diff --git a/Lecture 4/Central Limit Theorem.ipynb b/Lecture 4/Central Limit Theorem.ipynb
index 4085800..1c84dff 100644
--- a/Lecture 4/Central Limit Theorem.ipynb	
+++ b/Lecture 4/Central Limit Theorem.ipynb	
@@ -1,118 +1,179 @@
 {
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Central Limit Theorem\n",
     "# One of the most important, powerful theorems in statistics (arguably THE most important)\n",
     "# Independent of the PDF of a distribution, when one computes the average of a large enough number of samples,\n",
     "# these averaged experiments will follow a normal distribution with the same mean as the original, non-normal distribution!\n",
     "options(repr.plot.width=20, repr.plot.height=12.5)   # this command just formats the size of the figures. Adapt to view them nicelyin your browser.\n",
     "\n",
     "\n",
     "\n",
     "# Create a non-uniform population of 100,000 numbers between 1 and 100\n",
     "pop1 <- rnorm(20000, mean = 10, sd = 3)\n",
     "pop2 <- rnorm(80000, mean = 70, sd = 10)\n",
-    "pop <- c(pop1, pop2)"
+    "pop3 <- runif(80000, 10,50)\n",
+    "pop <- c(pop1, pop2, pop3)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "Plot with title “PDF of the real distribution”"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 750,
+       "width": 1200
+      },
+      "text/plain": {
+       "height": 750,
+       "width": 1200
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#Lets look at the real underlying probability distribution function.\n",
     "#In real life, this is unknown, and it is what you want to figure out from experiments\n",
     "#with finite numbers of samples.\n",
     "\n",
     "mu <- mean(pop) #calculate the population mean\n",
     "sigma <- sd(pop) #calculate the population standard deviation\n",
     "hist(pop,xlab=\"X\", main=\"PDF of the real distribution\")\n",
     "abline(v=mu,col=\"red\",lwd=2)\n",
     "abline(v=mu+sigma,col=\"blue\",lwd=2)\n",
     "abline(v=mu-sigma,col=\"blue\",lwd=2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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JLAAgCqCKwksACAKgIrCSwAoIrASgILAKgisJLAAgCqCKwksACAKgIrCSwAoIrA\nSgILAKgisJLAAgCqCKwksACAKgIrCSwAoIrASgILAKgisJLAAgCqCKwksACAKgIrCSwAoIrA\nSgILAKgisJLAAgCqCKwksACAKgIrCSwAoIrASgILAKgisJLAAgCqCKwksACAKgIrCSwAoIrA\nSgILAKgisJLAAgCqCKwksACAKgIrCSwAoIrASgILAKgisJLAAgCqCKwksACAKgIrCSwAoIrA\nSgILAKgisJLAAgCqCKwksACAKgIrCSwAoIrASgILAKgisJLAAgCqCKwksACAKgIrCSwAoIrA\nSgILAKgisJLAAgCqCKwksACAKgIrCSwAoIrASgILAKgisJLAAgCqCKwksACAKgIrCSwAoIrA\nSgILAKgisJLAAgCqCKwksACAKgIrCSwAoIrASgILAKgisJLAAgCqCKwksACAKgIrCSwAoIrA\nSgILAKgisJLAAgCqCKwksACAKgIrCSwAoIrASgILAKgisJLAAgCqCKwksACAKgIrCSwAoIrA\nSgILAKgisJLAAgCqCKwksACAKgIrCSwAoIrASgILAKgisJLAAgCqCKwksACAKgIrCSwAoIrA\nSgILAKgisJLAAgCqCKwksACAKgIrCSwAoIrASgILAKgisJLAAgCqCKwksACAKgIrCSwAoIrA\nSgILAKgisJLAAgCqCKw0GVhf2SMrCCwAmBcCK00G1r23fmAPrCCwAGBeCKw0GVgPGYajX/v1\n6hUEFgDMC4GVJgNr+7tP3TBs+qkP164gsABgXgistMOb3G/45aOG4RFvuKlwBYEFAPNCYKWd\n/hbh9r98xvrhPs+5pmwFgQUA80JgpZ0v0/DpVzxgGIa9fuxrRSsILACYFwIr7RBYt//Jj6wb\nvuMXPvdnjxnOKFpBYAHAvBBYaUVgfeaiQ4a9nnj57QvD7U+7b9EKAgsA5oXASpOB9dS9h/u9\n+JN551VV13gXWAAwLwRWmsyo4fjLvrF05+rXFK0gsABgXgisNBlYf7tHVhBYADAvBFbyYc8A\nQBWBlSYD6/cf89m4/eyj/6hwBYEFAPNCYKXJwHrClhwc86TCFQQWAMwLgZUmA2vzuTn4qQcV\nriCwAGBeCKw0GVjrX5aDCzcUriCwAGBeCKw0GVgPPCUHpzygcAWBBQDzQmClycB65sZ/jNtP\nbPy3hSsILACYFwIrTQbWB9bd79WfvPWTr77fur8uXEFgAcC8EFhpxXWwXrf3MLb36ypXEFgA\nMC8EVlp5odGPnXvsEcc+5+9LVxBYADAvBFZyJXcAoIrASgILAKgisJLAAgCqCKy0IrDe+7TN\nG/YOhSsILACYFwIrTQbWO9YN+z/8mFC4gsACgHkhsNJkYG3Z+3e3168gsABgXgisNBlYG5+5\nJ1YQWAAwLwRWmgys+z1vT6wgsABgXgisNBlYp27ZEysILACYFwIrTQbWpzf//O31KwgsAJgX\nAitNBtbWxw2Hn7Q1FK4gsABgXgisNBlYw7LCFQQWAMwLgZUmU+qjywpXEFgAMC8EVvJROQBA\nFYGVdgisT3/ga9UrCCwAmBcCK60IrA8ePQzvHI1+7+HvXdVzt197xZt/54pr7+Tq7wILAOaF\nwEqTgfWJTfueNA6smzY9fxXPvPWVhyy+If7QV946bZ7AAoB5IbDSZGCdvuHvvzQOrNFTV/Fh\nzzc/clh33Klnn3PqseuGE2+ZMlFgAcC8EFhpMrA2P2u0GFgXHHjnT3zZcMbnF0efO214+ZSJ\nAgsA5oXASpOBtc9LM7BeuuHOn3jklm1tuO0RD54yUWABwLwQWGkysA76yQysHzr8zp+44fzl\n8Xkbp0wUWAAwLwRWmgysp2/+ZgTWu/faeudPPOik5fHTNk+ZKLAAYF4IrDQZWH+17snvG674\n8IvXr//YnT/xtHVvasPL9jp9ykSBBQDzQmClFdfBet0+cd2F9W+6o9kTrtt/OO7CN15++Rsv\nPHY44LopEwUWAMwLgZVWXsn94y/YcsQx5358Vc+85oT2ydAnXDNtnsACgHkhsNLufBbh1Ref\ndcopZ1189fRZAgsA5oXASj7sGQCoIrCSwAIAqgisNBlYRy27S+e44aqrpjwqsABgXgisNBlY\n+4d9hmG//e/SOS4ddvw52Feed86Sk/ZAYL36nDX3R+UvCr5N/cpa/2k854d/eK13cNqJa72D\ns4/7ibXewg+evNY7OOcta/2H4cbnrvW34Jzjf3ytd/CdJ6/1v4Z7YGAt+tbfPOpp37pL57hs\np5947enAetgPrN1/Oj8lSoMAACAASURBVIu+Z83/A4J7ioMfv9Z/Hvfbb6138D17rfUOfmx4\n4lpvYZ/D1noHxz1hrf8w/M/hrLX+Jgxb1noHm45Z638N99jAWsijg3++cIU98SvCh72+/JR3\n0YsEFqSD37rWO3jwtI9DvVu8YN1a7+C64Q/XegubnrLWO3jFPSCwblvrLQwXrfUOHiSwFu3y\nTe5nfmfhCgILZpvAElhBYAmsILDSLgPrrA2FKwgsmG0CS2AFgSWwgsBKuwqsL2z2E6w7I7Cg\nEVgCKwgsgRUEVpoMrIvCy5+93/ALd+0kLzl8yoMCC2abwBJYQWAJrCCw0mRgtc8WvPcF2+7a\nSbZOu1ypwILZJrAEVhBYAisIrDSZRu8IV37gprt6EoEFc0xgCawgsARWEFip+6NynjXhCIEF\n80tgCawgsARWEFipO7CGFaZMFFgw2wSWwAoCS2AFgZW6A2vTQ9+x5PECC+aXwBJYQWAJrCCw\n0mQaHb7S9Cc+ar/tS2PvwYI5JrAEVhBYAisIrDSZRgceMAzDpoV/DjhwbPoTnz9ctzQWWDDH\nBJbACgJLYAWBlSbT6Kbvf8SVN41uuvK471/F3yN825b3Lo9fPmWiwILZJrAEVhBYAisIrDQZ\nWOcfeUvc3nLk+YUrCCyYbQJLYAWBJbCCwEqTgXXIBTm44NDCFQQWzDaBJbCCwBJYQWClycDa\n8JIcvGRj4QoCC2abwBJYQWAJrCCw0mRgPfSIm+P25sP/deEKAgtmm8ASWEFgCawgsNJkYP3q\ncMzlN45uvPyY4dLCFQQWzDaBJbCCwBJYQWClycDadvYwDPss/HPOXfyw56kEFsw2gSWwgsAS\nWEFgpZVXsHrP1qMPP3rr/yhdQWDBbBNYAisILIEVBFbq/qicVRNYMNsElsAKAktgBYGVdgis\nT3/ga9UrCCyYbQJLYAWBJbCCwEorAuuDRw/DO0ej33v4e+9oegeBBbNNYAmsILAEVhBYaTKw\nPrFp35PGgXXTpucXriCwYLYJLIEVBJbACgIrTQbW6Rv+/kvjwBo9tfK7I7BgtgksgRUElsAK\nAitNBtbmZ40WA+uCAwtXEFgw2wSWwAoCS2AFgZUmA2ufl2ZgvXRD4QoCC2abwBJYQWAJrCCw\n0mRgHfSTGVg/dHjhCgILZpvAElhBYAmsILDSZGA9ffM3I7DevdfWwhUEFsw2gSWwgsASWEFg\npcnA+qt1T37fcMWHX7x+/ccKVxBYMNsElsAKAktgBYGVVlwH63XjDyIchvVvqlxBYMFsE1gC\nKwgsgRUEVlp5JfePv2DLEcec+/HSFQQWzDaBJbCCwBJYQWClycD64Ef3xAoCC2abwBJYQWAJ\nrCCw0mRg7fWMPbGCwILZJrAEVhBYAisIrDQZWPc/c0+sILBgtgksgRUElsAKAitNBtYzH3b7\nHlhBYMFsE1gCKwgsgRUEVpoMrH868AW31K8gsGC2CSyBFQSWwAoCK00G1tbHDvd/wrO3jhWu\nILBgtgksgRUElsAKAitNBtawrHAFgQWzTWAJrCCwBFYQWGkypT66rHAFgQWzTWAJrCCwBFYQ\nWKnyZ1W7JrBgtgksgRUElsAKAistBdbvfWgPrSCwYLYJLIEVBJbACgIrLQXWsHXhyyVPql9B\nYMFsE1gCKwgsgRUEVloZWFv3wG8MBRbMNoElsILAElhBYCWB1UdgQSOwBFYQWAIrCKwksPoI\nLGgElsAKAktgBYGVBFYfgQWNwBJYQWAJrCCwksDqI7CgEVgCKwgsgRUEVloOrPX777//+mH/\nRYUrCCyYbQJLYAWBJbCCwErLgbVC4QoCC2abwBJYQWAJrCCw0lJKfWOFwhUEFsw2gSWwgsAS\nWEFgJR+V00dgQSOwBFYQWAIrCKwksPoILGgElsAKAktgBYGVBFYfgQWNwBJYQWAJrCCwksDq\nI7CgEVgCKwgsgRUEVhJYfQQWNAJLYAWBJbCCwEoCq4/AgkZgCawgsARWEFhJYPURWNAILIEV\nBJbACgIrCaw+AgsagSWwgsASWEFgJYHVR2BBI7AEVhBYAisIrCSw+ggsaASWwAoCS2AFgZUE\nVh+BBY3AElhBYAmsILCSwOojsKARWAIrCCyBFQRWElh9BBY0AktgBYElsILASgKrj8CCRmAJ\nrCCwBFYQWElg9RFY0AgsgRUElsAKAisJrD4CCxqBJbCCwBJYQWAlgdVHYEEjsARWEFgCKwis\nJLD6CCxoBJbACgJLYAWBlQRWH4EFjcASWEFgCawgsJLA6iOwoBFYAisILIEVBFYSWH0EFjQC\nS2AFgSWwgsBKAquPwIJGYAmsILAEVhBYSWD1EVjQCCyBFQSWwAoCKwmsPgILGoElsILAElhB\nYCWB1UdgQSOwBFYQWAIrCKwksPoILGgElsAKAktgBYGVBFYfgQWNwBJYQWAJrCCwksDqI7Cg\nEVgCKwgsgRUEVhJYfQQWNAJLYAWBJbCCwEoCq4/AgkZgCawgsARWEFhJYPURWNAILIEVBJbA\nCgIrCaw+AgsagSWwgsASWEFgJYHVR2BBI7AEVhBYAisIrCSw+ggsaASWwAoCS2AFgZUEVh+B\nBY3AElhBYAmsILCSwOojsKARWAIrCCyBFQRWElh9BBY0AktgBYElsILASgKrj8CCRmAJrCCw\nBFYQWElg9RFY0AgsgRUElsAKAisJrD4CCxqBJbCCwBJYQWAlgdVHYEEjsARWEFgCKwisJLD6\nCCxoBJbACgJLYAWBlQRWH4EFjcASWEFgCawgsJLA6iOwoBFYAisILIEVBFYSWH0EFjQCS2AF\ngSWwgsBKAquPwIJGYAmsILAEVhBYSWD1EVjQCCyBFQSWwAoCKwmsPgILGoElsILAElhBYCWB\n1UdgQSOwBFYQWAIrCKwksPoILGgElsAKAktgBYGVBFYfgQWNwBJYQWAJrCCwksDqI7CgEVgC\nKwgsgRUEVhJYfQQWNAJLYAWBJbCCwEoCq4/AgkZgCawgsARWEFhJYPURWNAILIEVBJbACgIr\nCaw+AgsagSWwgsASWEFgJYHVR2BBI7AEVhBYAisIrCSw+ggsaASWwAoCS2AFgZUEVh+BBY3A\nElhBYAmsILCSwOojsKARWAIrCCyBFQRWElh9BBY0AktgBYElsILASgKrj8CCRmAJrCCwBFYQ\nWElg9RFY0AgsgRUElsAKAisJrD4CCxqBJbCCwBJYQWAlgdVHYEEjsARWEFgCKwisJLD6CCxo\nBJbACgJLYAWBlQRWH4EFjcASWEFgCawgsJLA6iOwoBFYAisILIEVBFYSWH0EFjQCS2AFgSWw\ngsBKAquPwIJGYAmsILAEVhBYSWD1EVjQCCyBFQSWwAoCKwmsPgILGoElsILAElhBYCWB1Udg\nQSOwBFYQWAIrCKwksPoILGgElsAKAktgBYGVBFYfgQWNwBJYQWAJrCCwksDqI7CgEVgCKwgs\ngRUEVhJYfQQWNAJLYAWBJbCCwEoCq4/AgkZgCawgsARWEFhJYPURWNAILIEVBJbACgIrCaw+\nAgsagSWwgsASWEFgJYHVR2BBI7AEVhBYAisIrCSw+ggsaASWwAoCS2AFgZUEVh+BBY3AElhB\nYAmsILCSwOojsKARWAIrCCyBFQRWElh9BBY0AktgBYElsILASgKrj8CCRmAJrCCwBFYQWElg\n9RFY0AgsgRUElsAKAisJrD4CCxqBJbCCwBJYQWAlgdVHYEEjsARWEFgCKwisJLD6CCxoBJbA\nCgJLYAWBlQRWH4EFjcASWEFgCawgsJLA6iOwoBFYAisILIEVBFYSWH0EFjQCS2AFgSWwgsBK\nAquPwIJGYAmsILAEVhBYSWD1EVjQCCyBFQSWwAoCKwmsPgILGoElsILAElhBYCWB1UdgQSOw\nBFYQWAIrCKwksPoILGgElsAKAktgBYGVBFYfgQWNwBJYQWAJrCCwksDqI7CgEVgCKwgsgRUE\nVhJYfQQWNAJLYAWBJbCCwEoCq4/AgkZgCawgsARWEFhJYPURWNAILIEVBJbACgIrCaw+Agsa\ngSWwgsASWEFgJYHVR2BBI7AEVhBYAisIrCSw+ggsaASWwAoCS2AFgZUEVh+BBY3AElhBYAms\nILCSwOojsKARWAIrCCyBFQRWElh9BBY0AktgBYElsILASgKrj8CCRmAJrCCwBFYQWElg9RFY\n0AgsgRUElsAKAisJrD4CCxqBJbCCwBJYQWAlgdVHYEEjsARWEFgCKwisJLD6CCxoBJbACgJL\nYAWBlQRWH4EFjcASWEFgCawgsJLA6iOwoBFYAisILIEVBFYSWH0EFjQCS2AFgSWwgsBKAquP\nwIJGYAmsILAEVhBYSWD1EVjQCCyBFQSWwAoCKwmsPgILGoElsILAElhBYCWB1UdgQSOwBFYQ\nWAIrCKwksPoILGgElsAKAktgBYGVBFYfgQWNwBJYQWAJrCCwksDqI7CgEVgCKwgsgRUEVhJY\nfQQWNAJLYAWBJbCCwEoCq4/AgkZgCawgsARWEFhJYPURWNAILIEVBJbACgIrCaw+AgsagSWw\ngsASWEFgJYHVR2BBI7AEVhBYAisIrCSw+ggsaASWwAoCS2AFgZUEVh+BBY3AElhBYAmsILCS\nwOojsKARWAIrCCyBFQRWElh9BBY0AktgBYElsILASgKrj8CCRmAJrCCwBFYQWElg9RFY0Ags\ngRUElsAKAisJrD4CCxqBJbCCwBJYQWAlgdVHYEEjsARWEFgCKwisJLD6CCxoBJbACgJLYAWB\nlQRWH4EFjcASWEFgCawgsJLA6iOwoBFYAisILIEVBFYSWH0EFjQCS2AFgSWwgsBKAquPwIJG\nYAmsILAEVhBYSWD1EVjQCCyBFQSWwAoCKwmsPgILGoElsILAElhBYCWB1UdgQSOwBFYQWAIr\nCKwksPoILGgElsAKAktgBYGVBFYfgQWNwBJYQWAJrCCwksDqI7CgEVgCKwgsgRUEVhJYfQQW\nNAJLYAWBJbCCwEoCq4/AgkZgCawgsARWEFhJYPURWNAILIEVBJbACgIrCaw+AgsagSWwgsAS\nWEFgJYHVR2BBI7AEVhBYAisIrCSw+ggsaASWwAoCS2AFgZUEVh+BBY3AElhBYAmsILCSwOoj\nsKARWAIrCCyBFQRWElh9BBY0AktgBYElsILASgKrj8CCRmAJrCCwBFYQWElg9RFY0AgsgRUE\nlsAKAisJrD4CCxqBJbCCwBJYQWAlgdVHYEEjsARWEFgCKwisJLD6CCxoBJbACgJLYAWBlQRW\nH4EFjcASWEFgCawgsJLA6iOwoBFYAisILIEVBFYSWH0EFjQCS2AFgSWwgsBKAquPwIJGYAms\nILAEVhBYSWD1EVjQCCyBFQSWwAoCKwmsPgILGoElsILAElhBYCWB1UdgQSOwBFYQWAIrCKwk\nsPoILGgElsAKAktgBYGVBFYfgQWNwBJYQWAJrCCwksDqI7CgEVgCKwgsgRUEVhJYfQQWNAJL\nYAWBJbCCwEoCq4/AgkZgCawgsARWEFhJYPURWNAILIEVBJbACgIrCaw+AgsagSWwgsASWEFg\nJYHVR2BBI7AEVhBYAisIrCSw+ggsaASWwAoCS2AFgZUEVh+BBY3AElhBYAmsILCSwOojsKAR\nWAIrCCyBFQRWElh9BBY0AktgBYElsILASgKrj8CCRmAJrCCwBFYQWElg9RFY0AgsgRUElsAK\nAisJrD4CCxqBJbCCwBJYQWAlgdVHYEEjsARWEFgCKwisJLD6CCxoBJbACgJLYAWBlQRWH4EF\njcASWEFgCawgsJLA6iOwoBFYAisILIEVBFYSWH0EFjQCS2AFgSWwgsBKAquPwIJGYAmsILAE\nVhBYSWD1EVjQCCyBFQSWwAoCKwmsPgILGoElsILAElhBYCWB1UdgQSOwBFYQWAIrCKwksPoI\nLGgElsAKAktgBYGVBFYfgQWNwBJYQWAJrCCwksDqI7CgEVgCKwgsgRUEVhJYfQQWNAJLYAWB\nJbCCwEoCq4/AgkZgCawgsARWEFhJYPURWNAILIEVBJbACgIrCaw+AgsagSWwgsASWEFgJYHV\nR2BBI7AEVhBYAisIrCSw+ggsaASWwAoCS2AFgZUEVh+BBY3AElhBYAmsILCSwOojsKARWAIr\nCCyBFQRWElh9BBY0AktgBYElsILASgKrj8CCRmAJrCCwBFYQWElg9RFY0AgsgRUElsAKAisJ\nrD4CCxqBJbCCwBJYQWAlgdVHYEEjsARWEFgCKwisJLD6CCxoBJbACgJLYAWBlQRWH4EFjcAS\nWEFgCawgsJLA6iOwoBFYAisILIEVBFYSWH0EFjQCS2AFgSWwgsBKAquPwIJGYAmsILAEVhBY\nSWD1EVjQCCyBFQSWwAoCKwmsPgILGoElsILAElhBYCWB1UdgQSOwBFYQWAIrCKwksPoILGgE\nlsAKAktgBYGVBFYfgQWNwBJYQWAJrCCwksDqI7CgEVgCKwgsgRUEVhJYfQQWNAJLYAWBJbCC\nwEoCq4/AgkZgCawgsARWEFhJYPURWNAILIEVBJbACgIrCaw+AgsagSWwgsASWEFgJYHVR2BB\nI7AEVhBYAisIrCSw+ggsaASWwAoCS2AFgZUEVh+BBY3AElhBYAmsILCSwOojsKARWAIrCCyB\nFQRWElh9BBY0AktgBYElsILASgKrj8CCRmAJrCCwBFYQWGk3A+v9Tz5w32MumfoflMCC2Saw\nBFYQWAIrCKzUHVibX7Tw5a17D2MnbZ8yUWDBbBNYAisILIEVBFbqDqxh62j05X3XveJTX3n7\nwcNbpkwUWDDbBJbACgJLYAWBlXYrsF4/nD8efmh44pSJAgtmm8ASWEFgCawgsNJuBdbzhn+I\n8bEHTZkosGC2CSyBFQSWwAoCK+1WYP3EcGuMT14/ZaLAgtkmsARWEFgCKwistFuB9arhizF+\n7P2nTBRYMNsElsAKAktgBYGV+gNr3caN64d3xfiI46ZMFFgw2wSWwAoCS2AFgZW6A+th4ZfG\nw6uH50yZKLBgtgksgRUElsAKAitVXMn9wxd/ZMqjAgtmm8ASWEFgCawgsJKPyukjsKARWAIr\nCCyBFQRWElh9BBY0AktgBYElsILAShWBdcNVV015VGDBbBNYAisILIEVBFaqCKxLhx3PcvNF\nL13y4wILZprAElhBYAmsILBSRWBddtRROxz54o88YcnxwzcL1lhJYME9h8ASWEFgCawgsJL3\nYPURWNAILIEVBJbACgIrCaw+AgsagSWwgsASWEFgJYHVR2BBI7AEVhBYAisIrLQ7gbX92ive\n/DtXXLt9+iyBBbNNYAmsILAEVhBYqT+wbn3lIUM49JW3TpsnsGC2CSyBFQSWwAoCK3UH1s2P\nHNYdd+rZ55x67LrhxFumTBRYMNsElsAKAktgBYGVugPrZcMZn18cfe604eVTJgosmG0CS2AF\ngSWwgsBK3YF15JZtbbjtEdP+7yawYLYJLIEVBJbACgIrdQfWhvOXx+dtnDJRYMFsE1gCKwgs\ngRUEVuoOrINOWh4/bfOUiQILZpvAElhBYAmsILBSd2Cdtu5NbXjZXqdPmSiwYLYJLIEVBJbA\nCgIrdQfWdfsPx134xssvf+OFxw4HXDdlosCC2SawBFYQWAIrCKzUfx2sa04Y0gnXTJsnsGC2\nCSyBFQSWwAoCK+3OldyvvvisU0456+Krp88SWDDbBJbACgJLYAWBlXwWYR+BBY3AElhBYAms\nILCSwOojsKARWAIrCCyBFQRWElh9BBY0AktgBYElsILASgKrj8CCRmAJrCCwBFYQWElg9RFY\n0AgsgRUElsAKAisJrD4CCxqBJbCCwBJYQWAlgdVHYEEjsARWEFgCKwisJLD6CCxoBJbACgJL\nYAWBlQRWH4EFjcASWEFgCawgsJLA6iOwoBFYAisILIEVBFYSWH0EFjQCS2AFgSWwgsBKAquP\nwIJGYAmsILAEVhBYSWD1EVjQCCyBFQSWwAoCKwmsPgILGoElsILAElhBYCWB1UdgQSOwBFYQ\nWAIrCKwksPoILGgElsAKAktgBYGVBFYfgQWNwBJYQWAJrCCwksDqI7CgEVgCKwgsgRUEVhJY\nfQQWNAJLYAWBJbCCwEoCq4/AgkZgCawgsARWEFhJYPURWNAILIEVBJbACgIrCaw+AgsagSWw\ngsASWEFgJYHVR2BBI7AEVhBYAisIrCSw+ggsaASWwAoCS2AFgZUEVh+BBY3AElhBYAmsILCS\nwOojsKARWAIrCCyBFQRWElh9BBY0AktgBYElsILASgKrj8CCRmAJrCCwBFYQWElg9RFY0Ags\ngRUElsAKAisJrD4CCxqBJbCCwBJYQWAlgdVHYEEjsARWEFgCKwisJLD6CCxoBJbACgJLYAWB\nlQRWH4EFjcASWEFgCawgsJLA6iOwoBFYAisILIEVBFYSWH0EFjQCS2AFgSWwgsBKAquPwIJG\nYAmsILAEVhBYSWD1EVjQCCyBFQSWwAoCKwmsPgILGoElsILAElhBYCWB1UdgQSOwBFYQWAIr\nCKwksPoILGgElsAKAktgBYGVBFYfgQWNwBJYQWAJrCCwksDqI7CgEVgCKwgsgRUEVhJYfQQW\nNAJLYAWBJbCCwEoCq4/AgkZgCawgsARWEFhJYPURWNAILIEVBJbACgIrCaw+AgsagSWwgsAS\nWEFgJYHVR2BBI7AEVhBYAisIrCSw+ggsaASWwAoCS2AFgZUEVh+BBY3AElhBYAmsILCSwOoj\nsKARWAIrCCyBFQRWElh9BBY0AktgBYElsILASgKrj8CCRmAJrCCwBFYQWElg9RFY0AgsgRUE\nlsAKAisJrD4CCxqBJbCCwBJYQWAlgdVHYEEjsARWEFgCKwisJLD6CCxoBJbACgJLYAWBlQRW\nH4EFjcASWEFgCawgsJLA6iOwoBFYAisILIEVBFYSWH0EFjQCS2AFgSWwgsBKAquPwIJGYAms\nILAEVhBYSWD1EVjQCCyBFQSWwAoCKwmsPgILGoElsILAElhBYCWB1UdgQSOwBFYQWAIrCKwk\nsPoILGgElsAKAktgBYGVBFYfgQWNwBJYQWAJrCCwksDqI7CgEVgCKwgsgRUEVhJYfQQWNAJL\nYAWBJbCCwEoCq4/AgkZgCawgsARWEFhJYPURWNAILIEVBJbACgIrCaw+AgsagSWwgsASWEFg\nJYHVR2BBI7AEVhBYAisIrCSw+ggsaASWwAoCS2AFgZUEVh+BBY3AElhBYAmsILCSwOojsKAR\nWAIrCCyBFQRWElh9BBY0AktgBYElsILASgKrj8CCRmAJrCCwBFYQWElg9RFY0AgsgRUElsAK\nAisJrD4CCxqBJbCCwBJYQWAlgdVHYEEjsARWEFgCKwisJLD6CCxoBJbACgJLYAWBlQRWH4EF\njcASWEFgCawgsJLA6iOwoBFYAisILIEVBFYSWH0EFjQCS2AFgSWwgsBKAquPwIJGYAmsILAE\nVhBYSWD1EVjQCCyBFQSWwAoCKwmsPgILGoElsILAElhBYCWB1UdgQSOwBFYQWAIrCKwksPoI\nLGgElsAKAktgBYGVBFYfgQWNwBJYQWAJrCCwksDqI7CgEVgCKwgsgRUEVhJYfQQWNAJLYAWB\nJbCCwEoCq4/AgkZgCawgsARWEFhJYPURWNAILIEVBJbACgIrCaw+AgsagSWwgsASWEFgJYHV\nR2BBI7AEVhBYAisIrCSw+ggsaASWwAoCS2AFgZUEVh+BBY3AElhBYAmsILCSwOojsKARWAIr\nCCyBFQRWElh9BBY0AktgBYElsILASgKrj8CCRmAJrCCwBFYQWElg9RFY0AgsgRUElsAKAisJ\nrD4CCxqBJbCCwBJYQWAlgdVHYEEjsARWEFgCKwisJLD6CCxoBJbACgJLYAWBlQRWH4EFjcAS\nWEFgCawgsJLA6iOwoBFYAisILIEVBFYSWH0EFjQCS2AFgSWwgsBKAquPwIJGYAmsILAEVhBY\nSWD1EVjQCCyBFQSWwAoCKwmsPgILGoElsILAElhBYCWB1UdgQSOwBFYQWAIrCKwksPoILGgE\nlsAKAktgBYGVBFYfgQWNwBJYQWAJrCCwksDqI7CgEVgCKwgsgRUEVhJYfQQWNAJLYAWBJbCC\nwEoCq4/AgkZgCawgsARWEFhJYPURWNAILIEVBJbACgIrCaw+AgsagSWwgsASWEFgJYHVR2BB\nI7AEVhBYAisIrCSw+ggsaASWwAoCS2AFgZUEVh+BBY3AElhBYP3/9u43Vu/yLOD4fU5ZaynS\nMuiKaxmj02waF1vQUlQcS3DuDzCnrZaq6PizOZHiCzcBCUYwS2CxiToIkqwF4rYXKqSQkCFu\n73wDfcEaQ006ma5kTsQgf6rITk885zrX3T+YnBc39/Z7nvN8Pi/O/fuVX8r14oLzTc/T5xFY\nQWAlgdVGYEElsARWEFgCKwisJLDaCCyoBJbACgJLYAWBlQRWG4EFlcASWEFgCawgsJLAaiOw\noBJYAisILIEVBFYSWG0EFlQCS2AFgSWwgsBKAquNwIJKYAmsILAEVhBYSWC1EVhQCSyBFQSW\nwAoCKwmsNgILKoElsILAElhBYCWB1UZgQSWwBFYQWAIrCKwksNoILKgElsAKAktgBYGVBFYb\ngQWVwBJYQWAJrCCwksBqI7CgElgCKwgsgRUEVhJYbQQWVAJLYAWBJbCCwEoCq43AgkpgCawg\nsARWEFhJYLURWFAJLIEVBJbACgIrCaw2AgsqgSWwgsASWEFgJYHVRmBBJbAEVhBYAisIrCSw\n2ggsqASWwAoCS2AFgZUEVhuBBZXAElhBYAmsILCSwGojsKASWAIrCCyBFQRWElhtBBZUAktg\nBYElsILASgKrjcCCSmAJrCCwBFYQWElgtRFYUAksgRUElsAKAisJrDYCCyqBJbCCwBJYQWAl\ngdVGYEElsARWEFgCKwisJLDaCCyoBJbACgJLYAWBlQRWG4EFlcASWEFgCawgsJLAaiOwoBJY\nAisILIEVBFYS+falmwAAEbdJREFUWG0EFlQCS2AFgSWwgsBKAquNwIJKYAmsILAEVhBYSWC1\nEVhQCSyBFQSWwAoCKwmsNgILKoElsILAElhBYCWB1UZgQSWwBFYQWAIrCKwksNoILKgElsAK\nAktgBYGVBFYbgQWVwBJYQWAJrCCwksBqI7CgElgCKwgsgRUEVhJYbQQWVAJLYAWBJbCCwEoC\nq43AgkpgCawgsARWEFhJYLURWFAJLIEVBJbACgIrCaw2AgsqgSWwgsASWEFgJYHVRmBBJbAE\nVhBYAisIrCSw2ggsqASWwAoCS2AFgZUEVhuBBZXAElhBYAmsILCSwGojsKASWAIrCCyBFQRW\nElhtBBZUAktgBYElsILASgKrjcCCSmAJrCCwBFYQWElgtRFYUAksgRUElsAKAisJrDYCCyqB\nJbCCwBJYQWAlgdVGYEElsARWEFgCKwisJLDaCCyoBJbACgJLYAWBlQRWG4EFlcASWEFgCawg\nsJLAaiOwoBJYAisILIEVBFYSWG0EFlQCS2AFgSWwgsBKAquNwIJKYAmsILAEVhBYSWC1EVhQ\nCSyBFQSWwAoCKwmsNgILKoElsILAElhBYCWB1UZgQSWwBFYQWAIrCKwksNoILKgElsAKAktg\nBYGVBFYbgQWVwBJYQWAJrCCwksBqI7CgElgCKwgsgRUEVhJYbQQWVAJLYAWBJbCCwEoCq43A\ngkpgCawgsARWEFhJYLURWFAJLIEVBJbACgIrCaw2AgsqgSWwgsASWEFgJYHVRmBBJbAEVhBY\nAisIrCSw2ggsqASWwAoCS2AFgZUEVhuBBZXAElhBYAmsILCSwGojsKASWAIrCCyBFQRWElht\nBBZUAktgBYElsILASgKrjcCCSmAJrCCwBFYQWElgtRFYUAksgRUElsAKAisJrDYCCyqBJbCC\nwBJYQWAlgdVGYEElsARWEFgCKwisJLDaCCyoBJbACgJLYAWBlQRWG4EFlcASWEFgCawgsJLA\naiOwoBJYAisILIEVBFYSWG0EFlQCS2AFgSWwgsBKAquNwIJKYAmsILAEVhBYSWC1EVhQCSyB\nFQSWwAoCKwmsNgILKoElsILAElhBYCWB1UZgQSWwBFYQWAIrCKwksNoILKgElsAKAktgBYGV\nBFYbgQWVwBJYQWAJrCCwksBqI7CgElgCKwgsgRUEVhJYbQQWVAJLYAWBJbCCwEoCq43Agkpg\nCawgsARWEFhJYLURWFAJLIEVBJbACgIrCaw2AgsqgSWwgsASWEFgJYHVRmBBJbAEVhBYAisI\nrCSw2ggsqASWwAoCS2AFgZUEVhuBBZXAElhBYAmsILCSwGojsKASWAIrCCyBFQRWElhtBBZU\nAktgBYElsILASgKrjcCCSmAJrCCwBFYQWElgtRFYUAksgRUElsAKAisJrDYCCyqBJbCCwBJY\nQWAlgdVGYEElsARWEFgCKwisJLDaCCyoBJbACgJLYAWBlQRWG4EFlcASWEFgCawgsJLAaiOw\noBJYAisILIEVBFYSWG0EFlQCS2AFgSWwgsBKAquNwIJKYAmsILAEVhBYSWC1EVhQCSyBFQSW\nwAoCKwmsNgILKoElsILAElhBYCWB1UZgQSWwBFYQWAIrCKwksNoILKgElsAKAktgBYGVBFYb\ngQWVwBJYQWAJrCCwksBqI7CgElgCKwgsgRUEVhJYbQQWVAJLYAWBJbCCwEoCq43AgkpgCawg\nsARWEFhJYLURWFAJLIEVBJbACgIrCaw2AgsqgSWwgsASWEFgJYHVRmBBJbAEVhBYAisIrCSw\n2ggsqASWwAoCS2AFgZUEVhuBBZXAElhBYAmsILCSwGojsKASWAIrCCyBFQRWElhtBBZUAktg\nBYElsILASgKrjcCCSmAJrCCwBFYQWElgtRFYUAksgRUElsAKAisJrDYCCyqBJbCCwBJYQWAl\ngdVGYEElsARWEFgCKwisJLDaCCyoBJbACgJLYAWBlQRWG4EFlcASWEFgCawgsJLAaiOwoBJY\nAisILIEVBFYSWG0EFlQCS2AFgSWwgsBKAquNwIJKYAmsILAEVhBYSWC1EVhQCSyBFQSWwAoC\nKwmsNgILKoElsILAElhBYCWB1UZgQSWwBFYQWAIrCKwksNoILKgElsAKAktgBYGVBFYbgQWV\nwBJYQWAJrCCwksBqI7CgElgCKwgsgRUEVhJYbQQWVAJLYAWBJbCCwEoCq43AgkpgCawgsARW\nEFhJYLURWFAJLIEVBJbACgIrCaw2AgsqgSWwgsASWEFgJYHVRmBBJbAEVhBYAisIrCSw2ggs\nqASWwAoCS2AFgZUEVhuBBZXAElhBYAmsILCSwGojsKASWAIrCCyBFQRWElhtBBZUAktgBYEl\nsILASgKrjcCCSmAJrCCwBFYQWElgtRFYUAksgRUElsAKAisJrDYCCyqBJbCCwBJYQWAlgdVG\nYEElsARWEFgCKwis9GYC6+jBfQ8+sO/g0cWfEliwtAksgRUElsAKAiu1B9aR29eXsOH2I4s9\nJ7BgaRNYAisILIEVBFZqDqxXLizTm7dfe932TdNl66uLPCiwYGkTWAIrCCyBFQRWag6sm8vO\n5xauDu8otyzyoMCCpU1gCawgsARWEFipObA2XjBTL2fOX+z/bgILljaBJbCCwBJYQWCl5sBa\nfuPx610rFnlQYMHSJrAEVhBYAisIrNQcWGuvOH592bpFHhRYsLQJLIEVBJbACgIrNQfWjun7\n6+WeqSsXeVBgwdImsARWEFgCKwis1BxYh1aXzTftfeihvTdtKmsOLfKgwIKlTWAJrCCwBFYQ\nWKn9fbAObClpy4HFnhNYsLQJLIEVBJbACgIrvZl3ct9/1zXbtl1z1/7FnxJYsLQJLIEVBJbA\nCgIr+SzCNgILKoElsILAElhBYCWB1UZgQSWwBFYQWAIrCKzUI7C+8+STi/xTgQVLm8ASWEFg\nCawgsFKPwNpd3vi7vLbn3mM+/b0IrJ33DuyizUNP8Gc3DD3Bvdd/fugJbv7s0BPc+ZmhJ7jn\nU0NPcO/q3xh6grVrh57gkqmhJ7ijfGLoEVa8d+gJLvnRoSf4/XLP0COUjww9wZoNQ0+wc+kE\n1p53vesNv/KvP7bxmHes/m6Hf8fJrto4tLVvHXqCc1YNPcHGU98x9ARnrBt6grNXDz3BuSvP\nG3qE09YPPcGZZw49wfrThp7gvJXnDj3C6rOHnmDdmqEneMepQ0+wcdWGoSd469qhJ9h4Vffs\naPG9fw0WAMCEEVgAAJ0JLACAzt5MYB09uO/BB/YdPNptGACApaA9sI7cvn7hk3I23H6k40AA\nAOOuObBeubBMb95+7XXbN02Xra/2HAkAYLw1B9bNZedzC1eHd5Rbeo0DADD+mgNr4wUz9XLm\n/MHfRhkAYHQ0B9byG49f71rRYxQAgKWhObDWXnH8+rJ1PUYBAFgamgNrx/T99XLP1JV9hgEA\nWAqaA+vQ6rL5pr0PPbT3pk1lzaGeIwEAjLf298E6sKWkLQc6DgQAMO7ezDu577/rmm3brrlr\nf7dhAACWAp9FCADQmcACAOhMYAEAdCawAAA6E1gAAJ0JLACAzgQWAEBnAgsAoDOBBQDQmcAC\nAOhMYAEAdCawAAA6E1gAAJ0JLACAzgQWAEBnAgsAoDOBBQDQmcACAOhMYAEAdCawAAA6E1gA\nAJ0JLACAzgQWAEBnAgsAoDOBBQDQmcACAOhMYAEAdCawAAA6E1gAAJ0JLACAzgQWAEBnAgsA\noDOBNa72FgB4g71Df3ciCaxx9cjKp+Cpp75Uvjr0CIyCr5YvDT0CI2HlI0N/dyIJrHH16Kqh\nJ2AkPF1eGHoERsEL5emhR2AkrHp06AlIAmtcCSyCwCIILBYIrJEhsMaVwCIILILAYoHAGhkC\na1wJLILAIggsFgiskSGwxpXAIggsgsBigcAaGQJrXAksgsAiCCwWCKyRIbDGlcAiCCyCwGKB\nwBoZAmtcCSyCwCIILBYIrJEhsMaVwCIILILAYoHAGhkCa1wJLILAIggsFgiskSGwxtXjZww9\nASPhmamXhh6BUfDS1DNDj8BIOOPxoScgCaxxNfPs0BMwGr4x9ACMBotAeHZm6AlIAgsAoDOB\nBQDQmcACAOhMYAEAdCawAAA6E1gAAJ0JLACAzgQWAEBnAgsAoDOBBQDQmcACAOhMYAEAdCaw\nAAA6E1gAAJ0JLACAzgQWAEBnAmusvPzlX33PytN/+r6ZhdtDV65b8cO3vDrsTAxlXym3LFxZ\nhEn2xBVvW77h8q/FtU2YWEf/9v3rf+C8X/6HhTuLMBIE1ljZXZZv3XbxKeXyKKwDa6Yu23V+\n2Xpk6LEYwr+vOy0DyyJMsj8oK35u+yVnxirYhMn1qbL613Z9cHpq7/yNRRgNAmus/PXdL859\n/ce3lS/O320pe2ZnZ3aU24cdimF89IduzcCyCBPsC+Wiw3PHzH/M39iEifWNctZzc8fD5Zz5\nO4swGgTWOPps+cTc1/1l0/zN4ekNRweehwF8oTy6eyGwLMIEe+3sVf927MYmTK4nyofmj5lT\nVs5ahJEhsMbR3eWGua93lZviblM5OOw4DODZH/yt2QwsizDBHis7//vLf/gnT8Q3UpswuQ4v\nW/vtueOR8tFZizAyBNYYOrq1/N3ccU2Jn7bPbi/7hp2H77+Zi895sQaWRZhgf1xu+JEy56L5\nP8eyCRPsjrLm12/88Ckffn7WIowMgTWGbisfmz+2lYfi9rrywKDjMIA7y+OzNbAswgS7vix7\n99de/vql5X2zNmGyffH0udB+d7w81yKMCIE1fv68nP9f82f9j+ja8uCg8/D99/UVn5z9f4Fl\nESbQb5dTnpk7Xnl7edImTLQ/mvr0s6/u//n44aBFGBECa+x8rlzwn3Hhj4En1tGfOO/l2Vk/\nImT25vLjcV5V7rEJk+wrZcf8ceScZd+0CCNDYI2b28pFLy5c1RcybvZCxknzejnmaosw0e4v\nPxPnrrLbJkyyG8pfxrmtPGwRRobAGjO/V973cl7uL5vnj+em1/uruBNm5uqwtWy6eq9FmGiH\np8763/nz/fPfV23C5PpkuSPOi8tjFmFkCKyxMnNt+cDx9+bdUu6f+6Wd3kxuUu0+9kajFmFi\nfazcNjv/t/PPemXWJkywvypnf2vu2Dd16vxPOCzCaBBYY+XOMr3jqnmfm787sHr6ihsvKBf6\nOIQJVQPLIkyw595ZLvqdj0y/5eH5G5swsb57SVn1KzdcWuZfi2cRRoXAGiufqa+8+UDcHtqx\ndvnGm18ZeCiGsvvYhz1bhMn1/O+e+5Yzf/HJhRubMLFe+9Mtpy1be9nfL9xZhJEgsAAAOhNY\nAACdCSwAgM4EFgBAZwILAKAzgQUA0JnAAgDoTGABAHQmsAAAOhNYAACdCSwAgM4EFgBAZwIL\nAKAzgQUA0JnAAgDoTGABAHQmsAAAOhNYAACdCSwAgM4EFgBAZwILAKAzgQUA0JnAAgDoTGAB\nAHQmsAAAOhNYAACdCSwAgM4EFgBAZwILAKAzgQUA0JnAAgDoTGABAHQmsAAAOhNYAACdCSxg\njN1XfmHh4kPl88NOAnAigQWMs8vLX8wfd5cPDj0JwAkEFjDOvrP21IOzs/906pnfHnoSgBMI\nLGCsPVx+8vXXf6r8zdBzAJxIYAHj7ePl1lvLbw49BcBJBBYw3l46b9myd7409BQAJxFYwJjb\nU8pjQ88AcDKBBYy3I+8p5eNDDwFwMoEFjLfry65NZd/QUwCcRGABY+0rU+/9nwMr1j0/9BwA\nJxJYwDh74e3Ln56dvbP80tCDAJxIYAHjbHu5c+7rzM+WB4eeBOAEAgsYYw+Ui2fmz38+bc23\nhp4F4DiBBYyvf1l9+jcXru4rlx4ddhaAEwgsAIDOBBYAQGcCCwCgM4EFANCZwAIA6ExgAQB0\nJrAAADoTWAAAnQksAIDOBBYAQGcCCwCgM4EFANCZwAIA6ExgAQB0JrAAADoTWAAAnQksAIDO\nBBYAQGcCCwCgM4EFANCZwAIA6ExgAQB0JrAAADoTWAAAnQksAIDOBBYAQGcCCwCgM4EFANCZ\nwAIA6ExgAQB0JrAAADr7P4dQbVYK2LrnAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "Plot with title “Histogram of finite sample”"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 750,
