2 files changed, 1304 insertions(+), 249 deletions(-)

diff --git a/notebooks/scipy_TP_solutions.ipynb b/notebooks/scipy_TP_solutions.ipynb
index 213e271..878502a 100644
--- a/notebooks/scipy_TP_solutions.ipynb
+++ b/notebooks/scipy_TP_solutions.ipynb
@@ -5,14 +5,28 @@
    "id": "a5a5210d",
    "metadata": {},
    "source": [
-    "Import `numpy`, `pandas`, the `pyplot` module from `matplotlib`, `seaborn`, and the `stats` module from `scipy`:"
+    "## Q\n",
+    "\n",
+    "Import `numpy`, `pandas`, the `pyplot` module from `matplotlib`, `seaborn`, and the `stats` module from `scipy`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ac6cc32",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## A"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "id": "529c5f56",
-   "metadata": {},
+   "metadata": {
+    "hidden": true
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -24,7 +38,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bb564f37",
+   "id": "93ad4aaf",
    "metadata": {},
    "source": [
     "# Comparison of two group means"
@@ -32,17 +46,31 @@
   },
   {
    "cell_type": "markdown",
-   "id": "08c1dd12",
+   "id": "0e4fd0d9",
    "metadata": {},
    "source": [
-    "Load the `mi.csv` data file located in the `data` directory of the course repository:"
+    "## Q\n",
+    "\n",
+    "Load the `mi.csv` data file located in the `data` directory of the course repository."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08c1dd12",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## A"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "id": "00130518",
-   "metadata": {},
+   "metadata": {
+    "hidden": true
+   },
    "outputs": [
     {
      "data": {
@@ -261,14 +289,28 @@
    "id": "9cc036b2",
    "metadata": {},
    "source": [
-    "Question: anything missing?"
+    "## Q\n",
+    "\n",
+    "Anything missing?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99d5dc74",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## A"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "id": "8a648a9b",
-   "metadata": {},
+   "metadata": {
+    "hidden": true
+   },
    "outputs": [
     {
      "data": {
@@ -289,7 +331,9 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "2f0a8116",
-   "metadata": {},
+   "metadata": {
+    "hidden": true
+   },
    "outputs": [
     {
      "data": {
@@ -666,14 +710,28 @@
    "id": "3512f950",
    "metadata": {},
    "source": [
-    "Show a summary table for these data:"
+    "## Q\n",
+    "\n",
+    "Show a summary table for these data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6984434b",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## A"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "id": "a7a7d087",
-   "metadata": {},
+   "metadata": {
+    "hidden": true
+   },
    "outputs": [
     {
      "data": {
@@ -933,17 +991,31 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ec2a049f",
+   "id": "04163591",
    "metadata": {},
    "source": [
-    "Inspect the distribution of variables `Age` and `OwnsHouse`:"
+    "## Q\n",
+    "\n",
+    "Inspect the distribution of variables `Age` and `OwnsHouse`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6baac23",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## A"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "14572a36",
-   "metadata": {},
+   "id": "5de6412d",
+   "metadata": {
+    "hidden": true
+   },
    "outputs": [
     {
      "data": {
@@ -968,8 +1040,10 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "9c625646",
-   "metadata": {},
+   "id": "f793503f",
+   "metadata": {
+    "hidden": true
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -993,17 +1067,23 @@
   },
   {
    "cell_type": "markdown",
-   "id": "af5660e7",
-   "metadata": {},
+   "id": "1e94c17b",
+   "metadata": {
+    "heading_collapsed": true
+   },
    "source": [
-    "Isolate the house-owners group from the others, draw their respective age distributions and check they are normally distributed:"
+    "## Q\n",
+    "\n",
+    "Isolate the house-owners group from the others, draw their respective age distributions and check they are normally distributed."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "86252da3",
-   "metadata": {},
+   "id": "55d18f16",
+   "metadata": {
+    "hidden": true
+   },
    "outputs": [],
    "source": [
     "group = df.groupby('OwnsHouse').groups\n",
@@ -1016,8 +1096,10 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "1f4abc62",
-   "metadata": {},
+   "id": "3d1a44e6",
+   "metadata": {
+    "hidden": true
+   },
    "outputs": [
     {
      "data": {
@@ -1039,8 +1121,10 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "b67a8ea8",
-   "metadata": {},
+   "id": "ddf5d4b0",
+   "metadata": {
+    "hidden": true
+   },
    "outputs": [
     {
      "data": {
@@ -1063,19 +1147,21 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1d0bb402",
-   "metadata": {},
+   "id": "24b49c4c",
+   "metadata": {
+    "hidden": true
+   },
    "source": [
-    "\\[CORR\\]\n",
-    "\n",
     "The red line is fitted to the blue points and does not align well on the linear part. To better illustrate what is the linear part, we reimplement the regression (the exact implementation is out of the scope of this session):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "8f971da3",
-   "metadata": {},
+   "id": "0f888c53",
+   "metadata": {
+    "hidden": true
+   },
    "outputs": [
     {
      "data": {
@@ -1101,34 +1187,48 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b8ecfebf",
-   "metadata": {},
+   "id": "f35584b7",
+   "metadata": {
+    "hidden": true
+   },
    "source": [
-    "\\[CORR\\]\n",
-    "\n",
-    "The misalignment of the default regression line on the central part of the distribution is indicative of some asymmetry, while the diverging tails also hint at some departure from normality (kurtosis).\n",
+    "The misalignment of the default regression line on the central part of the distribution is indicative of some asymmetry, while the diverging tails also hint at some departure from normality (kurtosis). The sampling procedure clearly excluded people younger than 20 years old or elder than 70, which results in truncated distributions.\n",
     "\n",
     "Here, we have comfortable sample sizes and these departures from normality may not affect the power of the statistical test."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f6de8e66",
+   "id": "2cc80be1",
    "metadata": {},
    "source": [
+    "## Q\n",
+    "\n",
     "Are the sample size and variance of the two groups similar enough for running a standard $t$ test?"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "cd58c73a",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## A"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "d5ac4dc5",
-   "metadata": {},
+   "id": "0dbb79f7",
+   "metadata": {
+    "hidden": true
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(288, 528, 219.94736689332564, 138.81976459481174)"
+       "(288, 528, 14.830622606395378, 11.782179959362857)"
       ]
      },
      "execution_count": 12,
@@ -1137,30 +1237,46 @@
     }
    ],
    "source": [
-    "len(house_owners_age), len(others_age), np.var(house_owners_age), np.var(others_age)"
+    "len(house_owners_age), len(others_age), np.std(house_owners_age), np.std(others_age)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e177b52b",
-   "metadata": {},
+   "id": "3e221ea2",
+   "metadata": {
+    "hidden": true
+   },
    "source": [
-    "\\[CORR\\] `ttest_ind` allows variance ratios up to $2$. The groups can have different sample sizes."
+    "`ttest_ind` allows standard deviation ratios [up to $2$](https://en.wikipedia.org/wiki/Student%27s_t-test#Equal_or_unequal_sample_sizes,_similar_variances_(1/2_%3C_sX1/sX2_%3C_2)). The groups can have different sample sizes."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "cd273f22",
+   "id": "d61f454a",
    "metadata": {},
    "source": [
-    "Test the group mean ages equal:"
+    "## Q\n",
+    "\n",
+    "Test the group mean ages equal."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b076e8e6",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## A"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "ed503837",
-   "metadata": {},
+   "id": "1d238900",
+   "metadata": {
+    "hidden": true
+   },
    "outputs": [
     {
      "data": {
@@ -1183,25 +1299,39 @@
   },
   {
    "cell_type": "markdown",
-   "id": "967b1f9f",
+   "id": "62b30b76",
    "metadata": {},
    "source": [
+    "## Q\n",
+    "\n",
     "How would you report the result of this test?"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "efeac3ab",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## A"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "de90c6e8",
-   "metadata": {},
+   "execution_count": 54,
+   "id": "341157b6",
+   "metadata": {
+    "hidden": true
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(814, -10.305953282828284, -0.7954424784394866)"
+       "(814, -10.305953282828284, -0.7954424784394866, -0.7954424784394866)"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1212,23 +1342,27 @@
     "n1, n2 = len(house_owners_age), len(others_age)\n",
     "degrees_of_freedom = n1 + n2 - 2\n",
     "\n",
-    "# * the mean difference (this is almost an effect size in itself, an intuitive one),\n",
+    "# * the mean difference (this is almost an effect size, not compared with the associated variability),\n",
     "mean_difference = np.mean(house_owners_age) - np.mean(others_age)\n",
     "\n",
     "# * and the effect size.\n",
-    "f, _ = stats.ttest_ind(house_owners_age, others_age)\n",
-    "cohen_d = f * np.sqrt(1/n1 + 1/n2)\n",
+    "t, _ = stats.ttest_ind(house_owners_age, others_age)\n",
+    "cohen_d = t * np.sqrt(1/n1 + 1/n2)\n",
     "\n",
-    "degrees_of_freedom, mean_difference, cohen_d"
+    "#   alternatively:\n",
+    "import pingouin as pg\n",
+    "unbiased_cohen_d = pg.compute_effsize(house_owners_age, others_age)\n",
+    "\n",
+    "degrees_of_freedom, mean_difference, cohen_d, unbiased_cohen_d"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "21e69d05",
-   "metadata": {},
+   "id": "b79f8a5c",
+   "metadata": {
+    "hidden": true
+   },
    "source": [
-    "\\[CORR\\]\n",
-    "\n",
     "«**In our study**, house owners ($n=288$) were found to be significantly younger than the other surveyed people ($n=528$; $10.3$ years younger on average, $t(814)=-10.9$, $p<0.05$). This effect was found to be large (Cohen's $d \\approx 0.8$).»\n",
     "\n",
     "Note: as we report the sample size for each group, we may omit the (still nice-to-have) information of the number of degrees of freedom."
@@ -1236,16 +1370,613 @@
   },
   {
    "cell_type": "markdown",
-   "id": "54db26df",
+   "id": "f72698b7",
+   "metadata": {},
+   "source": [
+    "## Q\n",
+    "\n",
+    "\\[optional; good for playing with Python rather than statistical methods\\]\n",
+    "\n",
+    "Although tractable in principle, the group difference in variance is quite large and -- had we smaller samples -- we could instead use the Welch's $t$ test that is known to better control for type-1 errors in cases of differing variances, but also a slightly lower power.\n",
+    "\n",
+    "As it is now clear we have a relationship between age and owning a house, let us compute the rejection rate (or power) as a function of sample size.\n",
+    "\n",
+    "Proposal:\n",
+    "* loop over decreasing sample sizes (*e.g.* 200, 50, 20, 10, 5),\n",
+    "* randomly pick a subsample of that size from each group,\n",
+    "* compare their means using the standard Student $t$-test and Welch $t$-test,\n",
+    "* observe whether each test successfully rejects $H_0$ for a constant significance level (*e.g.* 5%),\n",
+    "* replicate this procedure many times (*e.g.* 100)\n",
+    "* and compute the rejection rate for each sample size and type of test."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a2bc253",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## Help: subsampling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "fd050fbc",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# let us consider an example sample\n",
+    "sample = others_age\n",
+    "\n",
+    "# and a subsample size\n",
+    "n = 200\n",
+    "\n",
+    "# we need a random generator\n",
+    "rng = np.random.default_rng()\n",
+    "\n",
+    "# now we can pick n observations from the original sample\n",
+    "# calling the `choice` method of the random generator\n",
+    "subsample = rng.choice(sample, n)\n",
+    "\n",
+    "# in principle the smaller sample will exhibit similar\n",
+    "# properties as the original sample; both are drawn from\n",
+    "# the population in similar ways\n",
+    "bins = np.arange(20, 70+1, 5)\n",
+    "sns.histplot(sample, bins=bins)\n",
+    "sns.histplot(subsample, bins=bins);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b44a7b2b",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "35541f89",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": [
+    "significance_level = 0.05\n",
+    "\n",
+    "sample1 = house_owners_age\n",
+    "sample2 = others_age\n",
+    "sample_size = min(len(sample1), len(sample2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "2ae175e5",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": [
+    "from collections import defaultdict\n",
+    "\n",
+    "sample_sizes = []\n",
+    "test_types = []\n",
+    "rejection_rates = []\n",
+    "\n",
+    "rng = np.random.default_rng()\n",
+    "\n",
+    "for relative_sample_size in (1, .2, .1, .05, .025):\n",
+    "    n = int(relative_sample_size * sample_size)\n",
+    "    nreplicates = 100\n",
+    "    rejections = defaultdict(lambda: 0)\n",
+    "    for _ in range(nreplicates):\n",
+    "        subsample1 = rng.choice(sample1, n)\n",
+    "        subsample2 = rng.choice(sample2, n)\n",
+    "        for test_type in ('Student', 'Welch'):\n",
+    "            t, pv = stats.ttest_ind(subsample1, subsample2, equal_var=test_type=='Student')\n",
+    "            if pv <= significance_level:\n",
+    "                rejections[test_type] = rejections[test_type] + 1\n",
+    "    for test_type in rejections:\n",
+    "        rejection_rates.append(rejections[test_type] / nreplicates)\n",
+    "        sample_sizes.append(n)\n",
+    "        test_types.append(test_type)\n",
+    "            \n",
+    "result = pd.DataFrame({'sample size': sample_sizes, 'test': test_types, 'power': rejection_rates})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "d9641fa6",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sample size</th>\n",
+       "      <th>test</th>\n",
+       "      <th>power</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>288</td>\n",
+       "      <td>Student</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>288</td>\n",
+       "      <td>Welch</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>57</td>\n",
+       "      <td>Student</td>\n",
+       "      <td>0.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>57</td>\n",
+       "      <td>Welch</td>\n",
+       "      <td>0.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>28</td>\n",
+       "      <td>Student</td>\n",
+       "      <td>0.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>28</td>\n",
+       "      <td>Welch</td>\n",
+       "      <td>0.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>14</td>\n",
+       "      <td>Student</td>\n",
+       "      <td>0.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>14</td>\n",
+       "      <td>Welch</td>\n",
+       "      <td>0.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>7</td>\n",
+       "      <td>Student</td>\n",
+       "      <td>0.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>7</td>\n",
+       "      <td>Welch</td>\n",
+       "      <td>0.23</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sample size     test  power\n",
+       "0          288  Student   1.00\n",
+       "1          288    Welch   1.00\n",
+       "2           57  Student   0.97\n",
+       "3           57    Welch   0.97\n",
+       "4           28  Student   0.80\n",
+       "5           28    Welch   0.80\n",
+       "6           14  Student   0.55\n",
+       "7           14    Welch   0.55\n",
+       "8            7  Student   0.25\n",
+       "9            7    Welch   0.23"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0ffbdab",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "Both tests give similar results and quickly loose quite a lot of power as the sample size decreases.\n",
+    "$0.8$ is often considered as a reasonnable (some would say «minimal») power for a(ny) test.\n",
+    "\n",
+    "Here, the quick decrease in power is likely to be intensified by the asymmetries in opposite directions, known to be deleterous for the $t$-tests."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7d98432",
+   "metadata": {},
+   "source": [
+    "# Comparing two distributions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f5453a9",
+   "metadata": {},
+   "source": [
+    "Now let proceed to comparing age between people living with kids and those living without kids.\n",
+    "Plot the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "0aeaeee7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x='LivesWithKids', y='Age', data=df)\n",
+    "sns.swarmplot(x='LivesWithKids', y='Age', data=df, linewidth=1, size=3);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02cbfb3c",
+   "metadata": {},
+   "source": [
+    "## Q\n",
+    "\n",
+    "How do the common descriptive statistics (mean, variance) compare?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2f481d2",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "e9c1cc3c",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(47.758187772925766, 44.85779329608938, 16.298908849529322, 9.611832029475966)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lives_with_kids = df['Age'][df['LivesWithKids']=='Yes']\n",
+    "lives_without_kids = df['Age'][df['LivesWithKids']=='No']\n",
+    "np.mean(lives_without_kids), np.mean(lives_with_kids), np.std(lives_without_kids), np.std(lives_with_kids)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d7ad6b1",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "A difference in group means is very unlikely, and the ratio of the group standard deviations is large but $<2$.\n",
+    "\n",
+    "The main feature to notice is the double mode in the *lives without kids* group.\n",
+    "Similar samples drawn from the same population could be (more) biased towards elder or younger people, and this could result in mean differences, in a direction or another, even to the point such differences become significant."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "b6bfdb58",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(lives_without_kids);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bef7a30a",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "As a consequence, there is no point in comparing the two groups in terms of central tendency (means). A $t$-test is not suitable."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34b67889",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## Q\n",
+    "\n",
+    "How can we compare the two groups to state they differ from one another?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89181560",
+   "metadata": {},
+   "source": [
+    "## A"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef4093df",
+   "metadata": {},
+   "source": [
+    "We need a two-sample goodness-of-fit test.\n",
+    "\n",
+    "This can be done in two ways:\n",
+    "\n",
+    "* with a $\\chi^2$ test of homogeneity, binning the age;\n",
+    "* with a two-sample Kolmogorov-Smirnov test.\n",
+    "\n",
+    "### Q\n",
+    "\n",
+    "Bin the two groups, extract frequencies and proceed to performing a $\\chi^2$ test."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4338fe92",
+   "metadata": {},
+   "source": [
+    "### A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f57a8ff6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([75, 32, 26, 23, 22, 30, 39, 56, 94, 61]),\n",
+       " array([ 2, 12, 44, 67, 65, 53, 57, 34, 16,  8]))"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bins = np.arange(20, 70+1, 5)\n",
+    "lives_without_kids_freqs, _ = np.histogram(lives_without_kids, bins)\n",
+    "lives_with_kids_freqs, _ = np.histogram(lives_with_kids, bins)\n",
+    "lives_without_kids_freqs, lives_with_kids_freqs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "980a9794",
+   "metadata": {},
+   "source": [
+    "Let us check we did not miss any observation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "de19e80b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "816"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "assert np.sum(lives_without_kids_freqs) + np.sum(lives_with_kids_freqs) == len(df)\n",
