diff --git a/doc/pub/week38/ipynb/week38.ipynb b/doc/pub/week38/ipynb/week38.ipynb
index 1d25f9941..8aa0ec620 100644
--- a/doc/pub/week38/ipynb/week38.ipynb
+++ b/doc/pub/week38/ipynb/week38.ipynb
@@ -1364,7 +1364,10 @@
    "id": "ff4790ba",
    "metadata": {
     "collapsed": false,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -1424,7 +1427,10 @@
    "id": "3cf4144d",
    "metadata": {
     "collapsed": false,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -1635,7 +1641,10 @@
    "id": "01c3b507",
    "metadata": {
     "collapsed": false,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -1707,13 +1716,103 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 11,
    "id": "7e7f4926",
    "metadata": {
     "collapsed": false,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Polynomial degree: 0\n",
+      "Error: 0.2937910450030775\n",
+      "Bias^2: 0.2929212799917661\n",
+      "Var: 0.0008697650113114114\n",
+      "0.2937910450030775 >= 0.2929212799917661 + 0.0008697650113114114 = 0.2937910450030775\n",
+      "Polynomial degree: 1\n",
+      "Error: 0.06894146856540673\n",
+      "Bias^2: 0.06832043024896824\n",
+      "Var: 0.0006210383164384981\n",
+      "0.06894146856540673 >= 0.06832043024896824 + 0.0006210383164384981 = 0.06894146856540674\n",
+      "Polynomial degree: 2\n",
+      "Error: 0.06106765054837855\n",
+      "Bias^2: 0.060547654220995305\n",
+      "Var: 0.0005199963273832366\n",
+      "0.06106765054837855 >= 0.060547654220995305 + 0.0005199963273832366 = 0.06106765054837854\n",
+      "Polynomial degree: 3\n",
+      "Error: 0.03346202229536658\n",
+      "Bias^2: 0.033140956468054594\n",
+      "Var: 0.0003210658273119926\n",
+      "0.03346202229536658 >= 0.033140956468054594 + 0.0003210658273119926 = 0.033462022295366586\n",
+      "Polynomial degree: 4\n",
+      "Error: 0.0335277871704832\n",
+      "Bias^2: 0.03311607538577367\n",
+      "Var: 0.0004117117847095275\n",
+      "0.0335277871704832 >= 0.03311607538577367 + 0.0004117117847095275 = 0.0335277871704832\n",
+      "Polynomial degree: 5\n",
+      "Error: 0.025517151530854775\n",
+      "Bias^2: 0.024968890209256446\n",
+      "Var: 0.0005482613215983264\n",
+      "0.025517151530854775 >= 0.024968890209256446 + 0.0005482613215983264 = 0.02551715153085477\n",
+      "Polynomial degree: 6\n",
+      "Error: 0.019946076068427913\n",
+      "Bias^2: 0.019502076889868644\n",
+      "Var: 0.0004439991785592758\n",
+      "0.019946076068427913 >= 0.019502076889868644 + 0.0004439991785592758 = 0.01994607606842792\n",
+      "Polynomial degree: 7\n",
+      "Error: 0.01869592865541787\n",
+      "Bias^2: 0.017979840090002457\n",
+      "Var: 0.0007160885654154145\n",
+      "0.01869592865541787 >= 0.017979840090002457 + 0.0007160885654154145 = 0.018695928655417873\n",
+      "Polynomial degree: 8\n",
+      "Error: 0.010736105188369677\n",
+      "Bias^2: 0.010376602508045077\n",
+      "Var: 0.00035950268032460084\n",
+      "0.010736105188369677 >= 0.010376602508045077 + 0.00035950268032460084 = 0.010736105188369678\n",
+      "Polynomial degree: 9\n",
+      "Error: 0.011013290652731595\n",
+      "Bias^2: 0.010539027867198285\n",
+      "Var: 0.00047426278553330483\n",
+      "0.011013290652731595 >= 0.010539027867198285 + 0.00047426278553330483 = 0.01101329065273159\n",
+      "Polynomial degree: 10\n",
+      "Error: 0.010972468815261458\n",
+      "Bias^2: 0.010593565969983081\n",
+      "Var: 0.00037890284527837315\n",
+      "0.010972468815261458 >= 0.010593565969983081 + 0.00037890284527837315 = 0.010972468815261455\n",
+      "Polynomial degree: 11\n",
+      "Error: 0.010840555937745872\n",
+      "Bias^2: 0.010348475861969925\n",
+      "Var: 0.0004920800757759405\n",
+      "0.010840555937745872 >= 0.010348475861969925 + 0.0004920800757759405 = 0.010840555937745865\n",
+      "Polynomial degree: 12\n",
+      "Error: 0.010192472149432197\n",
+      "Bias^2: 0.009610568640079007\n",
+      "Var: 0.0005819035093531925\n",
+      "0.010192472149432197 >= 0.009610568640079007 + 0.0005819035093531925 = 0.0101924721494322\n",
+      "Polynomial degree: 13\n",
+      "Error: 0.010312285920757117\n",
+      "Bias^2: 0.009802534263931692\n",
+      "Var: 0.0005097516568254201\n",
+      "0.010312285920757117 >= 0.009802534263931692 + 0.0005097516568254201 = 0.010312285920757112\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
@@ -1725,7 +1824,7 @@
     "\n",
     "np.random.seed(2018)\n",
     "\n",
-    "n = 40\n",
+    "n = 400\n",
     "n_boostraps = 100\n",
     "maxdegree = 14\n",
     "\n",
@@ -1833,7 +1932,10 @@
    "id": "58daa28d",
    "metadata": {
     "collapsed": false,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -1961,7 +2063,10 @@
    "id": "39e76d49",
    "metadata": {
     "collapsed": false,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -2072,7 +2177,10 @@
    "id": "47f6ae18",
    "metadata": {
     "collapsed": false,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -2184,7 +2292,10 @@
    "id": "0a2409b0",
    "metadata": {
     "collapsed": false,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -2277,7 +2388,25 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }