diff --git a/lecture_2.ipynb b/lecture_2.ipynb
index f657ebbf0b5ba776d4179ef74a51d178632def33..da9547978e8d7b9ff680e63d3456004e27800cf7 100644
--- a/lecture_2.ipynb
+++ b/lecture_2.ipynb
@@ -30,7 +30,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "229b45c8",
+   "id": "d68538c1",
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -595,7 +595,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "081552b2",
+   "id": "1c7ec301",
    "metadata": {},
    "outputs": [
     {
@@ -812,7 +812,7 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "3da79f92",
+   "id": "179d1ee8",
    "metadata": {},
    "outputs": [
     {
@@ -885,7 +885,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "1d904c23",
+   "id": "45fb0d7d",
    "metadata": {},
    "outputs": [
     {
@@ -960,7 +960,7 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "id": "9a142f1d",
+   "id": "3563f21f",
    "metadata": {},
    "outputs": [
     {
@@ -999,7 +999,7 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "8b9b9ed6",
+   "id": "1d83b0cb",
    "metadata": {},
    "outputs": [
     {
@@ -1707,7 +1707,7 @@
     "**Bundesliga**:\n",
     "Hypothesis: \"The $k_i$ goals in each Bundesliga match $i$ are Poisson distributed with a common parameter $\\mu = <k>$.\"\n",
     "\n",
-    "Alternative hypothesis: \"The goals in each Bundesliga match $k_i$ are Poisson distributed with parameter $\\mu_i = ki$ for each match.\"\n",
+    "Alternative hypothesis: \"The goals in each Bundesliga match $k_i$ are Poisson distributed with parameter $\\mu_i = k_i$ for each match.\"\n",
     "\n",
     "Neyman-Pearson: likelihood ratio: $\\frac{P_{A}(x)}{P_{H}(x)} = \\frac{\\hat L(\\vec k; \\vec k)}{L(\\mu; \\vec k)} > c$\n",
     "\n",
@@ -1722,7 +1722,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 61,
    "id": "75234801-341f-4474-a2bf-93aafe9add77",
    "metadata": {},
    "outputs": [
@@ -1734,7 +1734,8 @@
       "P(H): 5.9616248936886925e-254\n",
       "P(A): 4.088714274859846e-178\n",
       "-ln P(H): 583.0712705433988\n",
-      "-ln P(A): 408.4519159905313\n"
+      "-ln P(A): 408.4519159905313\n",
+      "d: 174.6193545528675\n"
      ]
     }
    ],
@@ -1766,7 +1767,7 @@
   {
    "cell_type": "code",
    "execution_count": 59,
-   "id": "0cca952a",
+   "id": "fd26d907",
    "metadata": {
     "cell_style": "center",
     "jupyter": {
@@ -1809,7 +1810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 63,
    "id": "90e27f87-36fd-4b11-add1-c82a4eeb79dd",
    "metadata": {
     "cell_style": "center",
@@ -1859,12 +1860,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "147415f4",
+   "execution_count": 64,
+   "id": "49ea2122",
    "metadata": {
     "cell_style": "split"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.hist(mu, bins=50)\n",
     "plt.plot([muobs, muobs], [0, 100], linestyle = 'dotted')\n",
@@ -1875,12 +1887,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "bcc2f650",
+   "execution_count": 65,
+   "id": "a29e98bf",
    "metadata": {
     "cell_style": "split"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "dobs = -np.sum(scipy.stats.poisson.logpmf(summe,muobs)) + np.sum(scipy.stats.poisson.logpmf(summe,summe))\n",
     "plt.hist(d, bins=50)\n",
@@ -1893,10 +1916,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 66,
    "id": "6647f14e-2282-4080-9139-c332db4af23a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "p: 0.32\n"
+     ]
+    }
+   ],
    "source": [
     "print(\"p:\", np.sum(d > dobs)/len(d))"
    ]
@@ -1926,7 +1957,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b4f420a2",
+   "id": "8eb982c9",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1947,15 +1978,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "178a5a0f",
+   "execution_count": 68,
+   "id": "3cf72baa",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "p-Wert via Chi2: 0.04111257571008784\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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pVoZh8NDKh9hcuplEVyIvZ7/MGV3PaNK54uxxPDfmOdI7pFNYUch9H9+HN9D4aDA5hmAQDu8KbcGg2dGImE6Jj4g0qw92fcDHuz/GbrXzl7F/aXLSUycpLok5l80hwZHA5yWf8/BnD2MYRjNFGwP8NfDnc0Kbv8bsaERMp8RHRJrNkdojzFwT6kj7y7N/yVndzmqW857W+TSeufgZ7BY7ywqW8fb2t5vlvDHDER/aRESJj4g0nyfXPsmh2kP069yPO86+o1nPPSJ9BFOzpgLw58//zOHaw816/qjl7AAPFYU2LVAqosRHRJrHp3s+5b1v38NqsfLoyEdbZNmJnw/4Of279KfMU8bsz2c3+/lFJPop8RGRU1blq+LR1Y8C8IsBv+Ds5LNb5Dp2q52Hhz8MwOLti9lYsrFFriMi0UuJj4icstnrZ1NcVUzPjj2567y7WvRa53U/j2v6XQPA/675X/xBf4ter93ze+Dde0Kb32N2NCKmU+IjIqfk2yPfsvDrhQA8MvKRVllba1rWNDo5O/HVoa/C15ZjCPrh89dCm5JEESU+InJqXtr8EgYGY3qNabU1tbq6u3Lf+fcB8PyG5zlQfaBVrtsuWR0w5nehzaqZm0WU+IhIkxWWF7KsYBkAvzz3l6167QmnT+CsbmdR6avkz59r8c1jsjvhov8T2uxaq0tEiY+INNlLm18iaAQZ1WNUs83Zc7JsVhsPDnsQgH9++0/2VOxp1euLSPvUpMRnzpw5ZGZm4na7ycrK4tNPPz1u+dzcXLKysnC73fTt25d58+bVe3zLli1MmDCBPn36YLFYmD17doNzPPLII1gslnpbampqvTKGYfDII4+Qnp5OXFwcF198MVu2bGnKSxSRE9hbuZd/fvNPAH517q9MieGc5HMYkTaCgBHglS9fMSWGNs8woKo0tGnGa5HIE5+FCxcydepUHnroITZs2MCoUaMYN24chYWFjZYvKChg/PjxjBo1ig0bNvDggw9y7733smjRonCZ6upq+vbty6xZsxokM9931llnUVRUFN42b95c7/Enn3ySZ599lueff561a9eSmprK2LFjqaioiPRlisgJzN88H7/hZ3jacM5NPte0OCafMxmAJTuWUFJdYlocbZavGp46LbT5qs2ORsR0ESc+zz77LLfffjt33HEHAwYMYPbs2fTq1Yu5c+c2Wn7evHn07t2b2bNnM2DAAO644w5uu+02nn766XCZoUOH8tRTT3HjjTficrmOeW273U5qamp4S05ODj9mGAazZ8/moYce4rrrrmPQoEG8+uqrVFdX88Ybb0T6MkXkOIqrinlnxzsATDl3iqmxDEkZwuDug/EFfby25TVTYxGRti+ixMfr9bJ+/Xqys7PrHc/OzmbVqlWNPicvL69B+csvv5x169bh8/kiCnb79u2kp6eTmZnJjTfeyLfffht+rKCggOLi4nrXcrlcjB49+pixeTweysvL620icmKvfPkKvqCPISlDyErJMjUWi8XC5LNDtT5/3/Z3jtQeMTWeNsfZAR4pC21askIkssSntLSUQCBASkpKveMpKSkUFxc3+pzi4uJGy/v9fkpLS0/62sOGDeO1117jgw8+4KWXXqK4uJiRI0dy8ODB8HXqzn2ysc2cOZPExMTw1qtXr5OORyRWHag+wKLtoaZqs/r2/NCFPS5kQNcB1PhreD3/dbPDEZE2rEmdmy0WS72fDcNocOxE5Rs7fjzjxo1jwoQJnH322Vx22WUsXboUgFdffbXJsc2YMYOysrLwtnv37pOORyRWLfx6IZ6Ah3OTz2VYauvM23MiFoslvCjqG1+9QaW30uSIRKStiijxSUpKwmazNahBKSkpaVDTUic1NbXR8na7nW7dukUY7lEdOnTg7LPPZvv27eHrABHF5nK56NSpU71NRI7NH/SzZPsSAH4x8BcR/fHS0i7LuIzMxEwqvBWazfn7/B54/4HQpiUrRLBHUtjpdJKVlUVOTg7XXntt+HhOTg5XX311o88ZMWIE7733Xr1jy5cvZ8iQITgcTZ9F1OPxkJ+fz6hRowDIzMwkNTWVnJwcBg8eDIT6JOXm5vLEE080+ToictSnez6lpKaEru6uXNrrUrPDqcdqsXL7oNv53We/47Wtr/GLgb/AZTv2YIm2qs8DS5v83J2zrmx4MOiHNd8NPrn0YaD93ROR5hRxU9f06dN5+eWXWbBgAfn5+UybNo3CwkKmTAmN7JgxYwa33HJLuPyUKVPYtWsX06dPJz8/nwULFjB//nzuv//+cBmv18vGjRvZuHEjXq+XvXv3snHjRnbs2BEuc//995Obm0tBQQFr1qzh+uuvp7y8nFtvvRUIVXVPnTqVxx9/nCVLlvDll18yadIk4uPj+dnPftbkGyQiR/1j2z8AuPq0q3HY2t7yB+P7jie1QyqHag+xfOdys8NpG6wOGPXr0KYlK0Qiq/EBmDhxIgcPHuTRRx+lqKiIQYMGsWzZMjIyMgAoKiqqN6dPZmYmy5YtY9q0abzwwgukp6fz3HPPMWHChHCZffv2hWtpAJ5++mmefvppRo8ezYoVKwDYs2cPN910E6WlpSQnJzN8+HBWr14dvi7Ab37zG2pqarjzzjs5fPgww4YNY/ny5SQkJER8Y0SkvqLKIlbuXQnAdadfZ3I0jXNYHVx/+vU8v/F53t72NleddpXZIZnP7oRLf292FCJthsUwNJVnnfLychITEykrK1N/H5EfeGHjC8z7Yh4XpF7A/Mvnmx3OMZVUl5D9djYBI8CSnyyhX5d+LXKd4zVJNdrk1AznPZFTua5IexbJ72+t1SUiJ+QP+lm8fTEAP+3/U5OjOb7u8d25pNclwNGmuZhmGOCtCm36O1dEiY+InNjKvSspqS6hi6sLY3qPMTucE6pLzt775j1q/DUmR2MyXzU8nh7atGSFiBIfETmxcKfmflfjtDlNjubEhqcPp2fHnlT4KvhXwb/MDkdE2hAlPiJyXN/v1Dzh9AknKN02WC1Wru9/PaDmLhzx8OC+0OaINzsaEdMp8RGR41qyYwlBI8jQ1KH0Sexjdjgn7Zp+12C32tlcupn8g/lmh2MeiyW0RpezQ2hfJMYp8RGRYzIMg3e/eReA60+/3uRoItMtrhtje48FVOsjIkcp8RGRY9pUuom9lXuJt8dzSe9LzA4nYj89I9TJeem3S6nyVZkcjUn8Xvj3o6HN7zU7GhHTKfERkWOq6xh8Se9LiLPHmRxN5IakDKFPpz5U+6tZVrDM7HDMEfTBp8+EtqDP7GhETKfER0QaFQgG+GDnBwCM6zPO5GiaxmKxhDtk//Obf5ocjUmsdhj236HNGvFk/SJRR4mPiDTq85LPOVBzgARnAiPTR5odTpONyxyHBQufl3zO3sq9ZofT+uwuGDcrtNm1QKmI0n8RaVRd09DYjLFtckHSkzXssXXE9T4Ne4cdXDL3T3gPHu2r1FJLPJxo2QktLSFiHtX4iEgDvqCPnF05QKjGpL3zlZ0HgD1xA6BlG0RimRIfEWlg9b7VlHnK6ObuxtCUoWaHc8r8FYMwgnZsrhKsrn1mh9O6vFXwSGJo88boyDaR71HiIyIN/GtnaDRXdp9sbFabydE0g6Abf+UAAByJG0wORkTMpD4+IlKPJ+Dh34X/BqKjmauOr2wwjk6bsXf6Ak/JeKLx777G+xYZdGUenz88VktWiBCNn3wROSUr96ykyldFaodUzk0+1+xwmk2gsj+GPx6rowJb/Ddmh9OKLByiE3RI0pIVIijxEZEfeH/n+wBc0ecKrJZo+oqw46s4G1Bzl0gsi6ZvNRE5RdW+anJ35wJwReYVJkfT/PxlgwGwJ2wBS2ws3+DAz122d+CTp7RkhQhKfETke1buXUltoJZeCb0Y2HWg2eE0u0BNb4LeLlhsHuwdY2PFdjt+/o/j7/DR/2jJChHUuVlEvufj3R8DMKbXGCxR2R/Eiq/8PFxJH5+wuetEkxC2FwFsvOm/hJsu6KUlK0RQjY+IfMcX9PHJnk8AuLjXxeYG04L8301maOu4nXJvubnBtAIvDmb4J8NP/q+WrBBBiY+IfGfD/g2Ue8vp7OrMed3PMzucFhP0phDwdMdiCYT7M4lI7FDiIyLA0Waui3pehD3Km0T85aHRXXXLcohI7FDiIyIYhlGvf0+08383rP2zvZ9R5YvuZRziqGWr67/gf9O0ZIUISnxEBNh+ZDt7K/fisrkYkT7C7HBaXNCTQtCThDfoDfdrimbxFg/4qs0OQ6RNUOIjInxcGKrtGZ42nPiYWNbAgq9iEBD9zV21OLnQ82e4bxPY48wOR8R0SnxEJNzMdUmvS0yOpPXUNXd9uudTqqO4NsTAyh4jGbpkgFVf+SL6FIjEuP1V+9lycAsWLIzuNdrscFpNsDadHh17UBuo5bN9n5kdjoi0EiU+IjFuxe4VAJyTfA5JcUmmxtK6LGRnZAOQszN6m7vs+LnN9j7kzYGAZm4WUeIjEuNisZmrztiMsQDk7sml1l9rcjQtw4Gf3zv+H3wwAwJaq0tEiY9IDKv0VrKmeA0Qm4nPoKRBpHZIpdpfzap9q8wOp0UEsfJOYCSc/VOw2MwOR8R0SnxEYtjKfSvxB/1kdMogMzHT7HBancViCdf6ROvoLg9Opvruhgkvg8NtdjgiplPiIxLDPtn93dpcPS+O0kVJTyzc3LU7F6+agkSiXpMSnzlz5pCZmYnb7SYrK4tPP/30uOVzc3PJysrC7XbTt29f5s2bV+/xLVu2MGHCBPr06YPFYmH27NkNzjFz5kyGDh1KQkIC3bt355prruHrr7+uV2bSpElYLJZ62/Dhw5vyEkWiXtAIhkczXdTzIpOjMc+5yefSPa47Fb4K1hStMTscEWlhESc+CxcuZOrUqTz00ENs2LCBUaNGMW7cOAoLCxstX1BQwPjx4xk1ahQbNmzgwQcf5N5772XRokXhMtXV1fTt25dZs2aRmpra6Hlyc3O56667WL16NTk5Ofj9frKzs6mqqj8F+xVXXEFRUVF4W7ZsWaQvUSQm5B/M51DtIeLt8QzuPtjscExjtVjDq9HXdfSOJnHUst71K3iyr5asEAEiXonw2Wef5fbbb+eOO+4AYPbs2XzwwQfMnTuXmTNnNig/b948evfuHa7FGTBgAOvWrePpp59mwoQJAAwdOpShQ4cC8MADDzR63X/961/1fn7llVfo3r0769ev56KLjv616nK5jpk8ichRn+4N1dQOTxuOw+YwORpzXdL7Ev6+7e+s2L2C3w3/HVZLdPUC6GapgOido1EkIhF9ur1eL+vXryc7O7ve8ezsbFatanxERF5eXoPyl19+OevWrcPna/qcEmVlZQB07dq13vEVK1bQvXt3+vfvz+TJkykpKTnmOTweD+Xl5fU2kVixcu9KAC7seaHJkZjvgtQL6ODowIGaA2wp3WJ2OM2qFidjPU/Cnau1ZIUIESY+paWlBAIBUlJS6h1PSUmhuLi40ecUFxc3Wt7v91NaWhphuCGGYTB9+nQuvPBCBg0aFD4+btw4/va3v/HRRx/xzDPPsHbtWsaMGYPH42n0PDNnziQxMTG89erVq0nxiLQ3ZZ4yNpduBmBUj1EmR2M+p83Jj9J/BERfc5eBle1GT+g+QEtWiNDEzs0/HP1hGMZxR4Q0Vr6x4yfr7rvvZtOmTbz55pv1jk+cOJErr7ySQYMGcdVVV/H++++zbds2li5d2uh5ZsyYQVlZWXjbvXt3k+IRaW9W7VtF0AjSr3M/UjuoaRhCzV0QfYmPiNQXUR+fpKQkbDZbg9qdkpKSBrU6dVJTUxstb7fb6datW4Thwj333MO7777LJ598Qs+ePY9bNi0tjYyMDLZv397o4y6XC5fLFXEMIu1duJmrh5q56ozqMQqbxcaOIzvYXRE9fwTZ8XO97RNYfwDO+znEeH8ukYhqfJxOJ1lZWeTk1J/oKycnh5EjRzb6nBEjRjQov3z5coYMGYLDcfIfQMMwuPvuu1m8eDEfffQRmZknnmzt4MGD7N69m7S0tJO+jki0CxpBJT6NSHQlkpWSBcDHhdFT6+PAzyzHy/DefVqyQoQmNHVNnz6dl19+mQULFpCfn8+0adMoLCxkypQpQKj56JZbbgmXnzJlCrt27WL69Onk5+ezYMEC5s+fz/333x8u4/V62bhxIxs3bsTr9bJ37142btzIjh07wmXuuusuXn/9dd544w0SEhIoLi6muLiYmpoaACorK7n//vvJy8tj586drFixgquuuoqkpCSuvfbaJt8gkWiTf+joMPbzu59vdjhtSt2yHdHU3BXEyvJAFpxxpZasEKEJic/EiROZPXs2jz76KOeddx6ffPIJy5YtIyMjA4CioqJ6c/pkZmaybNkyVqxYwXnnncdjjz3Gc889Fx7KDrBv3z4GDx7M4MGDKSoq4umnn2bw4MHhIfMAc+fOpaysjIsvvpi0tLTwtnDhQgBsNhubN2/m6quvpn///tx6663079+fvLw8EhISmnyDRKLNyj2h2p5hacNifhj7D9XN5/N5yedgi445bzw4+aXv13DTG1qyQoQmzOMDcOedd3LnnXc2+thf//rXBsdGjx7N559/fszz9enTJ9zh+VhO9HhcXBwffPDBccuIiPr3HE/PhJ6c3uV0th/ejr3jV/jLsswOSUSamcY2isSQMk8Zm0o3ARrGfix1zV32jltNjkREWoISH5EYkrcvj6AR5LTE00jrqE7/jRnTawwA9o7bwdL0SVbbCjceVrruhT+dDV5N3yyixEckhtQtU6FmrmMb2G0g3eO7Y7F6scV/Y3Y4p8yCQU9LKZQVAsfvMiASC5T4iMQIwzBYtS+0tIyWqTg2i8VytLkrof03d3lw8hPPYzD5I7Crc7OIEh+RGLHt8DZKa0qJs8dpGPsJjO45GgB7x69o77UkQaxsMk6DHllg1XB2ESU+IjFiddFqALJSsnDanCZH07ZdkHYBRtCB1VGO1VVkdjgi0oyU+IjEiLx9eQCMSBthciRtn8vmwl/VDwB7x3yTozk1NgJcbV0Jm/4OAb/Z4YiYTomPSAzwBDys378egBHpSnxORqByAAD2hK9MjuTUOPHxZ+ccWDwZAh6zwxExnRIfkRiwsWQjtYFakuOS6de5n9nhtAv+yjMAsLr3YLFVmhxN0wWx8mlgEPS9GCz6yhfRp0AkBtSN5hqeNhyLxWJyNO2D4U8kUNMDi8XA1vFrs8NpMg9ObvY9CLf8f+CIMzscEdMp8RGJAeH+PWrmikhdrU9odJeIRIMmrdUlIu3H4drDfHUo9It7eNpwk6NpW/o8sPS4j/srB+BK/gh7h22AH31lirR/qvERiXJritZgYHB6l9NJjk82O5x2JVjbg6C/IxabB1v8TrPDaRI3HpY7/w+8MExLVoigxEck6uUVaRh701kJhJu72uewdgsG/a174UD7n4xRpDko8RGJYt9fpkL9e5rG386HtXtwcqP3d3DrP7VkhQhKfESi2s7ynRRXFeOwOshKyTI7nHbJX3U6hmHD6jyIxXnA7HAiFsTK6uBAyBylJStEUOIjEtXqRnMN7j6YOLuGMjdJ0EWgKhPQ6C6RaKDERySKhfv3qJnrlPgrzwTaZz8fGwGyrWsh/59askIEJT4iUcsX9LG2eC2gjs2nqq6fjy1+J1hrzQ0mQk58vOj8Eyz8uZasEEGJj0jU+rL0S6p8VXR2debMrmeaHU67Zvi6EfAkY7EEsXfYbnY4EQliZV2wP/QapiUrRFDiIxK1Vu9bDcAFqRdgU6fWUxZop7M4e3ByvfcRuH25lqwQQYmPSNRaXRRKfIalDTM5kuhQ188ntG5X0NxgRKTJlPiIRKFqXzWbSjcB6t/TXALVfTACTqz2SqzufWaHIyJNpMRHJAp9XvI5/qCf9A7p9EzoaXY4UcKOv+r00F47Wq3dhZf/z/k7ePFi8NWYHY6I6ZT4iEShuv49w9KGYbFYTI4megSq2l8/HytBzrV+C/s2gKEmOhEtNSwShdYUrwHUv6e5+b/r4Gx178Fiq8QIdGzSeU60Knxz8uLgv7z/B4BPfv9vAjTe0X3nrCuPeY7jxXu854m0RUp8RKLM4drDfHUoVCOhxKd5Gf5EArVp2NxF2Dpsx18+2OyQTiiAjY+DbT9Okdaipi6RKPOf4v8A0K9zP5LikkyOJvocncW5/TR3ichRSnxEosyaolAz1/C04SZHEp2OJj7bgIC5wZwEK0EutG7mQutmrBqGL6KmLpFoU5f4qJmrZQRremH447HYq7HF7SZQ08fskI7LhZfXnTMBGFC7gBrcJkckYi7V+IhEkX2V+yisKMRmsTEkZYjZ4UQpa3hYu60dNHcFsbI1mMHWYAZBfeWLqMZHJJrU1faclXQWHZ1NG3EkJ+avPBNH4hfYO36N98AVZodzXB6cjPfONDsMkTZD6b9IFKlbpkL9e1pWoKo/hmHB5i7CYi8zOxwRiUCTEp85c+aQmZmJ2+0mKyuLTz/99Ljlc3NzycrKwu1207dvX+bNm1fv8S1btjBhwgT69OmDxWJh9uzZTbquYRg88sgjpKenExcXx8UXX8yWLVua8hJF2h3DMMIjupT4tCwj0IFgTS9Ao7tE2puIE5+FCxcydepUHnroITZs2MCoUaMYN24chYWFjZYvKChg/PjxjBo1ig0bNvDggw9y7733smjRonCZ6upq+vbty6xZs0hNTW3ydZ988kmeffZZnn/+edauXUtqaipjx46loqIi0pcp0u58c+QbSmtKcdvcnJt8rtnhRL26yQxtbXz5Chde3nI+xlvOx3DhNTscEdNFnPg8++yz3H777dxxxx0MGDCA2bNn06tXL+bOndto+Xnz5tG7d29mz57NgAEDuOOOO7jtttt4+umnw2WGDh3KU089xY033ojL5WrSdQ3DYPbs2Tz00ENcd911DBo0iFdffZXq6mreeOONSF+mSLtTN1vz4O6DcdqcJkcT/fxV3w1rj98BFr/J0RyblSDDrfkMt+ZrOLsIESY+Xq+X9evXk52dXe94dnY2q1atavQ5eXl5DcpffvnlrFu3Dp/P12zXLSgooLi4uF4Zl8vF6NGjjxmbx+OhvLy83ibSXtX179Ew9tYRrE0j6E/AYvNii9tpdjjH5MXBnd57udN7L14cZocjYrqIEp/S0lICgQApKSn1jqekpFBcXNzoc4qLixst7/f7KS0tbbbr1v0/kthmzpxJYmJieOvVq9dJxSPS1viDftYVrwPUv6f1WAlU9gfadj+fADaWBYezLDj8mOt0icSSJnVu/uFqz4ZhHHcF6MbKN3a8Oa4bSWwzZsygrKwsvO3evTuieETaivyD+VT6KklwJnBm1zPNDidm1M3i3Nb7+YjIURHN45OUlITNZmtQg1JSUtKgpqVOampqo+XtdjvdunVrtuvWdYouLi4mLS3tpGJzuVzH7FMk0p7U9e8ZmjIUm1V/1bcWf9XpGIYVm+sAFsdBDN/Jfae1JitBBlu2A7DBOP2Ykxi25orxImaKqMbH6XSSlZVFTk5OveM5OTmMHDmy0eeMGDGiQfnly5czZMgQHI6Ta28+metmZmaSmppar4zX6yU3N/eYsYlECy1TYZKgm0B1HwDsbbTWx4WXRa4/ssj1R43qEqEJMzdPnz6dm2++mSFDhjBixAhefPFFCgsLmTJlChBqPtq7dy+vvfYaAFOmTOH5559n+vTpTJ48mby8PObPn8+bb74ZPqfX62Xr1q3h/b1797Jx40Y6duxIv379Tuq6FouFqVOn8vjjj3P66adz+umn8/jjjxMfH8/PfvazU7tLIm2YJ+BhQ8kGQImPGfyVZ2Dv8C32jl/jO9z2/sgysFAQTAnvi8S6iBOfiRMncvDgQR599FGKiooYNGgQy5YtIyMjA4CioqJ6c+tkZmaybNkypk2bxgsvvEB6ejrPPfccEyZMCJfZt28fgwcPDv/89NNP8/TTTzN69GhWrFhxUtcF+M1vfkNNTQ133nknhw8fZtiwYSxfvpyEhISIb4xIe7HpwCY8AQ9JcUn0TexrdjgxJ1B5JqS8jy3+G7B4wWhbUwnU4uIS75/MDkOkzbAYdT2NhfLychITEykrK6NTp05mhyNyUp7f8Dx/2fQXxmeO54mLnjA7nDan5fuuGHTo9wRWxxGqCycRqIqtzuU7Z11pdggiEf3+1lpdIu1cXf8eDWM3iyU8i3Nb7ecjIkcp8RFpx6p8VXxZ+iUAF6RdYHI0seto4vMV0LYq0V14WeB4kgWOJ9W5WYQm9PERkbZj/f71+A0/PTv2pEfHHmaHE7MCVf0wgnaszsNYnQcIerubHVKYlSBjbBtD+z4tWSGixEekHftPUWg1do3mMpnhJFDdF3vHbdg6fkXwUNtJfHzYud/3q/C+SKzTp0CkHaubuPCCVDVzmc1feQb2jtuwd/wK36GLzA4nzI+dtwOjW+z8J+o8rs7P0taoj49IO3Wk9ghfHQqtEaX+Pear6+dji98J1lpzgxGRY1LiI9JO/ac41MzVr3M/kuKSTI5GDF8SQU8SFksQe4ftZocTZiXIQMtOBlp2YkV9fESU+Ii0U3WJj/r3tB11i5a2pdXaXXhZ5nqQZa4HNapLBCU+Iu1W3fw96t/TdtRfrb1t1K4YWCg2ulBsdNGSFSKoc7NIu7S/aj87y3ditVgZkjrE7HDkO4HqPhgBF1Z7JVb3XoK1vcwOiVpcDPe8YHYYIm2GanxE2qG60VwDuw6kk1PLq7QddvxVp4f22lBzl4gcpcRHpB1avW81AMPTtUxFW3O0n4+WrxBpi5T4iLQzhmGE+/eoY3PbE6gb1h63B4utwuRoQp2bX3DM5gXHbHVuFkGJj0i7U1BeQElNCU6rk/OSzzM7HPkBI5BAoKYnUNfJ2VxWglxp+w9X2v6j4ewiqHOzSLtTV9szuPtg3Ha3ydFIY/yVZ2CL24O9Yz7+MnM7n/uw87BvUnhfJNbpUyDSzqiZq+3zV56JK/nf2DvsAPyY+VXrx87/C2Sbdn2RtkZNXSLtSCAY0MSF7UCwtgdBf0csNk9oCQsRaTOU+Ii0I18d+ooKbwUdHR0Z2G2g2eHIMVnDnZzNHtZuIUgfSxF9LEVY1MdHRImPSHuyuig0jH1I6hDsVrVUt2VtZfkKN15WuH7NCtevcWtUl4gSH5H2pK5/z/A0zd/T1vmrTscwbFhdpVgcpabGUm7EU27EmxqDSFuhPxlF2glvwMuGkg0ADEtV/542L+gmUJ2JvcMO7An5+A6NMiWMGtyc43nZlGuLtEWq8RFpJ7448AW1gVqS4pI4rfNpZocjJ8FfUdfclW9yJCJSR4mPSDtR17/ngtQLsFi0ynZ74K8cABAa2WWtNjcYEQHU1CXSbkRz/54+Dyw97uM7Z13ZSpE0L8PXjYCnOzZXCfaO2/CXn9fqMTjx8bhjPgAP+m7Hi6NVr3+8f9v2+u8q7ZtqfETagUpvJV+WfglEZ+ITzfwVoVofs5q7bAS43vYJ19s+wUbAlBhE2hLV+Ii0A+v3rydgBOid0Ju0jmlmhyMRCFQOgKTc71ZrDwC2Vr2+HzuP+24K74vEOn0KRNqBuv49mq25/QnU9Cbo74DVXoUtfieB6tbtmO7DzouBq1r1miJtmRIfkXYgb18eACPSR5gciTlO1AeobQvN4mzt/Dn2jvmtnviISH3q4yPSxu2v2s83Zd9gwcIFqReYHY40Qd3oLntCPmC06rUtBEnhECkc0pIVIijxEWnz6pq5zup2FomuRJOjkabwV/XHCNqwOg9idR5o1Wu78bLGfTdr3HdryQoRlPiItHl5RbHdzBUVgi4C1X0BsJkwustn2PAZrdupWqStUh8fkTbMMAxW7wvV+Cjxad/8lQOwd9z+3fIVo1vtujW4Od3z/1rteiJtnWp8RNqwbYe3cbD2IHH2OM5NPtfscOQU1M3nY4vbBbYqk6MRiV1NSnzmzJlDZmYmbrebrKwsPv300+OWz83NJSsrC7fbTd++fZk3b16DMosWLWLgwIG4XC4GDhzIkiVL6j3ep08fLBZLg+2uu+4Kl5k0aVKDx4cP12Rv0n7V9e/JSsnCaXOaHI2cCsPfhUBtKhaLgb3D12aHIxKzIk58Fi5cyNSpU3nooYfYsGEDo0aNYty4cRQWFjZavqCggPHjxzNq1Cg2bNjAgw8+yL333suiRYvCZfLy8pg4cSI333wzX3zxBTfffDM33HADa9asCZdZu3YtRUVF4S0nJweAn/70p/Wud8UVV9Qrt2zZskhfokibEe7fk6ZmrmhQf3RX63Di41H7KzxqfwUnvla7rkhbZTEMI6KxlcOGDeP8889n7ty54WMDBgzgmmuuYebMmQ3K//a3v+Xdd98lP//oB33KlCl88cUX5OWFvtQnTpxIeXk577//frjMFVdcQZcuXXjzzTcbjWPq1Kn885//ZPv27eEFGydNmsSRI0d45513InlJYeXl5SQmJlJWVkanTp2adA6R5uINePnRmz+iNlDLop8son+X/maH1GLa9zw9J8/q3kOHzOcxgk4qtz0MRsuvmxVHLfnu2wAYULuAGtwtfs2TpbW6pLlE8vs7ohofr9fL+vXryc7Ornc8OzubVatWNfqcvLy8BuUvv/xy1q1bh8/nO26ZY53T6/Xy+uuvc9tttzVYpXrFihV0796d/v37M3nyZEpKSo75ejweD+Xl5fU2kbZiY8lGagO1JMUlcXrn080OR5pBsLYHQV8iFqsXW4cdrXJNP3Zm+69jtv86LVkhQoSJT2lpKYFAgJSUlHrHU1JSKC4ubvQ5xcXFjZb3+/2UlpYet8yxzvnOO+9w5MgRJk2aVO/4uHHj+Nvf/sZHH33EM888w9q1axkzZgwej6fR88ycOZPExMTw1qtXr2O+dpHWVtfMNTxteIMEX9orC/6KgQDYE7a0yhV92Jntv57Z/uvxKfERaVrn5h9+CRuGcdwv5sbK//B4JOecP38+48aNIz09vd7xiRMncuWVVzJo0CCuuuoq3n//fbZt28bSpY1Xo8+YMYOysrLwtnv37mO+BpHWFuvLVEQrf8VZQN1q7ZpJWaS1RZT+JyUlYbPZGtTElJSUNKixqZOamtpoebvdTrdu3Y5bprFz7tq1iw8//JDFixefMN60tDQyMjLYvn17o4+7XC5cLtcJzyPS2o7UHmHrwa1AqMZHokegOhMj4A4tWhpXSKCmTwtf0aAT1QCUEw+o9lBiW0Q1Pk6nk6ysrPCIqjo5OTmMHDmy0eeMGDGiQfnly5czZMgQHA7Hccs0ds5XXnmF7t27c+WVJ+4Ud/DgQXbv3k1aWtoJy4q0Jf8p/g8GBv0696N7fHezw5FmZfve6K6Wb+6Kw8Mm92Q2uScTR+PN/iKxJOKmrunTp/Pyyy+zYMEC8vPzmTZtGoWFhUyZMgUINR/dcsst4fJTpkxh165dTJ8+nfz8fBYsWMD8+fO5//77w2Xuu+8+li9fzhNPPMFXX33FE088wYcffsjUqVPrXTsYDPLKK69w6623YrfXr6yqrKzk/vvvJy8vj507d7JixQquuuoqkpKSuPbaayN9mSKm+n7/Hok+4eauhC209qKlIrEu4p5uEydO5ODBgzz66KMUFRUxaNAgli1bRkZGBgBFRUX15vTJzMxk2bJlTJs2jRdeeIH09HSee+45JkyYEC4zcuRI3nrrLX73u9/x8MMPc9ppp7Fw4UKGDRtW79offvghhYWF3HbbbQ3istlsbN68mddee40jR46QlpbGJZdcwsKFC0lISIj0ZYqYxjAM9e+Jcv7K/hhBO1bnIayu/QQ9qS12rRpc9Kt9LXRdtF6XSMTz+EQzzeMjbcHOsp1c9c5VOKwOVt64knhHvNkhtbhYmcfn++J6voo9IR/PgbF4Sy81OxxTaB4faS4tNo+PiLS8z/Z9BsD5KefHRNITq3x1w9o7bjU5EpHYokkdRNqYlXtXAnBh+oUmRyItKVA5AMOwYIvbi8V+BMPfuUWu48DP/faFADztn9im5vI5UU2faoSkJajGR6QNqfXXsq54HQA/6vEjk6ORlmQEOhKo7gO07OguO35+ZV/Kr+xLseNvseuItBdtJ/UXET7f/zm1gVq6x3enX+d+ZocjLcxfORB7hwLsCVvwHW6ZRNePnb/4rwzvi8Q6fQpE2pCV+75r5upxoZapiAH+irMgZSm2+AIstkqMQMdmv4YPOzP9P2/284q0V2rqEmlDPtsb6tg8Mr3xCUEluhi+rgRqemCxGNgT1MlZpDUo8RFpI4oqi/i27FusFqsmLowh/oqzAbAnbG6hKxjY8X/Xv0ezl4go8RFpI+qGsZ+TdA6JrkSTo5HW4isfBICtwzdgq2r288fhYYf7Fna4b9GSFSIo8RFpM+qauTSaK7YYviQCtWlYLEEcmtNHpMWpc7NIG+AL+lhdtBoIdWyW2OIvPwebuwh7p834yoY267lrcHFO7UvhfZFYpxofkTZg04FNVPoq6eLqwsBuA80OR1qZr6KuuWsHWKub+ewWyulAOR0AjRQUUeIj0gbUNXMNTx+O1aKPZawxvMkEalOxWIIa3SXSwvQNK9IGhJepUDNXzKob3eXo1Lyjuxz4mWp/m6n2t3Fo5mYRJT4iZiutKSX/UD6g+Xtimb88lPiEmrtqmu28dvxMtS9mqn2xlqwQQZ2bRUxX18w1oOsAkuKSTI5GzBL0didQm4LNvR97wlb8ZVnNct4ANl7zjw3vi8Q6JT4iJsvdkwvART0vMjkSMZu/YhA2934cCV82W+LjxcHv/f/VLOcSiQZq6hIxkS/gC9f4XNzrYnODEdP5y88BwNZhG1hrTY5GJDop8REx0br966j2V9PN3U3D2CXU3OVJxmINYE/YYnY4IlFJiY+IiT7Z8wkQaubSMHYBC/7ycwFwdPqiWc4YRy3bXTez3XUzcagWSUTftCImMQyDFbtXADC652hTY5G2w1d2HhAa3WWxVTbLOR2WAA5LoFnOJdLeqXOziEkKygvYU7kHh9XB8HStxi4hhi+JQE1PbHF7QktYHB5xSuerxcmw2ufD++1JnweWHvOxnbOubMVIJJqoxkfEJLm7Q6O5hqYOpYOjg8nRSFviCzd3bTzlcxlY2U9X9tMVQ1/5IvoUiJilbhi7mrnkh/zl52IYFmzxu7A4DpkdjkhUUeIjYoIyTxkbSzYCmr9HGjL8nQhU9wXA0WnTKZ3LgZ9f2t7jl7b3tGSFCEp8REzx2d7PCBgB+nXuR8+EnmaHI22Q/7tOzvZTbO6y4+dBx5s86HhTS1aIoM7NIqbQbM1yIr6Ks3AF38HmLsbqKiboSW3SeQLYeDtwUXhfJNYp8RFpZf6gP7wau/r3yDEF4/FXnYEjYSv2ThvxHriiSafx4uB+35RmDk6k/VJTl0gr21iykXJvOYmuRM5JPsfscKQNq2vuCo3uMswMRSRqKPERaWV1zVyjeozCblWlqxybv/JMjIATq/MI1rhCs8MRiQr61hVpRYZh8FHhRwCM7qVmLjkBw4m/4iwcnTfg6LQBT01GxKeIo5Y1rrsBGOZ5nhrczR2lKTS5oTSVanxEWtH2I9sprCjEaXVyUQ91bJYT85UPBr4b1m5p2qisTpZqOlmqmzMskXZLNT4irejfu/4NwMgeI4l3xJscjbQHgap+BH2JWB1l2DtuxV8RWb+wWpxc7HkmvC8S61TjI9KKPiz8EIDLel9mciTSfljxlZ0PgKPz+oifbWBlp5HGTiNNS1aI0MTEZ86cOWRmZuJ2u8nKyuLTTz89bvnc3FyysrJwu9307duXefPmNSizaNEiBg4ciMvlYuDAgSxZsqTe44888ggWi6Xelppaf14LwzB45JFHSE9PJy4ujosvvpgtW7Y05SWKNLvC8kK2Hd6GzWLj4l4Xmx2OtCN1iY+twzYs9nKToxFp3yJu6lq4cCFTp05lzpw5/OhHP+Ivf/kL48aNY+vWrfTu3btB+YKCAsaPH8/kyZN5/fXX+eyzz7jzzjtJTk5mwoQJAOTl5TFx4kQee+wxrr32WpYsWcINN9zAypUrGTZsWPhcZ511Fh9++GH4Z5ut/mRcTz75JM8++yx//etf6d+/P//zP//D2LFj+frrr0lISIj0pYo0q38Xhpq5hqYOJdGVaHI0x6ZOo22P4U3GX52BPX4XjsTP8R68+KSfa8fPTbZQh/o3A2Pwq4eDxLiIa3yeffZZbr/9du644w4GDBjA7Nmz6dWrF3Pnzm20/Lx58+jduzezZ89mwIAB3HHHHdx22208/fTT4TKzZ89m7NixzJgxgzPPPJMZM2Zw6aWXMnv27HrnstvtpKamhrfk5OTwY4ZhMHv2bB566CGuu+46Bg0axKuvvkp1dTVvvPFGpC9TpNmpmUtOhf/IEADsieuJZE4fB34ec/yVxxx/1VpdIkSY+Hi9XtavX092dna949nZ2axatarR5+Tl5TUof/nll7Nu3Tp8Pt9xy/zwnNu3byc9PZ3MzExuvPFGvv322/BjBQUFFBcX1zuPy+Vi9OjRx4zN4/FQXl5ebxNpCfur9rPpwCYsWBjTe4zZ4Ug75Ks4GyPowOY6gNW9+6SfF8TK0sAFLA1cQFB9fEQi+xSUlpYSCARISUmpdzwlJYXi4uJGn1NcXNxoeb/fT2lp6XHLfP+cw4YN47XXXuODDz7gpZdeori4mJEjR3Lw4MHwOeqed7KxzZw5k8TExPDWq1evE90CkSb5aHeoqeHc5HNJjk8+QWmRRgTd+MsHAeDovO6kn+bByV2+qdzlm4pHo7pEmpb+WyyWej8bhtHg2InK//D4ic45btw4JkyYwNlnn81ll13G0qWhfgivvvpqk2ObMWMGZWVl4W337pP/K0okEnXD2C/LUDOXNJ2vLNTc5ej0BVh8Jkcj0j5FlPgkJSVhs9ka1KCUlJQ0qGmpk5qa2mh5u91Ot27djlvmWOcE6NChA2effTbbt28PnwOI6Dwul4tOnTrV20Sa2+Haw6zbH/oLXc1ccioC1ZkEvV2w2DzYEzRiVaQpIkp8nE4nWVlZ5OTk1Duek5PDyJEjG33OiBEjGpRfvnw5Q4YMweFwHLfMsc4Jof45+fn5pKWlAZCZmUlqamq983i9XnJzc497HpGWtmL3CgJGgDO7nkmvBDWnyqn43pw+iSfX3OXGw2rXXax23YUbT0sGJ9IuRNzUNX36dF5++WUWLFhAfn4+06ZNo7CwkClTpgCh5qNbbrklXH7KlCns2rWL6dOnk5+fz4IFC5g/fz73339/uMx9993H8uXLeeKJJ/jqq6944okn+PDDD5k6dWq4zP33309ubi4FBQWsWbOG66+/nvLycm699VYg1MQ1depUHn/8cZYsWcKXX37JpEmTiI+P52c/+1lT74/IKasbxn5p70tNjkSiga8sCwBbh2+wOA6dsLwFg1TLYVIth7FohXeRyCd0mDhxIgcPHuTRRx+lqKiIQYMGsWzZMjIyQovnFRUVUVh4dBXhzMxMli1bxrRp03jhhRdIT0/nueeeC8/hAzBy5Ejeeustfve73/Hwww9z2mmnsXDhwnpz+OzZs4ebbrqJ0tJSkpOTGT58OKtXrw5fF+A3v/kNNTU13HnnnRw+fJhhw4axfPlyzeEjpqnwVrBqX2hUoYaxS3MwfF3xV56OveN2HJ3X4D0w7rjlPTgZ73k8vC8S6yxGXU9joby8nMTERMrKytTfR5rFku1L+P2q39Ovcz8W/2TxcQcBtBVmTGB4vGtKQ/aEL4nr+TpBfweqdswAQ5MSfp8m2ow9kfz+1qQOIi3o/YL3ARiXOa5dJD3SPvgrBhD0dcJqr8Ke8KXZ4Yi0K0p8RFpIaU0pa4rXAKHER6T52PAduQAAR5fVxy1px8/1tlyut+Vi18zNIkp8RFrKBzs/IGgEOSf5HI3mkmbnO3IBhmHFHr8Tq6vxSVohtGTF046/8LTjL1qyQoQmdG4WkZOzrGAZAOMzx5scSevRAqetx/B3wl8xEEenL3F0Xo1n/zWNlgti5aPAeeH9WHCiPmN6L8Y2JT4iLWB3xW42HdiE1WLl8j6Xmx2ORCnf4eGhxCdxA56ScWC4GpTx4OQ2329MiE6kbYqN9F+klf2r4F8AXJB6AUlxSSZHI9EqUH0aQU8SFpsHR+JGs8MRaReU+Ii0gFhs5hIzWPAeCc135uiSB5qgUOSElPiINLNth7ex48gOHFYHl2ZotmZpWb4jWRhBOzZ3Mda4wgaPu/HwsXMaHzunackKEdTHR6TZLfs2VNtzUc+L6OSMrokwT2WiQU1S2EKC8fjLz8XReT3Oriup3ZtR72ELBpnW/eF9kVinxEekGRmGUW/SQpHW4D10IY7O67EnfInFcQjD1zX8mAcnEzx/CO+LxDo1dYk0o40HNrKvah/x9nhG9xxtdjgSI4KeNPyVp2OxGDi7flb/MaysN85gvXFGzAxnFzkefQpEmtE7O94B4LKMy3Db3eYGIzHFe2gUAI7Oa8FabXI0Im2XEh+RZlLtqw4PY7+237UmRyOxJlB1OoHaVCxWL84u/wkftxFgvHU1462rsREwMUKRtkGJj0gz+WDnB1T7q8nolEFWSpbZ4UjMseA9dCEAji6r4LvlKZz4mON8jjnO53DiMzE+kbZBiY9IM1m0fREQqu3RSuxiBn/5eQT9CVgd5dg7bQJCfXxWBwewOjhAfXxE0KgukWbx7ZFv+eLAF9gsNq7ud7XZ4UisMuz4Do3E1f0DnN0+xV8+GA9ObvQ+bHZkIm2G0n+RZrB4+2IgNHePlqgQM3kPD8MIOrC5i7DFf2N2OCJtjhIfkVPkC/h495t3Abju9OtMjkZiXjAe35EhADi7fWJyMCJtjxIfkVO0Ys8KDnsOkxyXzIU9LjQ7HBG8hy7EMKzYO27D7f6WZc4ZLHPOwIXX7NBETKfER+QU1TVzXd3vauxWdZsT8xm+bvjLBgNgS/6YgdZdDLTuwkrQ5MhEzKdvaZFTUFxVzGd7QzPlau4eaUs8pZdgT9yAteN2rjs4mfjaJC1ZIYJqfEROyTs73sHAYEjKEHp36m12OCJhhi8pXOuT320nK4Nnazi7CEp8RJrMF/Tx9ra3AXVqlrbJU3rJd319vsbq3m12OCJtghIfkSb6965/s796P13dXbm8z+VmhyPSgOFLIlB2LgDpyYu1ZIUISnxEmuz1/NcBmHjGRJw29Z2QNqr0QmyGQVnHIpzunWZHI2I6JT4iTbDpwCa+OPAFDquDG864wexwRI4p6EtmRGXoq96alGtyNCLm06gukSaoq+0Zlzmu3c3U3OeBpWaHIK3Ig5N/7f81HTo+gzVhG1Z3IcFadcSX2KUaH5EI7a/aT87OHAB+PuDnJkcjcmKGLwlfWRYA7pSlgGFuQCImUuIjEqGFXy/Eb/jJSsliYLeBZocjclK8B7JDa3jF78Ke8KXZ4YiYRomPSARq/DX8Y9s/APjFgF+YHI3Iibnw8rbzEf5hfZbgwR+FjnV/H/CbG5iISZT4iERg6bdLOeI5Qo+OPbik1yVmhyNyQlaCDLFuY4h1G8bBEQT9CVidh3B0zTM7NBFTqHOzyEkyDIO/5f8NgJvOvAmb1WZyRMemDsxSx4uDX3qnhfaNjlhLsnGnL8KV9BG+I1kQjDc5QpHWpRofkZOUuyeXHUd2EGeP49rTtS6XtA8BbCwPDmV5cCgBbPjKsgjUpmCx1eBK+sjs8ERaXZMSnzlz5pCZmYnb7SYrK4tPP/30uOVzc3PJysrC7XbTt29f5s2b16DMokWLGDhwIC6Xi4EDB7JkyZJ6j8+cOZOhQ4eSkJBA9+7dueaaa/j666/rlZk0aRIWi6XeNnz48Ka8RJF6DMNg7hdzAbjxzBvp5OxkckQiTWXFU3IlAI6ueVgcpSbHI9K6Ik58Fi5cyNSpU3nooYfYsGEDo0aNYty4cRQWFjZavqCggPHjxzNq1Cg2bNjAgw8+yL333suiRYvCZfLy8pg4cSI333wzX3zxBTfffDM33HADa9asCZfJzc3lrrvuYvXq1eTk5OD3+8nOzqaqqqre9a644gqKiorC27JlyyJ9iSINfLLnE7Ye3EqcPY5JZ00yOxyRk2YlyHDrVoZbt2IlCECgqj/+yv5YLAHcKf9Ew9slllgMw4joHT9s2DDOP/985s6dGz42YMAArrnmGmbOnNmg/G9/+1veffdd8vPzw8emTJnCF198QV5eqHPdxIkTKS8v5/333w+XueKKK+jSpQtvvvlmo3EcOHCA7t27k5uby0UXXQSEanyOHDnCO++8E8lLCisvLycxMZGysjI6ddJf9BJiGAY3Lb2JLQe38F9n/RfTh0w3O6QTUh8fqRNHLfnu2wAYULuAGtwAWJ0lxPf9MxZLgJo9P8dfcbaZYbaqnbOuNDsEaWaR/P6OqMbH6/Wyfv16srOz6x3Pzs5m1apVjT4nLy+vQfnLL7+cdevW4fP5jlvmWOcEKCsrA6Br1671jq9YsYLu3bvTv39/Jk+eTElJyTHP4fF4KC8vr7eJ/NCnez9ly8EtxNnjuPWsW80ORyQiBha2BXuwLdgDA0v4eNDbHe/B0QC4Ut4Fa61ZIYq0qogSn9LSUgKBACkpKfWOp6SkUFxc3OhziouLGy3v9/spLS09bpljndMwDKZPn86FF17IoEGDwsfHjRvH3/72Nz766COeeeYZ1q5dy5gxY/B4PI2eZ+bMmSQmJoa3Xr16Hf8GSMwxDIO5G0O1mxPPmEi3uG4mRyQSmVpcZHufItv7FLW46j3mLb2EoCcJq6MCV/K/TIpQpHU1qXOzxWKp97NhGA2Onaj8D49Hcs67776bTZs2NWgGmzhxIldeeSWDBg3iqquu4v3332fbtm0sXdp4tf+MGTMoKysLb7t37z7ma5DYtHLvSr48+CVum1t9eyT6GA5qi68BwNFlDVZ34301RaJJRIlPUlISNputQU1MSUlJgxqbOqmpqY2Wt9vtdOvW7bhlGjvnPffcw7vvvsvHH39Mz549jxtvWloaGRkZbN++vdHHXS4XnTp1qreJ1Pn+SC7V9ki0ClT3w3fkfCwWA3faYiBgdkgiLSqiCQydTidZWVnk5ORw7bVH5zHJycnh6quvbvQ5I0aM4L333qt3bPny5QwZMgSHwxEuk5OTw7Rp0+qVGTlyZPhnwzC45557WLJkCStWrCAzM/OE8R48eJDdu3eTlpYWycsUAUK1PZtLN4dqewZNMjsckSZx4eVlx9MA3OG7Hw/OBmU8JVdi6/gVNncxzq4r8R4a3dphthknGhigjtHtX8RNXdOnT+fll19mwYIF5OfnM23aNAoLC5kyZQoQaj665ZZbwuWnTJnCrl27mD59Ovn5+SxYsID58+dz//33h8vcd999LF++nCeeeIKvvvqKJ554gg8//JCpU6eGy9x11128/vrrvPHGGyQkJFBcXExxcTE1NTUAVFZWcv/995OXl8fOnTtZsWIFV111FUlJSfWSNJGT4Q/6eXb9s0CoticpLsnkiESaxkqQUbYvGWX7Mjyc/YeMQIfw3D7O5Byszv2tGaJIq4p4yYqJEydy8OBBHn30UYqKihg0aBDLli0jIyMDgKKionpz+mRmZrJs2TKmTZvGCy+8QHp6Os899xwTJkwIlxk5ciRvvfUWv/vd73j44Yc57bTTWLhwIcOGDQuXqRs+f/HFF9eL55VXXmHSpEnYbDY2b97Ma6+9xpEjR0hLS+OSSy5h4cKFJCQkRPoyJcb9Y9s/2HFkB51dnZl8zmSzwxFpMi8O7vPeGd4/Fn/Z+fg7bcLe8WvcPd6keuddYBy7vEh7FfE8PtFM8/gIQJmnjCuXXEmZp4zfDfsdE8+caHZIEdM8PtIUFlsF8X1nY7VX4T14IZ6SH5sdUos4XnOVmrrapxabx0ckFszZOIcyTxn9OvdjQv8JJ36CSJQwAgnUFl0PgLPbSmwdtpkckUjz0+rsIt+z4/AOFn69EIDfXvBb7FZ9RKR9sxJkkKUAgC+NTIIn+Hs3UDkA76EROLvm4U7/B9Xf3ocR6NgaobaaU6kRPd5zVRvUPqjGR+Q7hmHw5NonCRgBxvQaw/A0LXAr7Z8LL++6HuZd18O48J7Uczwl4wl4umO1V+BKW4TW8pJoosRH5Du5e3LJK8rDYXVw/5D7T/wEkXbAwMIeI4k9RlK9JSuO/yQHtXtvxAjacCTk4+j6acsGKdKKVI8vAlT7qnniP08AcMvAW+jVScuXSHSoxcWFnucifl7Qk46n5Ercqe/i6v4+QU8qgar+LRChSOtS4iMC/Gn9n9hTuYfUDqntZvi6Rm5JS/MdHoHVvQ9n53XE9XiDqoK7MXya00raNzV1ScxbXbSat75+C4BHRz5KB0cHkyMSaSsseIqvIVCdgcVWS1yvV7WKu7R7SnwkplV6K/n9Z78HQjM0j0gfYXJEIs3LhZcXHc/wouOZk+7cXI9hp2bPLwj6ErG5DhCX/hYcYwZokfZAiY/EtKfWPUVRVRE9O/ZketZ0s8MRaXZWgmTb1pNtW3/MJStOxAgkULPnZoygHXvCV7i6L0MjvaS9Uh8fiVmf7PmExdsXY8HCYz96jHhHvNkhiTQ7H3Ye8N0R3m+qYG1PaouuJ67HWzi7rcQIxOM9OKa5whRpNUp8JCYdqT3CH1f9EYBfDPwFQ1KHmByRSMvwY+etQPMkKP7y86i1V+BOWYqr+3KMoAvf4R81y7mjnZbCaDvU1CUxxx/0c/8n91NSU0KfTn24d/C9Zock0m74Do3Cc+BSANyp72FPXG9yRCKRUeIjMedP6//EmqI1xNnjeObiZ3Db3WaHJNJiLAQ53bKH0y17sDRTp2Rv6WV4D4Vqetxpb2NP+LJZzivSGpT4SEx575v3eG3rawD874X/S/8umpBNopsbLzmu35Dj+g3upozqapQFz/4r8R3JwmIxcPd4E3unjc10bpGWpT4+EjO2HNzCH/NC/Xomnz2ZsRljTY7o+DRBoTSXg0ZCC5zVSm3RBLD4cSR+gTt9IR5blfr8SJunxEdiwsGag0z9eCqegIfRPUdz9+C7zQ5JpFXU4CbL85cWOruV2n0TMQLxodXcU9/DYqvCWzoWTnZdMJFWpqYuiXrl3nLu/PedFFcV06dTH2aOmonVore+SPOw4tn/EzwHQjWoruSPcKUuQZMcSlulb3+JalW+Kv77w/9m68GtdHF14c9j/kyCsyWq/UVimQVv6aXUFl2DYVhwdvkPcb1ewWKrMjswkQaU+EjUqvZVc+eHd7LpwCY6OTvxUvZL9E3sa3ZYIq3KhZfZjueZ7Xi+aUtWRMB3ZDi1e3+GEXRg77id+Mz/i9W9u0WvKRIp9fGRqFTrr+Xej+7l85LP6ejoyItjX+SMrmeYHZZIq7MS5BrbKgBmfDeDc0vyV5xN9c5k4nr+P6zOg8RnzMOz/2p8R4YS7f1+NCChfVDiI1Gn0lvJ9BXTWVO8hnh7PPPGzuOspLPMDkvEFD7sPOq7ObzfGoKeVKoK7sGd/nccCVtxpy3GFl9AbfFVENTSMGIuJT4SVfZW7uXuf9/NjiM7iLPHMeeyOZybfK7ZYYmYxo+dBYFxrX/hoJvaPb8g2C0XZ/JyHIkbsMXvoLb4WgKVA1s/HpHvqI+PRI2NJRv52dKfsePIDpLjkllw+QKyUrLMDkskhlnxHryE6l1TCHqSsDoqiO/1Gu70t0Adn8UkSnwkKrz3zXvc9sFtHKo9xICuA3jjyjcYlDTI7LBETGchSE/LAXpaDjTbkhWRCtZkUFVwH96DF2EYFhyJG+nQ9084Oq8BAqbEJLFLTV3SrlX5qnhq7VMs2r4IgEt7X8rjFz5OvKNt9CNQZ0cxmxsvK133ATCgdgE1mLQ2neHAUzIeX/kg3OlvY3OV4E5bgqPLKjwl4wlU9SfaOz9L26DER9qt/xT9h4c/e5h9VfuA0DIUdw++W5MTivxAteEyO4SwYG1vqr+9F0eXNbiS/o3NvZ/43q/grzwdz4FsgrW9zA5RopwSH2l3qn3VzP58Nm9+9SYAPTr24LEfPcbQ1KEmRybS9tTgZqDnFbPD+AE7vsM/wlc2GFfSChxdPsPecTv2jtvxV/XFe3C0aoCkxSjxkXbDH/Tzzo53mLtxLiU1JQDc0P8Gfj3k122maUtEIhCMx1MyHu/hYbiSPsKeuAF7h2+xd/iWQG0q3kOj8JefDYbT7EgliijxkTbPMAz+Xfhv/vz5n9lZvhMI1fL8fsTvGZk+slViOF5fnZ2zrmyVGESileHrRm3RT7EcGIuz60ocXf6DzV1MXPo/MFLexVd+Hr4jQwjW9kS1QJHRd1dDSnykzfIGvHyw8wP+lv83thzcAkAXVxd+ec4vueGMG3Da9FegyIk48fFH+18B+IN/El4c5gZ0HIa/M56SH+MpHYOzyxocnf+D1XkYZ5c1OLusIVCbgr/iHPzlgwh6u6MkSJpCiY+0OUWVRfx9299ZvH0xh2oPARBnj+OWgbcw6axJdHR2NDlCkfbDRoCb7B8D8Kj/ZmjDiU9YMB7vwUvwHhyNLf5bHJ3XYk/Ygs29H5s7B1dyDkFPEr6KswhUnkGgpjf6dSYnS+8UaRP2V+3n34X/5sPCD1m/fz1BIzTfSEp8Cj/t/1Ou73893eK6mRylSPvjx85TvhvC++2LlUB1PwLV/cBajT1hC46ELdg6bMfqKsXlyoWkXIygg0B1Jv6q0whU9yVYm4Z+vcmx6J0hpvAGvGwu3cy64nV8svcTNh3YVO/xYWnDuOmMmxjdazR2q96mIk3lw84LgWvMDuPUBePxlw3FXzYUrLXYO3yNPWErtg47sNqrsHfchr3jNgCMoJ1gbTqB2p4EanoR9KQR9CShX3kCTXwXzJkzh6eeeoqioiLOOussZs+ezahRo45ZPjc3l+nTp7NlyxbS09P5zW9+w5QpU+qVWbRoEQ8//DDffPMNp512Gv/7v//LtddeG9F1DcPgj3/8Iy+++CKHDx9m2LBhvPDCC5x1lhaoNFPQCLKnYg9fH/6arw59xef7P2fTgU14g95wGQsWzut+Hpf1voxLMy6lR8cejZ7rVCYEbKmOfJqkUKSVBd34K87FX3EuEMTqKsEWvwN7hx3Y4gqx2KuxxRdiiy8MP8UwrAQ9yQS9Kd/9vxtBXzcMb1eMQEfM7i8Ubd8jbblTdcSJz8KFC5k6dSpz5szhRz/6EX/5y18YN24cW7dupXfv3g3KFxQUMH78eCZPnszrr7/OZ599xp133klycjITJkwAIC8vj4kTJ/LYY49x7bXXsmTJEm644QZWrlzJsGHDTvq6Tz75JM8++yx//etf6d+/P//zP//D2LFj+frrr0lISDiV+yQn4A/6OVR7iH2V+9hTuYfdFbvZU7GHXeW72H54O9X+6gbP6ebuRlZKFsPShnFJr0tIjk82IXKRaGfQlQoADpGA2b/gm5+VoCeVoCcV3+ELAQOL4yC2uD3Y4nZjde/B5tqPxVb7XR+h/Q3OYASdBH2JGL5EDH8iQX8ihj8Bw98Bw9+RYCC0T9CNVnpq/yyGYRiRPGHYsGGcf/75zJ07N3xswIABXHPNNcycObNB+d/+9re8++675Ofnh49NmTKFL774gry8PAAmTpxIeXk577//frjMFVdcQZcuXXjzzTdP6rqGYZCens7UqVP57W9/C4DH4yElJYUnnniCX/3qVyd8beXl5SQmJlJWVkanTp0iuS3tVtAI4g/68Qa8eAIePAEPtYFavAEv1b5qqv3VVPuqqfJVUeWrosxbRpnn6FZaU8qBmgMcrj2MwbHfSk6rk35d+nFGlzM4J/kcslKy6NOpDxZLZF/CZtX4RNtfYxI74qgl330bYPKSFaYysNjLsbqKQ5vzYHiz2MuwWE7u16BhWCDowgjEYwTiMIIujKAbAqH/G0EnGI7Q/4PO7362YxgOCNpDjxm2747ZwbCFN8OwhvaxgWEllKC2bJLakjUvrV3jE8nv74hqfLxeL+vXr+eBBx6odzw7O5tVq1Y1+py8vDyys7PrHbv88suZP38+Pp8Ph8NBXl4e06ZNa1Bm9uzZJ33dgoICiouL613L5XIxevRoVq1a1Wji4/F48Hg84Z/LysqA0A1sTr6gjzs/vPOE5cKJg1H/WF1uatT99/2fDaPeccMwCBIkaAQJBr/7P0ECwQABI4A/6CdoBAkYAXxBHwGj+RYItFqsJMcl06Njj3rbaZ1Po3en3g366lRUVER8jaCnYc3RyTqVf9dTua6ImQLUUv7dL/aAp5qgSQuVms7jIFDVC/jBkhgWPxZ7GVZ7eSgJcpSHNmslFnsVVlsVFnslFlvd7wo/EFpZ3mINbS3VdSiUDH2XBH33f+P7SZHxg/9/77gRTpq++7/xg5+xcM3Cl77bCx37/h+i4WPfe8yChX5d+vHABfV/FzfmeN+Zzf079vvnPJm6nIj+uUpLSwkEAqSkpNQ7npKSQnFxcaPPKS4ubrS83++ntLSUtLS0Y5apO+fJXLfu/42V2bVrV6OxzZw5kz/+8Y8NjvfqpbViolHibLMjEDFHYnjvFhOjkLZmWxOfN5OGrTuRaMnv4oqKChITE49bpkl56g+bJwzDOG6TRWPlf3j8ZM7ZXGXqzJgxg+nTp4d/DgaDHDp0iG7dukXcBHMi5eXl9OrVi927d8dMM1pbovtvPv0bmEv333z6N2g5hmFQUVFBenr6CctGlPgkJSVhs9ka1O6UlJQ0qGmpk5qa2mh5u91Ot27djlum7pwnc93U1FQgVPOTlpZ2UrG5XC5crvqrFnfu3LnRss2lU6dOesObSPfffPo3MJfuv/n0b9AyTlTTUyei7ulOp5OsrCxycnLqHc/JyWHkyMbXTBoxYkSD8suXL2fIkCE4HI7jlqk758lcNzMzk9TU1HplvF4vubm5x4xNREREYowRobfeestwOBzG/Pnzja1btxpTp041OnToYOzcudMwDMN44IEHjJtvvjlc/ttvvzXi4+ONadOmGVu3bjXmz59vOBwO4+233w6X+eyzzwybzWbMmjXLyM/PN2bNmmXY7XZj9erVJ31dwzCMWbNmGYmJicbixYuNzZs3GzfddJORlpZmlJeXR/oym11ZWZkBGGVlZWaHEpN0/82nfwNz6f6bT/8GbUPEiY9hGMYLL7xgZGRkGE6n0zj//PON3Nzc8GO33nqrMXr06HrlV6xYYQwePNhwOp1Gnz59jLlz5zY45z/+8Q/jjDPOMBwOh3HmmWcaixYtiui6hmEYwWDQ+MMf/mCkpqYaLpfLuOiii4zNmzc35SU2u9raWuMPf/iDUVtba3YoMUn333z6NzCX7r/59G/QNkQ8j4+IiIhIe6UpKEVERCRmKPERERGRmKHER0RERGKGEh8RERGJGUp8TsHMmTMZOnQoCQkJdO/enWuuuYavv/66XhnDMHjkkUdIT08nLi6Oiy++mC1bttQr4/F4uOeee0hKSqJDhw785Cc/Yc+ePa35Utqlk7n/kyZNCq0x871t+PDh9cro/jfd3LlzOeecc8ITso0YMaLeYsN6/7esE91/vf9b18yZM7FYLEydOjV8TJ+BtkeJzynIzc3lrrvuYvXq1eTk5OD3+8nOzqaqqipc5sknn+TZZ5/l+eefZ+3ataSmpjJ27Nh6C3ROnTqVJUuW8NZbb7Fy5UoqKyv58Y9/TCDQfAuIRqOTuf8AV1xxBUVFReFt2bJl9R7X/W+6nj17MmvWLNatW8e6desYM2YMV199dfiLXe//lnWi+w96/7eWtWvX8uKLL3LOOefUO67PQBtk6mD6KFNSUmIA4fmFgsGgkZqaasyaNStcpra21khMTDTmzZtnGIZhHDlyxHA4HMZbb70VLrN3717DarUa//rXv1r3BbRzP7z/hhGaV+rqq68+5nN0/5tfly5djJdfflnvf5PU3X/D0Pu/tVRUVBinn366kZOTY4wePdq47777DMPQ74C2SjU+zaisrAyArl27AlBQUEBxcTHZ2dnhMi6Xi9GjR7Nq1SoA1q9fj8/nq1cmPT2dQYMGhcvIyfnh/a+zYsUKunfvTv/+/Zk8eTIlJSXhx3T/m08gEOCtt96iqqqKESNG6P3fyn54/+vo/d/y7rrrLq688kouu+yyesf1GWibmrQ6uzRkGAbTp0/nwgsvZNCgQQDhRVV/uEhqSkoKu3btCpdxOp106dKlQZkfLsoqx9bY/QcYN24cP/3pT8nIyKCgoICHH36YMWPGsH79elwul+5/M9i8eTMjRoygtraWjh07smTJEgYOHBj+0tb7v2Ud6/6D3v+t4a233uLzzz9n7dq1DR7T74C2SYlPM7n77rvZtGkTK1eubPCYxWKp97NhGA2O/dDJlJGjjnX/J06cGN4fNGgQQ4YMISMjg6VLl3Ldddcd83y6/yfvjDPOYOPGjRw5coRFixZx6623kpubG35c7/+Wdaz7P3DgQL3/W9ju3bu57777WL58OW63+5jl9BloW9TU1Qzuuece3n33XT7++GN69uwZPp6amgrQIGsvKSkJ/wWQmpqK1+vl8OHDxywjx3es+9+YtLQ0MjIy2L59O6D73xycTif9+vVjyJAhzJw5k3PPPZc///nPev+3kmPd/8bo/d+81q9fT0lJCVlZWdjtdux2O7m5uTz33HPY7fbwPdRnoG1R4nMKDMPg7rvvZvHixXz00UdkZmbWezwzM5PU1FRycnLCx7xeL7m5uYwcORKArKwsHA5HvTJFRUV8+eWX4TLSuBPd/8YcPHiQ3bt3k5aWBuj+twTDMPB4PHr/m6Tu/jdG7//mdemll7J582Y2btwY3oYMGcLPf/5zNm7cSN++ffUZaIvM6FEdLf77v//bSExMNFasWGEUFRWFt+rq6nCZWbNmGYmJicbixYuNzZs3GzfddJORlpZmlJeXh8tMmTLF6Nmzp/Hhhx8an3/+uTFmzBjj3HPPNfx+vxkvq9040f2vqKgwfv3rXxurVq0yCgoKjI8//tgYMWKE0aNHD93/ZjJjxgzjk08+MQoKCoxNmzYZDz74oGG1Wo3ly5cbhqH3f0s73v3X+98c3x/VZRj6DLRFSnxOAdDo9sorr4TLBINB4w9/+IORmppquFwu46KLLjI2b95c7zw1NTXG3XffbXTt2tWIi4szfvzjHxuFhYWt/GranxPd/+rqaiM7O9tITk42HA6H0bt3b+PWW29tcG91/5vutttuMzIyMgyn02kkJycbl156aTjpMQy9/1va8e6/3v/m+GHio89A22MxDMMwp65JREREpHWpj4+IiIjEDCU+IiIiEjOU+IiIiEjMUOIjIiIiMUOJj4iIiMQMJT4iIiISM5T4iIiISMxQ4iMiIiIxQ4mPiIiIxAwlPiIiIhIzlPiIiIhIzFDiIyIiIjHj/weWdNur6wBwDwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "plt.hist(2*d, bins=50, density=True)\n",
+    "plt.plot([2*dobs, 2*dobs], [0, 0.02], linestyle = 'dotted')\n",
+    "ds = np.linspace(200, 425, 100)\n",
+    "plt.plot(ds,scipy.stats.chi2.pdf(ds, 305))\n",
+    "\n",
+    "\n",
+    "\n",
+    "print(\"p-Wert via Chi2:\", scipy.stats.chi2.sf(2*dobs, 305))#\n",
+    "\n",
+    "#plt.grid()\n",
+    "#plt.xlabel(\"$-2\\ln(P(H)/P(A))$\")\n",
+    "#plt.show()"
+   ]
   },
   {
    "cell_type": "markdown",
-   "id": "c1fad78e",
+   "id": "0b5c869e",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1967,24 +2030,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 69,
    "id": "cda0957a-414f-4bea-ab33-e61218a4a4f5",
    "metadata": {
     "cell_style": "center"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.hist( scipy.stats.chi2.sf(2*d, 305), bins=50, density=True)\n",
     "plt.plot([ scipy.stats.chi2.sf(2*dobs, 305),  scipy.stats.chi2.sf(2*dobs, 305)], [0, 12], linestyle = 'dotted')\n",
     "\n",
-    "plt.grid()\n",
+    "plt.grid()  \n",
     "plt.xlabel(\"p\")\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c6e21298",
+   "id": "c7925145",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1996,7 +2070,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7025eed5",
+   "id": "348a367f",