+    "len(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bbbc965c",
+   "metadata": {},
+   "source": [
+    "Check there are at least 5 observations per combination of factor levels:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "32afe08a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([22, 23, 26]), array([ 2,  8, 12]))"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sort(lives_without_kids_freqs)[:3], np.sort(lives_with_kids_freqs)[:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "b021e03f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "χ²(9) = 228.0, p-value = 4.33e-44\n"
+     ]
+    }
+   ],
+   "source": [
+    "chi2, pvalue, dof, _ = stats.chi2_contingency(np.stack((lives_with_kids_freqs, lives_without_kids_freqs), axis=0))\n",
+    "print(f'χ²({dof}) = {chi2:.1f}, p-value = {pvalue:.3g}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b829782",
+   "metadata": {},
+   "source": [
+    "### Q\n",
+    "\n",
+    "Similarly, perform a two-sample Kolmogorov-Smirnov test."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e66c8ce",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "### A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "3c694746",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "KstestResult(statistic=0.31230026103290964, pvalue=1.1102230246251565e-16)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stats.ks_2samp(lives_with_kids, lives_without_kids)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "901dca51",
    "metadata": {},
    "source": [
-    "# Asymmetry and power"
+    "# Correlations"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b3e0f9d2",
+   "id": "73b4a48d",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -1272,7 +2003,7 @@
   "toc": {
    "base_numbering": 1,
    "nav_menu": {},
-   "number_sections": true,
+   "number_sections": false,
    "sideBar": false,
    "skip_h1_title": false,
    "title_cell": "Table of Contents",
diff --git a/notebooks/scipy_cours.ipynb b/notebooks/scipy_cours.ipynb
index 8237eb9..f6cea83 100644
--- a/notebooks/scipy_cours.ipynb
+++ b/notebooks/scipy_cours.ipynb
@@ -84,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 3,
    "id": "898957c8",
    "metadata": {},
    "outputs": [
@@ -1060,9 +1060,298 @@
     "hidden": true
    },
    "source": [
-    "<div class=\"alert alert-block alert-success\">\n",
     "The most important datum in a dataset is the sample size $n$. Here $n=816$.\n",
-    "</div>"
+    "\n",
+    "We also describe the sample reporting the mean value of variables of interest."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "id": "c58a021f",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "46.485710784313724"
+      ]
+     },
+     "execution_count": 153,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "age = dataframe['Age']\n",
+    "sample_mean = np.mean(age)\n",
+    "sample_mean"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "700ff9a1-e4e9-4ef5-9559-f6d6740977ed",
+   "metadata": {
+    "heading_collapsed": true,
+    "hidden": true
+   },
+   "source": [
+    "### Confidence intervals"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60f07aba",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "To report the value of an estimator such as the sample mean and account for the uncertainty about the estimated value, we can report a confidence interval instead. This is used mostly for the mean of an observed variable.\n",
+    "\n",
+    "Reminder: the sample mean is an estimator of the population mean. We are interested in drawing conclusions about the population the sample comes from, and not about the sample itself."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "id": "0b70263b",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# you can play with the three following parameters:\n",
+    "sample_mean = m = 46\n",
+    "sem = s = 1 # standard_error_of_the_mean = sample_sd / sqrt(sample_size)\n",
+    "alpha = 0.05\n",
+    "\n",
+    "b = 3\n",
+    "grid = np.linspace(m-b*s, m+b*s, 200) # possible population mean values\n",
+    "pdf = stats.norm(m, s).pdf\n",
+    "prob = pdf(grid) # probability for the population mean\n",
+    "\n",
+    "plt.plot(grid, prob, 'r-', zorder=3)\n",
+    "plt.axhline(0, color='k', linestyle=':', linewidth=1)\n",
+    "plt.xlabel('population mean')\n",
+    "plt.ylabel('probability density')\n",
+    "plt.axvline(m, color='k', linestyle=':', linewidth=1, label='sample mean')\n",
+    "\n",
+    "u = stats.norm().isf(alpha / 2)\n",
+    "ci_low = m - u * s\n",
+    "ci_high = m + u * s\n",
+    "\n",
+    "plt.fill_between(grid, np.zeros_like(prob), prob, where=(ci_low<=grid)&(grid<=ci_high), alpha=.1)\n",
+    "plt.plot([ci_low]*2, [0, pdf(ci_low)], color='b')#, label='confidence lower bound')\n",
+    "plt.plot([ci_high]*2, [0, pdf(ci_high)], color='b')#, label='confidence upper bound')\n",
+    "\n",
+    "ml = (grid[0]+4*ci_low)/5\n",
+    "pl = (2*pdf(ci_low)+pdf(m))/3\n",
+    "plt.annotate(f'${alpha/2*100}\\%$',\n",
+    "    [ml, .1*pdf(ml)], [ml, pl],\n",
+    "    arrowprops=dict(arrowstyle=\"->\"),\n",
+    "    horizontalalignment='center')\n",
+    "\n",
+    "ml1 = (4*m+ci_high)/5\n",
+    "ml2 = (m+ci_high)/2\n",
+    "plt.annotate(f'${(1-alpha)*100:.0f}\\%$ prob. mass',\n",
+    "    [ml1, pl], [ml2, (pdf(ml2)+pdf(m))/2],\n",
+    "    arrowprops=dict(arrowstyle=\"->\"))\n",
+    "\n",
+    "line_width, head_length, height = pdf(m)/30, b*s/10, .5*pdf(ci_low)\n",
+    "t = plt.arrow(ci_low+head_length, height, ci_high-ci_low-2*head_length, 0,\n",
+    "    width=line_width, head_length=head_length, linestyle='none')\n",
+    "t = plt.arrow(ci_high-head_length, height, ci_low-ci_high+2*head_length, 0,\n",
+    "    width=line_width, head_length=head_length, linestyle='none')\n",
+    "plt.text(m, height+line_width, f'${(1-alpha)*100:.0f}\\%$ confidence interval',\n",
+    "    ha='center')\n",
+    "\n",
+    "plt.legend(loc='upper left')\n",
+    "\n",
+    "plt.xlim([grid[0], grid[-1]]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77dfe22a",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "As they may differ, instead of reporting the sample mean alone, we can report a range of possible values for the population mean, and this is made possible by the fact the mean estimator is known to be normally distributed.\n",
+    "\n",
+    "Note: a normal distribution with mean $\\mu$ and variation $\\sigma^2$ can be represented in `scipy` with the `norm` class that features that many distribution related measurements:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "id": "8f54f1c4",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": [
+    "mean = mu = 30\n",
+    "standard_deviation = sigma = 10\n",
+    "normal_distribution = stats.norm(mu, sigma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "id": "8eef01b5",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.945200708300442"
+      ]
+     },
+     "execution_count": 151,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# cumulative distribution function\n",
+    "normal_distribution.cdf(46)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "id": "d93bb006",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.011092083467945555"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# probability density function\n",
+    "normal_distribution.pdf(46)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "128bee18",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "For example, we may report an interval around the sample mean that should include the population mean with a $1-\\alpha=95\\%$ probability:\n",
+    "\n",
+    "$$\n",
+    "\\bar{x} \\pm u_{\\alpha/2}\\frac{\\sigma}{\\sqrt{n}}\n",
+    "$$\n",
+    "\n",
+    "$u_{\\alpha/2}$ is calculated as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "id": "db08728d",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.9599639845400545"
+      ]
+     },
+     "execution_count": 139,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "alpha = 0.05\n",
+    "stats.norm().isf(alpha / 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "705c2e67",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "For a $95\\%$ confidence interval, we usually take $u\\approx 1.96$.\n",
+    "\n",
+    "$\\frac{\\sigma}{\\sqrt{n}}$ is the standard deviation of the sample mean and can be calculated using the `sem` function from `scipy.stats`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "id": "86912ddf",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.48500106556956185\n"
+     ]
+    }
+   ],
+   "source": [
+    "age = dataframe['Age']\n",
+    "sample_mean = np.mean(age)\n",
+    "sem = stats.sem(age)\n",
+    "print(sem)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "id": "9b723a68",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "46.49 ± 0.95 years old on average\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'{sample_mean:.2f} ± {1.96 * sem:.2f} years old on average')"
    ]
   },
   {
@@ -1134,7 +1423,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 957.6x295.2 with 2 Axes>"
       ]
@@ -1549,7 +1838,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 18,
    "id": "404476b6",
    "metadata": {
     "hidden": true,
@@ -1593,7 +1882,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 19,
    "id": "b162e92e",
    "metadata": {
     "hidden": true,
@@ -1640,7 +1929,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 20,
    "id": "67d046f0-ca64-4a12-8e32-58b8e7e142a0",
    "metadata": {
     "hidden": true,
@@ -1697,6 +1986,7 @@
    "cell_type": "markdown",
    "id": "ca6bf548-cadf-4c75-8130-fe0c3ef8de9a",
    "metadata": {
+    "heading_collapsed": true,
     "hidden": true
    },
    "source": [
@@ -1740,7 +2030,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "id": "b471633d-c9ad-455e-84e1-d32af085b32a",
    "metadata": {
     "hidden": true
@@ -1752,7 +2042,7 @@
        "Ttest_1sampResult(statistic=0.6024056396957578, pvalue=0.5658990587680466)"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1779,7 +2069,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "id": "b732c69f-1851-4ef2-bdc2-8f80b4c5281e",
    "metadata": {
     "hidden": true
@@ -1791,7 +2081,7 @@
        "Ttest_1sampResult(statistic=0.6024056396957578, pvalue=0.2829495293840233)"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1802,7 +2092,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "id": "dc360e24-d676-4398-8e4b-dba71e9e68e8",
    "metadata": {
     "hidden": true
@@ -1814,7 +2104,7 @@
        "(56.1713713175, 10.244544391411772)"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1827,6 +2117,7 @@
    "cell_type": "markdown",
    "id": "2144869e-9e4a-4e63-ae58-81c5a6be7205",
    "metadata": {
+    "heading_collapsed": true,
     "hidden": true
    },
    "source": [
@@ -1859,7 +2150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "id": "6231e214-ac36-4c4f-8a16-e1551c8484b4",
    "metadata": {
     "hidden": true
@@ -1871,7 +2162,7 @@
        "Ttest_indResult(statistic=-1.96174329619957, pvalue=0.06998888828308221)"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1895,13 +2186,13 @@
     "\n",
     "   ...but this should be defined prior to carring out any test!\n",
     "   \n",
-    "   More important than the *p*-value is the *effect size*. A common measure of effect size for two independent samples is Cohen's $d$: $d = \\frac{\\bar{X_2}-\\bar{X_1}}{\\sqrt{\\textrm{PooledVariance}}}$\n",
+    "   More important than the *p*-value is the *effect size*. A common measure of effect size for two independent samples is [Cohen's $d$](https://en.wikipedia.org/wiki/Effect_size#Cohen's_d): $d = \\frac{\\bar{X_2}-\\bar{X_1}}{\\sqrt{\\textrm{PooledVariance}}}$\n",
     "   "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "id": "2ba983bc-ef4c-4a15-ac23-776fffd88afb",
    "metadata": {
     "hidden": true
@@ -1913,13 +2204,15 @@
        "1.0485958993113402"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "def cohen_d(x1, x2):\n",
+    "    # note: there exists a corrected version\n",
+    "    #       that is less biased than this one\n",
     "    n1, n2 = len(x1), len(x2)\n",
     "    m1, m2 = np.mean(x1), np.mean(x2)\n",
     "    v1, v2 = np.var(x1), np.var(x2)\n",
@@ -1932,7 +2225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 26,
    "id": "3beb1fbb-a1ac-40aa-b4ce-b97f151392f5",
    "metadata": {
     "hidden": true
@@ -1989,7 +2282,7 @@
     "   To further conclude in terms of *causation*, it is necessary to rule out all possible [confounders](https://en.wikipedia.org/wiki/Confounding) (supplier, cage effect, etc).\n",
     "\n",
     "3. `scipy`'s implementation does not require equal numbers of observations per group.\n",
-    "   However, it assumes the groups are normally distributed (but is relatively robust to non-«extreme non-normality») and, more importantly, have similar variances ($0.5<\\frac{s_{X_1}}{s_{X_2}}<2$).\n",
+    "   However, it assumes the groups are normally distributed (but is relatively robust to non-«extreme non-normality») and, more importantly, have [similar variances ($0.5<\\frac{s_{X_1}}{s_{X_2}}<2$)](https://en.wikipedia.org/wiki/Student%27s_t-test#Equal_or_unequal_sample_sizes,_similar_variances_(1/2_%3C_sX1/sX2_%3C_2)).\n",
     "   For heterogeneous groups, `ttest_ind` embarks various variants of the *t* test that can be selected with additional arguments:\n",
     "    * Welch's *t* test with `equal_var=False`;\n",
     "    * Yuen's *t* test with `equal_var=False` and `trim=0.2` (requires more data).\n",
@@ -2003,6 +2296,7 @@
    "cell_type": "markdown",
    "id": "1083c04c-1221-446f-84a0-7084413a7722",
    "metadata": {
+    "heading_collapsed": true,
     "hidden": true
    },
    "source": [
@@ -2047,7 +2341,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "id": "25adcf4f-6611-434f-b619-27f341b3caad",
    "metadata": {
     "hidden": true,
@@ -2060,7 +2354,7 @@
        "1.2657389481669015"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2113,7 +2407,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "id": "2bf0aafa-d23f-44fe-b9e0-cf4bf13e7314",
    "metadata": {
     "hidden": true
@@ -2125,7 +2419,7 @@
        "F_onewayResult(statistic=2.3575322551335636, pvalue=0.11384795345837218)"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2252,7 +2546,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "id": "6bcaa795-3a85-4b8c-aad9-d554e244a2ec",
    "metadata": {
     "hidden": true
@@ -2285,7 +2579,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "id": "e1f2356b-dd02-47d5-8e2c-82c77a6a5c97",
    "metadata": {
     "hidden": true
@@ -2333,7 +2627,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "id": "e964be07-0696-4ff8-844a-3e7d318a7f3a",
    "metadata": {
     "hidden": true,
@@ -2402,6 +2696,7 @@
    "cell_type": "markdown",
    "id": "4c7a08d6-3c74-48ce-ad32-f164089b6ec7",
    "metadata": {
+    "heading_collapsed": true,
     "hidden": true,
     "tags": []
    },
@@ -2424,7 +2719,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "id": "8a31b653-d891-4f07-ab8b-73562b2ed86d",
    "metadata": {
     "hidden": true
@@ -2470,7 +2765,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "id": "280180fa-4f20-44a6-a463-2ce9b0ad75a4",
    "metadata": {
     "hidden": true
@@ -2482,7 +2777,7 @@
        "BartlettResult(statistic=3.3024375753550457, pvalue=0.19181598314036113)"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2512,7 +2807,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "id": "b8123d48-7285-4fa3-8473-04f316dedffc",
    "metadata": {
     "hidden": true,
@@ -2582,7 +2877,7 @@
     "tags": []
    },
    "source": [
-    "## $\\chi^2$ tests"
+    "## χ² tests"
    ]
   },
   {
@@ -2617,7 +2912,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "id": "84f4e9b3-a653-422c-8fc5-83b29869ba87",
    "metadata": {
     "hidden": true
@@ -2629,7 +2924,7 @@
        "410"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2642,7 +2937,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "id": "8bb2916a-e9a4-4bbe-b04f-14818bb68723",
    "metadata": {
     "hidden": true
@@ -2654,7 +2949,7 @@
        "array([98.4, 82. , 65.6, 57.4, 53.3, 53.3])"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2685,7 +2980,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "id": "a8898631-a5b5-49e8-a158-3e149345391a",
    "metadata": {
     "hidden": true
@@ -2697,7 +2992,7 @@
        "8.566983829178941"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2710,7 +3005,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "id": "7a8af457-13dc-483a-90e3-50d3949c8edf",
    "metadata": {
     "hidden": true
@@ -2722,7 +3017,7 @@
        "0.1276329790529603"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2744,7 +3039,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "id": "1e246915-e9be-4826-9677-d9f30348d0df",
    "metadata": {
     "hidden": true
@@ -2756,7 +3051,7 @@
        "Power_divergenceResult(statistic=8.566983829178941, pvalue=0.1276329790529603)"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2779,7 +3074,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 40,
    "id": "f2231df3-3ce3-40c4-ae75-f6159061e3d6",
    "metadata": {
     "hidden": true
@@ -2791,7 +3086,7 @@
        "2.9269410361636843"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2805,6 +3100,7 @@
    "cell_type": "markdown",
    "id": "1feb29a6-eeac-4096-8e90-c663fdeea8a8",
    "metadata": {
+    "heading_collapsed": true,
     "hidden": true,
     "tags": []
    },
@@ -2836,7 +3132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 41,
    "id": "13145d41-7dc3-4eff-9f9c-38d0abed1e8d",
    "metadata": {
     "hidden": true
@@ -2852,7 +3148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 42,
    "id": "fe4ce304-25ac-4734-9a0a-e20db59b742f",
    "metadata": {
     "hidden": true
@@ -2864,7 +3160,7 @@
        "array([0.50963855, 0.29518072, 0.14939759, 0.04578313])"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2892,7 +3188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 43,
    "id": "087ea780-304c-459d-a7c0-066817f3d82f",
    "metadata": {
     "hidden": true
@@ -2906,7 +3202,7 @@
        "       [167.16144578,  96.81927711,  49.00240964,  15.01686747]])"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2932,7 +3228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 44,
    "id": "d9585b87-f5dd-4cf2-ad91-0a79dcc95c86",
    "metadata": {
     "hidden": true
@@ -2944,7 +3240,7 @@
        "26.7075512595244"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2958,7 +3254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 45,
    "id": "a7cd985e-eb2b-4a72-a221-30e3e0fdf8ec",
    "metadata": {
     "hidden": true,
@@ -2971,7 +3267,7 @@
        "0.00016426084515914902"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2992,7 +3288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 46,
    "id": "4cd7ea08-110a-4ff7-9521-0f12e4169d35",
    "metadata": {
     "hidden": true
@@ -3009,7 +3305,7 @@
        "        [167.16144578,  96.81927711,  49.00240964,  15.01686747]]))"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3030,7 +3326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 47,
    "id": "d7e7fd75-068c-4f37-b981-498890c4a0d7",
    "metadata": {
     "hidden": true
@@ -3048,7 +3344,7 @@
        "        [ 12.04096386,  10.94216867,  15.01686747]]))"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3059,9 +3355,35 @@
   },
   {
    "cell_type": "markdown",
-   "id": "141dc10b-c964-4621-9e53-32f36c11d2da",
+   "id": "6277cebb",
    "metadata": {
     "heading_collapsed": true,
+    "hidden": true
+   },
+   "source": [
+    "### Two-sample goodness-of-fit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dde0e024",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "The $\\chi^2$ test is also used for comparing the distributions of a continuous variable for two samples (two groups) in a more general way than a $t$-test for independent samples.\n",
+    "\n",
+    "The procedure consists in binning the continuous variable so that the problem can be formulated as a homogeneity test, with bins as the levels of one factor, and the grouping criterion as another factor.\n",
+    "\n",
+    "As a consequence, we will also use the `chi2_contingency` function.\n",
+    "\n",
+    "A similar test is the two-sample Kolmogorov-Smirnov test implemented as [ks_2samp](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ks_2samp.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "141dc10b-c964-4621-9e53-32f36c11d2da",
+   "metadata": {
     "tags": []
    },
    "source": [
@@ -3072,7 +3394,6 @@
    "cell_type": "markdown",
    "id": "6efe325b",
    "metadata": {
-    "hidden": true,
     "tags": []
    },
    "source": [
@@ -3091,8 +3412,6 @@
    "cell_type": "markdown",
    "id": "344da42e-5707-4a0a-9b98-4cbf47781fbe",
    "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
     "tags": []
    },
    "source": [
@@ -3103,7 +3422,6 @@
    "cell_type": "markdown",
    "id": "0c8b61ee-e2da-4dc4-b3ad-02790d407d5b",
    "metadata": {
-    "hidden": true,
     "jp-MarkdownHeadingCollapsed": true,
     "tags": []
    },
@@ -3122,11 +3440,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 48,
    "id": "f5a36568-d5b5-4388-b3f9-f38855c58c80",
-   "metadata": {
-    "hidden": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -3134,7 +3450,7 @@
        "(0.3518680132574827, 0.05653920630309695)"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3150,24 +3466,20 @@
   {
    "cell_type": "markdown",
    "id": "a9fbd953",
-   "metadata": {
-    "hidden": true
-   },
+   "metadata": {},
    "source": [
     "The correlation coefficient is a commonly-used effect size for the linear relationship between the two variables, similarly to (but not to be confused with) a regression coefficient:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 49,
    "id": "3581eeb0-f98d-4b2f-a0f1-bef073d86980",
-   "metadata": {
-    "hidden": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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XwX3LwzsKALVRKJZ45UJmcShoPdXDdobVw8orgkYG+9izvadj9ga0kigCwyvArWaWJhhKehtwHHgceDfByqS7gUfq2Qh3p+hQXHFx29oqT3BLAWMpkFhFECnft6oNq7x20LcJejv/57sT/PL//DbJuLE1leDV6Sy/9Miz/FLhBm573eAq77H6dconzfL794r7XNaw1zyu4vDiSb7y5F9po9/ALezJ1XOt/lNjFzjy9DhnprPs3trDXTcPqyj9Os3mCuG3/2Ao6MTkLC9voHpYeVVQuXqYNL8o5hieNLMvAl8HCsA3CIaG/hdwxMx+JTz26Ua3baOCb7YOdZ5j/O3HTgLBt+tC0UnEYuSLRX73iZOM7uqr74s3QDlI1ctTYxd44LEXSMSCwHp+bp4HHnuB+zig4FCh5M7ZS7nF5aDl5aGrVQ/bkkosfvsvZw3dbPUwiV4kq5Lc/WPAxy47PAbcEkFzmt6Z6SxbL8vHkkrGODudjahF9VfLb/hHnh4nEbPFcetyuvMjT493bGAoVw8rbxArzwdkVqkedvX21OK3/5EwVcRQHaqHSfS087kF7N7aw/m5+aoJuVy+xFVbeyJsVf3U+ht+JwbWSpupHnZgaCkQVBaX2ggN47UeBYYWcNfNwzzw2Atk80VSyRi5fIlCybnr5uGom1YXtf6G3ymBtVhyxqcynCwXkg97AatVD9sRVg8bqVP1MA3jtSYFhhZwy8gA93GAI0+Pc3Y6y1Vt/q2r1t/w2zGwXkn1sGsG0owO9S0WkBkd6qM/Xd/qYRrGa00KDC3ilpGBjvmPVOtv+K0cWN2dV6fnq1YFrbd62OhQmCJiKLrqYZ0+jNeqFBik6dTjG34rBNaFQomXzs8trgoqF5GZm195QviqrSn2D4W7g5uwelinDOO1GwUGaTqt/A1/vS5mFqqKxpycnOOVdVcP62M0TBrX193c/4XbcRivEzT3vyrpWK3wDX89ytXDlvYFBIHg/CrVw/rTyaVMoS1ePawTgnw7UmAQqZHMQqEqR9DJDVYPGx0KfgZ66zsh3GjtEuQ7iQKDyAZVVg8rzwOcnJjj9MWVJ1TTXXFGdi71AFQ9TJqZAoPIKsrVw05UrAoam1xf9bDRoaWsobu3RVc9rN03mLX7+4uCAoNI6FJYPexkxaTwatXDEjFjX7hDuDwUNLKzt6mqh7X7BrN2f39RUWCQjlNZPWwxV9DEHJOzq1cPGy0PA4U7hFuheli7bzBb7/u7PK1+qcSGKuxVZlJeLgV/LGZV6d+BqvTxpTBtfLGcNj48XlkIqh7iMVv6MVv8O6xFgUHaWjZf5MWKCeETE7O8eG7t6mHlPQHl5aGtWj2s2TaYLZ5UyyfSWFBPIxGLYeHvQcr6ipNx+GdfLiv75GyO7T1JzGwxQ28ibpybzTE8kF58/HKfnXtQp6NYchxfTEtfmSa/kbVXyrVIKtPhVwWZsLZJyT0sTlYd7MqFyGzx8srbrcAgbaE8IVwuG1nuCZyayq6Y0juVjFVNCI8O9nHdzl56utpnQrjeG8wqT/SxsIpiLAbJWIxkIkai4tvqZisnLneOu2agl4mZHOmuBOWbM/kiwwO9a/bmzIyuRPME+1jMiNEc7VFgkJZTrh52oqJuwMnJOS6tUT1stGJF0OhQH1dv74lsQrhRrmSDWaziW3u5wl4yvjQkETNbPOFH3Yu697YR7j/6HJmFwuIwUr7o3HvbSKTtanUKDNLUytXDToTzAOuqHrYjvVg0Zn+HVw+7fIPZ7m09/Nit1/DP/9EgiViMRNyqvtU3w8l+Iw5dP8Rh4MFjY5yayrC3BcretgJr5Rq4b3jTTf7lrxyLuhlSAyV3zlzKBUNBYQA4uUb1sL7uxOKS0NGKHcKdXD0sGQ9O9sl4bHH4Jh4LTv7JeKwld09L7ZnZM+5+cKXb1WOQhpvPF3nx/NxiptCTE7OMnVu9etie7T1LPYAwGHRy9bBELEaqK0Z3PE4yEUzeJuOt9W1fmpcCg9RVuXrYYsK4iVnGp9auHjZasUHsup299DZ5srh6SsZj4Y/RlYiRSsabfpmstLbO/d8mNVUsOa9cyDBWGQQmZ5nKrDwhPNDbVZUortbVw5rZa3br3jLMW0Z30p0Ig0AiDATxmHoB0nAKDLJh5ephJyp2Cb94fvXqYdfu6K2YDA6CQb2rhzWjRCzG8Zcu8D8eP0EybuxIdzGdW+B3Hj/BVVtTmjSVpqDAICuqrB5W2QtYq3rY0r6AaKuH1dqV5OSJmZHuitPTFSfdlSAeM37hz75JKhkj3RX89+uNx8gsFHjw2JgCgzQFBQYBrqx62O5tqap9AfsH+9i1tT0nhDeSk6crEZz0011xuhNLQ0FPPD/Bg8fGeOqlC3THjaGtKbakgmW0Pck4p6YyDX9fUSn/LcanMgxriWnTiSQwmNl24FPA6wl2of848F3gT4B9wEvAe9x9Kor2tbupzEJVoriTk3O8fH5uxQnhcvWw0cG+sJZwa1QPq6XVcvK85cBOUsmgV5BKxEgsMzH8xPMT3H/0OZJxI5WIsVAs8b2LOa7eDltSSbL5Inv70w1+V9Go/Fts70kyMZPj/qPPcRgUHJrEqv+zzWwrMOjuJy87/gZ3/+YmXvcB4K/c/d1m1gWkgY8Cj7r7x83sw8CHgV/YxGt0vGLJOT2VXdogFvYGzs+tXj1saV9AEABatXpYLS3mHKrITbMllljMybOWB4+NkYwb6a4EO/u6+d6lLI4zMZ0jHrOO2q1b+bcASHclNJTWZFYMDGb2HuA3gQkzSwLvd/enw5s/C9x0JS9oZtuA24D3A7j7ArBgZncCh8K7PQw8gQLDul1ePaycLG5+lQnh4f40IxXDQPsHe9nR193glje3ZDxYHnrNQJrzs/NVy2YzCwWGB3rX9TzjUxm2h+m4y2m5z83OkyuUGNqS6qihlMq/RVmnDaU1u9V6DB8Fvt/dz5jZLcAfmtlH3P3PYVOZnq4DJoHfN7M3As8A9wG73P1MeJ+zwK7lHmxm9wD3AOzZ23kFxd2diZn5xVTR5UCw3uph5ayh+3aoethykvEYPV1xepLBTznx24cOjW4qJ89wf3ox2RsEwSERN4a2pPj8PbfW7f00o8v/FkBHDaW1gtUCQ7x8onb3p8zsXwJ/YWbDsGLCyvW+5k3AT7n7k2b2AMGw0SJ3dzNb9jXc/SHgIQhSYmyiHU0vXyzx8vnMa1YFzaxSPWzX1u6qJaGjg31cFWH1sGa2uHs4EV/cP7DSkNlGc/JcPrn65pEBvvj103VJ9tZqE7lKfNf8VsyVZGZ/A7y3cn7BzLYAXwZ+wN2vaMzBzK4Cvubu+8Lr/5wgMIwCh8Ieym7gCXd/3WrP1U65ki5l84uZQk+EAeCVVaqHJeO2uDegciiovMpFXsvMgmWiyQQ9XfG6LaGtnFytPPG9+6Y9/O3YhZome1vptQ7fcWNTB4dyMFPiu2hsJlfSfwBiZnaDu38bwN1nzOx24K4rbZC7nzWzcTN7nbt/F3gb8O3w527g4+HlI1f6Gs2s5OUJ4fIw0NrVw7b1JBkNJ4JHK6qHLbf6RarFw5VE6e4E6YqhoXpaaXL1b8cu1HzYqFUncg9dP9TU7et0KwYGd/97ADN71sz+EPjvQCq8PAj84SZe96eAz4UrksaADwAx4Atm9kHgZeA9m3j+plBZPaycNnrs3Cy5/PITwu1WPSwqlfsIophHaeTkqiZypR7WsxD9nwK/CvwNsAX4HPCWzbyou/8dQXC53Ns287xRqawedrKibsDpdVQPGx3aspgq4rrB3qpKW7I+5ZVD5d3EUS+tbeTkqiZypR7WExjyQBboIegxvOju66+i3WaupHrYYF93VQ9gdKi3I6qH1YtVpJnoacJMo42cXNVErtTDegLD0wTj/TcDO4HfM7N/6+7/rq4tawIzufziXEB5VdB6qoct5gka7GP/UB/bejQhvBkxM7qTweqhVDJGTzLe1ENrjawqpgpmUg9rVnAzs4PufvyyY+91983MMdRErVYlldw5eynHiYrqYScmZpmYWXlCeEsq8Zo8QZ1ePaxWYmZBiolkfHE5qYjUzqYruF0eFMJjkQeFK5XLF3nx3NzSqqArqB42OtjHYAdXD6uHeMzo6YrT151o+h6BSLtr6yxoF+YWwiGgoAcwNjm3avWw7nL1sIp9ASODvVUTe1I7yXiMdJiOOpVUQRqRZtEWZ7wrqR62o7crrBsQ9AD2D/axp78n8hUt7cws2FMQ1CaIZtK41XYJi0ShpQPD2Us5fvKPnuHFcytPCFdWDxupKB7TidXD6mW1AjblXkF5BVGUvQKlexZZn5YODJeyef7h1dnF673d8XBF0FKuoHapHtaslitg81uPv8Avpv4x/+r1VzXVUtJW3SUs0mgtHRj6uhO8/59du7g/oF2rhzWzcgGb8say7kSwlv6PnnyFH3rj1VE3r4p2CYusT0sHhj39PbzvzfuibkbHiseMV2dy9KeTxGNLPYNmPdlql7DI+jRPP19aRk9XnKGtKa4ZSLNvR+9rigE168n23ttGyBedzEIB9+BSu4RFXkuBQdalOxlnR1831+7oZfe2Hvq6E5hZS51sD10/xOE7bmRoS4pL2TxDW1JNn55aJAotPZQk9dWViNHXnaC3O7HiJHKrpWRQumeRtSkwSJV4zOjrTtCXSqw7FYVOtiLtRYFBiIerinq7o99rICLRU2DoUPGY0dudoK87EUkxGxFpXgoMHSRmRrpbiepEOlU5JUxycN8/We1+CgxtLmaVPQMlqhPpVJUpYfBSYbX7tvRy1bGJWf7Tn/w9T41diLopTaenK87glm6u3ZFmcEs3PV3qIYh0sstTwqympQNDLGacn5vngcdeUHAAUuFeg2sG0uze1sOWVFLBQESAICXMemvKt3RgMII0zomYceTp8aib03DxmNGXSoQ9g6CO9LaeJIkmSlwnIs1huD9NNr9yQbJKbXEGSSVjnJ3ORt2Mhkl3JdgVpqQY2pJiSyqpOhIisqrKLAVraYvJ51y+xFVbe6JuRl0lYjG2pBJsSSXUIxCRDavMUoDFVj33RxYYzCwOHAdOu/s7zew64AiwA3gGeK+7L6z2HI6TzRcplJy7bh6uf6MbzMzo7YrTl0qovKiIbFo5S4Hd+9K3VrtflGeb+4DvAFvD678K/Ia7HzGz3wM+CHxytScolZwdvd1VFcPaQTJe7h00dohIZS9FBCKaYzCzvcAPAZ8KrxvwVuCL4V0eBt611vOMDPXxiR9+Y1sEhUQsxraeJFdv72F4IM32dFfDg8L9R59jYiZXVfbyiecnGtYGEWkOUQ1W/ybw80A5kf8O4KK7l2dFTgF7lnugmd1jZsfN7PiFc+fq3tB6iseMLakku7f1cM2ONDv6uiNLT1G5xtksuEzGLRiPFJGO0vDAYGbvBCbc/Zkreby7P+TuB9394MDOnTVuXf3FLFhietW2YFVRefNZ1JZb49ysldhEpL6imGN4C3CHmb0DSBHMMTwAbDezRNhr2AucjqBtdREzI90Vp7c7QbpJdyCr7KWIlDW8x+DuH3H3ve6+D7gLeMzdfxR4HHh3eLe7gUca3bZaillQ12DX1hTX7kgztDVFb1j1rBm1UiU2EamvZloD+QvAETP7FeAbwKcjbs+GlZeXprsT9DZpz2AlrVaJTUTqx9w96jZcsTe86Sb/8leORd0MerqCVNa9XQliq6wk0nJQEWkGZvaMux9c6fZm6jG0lO5knL6w6tl6diJXprytXA56GBQcRKSpKLfCBiTjMfrTXQwPpNmzvYdt6fUnrNNyUBFpFeoxrKFWJTDHpzJs70lWHdNyUBFpRgoMy7BweWlfDZeXXslyUM1JiEgUNJRUobui0M2uGi8v3ehyUKWoEJGodHxgSMRibE93sbc/nDfoqU/iukPXD3H4jhsZ2pLiUjbP0JYUh++4ccUegOYkRCQqHTuU1Nsd1DZoZDrrcsrb9dCchIhEpaMCQzIeo6+7NYrdKEWFiESluc+ONVBOZ72nP0hn3d/b1fRBAZSiQkSi05Y9hlotMY2SUlSISFTaJjC0YxnMjcxJiIjUSkufQY1gErm3O0E6GV81T5GIiKxPSweGZDzGrq2pqJshItJWmn8WVkREGkqBQUREqigwiIhIFQUGERGposAgIiJVFBhERKSKAoOIiFRRYBARkSoKDCIiUkWBQUREqjQ8MJjZsJk9bmbfNrPnzOy+8PiAmX3VzF4IL/sb3TYREYmmx1AAfs7dbwBuBT5kZjcAHwYedfcDwKPhdRERabCGBwZ3P+PuXw9/nwG+A+wB7gQeDu/2MPCuRrdNREQinmMws33Am4AngV3ufia86Sywa4XH3GNmx83s+OTkZGMaKiLSQSILDGbWB/wZ8DPuPl15m7s74Ms9zt0fcveD7n5wcHCwAS0VEekskQQGM0sSBIXPufuXwsOvmtnu8PbdwEQUbRMR6XRRrEoy4NPAd9z9ExU3HQXuDn+/G3ik0W0TEZFoKri9BXgv8C0z+7vw2EeBjwNfMLMPAi8D74mgbSIiHa/hgcHd/x9BueblvK2RbRERkdfSzmcREamiwCAiIlUUGEREpIoCg4iIVFFgEBGRKgoMIiJSRYFBRESqKDCIiEgVBQYREamiwCAiIlUUGEREpIoCg4iIVFFgEBGRKgoMIiJSRYFBRESqKDCIiEgVBQYREamiwCAiIlUUGEREpIoCg4iIVFFgEBGRKgoMIiJSRYFBRESqNF1gMLPbzey7ZnbCzD4cdXtERDpNUwUGM4sDvwO8HbgB+BEzuyHaVomIdJamCgzALcAJdx9z9wXgCHBnxG0SEekozRYY9gDjFddPhccWmdk9ZnbczI5PTk42tHEiIp0gEXUDNsrdHwIeAjh48KBH3JxlPfH8BA8eG2N8KsNwf5p7bxvh0PVDUTdLRGRdmq3HcBoYrri+NzzWMp54foL7jz7HxEyO7T1JJmZy3H/0OZ54fiLqpomIrEuzBYangQNmdp2ZdQF3AUcjbtOGPHhsjGTcSHclMAsuk3HjwWNjUTdNRGRdmmooyd0LZvYfgb8G4sBn3P25iJu1IeNTGbb3JKuO9STjnJrKRNQiEZGNaarAAODufwn8ZdTtuFLD/WkmZnKku5b+tNl8kb396QhbJSKyfs02lNTy7r1thHzRySwUcA8u80Xn3ttGom6aiMi6KDDU2KHrhzh8x40MbUlxKZtnaEuKw3fcqFVJItIymm4oqR0cun5IgUBEWpZ6DCIiUkWBQUREqigwiIhIFQUGERGposAgIiJVzL0p89Cti5lNAi9H3Y7QTuBc1I1ogE55n9A577VT3id0zntd631e6+6DK93Y0oGhmZjZcXc/GHU76q1T3id0znvtlPcJnfNeN/s+NZQkIiJVFBhERKSKAkPtPBR1AxqkU94ndM577ZT3CZ3zXjf1PjXHICIiVdRjEBGRKgoMIiJSRYFhk8zsdjP7rpmdMLMPR92eWjKzYTN73My+bWbPmdl94fEBM/uqmb0QXvZH3dZaMLO4mX3DzP4ivH6dmT0ZfrZ/EpabbXlmtt3Mvmhmz5vZd8zsze34mZrZz4b/bp81s8+bWapdPlMz+4yZTZjZsxXHlv0MLfBb4Xv+ppndtNbzKzBsgpnFgd8B3g7cAPyImd0QbatqqgD8nLvfANwKfCh8fx8GHnX3A8Cj4fV2cB/wnYrrvwr8hruPAlPAByNpVe09APyVu18PvJHgPbfVZ2pme4CfBg66++sJSgXfRft8pp8Fbr/s2Eqf4duBA+HPPcAn13pyBYbNuQU44e5j7r4AHAHujLhNNePuZ9z96+HvMwQnkD0E7/Hh8G4PA++KpIE1ZGZ7gR8CPhVeN+CtwBfDu7TL+9wG3AZ8GsDdF9z9Im34mRLUm+kxswSQBs7QJp+pux8DLlx2eKXP8E7gDzzwNWC7me1e7fkVGDZnDzBecf1UeKztmNk+4E3Ak8Audz8T3nQW2BVVu2roN4GfB0rh9R3ARXcvhNfb5bO9DpgEfj8cNvuUmfXSZp+pu58Gfh14hSAgXAKeoT0/07KVPsMNn6cUGGRNZtYH/BnwM+4+XXmbB+udW3rNs5m9E5hw92eibksDJICbgE+6+5uAOS4bNmqTz7Sf4JvydcDVQC+vHXppW5v9DBUYNuc0MFxxfW94rG2YWZIgKHzO3b8UHn613BUNLyeial+NvAW4w8xeIhgOfCvBOPz2cBgC2uezPQWccvcnw+tfJAgU7faZ/iDwortPunse+BLB59yOn2nZSp/hhs9TCgyb8zRwIFzp0EUwuXU04jbVTDjO/mngO+7+iYqbjgJ3h7/fDTzS6LbVkrt/xN33uvs+gs/wMXf/UeBx4N3h3Vr+fQK4+1lg3MxeFx56G/Bt2uwzJRhCutXM0uG/4/L7bLvPtMJKn+FR4H3h6qRbgUsVQ07L0s7nTTKzdxCMT8eBz7j7f4u2RbVjZj8A/F/gWyyNvX+UYJ7hC8A1BGnP3+Pul0+EtSQzOwT8Z3d/p5mNEPQgBoBvAD/m7vMRNq8mzOz7CCbZu4Ax4AMEXxLb6jM1s18Gfphgdd03gJ8gGFtv+c/UzD4PHCJIr/0q8DHgyyzzGYaB8bcJhtIywAfc/fiqz6/AICIilTSUJCIiVRQYRESkigKDiIhUUWAQEZEqCgwiIlJFgUGkTszsr8zsYjlbq0irUGAQqZ9fA94bdSNENkqBQWSTzOzmMM99ysx6wxoAr3f3R4GZqNsnslGJte8iIqtx96fN7CjwK0AP8Efu/uwaDxNpWgoMIrVxmCB3Vo6gQIxIy9JQkkht7AD6gC1AKuK2iGyKAoNIbTwI/BLwOYLykSItS0NJIptkZu8D8u7+x2Ed8L8xs7cCvwxcD/SZ2Sngg+7+11G2VWQ9lF1VRESqaChJRESqKDCIiEgVBQYREamiwCAiIlUUGEREpIoCg4iIVFFgEBGRKv8fP6bCud6TO7YAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3179,17 +3491,15 @@