+       "width": 1200
+      },
+      "text/plain": {
+       "height": 750,
+       "width": 1200
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#Finite sample: draw n random numbers from this distribution\n",
     "n=10\n",
     "k=length(pop)\n",
     "rdu<-function(n,k) sample(1:k,n,replace=T) # generates n random samples of numbers 1:k\n",
     "selection <- rdu(n,k)\n",
     "hist(pop[selection],breaks = 20,xlab=\"X\", main=\"Histogram of finite sample\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "Plot with title “Histogram of averaged_data”"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 750,
+       "width": 1200
+      },
+      "text/plain": {
+       "height": 750,
+       "width": 1200
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Repeat this experiment: you draw m-times n random numbers, and compute the sample average over the n numbers.\n",
     "# output will then be m averaged results. Total of n*m measurements!\n",
     "\n",
-    "m=1000000  #number of experiments\n",
-    "n=10  #number of samples per experiment\n",
+    "m=100000  #number of experiments\n",
+    "n=100  #number of samples per experiment\n",
     "averaged_data <- numeric(length = m)\n",
     "\n",
     "for (i in seq(1,m,by=1)) {\n",
     "  selection <- rdu(n,k)\n",
     "  averaged_data[i]=sum(pop[selection])/n\n",
     "}\n",
     "\n",
-    "hist(averaged_data,breaks = seq(0,150,by=2))\n",
+    "hist(averaged_data,breaks = seq(0,150,by=0.5))\n",
     "abline(v=mu,col=\"red\",lwd=2)\n",
     "abline(v=mu+sigma/sqrt(n),col=\"blue\",lwd=2)\n",
     "abline(v=mu-sigma/sqrt(n),col=\"blue\",lwd=2)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "#Compare to original distribution\n",
     "p1 <- hist(averaged_data,plot = \"FALSE\")       \n",
     "p2 <- hist(pop,plot = \"FALSE\")                \n",
     "plot( p2, col=rgb(1,0,0,1/4), xlim=c(0,100), main=\"Comparison of experimental sample and theoretical distribution\", xlab=\"X\") \n",
     "plot( p1, col=rgb(0,0,1,1/4), xlim=c(0,100), add=T) \n",
     "abline(v=mu,col=\"red\",lwd=2)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
    "display_name": "R",
    "language": "R",
    "name": "ir"
   },
   "language_info": {
    "codemirror_mode": "r",
    "file_extension": ".r",
    "mimetype": "text/x-r-source",
    "name": "R",
    "pygments_lexer": "r",
-   "version": "3.6.2"
+   "version": "3.6.3"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
 }
diff --git a/Lecture 4/Law of large numbers.ipynb b/Lecture 4/Law of large numbers.ipynb
index c1f61a6..dda957d 100644
--- a/Lecture 4/Law of large numbers.ipynb	
+++ b/Lecture 4/Law of large numbers.ipynb	
@@ -1,80 +1,460 @@
 {
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "std =  100.995      std/n =  1.00995"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "Plot with title “Histogram of data”"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 750,
+       "width": 1200
+      },
+      "text/plain": {
+       "height": 750,
+       "width": 1200
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Frequentist definition of probability\n",
     "# Lets throw a dice n times\n",
     "options(repr.plot.width=20, repr.plot.height=12.5)   # this command just formats the size of the figures. Adapt to view them nicelyin your browser.\n",
     "                                                \n",
-    "n=10\n",
-    "k=6\n",
+    "n=100\n",
+    "k=2\n",
     "\n",
-    "rdu<-function(n,k) sample(1:k,n,replace=T) # generates n random samples of numbers 1:k\n",
+    "rdu  <-function(n,k) sample(1:k,n,replace=T) # generates n random samples of numbers 1:k\n",
     "data <- rdu(n,k)\n",
     "histdata=hist(data,breaks = seq(0.5,6.5,by=1),col=\"blue\")\n",
     "abline(h = n/k,col=\"red\")\n",
     "\n",
     "\n",
     "cat(\"std = \",sqrt( sum(  (histdata$counts-n/k)^2)),\"     \")\n",
     "cat(\"std/n = \", sqrt( sum(  (histdata$counts-n/k)^2))/n)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 750,
+       "width": 1200
+      },
+      "text/plain": {
+       "height": 750,
+       "width": 1200
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Law of large numbers\n",
     "\n",
     "# How does the dice game converge? Mean of dice: 3.5, variance 2.92\n",
-    "n=1000\n",
+    "n=100000\n",
     "data <-rdu(n,k)\n",
     "\n",
     "partialmean <- numeric(length = n) #compute a vector of averages, up to x_i\n",
     "bucket <- 0\n",
     "for (i in seq(1,n,by=1))\n",
     "{\n",
     "  bucket=bucket+data[i]\n",
     "  partialmean[i] <- bucket/i\n",
     "}\n",
     "plot(seq(1,n,by=1),partialmean ,xlab=\"number of throws\",ylab=\"average\")\n",
     "abline(h=3.5,col=\"red\")\n",
     "\n",
     "#Plot variance bounds\n",
     "x=seq(1,n,by=1)\n",
     "p0=0.9\n",
     "lines(x,3.5 + sqrt(2.92/(x*(1-p0))),col=\"red\",lwd=3)\n",
     "lines(x,3.5 - sqrt(2.92/(x*(1-p0))),col=\"red\",lwd=3)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>4</li>\n",
+       "\t<li>3</li>\n",
+       "\t<li>3</li>\n",
+       "\t<li>3</li>\n",
+       "\t<li>3</li>\n",
+       "\t<li>4</li>\n",
+       "\t<li>3</li>\n",
+       "\t<li>3</li>\n",
+       "\t<li>6</li>\n",
+       "\t<li>2</li>\n",
+       "\t<li>3</li>\n",
+       "\t<li>1</li>\n",
+       "\t<li>6</li>\n",
+       "\t<li>4</li>\n",
+       "\t<li>5</li>\n",
+       "\t<li>3</li>\n",
+       "\t<li>4</li>\n",
+       "\t<li>6</li>\n",
+       "\t<li>2</li>\n",
+       "\t<li>4</li>\n",
+       "\t<li>1</li>\n",
+       "\t<li>5</li>\n",
+       "\t<li>1</li>\n",
+       "\t<li>4</li>\n",
+       "\t<li>2</li>\n",
+       "\t<li>4</li>\n",
+       "\t<li>5</li>\n",
+       "\t<li>6</li>\n",
+       "\t<li>6</li>\n",
+       "\t<li>5</li>\n",
+       "\t<li>4</li>\n",
+       "\t<li>5</li>\n",
+       "\t<li>1</li>\n",
+       "\t<li>4</li>\n",
+       "\t<li>4</li>\n",
+       "\t<li>6</li>\n",
+       "\t<li>6</li>\n",
+       "\t<li>3</li>\n",
+       "\t<li>2</li>\n",
+       "\t<li>6</li>\n",
+       "\t<li>3</li>\n",
+       "\t<li>4</li>\n",
+       "\t<li>4</li>\n",
+       "\t<li>4</li>\n",
+       "\t<li>2</li>\n",
+       "\t<li>4</li>\n",
+       "\t<li>5</li>\n",
+       "\t<li>4</li>\n",
+       "\t<li>3</li>\n",
+       "\t<li>3</li>\n",
+       "\t<li>4</li>\n",
+       "\t<li>1</li>\n",
+       "\t<li>3</li>\n",
+       "\t<li>4</li>\n",
+       "\t<li>5</li>\n",
+       "\t<li>5</li>\n",
+       "\t<li>6</li>\n",
+       "\t<li>5</li>\n",
+       "\t<li>5</li>\n",
+       "\t<li>2</li>\n",
+       "\t<li>6</li>\n",
+       "\t<li>4</li>\n",
+       "\t<li>6</li>\n",
+       "\t<li>4</li>\n",
+       "\t<li>2</li>\n",
+       "\t<li>2</li>\n",
+       "\t<li>2</li>\n",
+       "\t<li>2</li>\n",
+       "\t<li>3</li>\n",
+       "\t<li>3</li>\n",
+       "\t<li>4</li>\n",
+       "\t<li>2</li>\n",
+       "\t<li>2</li>\n",
+       "\t<li>3</li>\n",
+       "\t<li>2</li>\n",
+       "\t<li>2</li>\n",
+       "\t<li>3</li>\n",
+       "\t<li>6</li>\n",
+       "\t<li>3</li>\n",
+       "\t<li>1</li>\n",
+       "\t<li>2</li>\n",
+       "\t<li>6</li>\n",
+       "\t<li>5</li>\n",
+       "\t<li>4</li>\n",
+       "\t<li>6</li>\n",
+       "\t<li>6</li>\n",
+       "\t<li>6</li>\n",
+       "\t<li>2</li>\n",
+       "\t<li>5</li>\n",
+       "\t<li>1</li>\n",
+       "\t<li>6</li>\n",
+       "\t<li>5</li>\n",
+       "\t<li>6</li>\n",
+       "\t<li>4</li>\n",
+       "\t<li>3</li>\n",
+       "\t<li>3</li>\n",
+       "\t<li>2</li>\n",
+       "\t<li>4</li>\n",
+       "\t<li>1</li>\n",
+       "\t<li>2</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 4\n",
+       "\\item 3\n",
+       "\\item 3\n",
+       "\\item 3\n",
+       "\\item 3\n",
+       "\\item 4\n",
+       "\\item 3\n",
+       "\\item 3\n",
+       "\\item 6\n",
+       "\\item 2\n",
+       "\\item 3\n",
+       "\\item 1\n",
+       "\\item 6\n",
+       "\\item 4\n",
+       "\\item 5\n",
+       "\\item 3\n",
+       "\\item 4\n",
+       "\\item 6\n",
+       "\\item 2\n",
+       "\\item 4\n",
+       "\\item 1\n",
+       "\\item 5\n",
+       "\\item 1\n",
+       "\\item 4\n",
+       "\\item 2\n",
+       "\\item 4\n",
+       "\\item 5\n",
+       "\\item 6\n",
+       "\\item 6\n",
+       "\\item 5\n",
+       "\\item 4\n",
+       "\\item 5\n",
+       "\\item 1\n",
+       "\\item 4\n",
+       "\\item 4\n",
+       "\\item 6\n",
+       "\\item 6\n",
+       "\\item 3\n",
+       "\\item 2\n",
+       "\\item 6\n",
+       "\\item 3\n",
+       "\\item 4\n",
+       "\\item 4\n",
+       "\\item 4\n",
+       "\\item 2\n",
+       "\\item 4\n",
+       "\\item 5\n",
+       "\\item 4\n",
+       "\\item 3\n",
+       "\\item 3\n",
+       "\\item 4\n",
+       "\\item 1\n",
+       "\\item 3\n",
+       "\\item 4\n",
+       "\\item 5\n",
+       "\\item 5\n",
+       "\\item 6\n",
+       "\\item 5\n",
+       "\\item 5\n",
+       "\\item 2\n",
+       "\\item 6\n",
+       "\\item 4\n",
+       "\\item 6\n",
+       "\\item 4\n",
+       "\\item 2\n",
+       "\\item 2\n",
+       "\\item 2\n",
+       "\\item 2\n",
+       "\\item 3\n",
+       "\\item 3\n",
+       "\\item 4\n",
+       "\\item 2\n",
+       "\\item 2\n",
+       "\\item 3\n",
+       "\\item 2\n",
+       "\\item 2\n",
+       "\\item 3\n",
+       "\\item 6\n",
+       "\\item 3\n",
+       "\\item 1\n",
+       "\\item 2\n",
+       "\\item 6\n",
+       "\\item 5\n",
+       "\\item 4\n",
+       "\\item 6\n",
+       "\\item 6\n",
+       "\\item 6\n",
+       "\\item 2\n",
+       "\\item 5\n",
+       "\\item 1\n",
+       "\\item 6\n",
+       "\\item 5\n",
+       "\\item 6\n",
+       "\\item 4\n",
+       "\\item 3\n",
+       "\\item 3\n",
+       "\\item 2\n",
+       "\\item 4\n",
+       "\\item 1\n",
+       "\\item 2\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 4\n",
+       "2. 3\n",
+       "3. 3\n",
+       "4. 3\n",
+       "5. 3\n",
+       "6. 4\n",
+       "7. 3\n",
+       "8. 3\n",
+       "9. 6\n",
+       "10. 2\n",
+       "11. 3\n",
+       "12. 1\n",
+       "13. 6\n",
+       "14. 4\n",
+       "15. 5\n",
+       "16. 3\n",
+       "17. 4\n",
+       "18. 6\n",
+       "19. 2\n",
+       "20. 4\n",
+       "21. 1\n",
+       "22. 5\n",
+       "23. 1\n",
+       "24. 4\n",
+       "25. 2\n",
+       "26. 4\n",
+       "27. 5\n",
+       "28. 6\n",
+       "29. 6\n",
+       "30. 5\n",
+       "31. 4\n",
+       "32. 5\n",
+       "33. 1\n",
+       "34. 4\n",
+       "35. 4\n",
+       "36. 6\n",
+       "37. 6\n",
+       "38. 3\n",
+       "39. 2\n",
+       "40. 6\n",
+       "41. 3\n",
+       "42. 4\n",
+       "43. 4\n",
+       "44. 4\n",
+       "45. 2\n",
+       "46. 4\n",
+       "47. 5\n",
+       "48. 4\n",
+       "49. 3\n",
+       "50. 3\n",
+       "51. 4\n",
+       "52. 1\n",
+       "53. 3\n",
+       "54. 4\n",
+       "55. 5\n",
+       "56. 5\n",
+       "57. 6\n",
+       "58. 5\n",
+       "59. 5\n",
+       "60. 2\n",
+       "61. 6\n",
+       "62. 4\n",
+       "63. 6\n",
+       "64. 4\n",
+       "65. 2\n",
+       "66. 2\n",
+       "67. 2\n",
+       "68. 2\n",
+       "69. 3\n",
+       "70. 3\n",
+       "71. 4\n",
+       "72. 2\n",
+       "73. 2\n",
+       "74. 3\n",
+       "75. 2\n",
+       "76. 2\n",
+       "77. 3\n",
+       "78. 6\n",
+       "79. 3\n",
+       "80. 1\n",
+       "81. 2\n",
+       "82. 6\n",
+       "83. 5\n",
+       "84. 4\n",
+       "85. 6\n",
+       "86. 6\n",
+       "87. 6\n",
+       "88. 2\n",
+       "89. 5\n",
+       "90. 1\n",
+       "91. 6\n",
+       "92. 5\n",
+       "93. 6\n",
+       "94. 4\n",
+       "95. 3\n",
+       "96. 3\n",
+       "97. 2\n",
+       "98. 4\n",
+       "99. 1\n",
+       "100. 2\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  [1] 4 3 3 3 3 4 3 3 6 2 3 1 6 4 5 3 4 6 2 4 1 5 1 4 2 4 5 6 6 5 4 5 1 4 4 6 6\n",
+       " [38] 3 2 6 3 4 4 4 2 4 5 4 3 3 4 1 3 4 5 5 6 5 5 2 6 4 6 4 2 2 2 2 3 3 4 2 2 3\n",
+       " [75] 2 2 3 6 3 1 2 6 5 4 6 6 6 2 5 1 6 5 6 4 3 3 2 4 1 2"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
    "display_name": "R",
    "language": "R",
    "name": "ir"
   },
   "language_info": {
    "codemirror_mode": "r",
    "file_extension": ".r",
    "mimetype": "text/x-r-source",
    "name": "R",
    "pygments_lexer": "r",
-   "version": "3.6.2"
+   "version": "3.6.3"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
 }
diff --git a/Lecture 4/Normal Distribution intervals.ipynb b/Lecture 4/Normal Distribution intervals.ipynb
index ce0ec9f..d5c8bde 100644
--- a/Lecture 4/Normal Distribution intervals.ipynb	
+++ b/Lecture 4/Normal Distribution intervals.ipynb	
@@ -1,84 +1,104 @@
 {
  "cells": [
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "options(repr.plot.width=20, repr.plot.height=12.5)   # this command just formats the size of the figures. Adapt to view them nicelyin your browser."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+8NGDCgXr163t7eTZo0GT9+fHx8/O1ps7OzdTpdixYtioqK7PYWAK5KZzKZqlxy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",
+      "text/plain": [
+       "Plot with title “Normal Distribution”"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      },
+      "text/plain": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Compute intervals in the normal distribution\n",
     "\n",
     "mean=67.5   # mean (mu) of the normal distribution\n",
     "sd=2      # standard deviation (sigma) of the normal distribution\n",
     "\n",
     "#We are interested in the probability of X being between a lower bound (lb) and an upper bound (ub), so P(lb<X<ub)\n",
     "lb=69.5      # lower bound of X\n",
-    "ub=1000    # upper bound of x   \n",
+    "ub=1000      # upper bound of x   \n",
     "\n",
     "#Compute P(lb<X<ub), as always through Phi(ub) - Phi(lb)\n",
     "prob <- pnorm(ub, mean, sd) - pnorm(lb, mean, sd)   #pnorm : \"P\"robability of \"NORM\" (=Phi(Z)) \n",
     "\n",
     "#############################################################\n",
     "# The rest is just plotting\n",
     "x <- seq(-4,4,length=1000)*sd + mean  # define an x vector as equally spaced points around the mean\n",
     "hx <- dnorm(x,mean,sd)  #dnorm  : \"D\"ensity of \"NORM\"al distribution\n",
     "\n",
     "plot(x, hx, type=\"n\", xlab=\"Parameter X\", ylab=\"\",\n",
     "     main=\"Normal Distribution\", axes=FALSE)\n",
     "\n",
     "i <- x >= lb & x <= ub\n",
     "lines(x, hx)\n",
     "polygon(c(lb,x[i],ub), c(0,hx[i],0), col=\"red\")\n",
     "\n",
     "\n",
     "result <- paste(\"P(\",lb,\"< X <\",ub,\") =\",\n",
     "                signif(prob, digits=4))\n",
     "mtext(result,3)\n",
     "axis(1, at=seq(40, 160, 20), pos=0)\n",
     "\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "1-0.8413"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
    "display_name": "R",
    "language": "R",
    "name": "ir"
   },
   "language_info": {
    "codemirror_mode": "r",
    "file_extension": ".r",
    "mimetype": "text/x-r-source",
    "name": "R",
    "pygments_lexer": "r",
    "version": "3.6.3"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
 }
diff --git a/Lecture 4/Visualize the normal distribution.ipynb b/Lecture 4/Visualize the normal distribution.ipynb
index ec9df4e..49d5773 100644
--- a/Lecture 4/Visualize the normal distribution.ipynb	
+++ b/Lecture 4/Visualize the normal distribution.ipynb	
@@ -1,56 +1,89 @@
 {
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
-    "options(repr.plot.width=20, repr.plot.height=12.5)   # this command just formats the size of the figures. Adapt to view them nicelyin your browser."
+    "options(repr.plot.width=20, repr.plot.height=10.5)   # this command just formats the size of the figures. Adapt to view them nicelyin your browser."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Etu7du9euXXvlypVBBwEA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VXAeEDzzwwNixYytUqPDpp5/OmTOnfPny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\ncnFmYyNdXaJ+KC4mLo4bN4iNJSZGfVBai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+      "text/plain": [
+       "Plot with title “Normal Distribution”"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 630,
+       "width": 1200
+      },
+      "text/plain": {
+       "height": 630,
+       "width": 1200
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Visualize some normal distributions\n",
     "\n",
     "mean=0   # mean (mu) of the normal distribution\n",
     "sd=4      # standard deviation (sigma) of the normal distribution\n",
     "\n",
     "\n",
     "x <- seq(-40,40,by=0.01)\n",
-    "hx <- dnorm(x,mean,sd)  #dnorm  : \"D\"ensity of \"NORM\"al distribution\n",
+    "#hx <- dnorm(x,mean,sd)  #dnorm  : \"D\"ensity of \"NORM\"al distribution\n",
+    "hx <- 1/(sd*sqrt(2*pi))*exp(- (x-mean)^2/(2*sd^2))    # use the definition of the PDF, does the exact same thing.\n",
     "\n",
     "plot(x, hx, type=\"l\", xlab=\"Parameter X\", ylab=\"PDF(X)\",\n",
-    "     main=\"Normal Distribution\", axes=TRUE)"
+    "     main=\"Normal Distribution\", axes=TRUE)\n",
+    "\n",
+    "hx2 <- dnorm(x,mean,2*sd)\n",
+    "lines(x,hx2,col=\"red\")\n",
+    "\n",
+    "hx2_sh <- dnorm(x,mean+20,2*sd)\n",
+    "lines(x,hx2_sh,col=\"red\")\n",
+    "\n",
+    "hx3 <- dnorm(x,mean,3*sd)\n",
+    "lines(x,hx3,col=\"blue\")\n",
+    "\n",
+    "hx15 <- dnorm(x,mean,1.5*sd)\n",
+    "lines(x,hx15,col=\"red\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
    "display_name": "R",
    "language": "R",
    "name": "ir"
   },
   "language_info": {
    "codemirror_mode": "r",
    "file_extension": ".r",
    "mimetype": "text/x-r-source",
    "name": "R",
    "pygments_lexer": "r",
    "version": "3.6.3"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
 }
diff --git a/Lecture 5/MyOwnEstimator.ipynb b/Lecture 5/MyOwnEstimator.ipynb
new file mode 100644
index 0000000..e43df09
--- /dev/null
+++ b/Lecture 5/MyOwnEstimator.ipynb	
@@ -0,0 +1,106 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "options(repr.plot.width=20, repr.plot.height=10.5)   # this command just formats the size of the figures. Adapt to view them nicelyin your browser."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Understand how estimator functions estimate parameters.\n",
+    "\n",
+    "# define the parameters of the mathematical model.\n",
+    "# we use a Normal distribution, N(mu,sigma^2)\n",
+    "\n",
+    "mu <- 10\n",
+    "sigma <- 2\n",
+    "n <- 15\n",
+    "\n",
+    "data <- rnorm(n, mu, sigma) # generate some normal distributed data\n",
+    "samplemean=mean(data)        # compute the sample mean. This will be different from mu!\n",
+    "samplesd=sd(data)\n",
+    "\n",
+    "histdata <-hist(data,col=\"grey\")\n",
+    "abline(v=samplemean, col=\"red\",lwd=2)\n",
+    "\n",
+    "\n",
+    "\n",
+    "x=seq(mu-5*sigma,mu+5*sigma,by=0.01)\n",
+    "lines(x,5*max(histdata$counts)*dnorm(x,mu,sigma), col=\"blue\",lwd=2)\n",
+    "abline(v=mu, col=\"blue\",lwd=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# myownestimator\n",
+    "\n",
+    "# Lets invent our own estimators for mu\n",
+    "# midpoint between the most closely spaced pair of datapoints.\n",
+    "# idea: points close to the mean are most likely, there datapoint density will be high\n",
+    "\n",
+    "m <- 100    #number of trials\n",
+    "n <- 10     #number of datapoints in each experiment\n",
+    "mu <- 10    #true mean\n",
+    "sigma <- 2  #true standard deviation\n",
+    "\n",
+    "meanlist <- numeric(length = m) \n",
+    "myownestlist <- numeric(length = m)\n",
+    "for (i in seq(1,m,by=1))\n",
+    "{\n",
+    "data <- rnorm(n, mu, sigma)\n",
+    "\n",
+    "index=which.min(diff(sort(data))) #find datapoints which are closest together\n",
+    "myownest=(sort(data)[index+1]+sort(data)[index])/2 # compute my estimator\n",
+    "\n",
+    "meanlist[i]=mean(data)\n",
+    "myownestlist[i]=myownest\n",
+    "}\n",
+    "\n",
+    "\n",
+    "p1 <- hist(meanlist,breaks = seq(0,20,by=0.5))                     # centered at 4\n",
+    "p2 <- hist(myownestlist,breaks = seq(0,20,by=0.5))                     # centered at 6\n",
+    "plot( p1, col=rgb(0,0,1,1/4), xlim=c(0,20))  # first histogram\n",
+    "plot( p2, col=rgb(1,0,0,1/4), xlim=c(0,20), add=T)  # second\n",
+    "\n",
+    "cat(\"Sample average: mean=\",mean(meanlist),\", sd=\",sd(meanlist))\n",
+    "cat(\"Myownestimator: mean=\",mean(myownestlist),\", sd=\",sd(myownestlist))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Lecture 5/Parameter estimation.ipynb b/Lecture 5/Parameter estimation.ipynb
new file mode 100644
index 0000000..9df5146
--- /dev/null
+++ b/Lecture 5/Parameter estimation.ipynb	
@@ -0,0 +1,101 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "options(repr.plot.width=20, repr.plot.height=10.5)   # this command just formats the size of the figures. Adapt to view them nicelyin your browser."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Parameter estimation\n",
+    "#\n",
+    "# goal: see the performance of sample mean and sample median as a point estimator for mu.\n",
+    "# \n",
+    "# x: x=(x1,x2,...,xn) are n independent, normal-distributed random variables.\n",
+    "\n",
+    "\n",
+    "\n",
+    "n=10        # number of samples in each realization\n",
+    "m=5         # number of realizations\n",
+    "mu=15       # the true, underlying parameter mu of N(mu,sigma^2) that we want to estimate\n",
+    "sigma=3     # the true, underlying parameter sigma of N(mu,sigma^2) that we want to estimate\n",
+    "\n",
+    "#compute mean and median m-times for a sample of size n each time\n",
+    "mean_est <- vector(length=m)    # initiate empty vector of length m to store the m means\n",
+    "median_est <- vector(length=m)  # initiate empty vector of length m to store the m medians\n",
+    "\n",
+    "for (i in seq(1,m,by=1))              # repeat m times. Index i counts from 1 to m\n",
+    "  \n",
+    "{\n",
+    "x <- rnorm(n, mean = mu, sd = sigma)      # draw n samples of X ~ N(mu,sigma^2) \n",
+    "mean_est[i] <- mean(x)\n",
+    "median_est[i] <- median(x)\n",
+    "}\n",
+    "\n",
+    "# Plotting\n",
+    "plot(seq(1,m,by=1),mean_est,col=\"red\", xlab = \"Realization index i\", ylab=\"Value X\")\n",
+    "mtext(\"Mean\",col=\"red\",side=3)\n",
+    "points(seq(1,m,by=1),median_est, col=\"blue\")\n",
+    "mtext(\"Median\",col=\"blue\",side=1)\n",
+    "abline(h=mu)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Compute bias variance\n",
+    "mean(mean_est)\n",
+    "sd(mean_est)\n",
+    "\n",
+    "mean(median_est)\n",
+    "sd(median_est)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# True parameters for the mean\n",
+    "mu\n",
+    "sqrt(sigma^2/n)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Lecture 5/Variance estimate.ipynb b/Lecture 5/Variance estimate.ipynb
new file mode 100644
index 0000000..4694469
--- /dev/null
+++ b/Lecture 5/Variance estimate.ipynb	
@@ -0,0 +1,57 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## why is there a n-1 in the formula for the variance?\n",
+    "\n",
+    "n=5\n",
+    "mu=10\n",
+    "sigma=2\n",
+    "\n",
+    "x <- rnorm(n,mean = mu,sd = sigma)\n",
+    "plot(seq(1,n,by=1),x)\n",
+    "abline(h=mean(x), col='blue')\n",
+    "abline(h=mu, col='red')\n",
+    "mtext(\"True parameter mu\",col=\"red\",side=3)\n",
+    "mtext(\"Sample mean\",col=\"blue\",side=4)\n",
+    "\n",
+    "\n",
+    "# define 3 estimators\n",
+    "s1=1/n*sum((x-mu)^2)\n",
+    "s2=1/n*sum((x-mean(x))^2)\n",
+    "s3=1/(n-1)*sum((x-mean(x))^2)\n",
+    "s4=sd(x)^2\n",
+    "\n",
+    "paste('s1=',s1,'   s2=',s2,'   s3=',s3,'   s4=',s4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}