fixed little mistake in exercise, added lecture

Author: arpoe

.DS_Store

@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 4,
    "id": "4801aaf4",
    "metadata": {},
    "outputs": [],
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "d43cea81",
    "metadata": {},
    "outputs": [],
@@ -107,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 7,
    "id": "622175af",
    "metadata": {},
    "outputs": [
@@ -144,7 +144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 8,
    "id": "17105b2e",
    "metadata": {},
    "outputs": [
@@ -183,13 +183,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 9,
    "id": "43e5d702",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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ltAW+qqL9mZlZjnIZU3hB0sWS2idnCLWW1LqyO4yISyOiXUR0AgYAEyPi52RCZ90tuQcCYyq7DzMzq5xcxhR+lbyfkzUvgB2ruJbrgVGSTgPmAv2quH0zM6tALlc0d87XziNiEplbchMRC4He+dqXmZlVrMJQkNQYOBv4MZkewqvAPRGxIs+1mZlZgeVy+OghYBlwezJ9EvAwPrxjZlbr5BIKXSNir6zplyS9l6+CzMyseHI5+2iapB7rJiR1B17PX0lmZlYsufQUugOnSJqbTHcAZkj6AIiI2DNv1ZmZWUHlEgqH5r0KMzOrFnI5JXVOIQoxM7Piy/UuqWZmVgc4FMzMLOVQMDOzlEPBzMxSDgUzM0s5FMzMLOVQMDOzlEPBzMxSDgUzM0s5FMzMLOVQMDOzlEPBzMxSDgUzM0s5FMzMLOVQMDOzlEPBzMxSDgUzM0s5FMzMLOVQMDOzlEPBzMxSDgUzM0s5FMzMLOVQMDOzlEPBzMxSDgUzM0sVPBQktZf0kqQZkqZLuiCZ31rSC5I+Tt5bFbo2M7O6rhg9hTXARRGxK9ADOEfSbsBgYEJEdAEmJNNmZlZABQ+FiJgXEe8kn5cBM4AdgGOA4clqw4FjC12bmVldV9QxBUmdgL2BycC2ETEPMsEBbFPGNoMkTZE0ZcGCBQWr1cysLihaKEjaCngSuDAilua6XUTcFxHdIqJbSUlJ/go0M6uDihIKkhqSCYRHI+KpZPZ8SW2T5W2Br4pRm5lZXVaMs48E/BmYERE3Zy0aCwxMPg8ExhS6NjOzuq5BEfbZE/gF8IGkd5N5lwHXA6MknQbMBfpVpvHVq1dTWlrKihUrKlz3/qPbVmYXdUYQzFmymtsnL2bpyn8VuxwzK4CCh0JEvAaojMW9N7f90tJSmjVrRqdOnch0Ssq2unTJ5u6uVosI2rRZynnAkFcWFrscMyuAWndF84oVK2jTpk2FgWAVk0SDps3p2LJhsUsxswKpdaEAOBCqkCRUZsfOzGqbWhkKZmZWOcUYaC6oToOfrdL2xp7bs0rby4fD9t+TEc++RKvWbYpdipnVMO4pVDNr1qwpdglmVofV+p5CMXz+2VzOOaUfe+/bg3envsU227bl1j8/yuxPZ3Ltpb9hxfLvadexM1ffeAfNW7bktH5HsteP9uPdKZM5qM9hvPLieHbZfU8+/OBdFi9ayLVD7+bPdw5l5j8+pO9Rx3HuJZcDcOFpJ/PlvM9ZuXIlJ//qDE48+dTifnEzq/HcU8iTubM+4WcDT+fpCW/SvEULXvzrWC6/8EwuvPQqRr/wOl122Y17brkhXX/Z0m8YNvpZBp5xLgANGjXkwSefo9/Pf8mFp53MZdf+iSdffIMxTzzGksWLAPjjjXcw8rlJPDZuIiOG3ZvONzOrLPcU8mSH9h3ZZfc9ANh1j70onTObZUu/odv+mTGJo088iYvPOjVdv+9Rx6+3/cF9DgNg5112Y6euu1Cy7XYAtOvQkS+/+JyWrVoz4sF7mTh+HADz533O3Fmf0LJV63x/NTOrxRwKedKwUaP0c/169Vn2zTflrt+kadP1phs12gKAevXqrddWvXr1WLtmDW+/+Rp/f20SD415niZNmnJavyNZuXJlFX4DM6uLfPioQLZq3pzmLVryzuQ3ABj31ON06175M5m+XbqU5i1a0qRJU2bN/CfvT5tSVaWaWR1W63sKs68/osxl7xf4NhfXDL37/waaO3Ti6pvurHRbPQ/uzROPDOPEPj3ptFMX9ty7WxVWamZ1lSKi2DVUWrdu3WLKlPX/Qp4xYwa77rprTtsXOhRqqvlzP+XXY+cVuwwzy1LeH7wVkTQ1Ijb6l6QPH5mZWcqhYGZmKYeCmZmlHApmZpZyKJiZWcqhYGZmqVp/nQJXtShz0Z6VaO790+ds8jZ333w9TZtuycAzz9vo8kULv+a8UwewevUqBv/xevbpfkAlKts8V/zn2Rx4SF/6HHFMwfdtZtVH7Q+FGmDyay/TeecuXDv07py3Wbt2LfXr189jVWZWF/nwUZ7cf9uNHH3Qvgw66Vhmf/IxAJ/NnsVZPz+RAYcfzKnHH8asmf/kH9M/4JbrruS1iS/Qv+9PWLF8OX99ZjQnHHIAx/fen6HXXZm22aNrO+688TpOPuoQ3pv6Fj26tmPodVcy4PCDGXTSsXwwbSqn9TuSw3v+kEnPPwdkwuPma6/g/x/RixP79OSJRx4EICK47vLfclyvHpw7sD+LFi4o/I9kZtWOQyEPPnz/XcaPfYrHx7/Mzfc9xPT3pgFw9eALGXzNDYx8bhK/ueIahvz+YnbZfQ/Ovugy/t9RxzHqb6+y9Jsl3PJfV3H/42MZ9bdXmf7eNCaOzzw9bvn337Fz11159C8vss9++7P8++/Yd/8fM/K5STTdcivu+NMQ7hnxNEPvf5i7bvovAJ4e+TBbNWvBiGcnMmLcRJ4a8RClc+cwYfw45nw6k9EvvM4fbriV96a+VbTfy8yqDx8+yoN33nqTXoceSZMmmTufHtTnMFauXMl7U97it2eemq63atWqf9t2+nvT6Lb/j2ndZmsADj+uH1Mnv0GvQ4+gfv36HHL40em6DRs1oufBhwDQZZfdaNSoEQ0bNqTLLrvzRelcAN585SX+OWM6Lz43BoBly5Yyd9YnvDP5DQ49+gTq16/PNtu1Zd8DDszLb2FmNYtDIU8krTf9r/gXzVq0YNTfXi13u/LuRdVoi8brjSM0aNAw3U+9evXWu932mjVr0/YGX30DPQ/uvV5br730wr/VaGbmw0d58KPuBzBx/DhWLF/Od98u45UXx9OkcRN2aN+B58c9A2T+s/7oww/+bds99v4RU//+OosXLWTt2rWMH/Mk3XpU/hbbBxzUiyceHsbq1asBmP3pTL7//jv26X4A48c+xdq1a1kw/0vefrP8sDKzuqH29xSuKvvhNvm6S+que+xF36OOo/+hB9J2h/bsvd/+AFx32/0Muewi7r/tRtasWUPfo4+n6257rLdtybbbcf7v/sDp/Y8iIvhJrz78tO/hla7l+JNO4YvP5jLgsIOICFq12ZpbHniE3oceyVuvv8KJfXrSsfNOm/VsBzOrPXzrbKuQb51tVv341tlmZpZ3DgUzM0vVylCoyYfEqpuIIPDvaVZX1LpQaNy4MQsXLnQwVIGIYM33S5mzZHWxSzGzAql1Zx+1a9eO0tJSFiyo+LYN8xcvL0BFNVcQzFmymtsnLy52KWZWILUuFBo2bEjnzp1zWvewwc/muRozs5ql2h0+knSopI8kzZQ0uNj1mJnVJdUqFCTVB+4EDgN2A06StFtxqzIzqzuqVSgA+wEzI+LTiFgFjAT81BczswKpbmMKOwCfZU2XAt2zV5A0CBiUTH4r6aMC1Wa2qbYGvi52EVY76YbN2rxjWQuqWyhs7Lad651bGhH3AfcVphyzypM0paxbCZhVV9Xt8FEp0D5ruh3wRZFqMTOrc6pbKLwNdJHUWVIjYAAwtsg1mZnVGdXq8FFErJF0LvA3oD4wLCKmF7kss8ryYU6rcWr0rbPNzKxqVbfDR2ZmVkQOBTMzSzkUzPJE0qmS7kg+XyXp4mLXZFYRh4KZmaUcCmabSNIpkt6X9J6khyWVSHpS0tvJq2cF258v6cOkjZGFqtssF9XqlFSz6k7S7sDvgZ4R8bWk1sAdwNCIeE1SBzKnVO9aTjODgc4RsVJSy7wXbbYJHApmm6YXMDoivgaIiEWSDgF2k9K7tDSX1KycNt4HHpX0DPBMHms122QOBbNNI/i3h1bXA/aPiPUe5ZcVEhs6AjgQOBq4QtLuEbGmqgs1qwyPKZhtmglAf0ltAJLDR88D565bQdIPy9pYUj2gfUS8BFwCtAS2ymO9ZpvEPQWzTRAR0yUNAV6WtBaYBpwP3CnpfTL/pl4BziyjifrAI5JakOl1DI2IJfmv3Cw3vs2FmZmlfPjIzMxSDgUzM0s5FMzMLOVQMDOzlEPBzMxSDgUzM0s5FMzMLPW/gqE2e/3HtMUAAAAASUVORK5CYII=\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -243,25 +243,26 @@
    "metadata": {},
    "source": [
     "The confidence intervals we are calculating are confidence intervals of a proportion, this means that they are going back to \"binomial variables\", which are represented as a proportion. There are several ways to calculate these intervals, the \"exact method\", the \"standard Wald method\", the \"modified Wald method\".    \n",
-    "The details are probably never to become relevant for you, so we will take the default standard implementation in python, the \"asymptotic normal approximation\". For this we need the measured proportion (a) and the total number (n). It will give us a lower and an upper interval.    "
+    "The details are probably never to become relevant for you, so we will take the default standard implementation in python, the \"asymptotic normal approximation\". For this we need the measured proportion (a) and the total number (n). It will give us a lower and an upper interval.    \n",
+    "alpha is 1 - the confidence level. It will be set by default to 0.05, so a 95% confidence level will be assumed, if not specified explicitely. (this was a problem in my original script, because I had accidentally deleted the specification of alpha... sorry!)   "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 11,
    "id": "d757af70",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(0.04120108046379837, 0.15879891953620165)\n"
+      "(0.050654391191455816, 0.1493456088085442)\n"
      ]
     }
    ],
    "source": [
-    "CI= sm.stats.proportion_confint(a, n)\n",
+    "CI= sm.stats.proportion_confint(a, n, alpha=1-confidence_level)\n",
     "print(CI)"
    ]
   },
@@ -426,7 +427,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,