     }
    ],
    "source": [
-    "df = pd.DataFrame(dict(x1=x1, x2=x2));\n",
+    "df = pd.DataFrame(dict(x1=x1, x2=x2))\n",
     "sns.regplot(x=\"x1\", y=\"x2\", data=df);"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 50,
    "id": "730a1bd8-51b6-4c06-9431-9a0fce53c42c",
-   "metadata": {
-    "hidden": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -3197,7 +3507,7 @@
        "(0.4132334074789644, 0.023224608467418917)"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3211,15 +3521,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 51,
    "id": "bfd1c00e-20b3-48bd-9724-7342ae70f626",
-   "metadata": {
-    "hidden": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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8cHaWTCqB2zTYnErGOJ0L/lbcTj84fo5PPv48Z2fKxAXUhcl8mdFMikRMeGT/ibYGCFeVF88W/DIVXnfRS+eCex5WDSXndRXdtD5Dqkeyg6UILUCo6rPArQAiEgdeBh7F22L0E6r6R0t9LcdVRjMp6/M2fSVo7clsucpfPHGM8Y1ZChUH11+8tiGb5uxsmXTTya1UddmQTXe83Y/sP0EiJriqxGKCILguTBcqbFqVbnnQminVOHw6x8GXcxycyHFkIsdsQHYQE7jBzw7qK5M3ZFM9mx0sRVS6mO4Ejqnqi1fyYdy8IcPDu9/Q+lYZ0watKklSX3uiqqiCo0o8FuOlc7PMlGvzHnvP6zfzycefp1h1SCVjlKouNVe55/WbW/W2lmwiVySbSpCMx6i5igiIeHsVXG3QclV56Vyh0VV0cCLHi2eDs4OV6eS8rqKbNmTmBVATnQBxD/Bw0+0PiMi7gAPAb6rq9MIniMhuYDfAtdde25FGGnO1WlWSRFXZuCLNmXyRwUQc/J6jUtUJPMHevnU197ONR/af4HSuyIYQB4TH/Gxm9fAAk7kyLgp+NrHcoDVTrnG4aZrp4Yn8RcERvOxg67qRuammG7NsXNHf2cFSyKXqp4hIFlinqscWHH+1qj7dkgaIDACngB2qekZE1gOv4P2T/z1gTFXfe6nX2Llzpx44cKAVzTGmre7d8+RFCwoLlRqjmdRls2DXVQpVh0K5RqHi8OSxs3zy8edJxGReVnD/Wzo/yLsc9TGIRExwXJdXZirUXGXL6iF233HDom13VTl5rsjBUxcaaw9ePFsg6Ay2Ip3kljGviN34WJbtG7KkByw7WOiG0cxTqrpzsfsXzSBE5B3AnwCTIpIE3qOq+/27Pwe8rkVtfCvwz6p6BqB+6bfhL4Fvtuj3GBO65ZYkqTkuBX/x2sKNdqKUFSzHwnaPj60IbPdsucaR0/lGV9HhiRz5UnB2cP3a4XmDydesTFt20AKX6mL6MHCbqk6IyO3AX4vIh1T1UaCVf/l7aepeEpExVZ3wb/4c8EwLf5cxobpcSRKvH96hVHUp15zLbg95+9bVkQ8IQRa2W5vHDvzs4MevzAZmB9lUojF2ML4xy/YNmXl/T9M6l/qrxusnalX9gYj8FPBNEdkMgZ/bsonIMPAzwH1Nh/9ARG71f8cLC+4zpqsFlSSp1Fx+5Sev5eXzxb7ZYKdQqXFkIt/oKjo8kSO3SHawZe3wvDIVm1ZZdtAplwoQeRG5oT7+4GcSu4CvAzta8ctVdRZYs+DYO1vx2sZEUXNJkpfOzbIhm+aXdm7ilo3Zng0OqsrL54uNrqJ6dhBU/y+TSnDL2FxX0fYNGYYHLTsIy6X+8v8JiInIuKoeAlDVvIjchTfryBizDK6rlGoOP7FpBR/7hVf3bDG8YsXhyOm5zW8OT+S5UKxe9DjByw6ay1RsWp0mZtlBZCwaIFT1/wGIyDMi8tfAHwAp/3In8NcdaaExEbOcdQzlmkOx4lCoOJRrbs/tuqaqnDpfmqtoeirH8VdmArODkcEE42OZRlfR9rEsI5YdRNpSPp2fBD4G/BOQAb4IvLGdjTImqi63jqFc8wJBueoNNvdaddRi1eG50/l5O6KdD8gOAK5bMzRv3cG1q4csO+gySwkQVaAIpPEyiB+ram/9qzdmiRaWt0gl4zhujT99/Hm2rB1ulLboBarKxIXS3G5op3IcmwrODoYH4tzStBvaLRuyjKQsO+h2S/kE9wPfAF4PrAU+LSK/oKq/1NaWGRNBL52bJZtKUnVcLxiot3fwqfPFrg8OparDs03ZweGJHNOFRbKD1UPzpppet8ayg160lADxPlWtL1OeAN4uIjbTyPS8Ss2l6rhUai6lmkO56rJuJBWZondXQ1U5nSt5M4v8gHB0Mjg7GBqIc8uGTKOraHwsSyaVvPiBpudcNkA0BYfmYzZAbXqC6ypV16XmaCMYVByXqqOBA8pRKnq3HOWqw7Nn8vOmmi6WHWxelW50FY2PZbluzTDxmGUH/cg6CU3Pclz1Tvg1l7Lj7ZHtOHMnfUVxgr4yX0I3lLdQVc7kyvMGko9OzQS+13Qyzi1NM4tuGcuyIm3ZgfFYgDBdS9U7wTuq1Bzvp+K41FwvKCz35L9UnS5vcbltOSs1l+fO5BtbYx6eyHF2thL4WptWpeetO9iy1rIDszgLEKZrlGsOpYo3HlCqOm0LAFHSXPk0m0rwykyJj//9c9y5fZSy43rZweQMtYC/RSoZY/uGua6i8bEsK4YsOzBLZwHCRIbrKq4qNdf7cfyMoOKPDfTaIrOl+NIPXsJxXaqOt+NaseplRg/vP3HRYzetSntTTf0NcK637MBcJQsQ5qo4/knd9Xc1A2g+jyvecdfvDqo6c91Crjt3X7dPEW2VqXzZ2++gaQOcIAK8ZvMKPxissOzAtIUFCHNJblMff9V1qda8WTsV/7Ifv9W3SqXmcnRypjGr6OCpC7wyEzx2kIwLqUScdDKOCGzIpvj4O27tbINN37EA0aNUFVf9gVz/231932LX//buNn17d13mZQJu0/NNa0zly41ZRQdP5Xh+Mk/VCRg7SMS42V93kIzF+PbB0wwmYvOm1d57u22za9rPAkQEOa5Sc72+Zn+xbuOEj3rdNo2Tu3+idxrX7cQeBVXHzw5Oze2XPJkvBz52bEWqMW4wvjHL1rXDJOKxxv3jY9lIT6s1vatvA0T9ZAr1E7B34gXm9aXX+9DnHqeN3ZLUP2Ev+jv04hN20O9Svz0w16dvusvZmXJTV1GO584EZweDiRg3rZ/bK3l8Y5bVwwOXfO1u3TXOdL/QA4SIvADkAQeoqepOEVkNfBnYgrer3DtUdXqx11C8GvRuvTvF9b5RO34f+dx1OwGbq1dzXI5OzcwrU3EmF5wdbMimGovQdmzMcsO6+dmBMVEWeoDw/ZSqvtJ0+4PAd1X190Xkg/7t317syTVHmbhQbHcbTZ86N1vxu4oucGgix7NnZgL3ik7GhZvr2cHGFYyPZVgzMhhCi003ExEEEAFBvEuBmIj3E4O4fx3mejbqzw18zfrrif96l3hss6gEiIXeDuzyr38eeIJLBAhjWqXmuBx/ZbaxKvnQqRync6XAx45mBhvjBuNjWW4cHSFp2cGiLrciPGpEhLgI8bgQ80/Q9RN1vOlkXT9x1+/znuu/xoLXu/jYxfdHSRQChALfEREFHlLVPcB6VZ3w7z8NrA+tdaanTRcq87qKnj2dp7xIdnDT+sy8weS1lh0s2cIV4Wdny3zy8ee5n20dCRLxmHcSj8eERMy/jMcuOvE3f1O3RYbRCBBvUtWXRWQUeExEjjTfqarqB495RGQ3sBtg0+ZoV9I00eC4yjF/7KC+Cc7EheDsYN3IXHbgjR2MMJCw7OBKPbL/BImYNMqkp5NxilWHR/afuGyAqH+Tj8UgEYuRiAvJWIxYbH53TLPmk7yd6K9c6AFCVV/2LydF5FHgduCMiIyp6oSIjAGTAc/bA+wBeM1rb7NRZ3OR84XKvN3Qnj2dp7RIdrBtdMTvKlrBjo1Z1mUsO7gSsUYft3dZP0GfyZdYkUrMdaMIjMQSTM2U2LgyHXgyv1y/umm/UAOEiAwDMVXN+9f/DfAgsBd4N/D7/uU3wmul6QaOq/zYHzuoL0Z7+XzwxIW1IwONaqbjG7NsG81YdrBAvQum3h0T87+px/wRznjM+1YvTddjl/imvmXNMJP5EkMDcxstFSo1rl09TKpp86VmFhjCF3YGsR541P+HkAC+pKrfFpH9wFdE5H3Ai8A7QmyjiaALhaoXCPwM4cjpHKXqxdlBIiZsWz8yN3YwlmU0mwqhxdFT/8be3G0zkPB+Wt0tc98dW3lg70EKlVqje6nqKPfdsbWlv8e0VqgBQlWPA68JOH4WuLPzLTJR5LjKC2dn561KPjkdnB2sGR6Yt+7gpvX9mx2ICMm4MBCPzcsI5vrwO/cNfdf2UR4EHtp3nJPTBTatGuK+O7aya/tox9pgli/sDMKYi+SKc9nBoVM5jpzOU6g4Fz0uHhNuHB1pdBWNb8yyPjPYd10TyXiMZD0IxLxpmQPxGIOJWKT+Fru2j7Y1IDxxZJKH9h3nxHSBzRaALqn+t0qu2/ITl3qcBQgTKsdVXjw7O28w+cQi2cGqoaRX2tofP7hp/QiDi/Rf96J6IBhIxLzMIBFjIB6tIBCWJ45M8sDegyTjwsp0ksl8iQf2HuRBsCCxQPPfCnVrl3qsBQjTUblilcOn/a4iPzuYXSQ7uGHd8Lx1Bxuyqb44GSZiMQaTMVKJOMmEkPS7hvrhvV+ph/YdJxkXhga8U9rQQIJCpcZD+45bgFhg4d/qUixAmLZxVXnxbGHe2MFL5wqBj101lGwUrxvfmOXm9ZlFZ7f0gph4YwED8Zg3NuCPCyTjYrWarsCJ6QIr0/M3TEon45ycDv731s+C/laLsQBhWiZfqnJ4Iu+tTJ7IcWQiF5gdxARuWDfSWIQ2PpZlbEXvZgcx8bqDUsk4gwlvbMCCQGttXjXkT6OdO6UVqw6bVg2F2KpoCvpbLcYChLki9ezgUNO6gxcXyQ5WpJONrqIdG7PctCHTWFHbi2IiDA3ESQ/EGUzE+3YWVSfZNNqla/5bXY4FCLMkM+UahyfmuooOTeSYLQdnB1vXjjS6inaMZdm4srezg7g/dTQRizE0EGdoIN6z7zeqbBrt0jX/rZDYJWOA9MLOY6957W366He+F3YzeoaryolzhUZX0aFTOV48WwjcGymbSszrKtq+IUt6oPeyg6Q/bXTQHziuLzCzOj+mm4nIU6q6c7H7LYMwzNazAz8YHD6dJ1+6OP2MCWxZO8yOpplF16xM9+S35fqYQToZJ5WMWyAwfckCRJe52pr6qsqJ6eK8sYMfvzJ7yeygPrto+4bMkga2ulHzuEE6GbdBZGOwANFVrqSmfqFS48hEvtFVdHgiR+4y2UE9KGxa1ZvZQV1MhKHBOCODCdJJGzcwZiELEF3kcjX1VZWT08VGZnBwIscLr8ziBqQHmVSCW+ozi8aybB/r3eygLhn3FqANJuamm1pQMGZxvX1G6DETuSLZ1NxH5rqKq8rRqTwffvRfOHQqODsQvOyg3lW0Y2OWzT2eHYCXIaQH4o2xBJtuaszyWIDoEqrK6qEBzuRKOKqUqu68rTGfPH6ucX1kMMH4WKbRVbR9LMvIYH981PGYMDyY8MYTrNvImKvSH2eNLlSsOjx7Ot/YL/nwRI7zxWrgY9dnBrlty6rG+MHm1UONzdP7RSoZJ5tOMmxrEIxpGQsQEaCqTFwoNWoWHZzIcXxqJnDsIJWMkYzFcFVZn03xzp+8jn+9fV3nGx2ymAipZJyhwThDXTzryEpUmygLLUCIyGbgC3i7yimwR1U/KSK/A/waMOU/9MOq+q1wWtkeparDs2fyjYqmhyZyTBeCs4PrVg/Nm2p63Zr+yw6apZJxMqkEI4OJK84UonJSthLVJurCzCBqwG+q6j+LSAZ4SkQe8+/7hKr+UYhtaxlV5XSu1OgqOjSR49jULE5AejA0EOeWDZnGnge3jGXIpJZWdbGXDSRiDA8kGB5MXPVAc5ROyldSojoqwc30h9AChKpOABP+9byIHAauCas9rVKuOjx3ZoaDEzkOnrrAoVOLZwebV6Xnlam4bs2wrdj1JeMxMikvKCRb2H0UpX0DlluiOkrBzfSHSIxBiMgW4LXA94E3Ah8QkXcBB/CyjOkQm7coVeVMvtzoKjo4kePo5ExgdpBOxrmlaWbR+FiW7BJrsveL+gykkcFE2/aCiNK+AcstUR2l4Gb6Q+gBQkRGgK8Bv6GqORH5FPB7eOMSvwf8MfDegOftBnYDbNq8uSNtrdRcnjuTb3QVHTqV4+xsJfCxm1alG5nB+MYsWyw7CCR+iYsRf2pqu2cgRWnfgOWWqI5ScDP9IdQAISJJvODwRVX9GwBVPdN0/18C3wx6rqruAfaAV821He2bzHkzi+plKo5OzlALyA5SyRjbN8ztd3DLWJYVlh1cUnogzvBgguGBREcDZ5T2DVhuieooBTfTH8KcxSTAZ4DDqvrxpuNj/vgEwM8Bz3SiPZWay/OT+Xklrl+ZCc4OrlmZbnQV7diY5fq1lh1cjoiQSsZCCQrNorZvwK7to0v+3VEKbqY/hJlBvBF4J/AvIvIj/9iHgXtF5Fa8LqYXgPva8cun8mW/q8gbSH5+coaqE5AdJGJsH8vM7Zc8lmXl0EA7mtSTBpNe99HIYHhBYaHlnJSjJGrBzfS+MGcx/R+8MkELtXzNQ6XmcnRyppEZHDqVY2qmHPjYsRWpRlfR+FiWretGInNi6xbxmHhBIZVgMNF7mweFqVuDm+lOoQ9St8NUvjxX0fRUjucn84HZwWAixs0bMo2uovGNWVZFMDu42j0gOkFEGB6IM5Ky0tnG9IqeCBClao2vPnWysSp5Mr94dtBc0XTr2uHIl2i4kj0gOintz0AaHkgQs0zLmJ7SEwHihbMF/uKJY/OODSRi3Lw+M2+q6erh6GUHl3O5PSDCkIh5i9gyqUTkA6wx5sr1RIAAWJ8dbHQV7di4ghvWRT87WIqFe0CAN632dK7Y0XY0777W6xsLGWM8PfE//cbRER7+tTeE3Yy2GMumOTtbbmQQAKWqy4ZsuiO/f2jAG2y2MtrG9J/u/4qN1+XRq+55/WZqrlKsOijeZc1V7nl9+1aPDyRirBke5NrVQ2xYkbqqyqnGmO7VExlEL7t962ruZxuP7D/B6VyRDW2axWRTU6+MVVc1vcwCRBe4fevqtgxI27jC1YladVULVqbVerdvxiwqlYyzLjPIdWuGGM2kLDhcoebqql7RwQTJuPDQvuMdb0s9WE3mS/OC1RNHJjveFtM7LED0iWQ8xsqhATatGmLjyjSZVNLGFa7SienCvMkDEF511SgFK9M77KtjD0vEYt4MpMG4jSu0QZSqq1opcNMOlkH0GBFvsHnDihTXrhli9fCABYc2ue+OrVQdpVCpoepdhlVddfOqIYpVZ94xKwVurpYFiB6RjMdYPTzAtauHGM3auEIn7No+yoN372A0k+JCscpoJsWDd+8IZWA4SsHK9A47i3S5oYEE2bTNQgpLVKqrWilw0w52VulCMfH2bl6RTjKQsCTQeKISrEzvsADRRq0u052Mx8imkmRSVjnVGNN+9vWzTeplus/OlueV6f7B8XPLfq2hAW/QefPqIVYMJS04GGM6IrIBQkTuEpFnReSoiHww7PYsV3OZbsG7TMSER/afWNLzYyKsSCfZ7NdDsjEGY0ynRfKsIyJx4M+BnwFOAvtFZK+qHgq3ZUt3pWW6k/EY2XSSzGD0upGslIMx/SWqGcTtwFFVPa6qFeAR4O0ht2lZxrJpSlV33rFLlelOD8TnupHS0etGslIOxvSfqAaIa4DmvpiT/rEGEdktIgdE5MC5s1MdbdxSLKVMt4iQSSW5ZlWasRXpSHcjWSkHY/pPVAPEZanqHlXdqao7V69ZF3ZzLnL71tXc/5ZtrBkeJF+qsWZ4kPvf4u0jnYx7+y1ct3qIdZnBrljpHKW6Q8aYzojqV9aXgeYdcTb5x7pKc5nuRCzWKK2dSkY/ICwUpbpDxpjOiGoGsR/YJiLXi8gAcA+wN+Q2LZv4C9rqdZHWjgx2ZXAAK+VgTD+KZAahqjUR+QDwd0Ac+KyqHgy5WUuWjMfIpBKMDCZIxKMag5fHSjkY038iGSAAVPVbwLfCbsdSedlCnGwq2bVZwuVYKQdj+ktkA0S3SMRiZNMJMqkk8WVOTbV1BcaYKLMAcQW8aZ5xhgcTDA/Er2hntqjtZ2yMMQv1Rgd5hwwm46zNeNNT12dTjAwmrnjbTltXYIyJOssgLiMe82YiZVKJlq5XsC0ijTFRZwEiQEyksWYhnbyyLqTLsXUFxpiosy4mX33Nwmg2xXVrhhjNpBrdP+1g6wqMMVHX9xnEQCJGJpVkZDCx7FlIV8PWFRhjoq4vA4SIMOKPK4S5ZsHWFRhjoqyvAkQ85lVPXZFe/poFY4zpN30RIOqb8GRT7RtTMMaYXtPTASKVjLMinWR4sKffpjHGtEXPnTljIoykEmRTSQYSNknLGGOuVM8EiMFk3KugOhC9vZyNMaYb9USASMaFa1YG7/VsTBArlGjM5VkfjOk79UKJk/nSvEKJTxyZDLtpxkSKBQjTd6xQojFLE0qAEJE/FJEjIvK0iDwqIiv941tEpCgiP/J/Ph1G+0xvOzFdIL1ggaQVSjTmYmFlEI8Br1LVVwPPAR9quu+Yqt7q/7w/nOaZXrZ51RDFqjPvmBVKNOZioQQIVf2Oqtb8m08Cm8JoR5ieODLJvXue5E0fe5x79zxp/d8dZIUSjVmaKIxBvBf426bb14vID0XkeyLy5sWeJCK7ReSAiByYmppqfytbyAZJw7Vr+ygP3r2D0UyKC8Uqo5kUD969w2YxGbOAqGp7Xljk74ENAXd9RFW/4T/mI8BO4OdVVUVkEBhR1bMichvwdWCHquYu9bt27typBw4caO0baKN79zx50V4QhUqN0UyKh3e/IcSWGWP6iYg8pao7F7u/besgVPWnL3W/iLwHeBtwp/pRSlXLQNm//pSIHANuArrn7L8EtpucMaYbhDWL6S7gt4C7VbXQdHydiMT961uBbUDPzT20QVJjTDcIawziz4AM8NiC6ax3AE+LyI+ArwLvV9VzIbWxbWyQ1BjTDUIptaGqNy5y/GvA1zrcnI6z3eSMMd2gJ2oxdSPbTc4YE3VRmOZqjDEmgixAGGOMCWQBwhhjTCALEMYYYwJZgDDGGBOobaU2OklEpoAXw26Hby3wStiN6IB+eZ/QP++1X94n9M97vdz7vE5V1y12Z08EiCgRkQOXqm3SK/rlfUL/vNd+eZ/QP+/1at+ndTEZY4wJZAHCGGNMIAsQrbcn7AZ0SL+8T+if99ov7xP6571e1fu0MQhjjDGBLIMwxhgTyAKEMcaYQBYgWkRE7hKRZ0XkqIh8MOz2tJKIbBaRfxCRQyJyUETu94+vFpHHROR5/3JV2G1tBRGJ+/uif9O/fb2IfN//bL8sIgNht7EVRGSliHxVRI6IyGER+Ve9+JmKyH/x/90+IyIPi0iqVz5TEfmsiEyKyDNNxwI/Q/H8qf+enxaR113u9S1AtIC/C96fA28FxoF7RWQ83Fa1VA34TVUdB94A/Lr//j4IfFdVtwHf9W/3gvuBw023PwZ8wt/HZBp4Xyitar1PAt9W1e3Aa/Dec099piJyDfCfgZ2q+iogDtxD73ymnwPuWnBssc/wrXi7dG4DdgOfutyLW4BojduBo6p6XFUrwCPA20NuU8uo6oSq/rN/PY93IrkG7z1+3n/Y54F/H0oDW0hENgH/Dvgr/7YAb8Hb4RB6532uwNvB8TMAqlpR1fP04GeKt+9NWkQSwBAwQY98pqq6D1i46+Zin+HbgS+o50lgpYiMXer1LUC0xjXAiabbJ/1jPUdEtgCvBb4PrFfVCf+u08D6sNrVQn+Ct1+6699eA5xX1Zp/u1c+2+uBKeB/+d1pfyUiw/TYZ6qqLwN/BLyEFxguAE/Rm59p3WKf4bLPUxYgzJKJyAjelrC/oaq55vvUmy/d1XOmReRtwKSqPhV2WzogAbwO+JSqvhaYZUF3Uo98pqvwvjlfD2wEhrm4S6ZnXe1naAGiNV4GNjfd3uQf6xkiksQLDl9U1b/xD5+pp6j+5WRY7WuRNwJ3i8gLeN2Eb8Hrp1/pd09A73y2J4GTqvp9//ZX8QJGr32mPw38WFWnVLUK/A3e59yLn2ndYp/hss9TFiBaYz+wzZ8ZMYA3CLY35Da1jN8P/xngsKp+vOmuvcC7/evvBr7R6ba1kqp+SFU3qeoWvM/wcVX9D8A/AL/oP6zr3yeAqp4GTojIzf6hO4FD9Nhnite19AYRGfL/HdffZ899pk0W+wz3Au/yZzO9AbjQ1BUVyFZSt4iI/Cxe/3Uc+Kyq/s9wW9Q6IvIm4B+Bf2Gub/7DeOMQXwGuxSu3/g5VXThg1pVEZBfw31T1bSKyFS+jWA38EPgVVS2H2LyWEJFb8QbjB4DjwK/ifWnsqc9URH4X+GW82Xg/BP4jXt9713+mIvIwsAuvrPcZ4KPA1wn4DP0A+Wd4XWwF4FdV9cAlX98ChDHGmCDWxWSMMSaQBQhjjDGBLEAYY4wJZAHCGGNMIAsQxhhjAlmAMKbNROTbInK+Xh3WmG5hAcKY9vtD4J1hN8KY5bIAYUyLiMjr/Tr7KREZ9vcgeJWqfhfIh90+Y5YrcfmHGGOWQlX3i8he4H8AaeB/q+ozl3maMZFlAcKY1noQrzZXCW+jGmO6lnUxGdNaa4ARIAOkQm6LMVfFAoQxrfUQ8N+BL+Jta2lM17IuJmNaRETeBVRV9Uv+PuX/JCJvAX4X2A6MiMhJ4H2q+ndhttWYpbBqrsYYYwJZF5MxxphAFiCMMcYEsgBhjDEmkAUIY4wxgSxAGGOMCWQBwhhjTCALEMYYYwL9fxBPMPeURFgRAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3231,16 +3539,14 @@
     }
    ],
    "source": [
-    "df = pd.DataFrame(dict(x1=x1, x2=x2_correlated));\n",
+    "df = pd.DataFrame(dict(x1=x1, x2=x2_correlated))\n",
     "sns.regplot(x=\"x1\", y=\"x2\", data=df);"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "53338c9a-9345-4ead-8487-1b51254d7330",
-   "metadata": {
-    "hidden": true
-   },
+   "metadata": {},
    "source": [
     "Pearson $r$ assumes the observations are drawn from normal distributions.\n",
     "\n",
@@ -3249,19 +3555,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 52,
    "id": "39423438-a688-4afa-b258-e17076ba1fbd",
-   "metadata": {
-    "hidden": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(4.5962170596136655e-05, 0.000184299645610984)"
+       "(7.087239246711281e-05, 8.421503263379038e-05)"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3279,15 +3583,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 53,
    "id": "bca6551e-9ffb-4f60-8b96-9a085b2e2282",
-   "metadata": {
-    "hidden": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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EnBARP46In0TERXVuS5L0RrUFfETsCPwtcCLwbuCMiHh3XduTJL1RnSP49wE/ycyfZuZrwA3AKTVuT5I0TGRmPYUj/gA4ITPPq27/EfD+zPzMFuvNAeZUNw8CfjxK6T2B5zrY6lupXi/31ul6vdxbp+v1cm+drtfLvXWr3m9mZt/WFkzoYCMtycyrgaubXT8iHszMwU5t/61Ur5d763S9Xu6t0/V6ubdO1+vl3nqxXp2HaNYC+w673V/dJ0kaB3UG/A+BAyJiekTsBJwO3Frj9iRJw9R2iCYzN0TEZ4A7gR2BazNzZQdKN304x3q11ur1er3cW6fr9XJvna7Xy731XL3aXmSVJHWX72SVpEIZ8JJUqO0q4Dt56oOIuDYinomIFR3oa9+IuCciVkXEyoi4oM16kyLigYhYVtW7rAM9romI5RGxNCIebLPW7hHxzYh4JCJWR8RvtVHroKqnTZdfR8TcNvv7r9XPbUVELIyISW3UuqCqs7KVvra2n0XEOyLiexHxaPV1jzbrnVb193pEjGlK3TbqXV79bh+OiJsjYvc26/3PqtbSiLgrIvZptdawZRdGREbEnm32dmlErB22/81up151/2ern9/KiPirNnq7cVhfayJiabO9bZaZ28WFxgu1jwH7ATsBy4B3t1FvFnAEsKIDve0NHFFdnwz8W5u9BfC26vpEYDHwgTZ7XAPs2aHfxf8Fzquu7wTs3sHf8S9ovHGj1RpTgceBnavb/wCc3WKtQ4EVwC40JiT8P2D/dvcz4K+Ai6rrFwF/2Wa9g2m8SfBeYLAD/f0uMKG6/pcd6G+3Ydc/ByxotVZ1/740Jm/8bCz79DZ6uxT4by3uH1ur99vVfvKfqtt7tfO9Dlv+JeCSsfa4PY3gO3rqg8z8AfDLTjSWmU9l5kPV9ReB1TSCptV6mZkvVTcnVpeeeDU8It5OY2e8BiAzX8vM5ztU/ljgscz8WZt1JgA7R8QEGuH88xbrHAwszsx/z8wNwPeBU8dSYBv72Sk0/klSff29dupl5urMHO0d4GOpd1f1/QL8K433sLRT79fDbu5Kk/vyCH+jfwP8abN1mqjXkm3U+xTwF5n5arXOM+32FhEBfAxYONYet6eAnwo8Oez2EG2EaF0iYhpwOI1Rdzt1dqyekj0DfC8z26pH44/hrohYEo3TQ7RqOvAs8NWI+FFE/F1E7Npmb5ucTgs78XCZuRa4AngCeAp4ITPvarHcCuDoiJgSEbsAs3njm/da9c7MfKq6/gvgnR2oWZdzgDvaLRIRfxYRTwJ/CFzSRp1TgLWZuazdnob5THUI6dqxHC7bhgNp7DOLI+L7EXFkB/o7Gng6Mx8d6wO3p4DveRHxNuBbwNwtRi1jlpkbM/O9NEZP74uIQ9ts70OZeQSNs3ueHxGzWqwzgcZTyasy83DgZRqHGdoSjTfDnQzc1GadPWiMkKcD+wC7RsSZrdTKzNU0DlHcBSwClgIb2+lvK9tIeuTZ2ZYi4mJgA3B9u7Uy8+LM3Leq9ZnR1t9GP7sA/4M2/kFsxVXAu4D30hgQfKnNehOAdwAfAOYB/1CNwNtxBi0OfLangO/pUx9ExEQa4X59Zn67U3Wrwx/3ACe0WWdt9fUZ4GYah7xaMQQMDXtG8U0agd+uE4GHMvPpNuv8DvB4Zj6bmeuBbwMfbLVYZl6TmTMzcxbwKxqvr7Tr6YjYG6D62tTT+PEUEWcDJwF/WP0T6pTrgf/S4mPfReMf97KIWEMjAx6KiN9otZnMfLoaTL0O/B9a/7vYZAj4dnWY9QHgdRonDGtJdZjxVODGVh6/PQV8z576oPoPfQ2wOjP/ugP1+jbNXIiInYHjgEfaqLdrREzedJ3Gi2gtzR7KzF8AT0bEQdVdxwKrWu1tmJZHKVt4AvhAROxS/V6OpfGaSEsiYq/q6wCNP7RvdKDHW4FPVNc/AdzSgZodExEn0DjGfXJm/nsH6h0w7OYptLgvZ+byzNwrM6dl5jQaYXpEtU+22tvew27+Pi3+XQzzHRovtBIRB9KYhNDO2SV/B3gkM4daenQrrx5360LjGOi/0ZhNc3GbtRbSeEq2nsaOcm4btT5E42n2wzSexi8FZrdR7z3Aj6p6K2jh1fMt6u1HY9bRMmBlB3527wUerPr7DrBHm/V2BdYBb+/QfnIZjRBZAXydakZDi7X+mcY/sGXAsZ3Yz4ApwD8Bj9KYcfGONuv9fnX9VeBp4M426/2Exutdm/blpma9jFDvW9Xv4mHgH4GprdbaYvkaxjaLZmu9fR1YXvV2K7B3m/V2Av6++n4fAo5p53sFvgZ8stX911MVSFKhtqdDNJKkMTDgJalQBrwkFcqAl6RCGfCSVCgDXpIKZcBLUqEMeGkbIuLI6iRUk6p3A6/swDmBpHHjG52kEUTEF4FJwM40zsHz511uSWqaAS+NoDrv0Q+BV4APZmZHzyYp1clDNNLIpgBvo/FJXS1/9J/UDY7gpRFExK00Pj1sOo0TUbV0LnOpGyZ0uwGpV0XEWcD6zPxGROwI3BcRx2Tm3d3uTWqGI3hJKpTH4CWpUAa8JBXKgJekQhnwklQoA16SCmXAS1KhDHhJKtT/B3ldyTeASXwmAAAAAElFTkSuQmCC\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3305,15 +3607,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 54,
    "id": "a2dee6dc-0f17-4b33-9431-c6dc2d2bd418",
-   "metadata": {
-    "hidden": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3332,9 +3632,7 @@
   {
    "cell_type": "markdown",
    "id": "18902dd3-022a-447a-9681-6713b2e9296e",
-   "metadata": {
-    "hidden": true
-   },
+   "metadata": {},
    "source": [
     "What could possibly go wrong?\n",
     "\n",
@@ -3348,29 +3646,26 @@
   {
    "cell_type": "markdown",
    "id": "9f26feda-d0fd-4d9d-a748-b0b82d0f84b4",
-   "metadata": {
-    "heading_collapsed": true
-   },
+   "metadata": {},
    "source": [
-    "## Effect sizes, confidence intervals and power"
+    "## Effect sizes and test power"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "5d8555e2-f91e-4ed8-b518-d95b1f46aa11",
-   "metadata": {
-    "hidden": true
-   },
+   "metadata": {},
    "source": [
-    "Many more effect size indicators are available and the easier approach to effect sizes in Python is offered by the `pingouin` library:"
+    "`scipy.stats` does not offer any helper for effect size and power calculation.\n",
+    "\n",
+    "The [`statsmodels`](https://www.statsmodels.org/dev/stats.html#power-and-sample-size-calculations) and `pingouin` ([1](https://pingouin-stats.org/api.html#effect-sizes) & [2](https://pingouin-stats.org/api.html#power-analysis)) libraries feature useful tools, therefore we will start using these libraries, which we will cover more extensively in the next session."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 55,
    "id": "78c838fa-6eff-459a-867c-b9e930b0cebd",
    "metadata": {
-    "hidden": true,
     "jupyter": {
      "outputs_hidden": true
     },
@@ -3382,48 +3677,47 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Requirement already satisfied: statsmodels in /home/flaurent/.local/lib/python3.8/site-packages (0.12.2)\n",
       "Requirement already satisfied: pingouin in /home/flaurent/.local/lib/python3.8/site-packages (0.4.0)\n",
-      "Requirement already satisfied: scipy>=1.7 in /home/flaurent/.local/lib/python3.8/site-packages (from pingouin) (1.7.1)\n",
-      "Requirement already satisfied: numpy>=1.19 in /home/flaurent/.local/lib/python3.8/site-packages (from pingouin) (1.21.1)\n",
-      "Requirement already satisfied: outdated in /home/flaurent/.local/lib/python3.8/site-packages (from pingouin) (0.2.1)\n",
-      "Requirement already satisfied: statsmodels>=0.12.0 in /home/flaurent/.local/lib/python3.8/site-packages (from pingouin) (0.12.2)\n",
-      "Requirement already satisfied: tabulate in /home/flaurent/.local/lib/python3.8/site-packages (from pingouin) (0.8.9)\n",
-      "Requirement already satisfied: pandas>=1.0 in /home/flaurent/.local/lib/python3.8/site-packages (from pingouin) (1.3.1)\n",
+      "Requirement already satisfied: scipy>=1.1 in /home/flaurent/.local/lib/python3.8/site-packages (from statsmodels) (1.7.1)\n",
+      "Requirement already satisfied: patsy>=0.5 in /home/flaurent/.local/lib/python3.8/site-packages (from statsmodels) (0.5.1)\n",
+      "Requirement already satisfied: numpy>=1.15 in /home/flaurent/.local/lib/python3.8/site-packages (from statsmodels) (1.21.1)\n",
+      "Requirement already satisfied: pandas>=0.21 in /home/flaurent/.local/lib/python3.8/site-packages (from statsmodels) (1.3.1)\n",
       "Requirement already satisfied: matplotlib>=3.0.2 in /home/flaurent/.local/lib/python3.8/site-packages (from pingouin) (3.4.2)\n",
+      "Requirement already satisfied: pandas-flavor>=0.2.0 in /home/flaurent/.local/lib/python3.8/site-packages (from pingouin) (0.2.0)\n",
+      "Requirement already satisfied: tabulate in /home/flaurent/.local/lib/python3.8/site-packages (from pingouin) (0.8.9)\n",
       "Requirement already satisfied: seaborn>=0.9.0 in /home/flaurent/.local/lib/python3.8/site-packages (from pingouin) (0.11.1)\n",
       "Requirement already satisfied: scikit-learn in /home/flaurent/.local/lib/python3.8/site-packages (from pingouin) (0.24.2)\n",
-      "Requirement already satisfied: pandas-flavor>=0.2.0 in /home/flaurent/.local/lib/python3.8/site-packages (from pingouin) (0.2.0)\n",
-      "Requirement already satisfied: requests in /usr/lib/python3/dist-packages (from outdated->pingouin) (2.22.0)\n",
-      "Requirement already satisfied: littleutils in /home/flaurent/.local/lib/python3.8/site-packages (from outdated->pingouin) (0.2.2)\n",
-      "Requirement already satisfied: patsy>=0.5 in /home/flaurent/.local/lib/python3.8/site-packages (from statsmodels>=0.12.0->pingouin) (0.5.1)\n",
-      "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/lib/python3/dist-packages (from pandas>=1.0->pingouin) (2.7.3)\n",
-      "Requirement already satisfied: pytz>=2017.3 in /usr/lib/python3/dist-packages (from pandas>=1.0->pingouin) (2019.3)\n",
-      "Requirement already satisfied: cycler>=0.10 in /home/flaurent/.local/lib/python3.8/site-packages (from matplotlib>=3.0.2->pingouin) (0.10.0)\n",
-      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/flaurent/.local/lib/python3.8/site-packages (from matplotlib>=3.0.2->pingouin) (1.3.1)\n",
-      "Requirement already satisfied: pyparsing>=2.2.1 in /home/flaurent/.local/lib/python3.8/site-packages (from matplotlib>=3.0.2->pingouin) (2.4.7)\n",
+      "Requirement already satisfied: outdated in /home/flaurent/.local/lib/python3.8/site-packages (from pingouin) (0.2.1)\n",
+      "Requirement already satisfied: six in /usr/lib/python3/dist-packages (from patsy>=0.5->statsmodels) (1.14.0)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/lib/python3/dist-packages (from pandas>=0.21->statsmodels) (2.7.3)\n",
+      "Requirement already satisfied: pytz>=2017.3 in /usr/lib/python3/dist-packages (from pandas>=0.21->statsmodels) (2019.3)\n",
       "Requirement already satisfied: pillow>=6.2.0 in /home/flaurent/.local/lib/python3.8/site-packages (from matplotlib>=3.0.2->pingouin) (8.3.1)\n",
+      "Requirement already satisfied: pyparsing>=2.2.1 in /home/flaurent/.local/lib/python3.8/site-packages (from matplotlib>=3.0.2->pingouin) (2.4.7)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/flaurent/.local/lib/python3.8/site-packages (from matplotlib>=3.0.2->pingouin) (1.3.1)\n",
+      "Requirement already satisfied: cycler>=0.10 in /home/flaurent/.local/lib/python3.8/site-packages (from matplotlib>=3.0.2->pingouin) (0.10.0)\n",
+      "Requirement already satisfied: xarray in /home/flaurent/.local/lib/python3.8/site-packages (from pandas-flavor>=0.2.0->pingouin) (0.19.0)\n",
       "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/flaurent/.local/lib/python3.8/site-packages (from scikit-learn->pingouin) (2.2.0)\n",
       "Requirement already satisfied: joblib>=0.11 in /home/flaurent/.local/lib/python3.8/site-packages (from scikit-learn->pingouin) (1.0.1)\n",
-      "Requirement already satisfied: xarray in /home/flaurent/.local/lib/python3.8/site-packages (from pandas-flavor>=0.2.0->pingouin) (0.19.0)\n",
-      "Requirement already satisfied: six in /usr/lib/python3/dist-packages (from patsy>=0.5->statsmodels>=0.12.0->pingouin) (1.14.0)\n",
+      "Requirement already satisfied: requests in /usr/lib/python3/dist-packages (from outdated->pingouin) (2.22.0)\n",
+      "Requirement already satisfied: littleutils in /home/flaurent/.local/lib/python3.8/site-packages (from outdated->pingouin) (0.2.2)\n",
       "Requirement already satisfied: setuptools>=40.4 in /usr/lib/python3/dist-packages (from xarray->pandas-flavor>=0.2.0->pingouin) (45.2.0)\n"
      ]
     }
    ],
    "source": [
     "import sys\n",
-    "!\"{sys.executable}\" -m pip install pingouin"
+    "!\"{sys.executable}\" -m pip install statsmodels pingouin"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 56,
    "id": "5eaf887e-9310-4b89-acca-ccde9dff5f02",
-   "metadata": {
-    "hidden": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
+    "from statsmodels.stats import power\n",
     "import pingouin as pg"
    ]
   },
@@ -3431,7 +3725,7 @@
    "cell_type": "markdown",
    "id": "5532ddbe-202e-48f5-96fd-825c465a503f",
    "metadata": {
-    "hidden": true
+    "heading_collapsed": true
    },
    "source": [
     "### Effect sizes"
@@ -3444,40 +3738,57 @@
     "hidden": true
    },
    "source": [
-    "[Reference tables](https://core.ecu.edu/wuenschk/docs30/EffectSizeConventions.pdf)\n",
+    "An effect is worth reporting only if it can be quantified in terms of magnitude:\n",
+    "* for comparisons between group means, we need to know the actual difference(s) between these group means;\n",
+    "* for correlation or association studies, we want the strength of the correlations or associations, if any; etc.\n",
     "\n",
-    "[compute_effsize](https://pingouin-stats.org/generated/pingouin.compute_effsize.html) ([source](https://github.com/raphaelvallat/pingouin/blob/v0.4.0/pingouin/effsize.py#L553-L717)) and [convert_effsize](https://pingouin-stats.org/generated/pingouin.convert_effsize.html)"
+    "The significance of an effect is reported as a $p$-value, but a $p$-value in itself is not a useful estimator of the magnitude of the effect.\n",
+    "Appart from the fact a $p$-value can be compared with a gold-standard significance level, it suffers from a major flaw: the lower its value, the less reliable it is.\n",
+    "For a very small $p$-value, a slight change in the value of one or more observations may lead to a dramatically different $p$-value.\n",
+    "In particular, $p$-values are not robust across the repetitions of an experiment.\n",
+    "\n",
+    "As a side note, we may also face situations with very little measurement noise and find consistent differences (that happen to be statistically significant) that are very small and, consequently, of no practical interest.\n",
+    "\n",
+    "Instead, we report the magnitude (or size) of the effect as a [measure](https://en.wikipedia.org/wiki/Effect_size) that can be admittedly translated into common language as \"small\", \"medium\", \"large\", etc, following some convention.\n",
+    "\n",
+    "More about the [MAGIC criteria](https://en.wikipedia.org/wiki/MAGIC_criteria) for good measures of effect size.\n",
+    "\n",
+    "For example, we have seen Cohen's $d$ that takes the difference between two group means (which itself already deserves to be reported) and standardize it dividing by the within group variance. Once calculated, we can refer to a [reference table](https://core.ecu.edu/wuenschk/docs30/EffectSizeConventions.pdf) (see also the table on this [webpage](https://www.statology.org/effect-size/)).\n",
+    "\n",
+    "The most useful and comprehensive interface for effect sizes is featured by the `pingouin` library.\n",
+    "We can calculate the effect size for many different tests using [compute_effsize](https://pingouin-stats.org/generated/pingouin.compute_effsize.html) ([source](https://github.com/raphaelvallat/pingouin/blob/v0.4.0/pingouin/effsize.py#L553-L717)) and, for the tests that feature several effect size measures, we can convert from one measure to another using [convert_effsize](https://pingouin-stats.org/generated/pingouin.convert_effsize.html)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 57,
    "id": "d934a53e-f2b5-423a-a101-21c69e1a472b",
    "metadata": {
     "hidden": true
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "0.9914036363636364"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.980871648099785\n",
+      "0.9914036363636364\n"
+     ]
     }
    ],
    "source": [
-    "pg.convert_effsize(1.0486, 'cohen', 'hedges', nx=8, ny=8)"
+    "x1 = np.array([49.47257879, 81.93967205, 64.030398, 17.25423608, 59.80082512,\n",
+    "              94.56012514, 69.91672899, 12.39640637])\n",
+    "x2 = np.array([64.22723692, 96.56483856, 101.94191774, 85.31918879,\n",
+    "               66.4952999, 63.88841224, 127.63861749, 55.00527005])\n",
+    "print(pg.compute_effsize(x1, x2, paired=False, eftype='cohen')) # unbiased Cohen's d\n",
+    "print(pg.convert_effsize(1.0486, 'cohen', 'hedges', nx=8, ny=8))"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "3b30414c-6575-4dff-9637-664cb0f29a5a",
-   "metadata": {
-    "hidden": true
-   },
+   "metadata": {},
    "source": [
     "### Power analysis"
    ]
@@ -3485,42 +3796,55 @@
   {
    "cell_type": "markdown",
    "id": "9b54a050-85cc-44c3-b406-650734d0d2ec",
-   "metadata": {
-    "hidden": true
-   },
+   "metadata": {},
    "source": [
-    "* `statsmodels`: [FTestAnovaPower](https://www.statsmodels.org/stable/generated/statsmodels.stats.power.FTestAnovaPower.html) ([examples](https://www.statsmodels.org/stable/generated/statsmodels.stats.oneway.effectsize_oneway.html))\n",
+    "Prior to collecting data, in the presence of preliminary data to roughly predict the expected effect size, one can estimate the sample size necessary for a test to detect such an effect.\n",
     "\n",
-    "* `pingouin`: [power_anova](https://pingouin-stats.org/generated/pingouin.power_anova.html)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "700ff9a1-e4e9-4ef5-9559-f6d6740977ed",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "### Confidence intervals"
+    "This is done in a *power analysis*. Reminder: the power of a test is the probability of detecting an effect (rejecting $H_0$) when $H_0$ is false.\n",
+    "\n",
+    "`statsmodels` features utilities for such analyses, for most of the tests previously mentioned.\n",
+    "\n",
+    "* one-sample $t$-test: `TTestPower`\n",
+    "* $t$-test for independent samples: `TTestIndPower`\n",
+    "* $t$-test for dependent samples: `TTestPower`\n",
+    "* one-way ANOVA: `FTestAnovaPower`\n",
+    "* (one-sample) goodnes-of-fit $\\chi^2$ test: `GofChisquarePower`\n",
+    "\n",
+    "All classes, once initialized, feature a `plot_power` helper method for quickly inspecting the relationship between various parameters that are key in the design of a test:"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "6c8d4e2f-183a-4a1b-a49c-fc7ba21c79d3",
-   "metadata": {
-    "hidden": true
-   },
+   "cell_type": "code",
+   "execution_count": 169,
+   "id": "de47ba11-f486-44e3-9f3b-9ddffacc95ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "For the purpose of communicating about effect sizes, in addition to the [various measurements proposed](https://en.wikipedia.org/wiki/Effect_size), one may consider expressing population estimates as *confidence intervals*."
+    "nobservations = np.arange(5, 30+1, 5)\n",
+    "power.TTestIndPower().plot_power(nobs=nobservations, effect_size=[0.3, 0.5, 0.8, 1.2])\n",
+    "plt.xticks(nobservations)\n",
+    "plt.axhline(0.8, color='r', linestyle=':', linewidth=1)\n",
+    "plt.xlim(nobservations[0], nobservations[-1]);"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "de47ba11-f486-44e3-9f3b-9ddffacc95ee",
-   "metadata": {
-    "hidden": true
-   },
+   "id": "fdbaf062",
+   "metadata": {},
    "outputs": [],
    "source": []
   }
-- 
GitLab