    "metadata": {
     "cell_style": "split"
    },
@@ -2006,7 +2080,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f57f892c",
+   "id": "285ba26a",
    "metadata": {
     "cell_style": "split"
    },
@@ -2016,12 +2090,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "615690a9",
+   "execution_count": 70,
+   "id": "6bb5f284",
    "metadata": {
     "cell_style": "split"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "pvalues = [np.sum(d > dsim)/len(d) for dsim in d]\n",
     "\n",
@@ -2032,12 +2117,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "11c062dc",
+   "execution_count": 71,
+   "id": "e97d9e5f",
    "metadata": {
     "cell_style": "split"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "d2 = scipy.stats.chi2.rvs(305, size=10000)\n",
     "pvalues = [np.sum(d2 > d2sim)/len(d2) for d2sim in d2]\n",
@@ -2064,11 +2160,70 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "13066eac",
+   "execution_count": 72,
+   "id": "5e36af5e",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#simuliere 5000 Spielzeiten\n",
+    "muobs = 35\n",
+    "\n",
+    "tore = scipy.stats.poisson.rvs(muobs,size=(5000,306))\n",
+    "\n",
+    "d = np.zeros(len(tore))\n",
+    "mu = np.zeros(len(tore))\n",
+    "for i in range(len(tore)):\n",
+    "    mu[i] = np.mean(tore[i,:])\n",
+    "    d[i] = np.sum(-scipy.stats.poisson.logpmf(tore[i],mu[i]) + scipy.stats.poisson.logpmf(tore[i],tore[i]))\n",
+    "    \n",
+    "plt.hist(mu, bins=50)\n",
+    "plt.grid()\n",
+    "plt.xlabel(\"$\\hat \\mu$\")\n",
+    "plt.show()\n",
+    "\n",
+    "plt.hist(2 * d, bins=50, density=True)\n",
+    "ds = np.linspace(200, 425, 100)\n",
+    "plt.plot(ds,scipy.stats.chi2.pdf(ds, 305))\n",
+    "plt.grid()\n",
+    "plt.xlabel(\"$-2\\ln(P(H)/P(A))$\")\n",
+    "plt.show()\n",
+    "\n",
+    "plt.hist( scipy.stats.chi2.sf(2*d, 305), bins=50)\n",
+    "plt.grid()\n",
+    "plt.xlabel(\"p\")\n",
+    "plt.show()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -2093,15 +2248,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "be5a52e7",
+   "execution_count": 73,
+   "id": "5a48462b",
    "metadata": {
     "slideshow": {
      "slide_type": ""
     },
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 0.6826894921370859 0.3934693402873665 0.19874804309879915\n",
+      "2 0.8427007929497151 0.6321205588285577 0.42759329552912023\n",
+      "3 0.9167354833364495 0.7768698398515702 0.6083748237289109\n",
+      "4 0.9544997361036416 0.8646647167633873 0.7385358700508897\n",
+      "5 0.9746526813225318 0.9179150013761012 0.8282028557032668\n"
+     ]
+    }
+   ],
    "source": [
     "def conv_chi2_nd(z, n):\n",
     "    return scipy.stats.chi2.cdf(z,n)\n",
@@ -2113,7 +2280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 74,
    "id": "2fd5c302-732b-4c6b-b224-5b9f79df57cc",
    "metadata": {
     "slideshow": {
@@ -2121,7 +2288,18 @@
     },
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "critical chi2 values\n",
+      "0.6826894921370859 0.9999999999999993 2.295748928898636 3.526740380261716\n",
+      "0.9544997361036416 3.9999999999999982 6.180074306244174 8.02488176026625\n",
+      "0.9973002039367398 9.00000000000005 11.82915808190077 14.156413609126696\n"
+     ]
+    }
+   ],
    "source": [
     "print(\"critical chi2 values\")\n",
     "for k in [1, 2, 3]:\n",
@@ -2131,10 +2309,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 75,
    "id": "23626cec-4198-44f4-a1c8-81e36992268f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.68 0.9889464814780241 2.27886856637673 3.505882355768182\n",
+      "0.9 2.705543454095412 4.605170185988093 6.2513886311703235\n",
+      "0.95 3.8414588206941267 5.99146454710798 7.81472790325118\n",
+      "0.99 6.63489660102121 9.210340371976173 11.344866730144364\n"
+     ]
+    }
+   ],
    "source": [
     "for c in [0.68, 0.90, 0.95, 0.99]:\n",
     "    print(c, scipy.optimize.brentq(lambda z: conv_chi2_nd(z, 1)-c,0, 30), scipy.optimize.brentq(lambda z: conv_chi2_nd(z, 2)-c,0, 30),scipy.optimize.brentq(lambda z: conv_chi2_nd(z,3)-c,0, 30))"
diff --git a/lecture_3.ipynb b/lecture_3.ipynb
index 18c09eca2fdb2b96aab90fd10ff4c6c66395f3db..514c4351144f4ff5d4c656a46477c67eec57d8d7 100644
--- a/lecture_3.ipynb
+++ b/lecture_3.ipynb
@@ -117,19 +117,73 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 235,
    "id": "79dce36a",
    "metadata": {
     "cell_style": "center"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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IIwDcqqhr1vRVO9Xc1u6yzmoJV1x0hAlVAQhFhBEAbtU2tqq5rV2FOeOVnhjjtC4uOkKpdqtJlQEINYQRAN1KT4xRZmqs2WUACGE8wAoAAExFGAEAAKYijAAAAFMRRgAAgKl4gBVAyHI3TorE20BAoCGMAAhJPY2Tsn3pVAIJECAIIwBCwndHhj1c1eB2nJTDVQ3KKypVbWMrYQQIEIQRAEEtLjpCVku48opKXdZZLeH63qghhA4gwBFGAAS1VLtV25dO5dkQIIgRRgAEvVS7ldABBDFe7QUAAKYijAAAAFMRRgAAgKk8DiO7du3SrFmzlJKSorCwMP31r3/tcZudO3cqKytLUVFROvPMM7Vu3TpvagUAACHI4zDS2NiocePG6fHHH+9V/yNHjujKK6/UlClTVFJSop/97GdasmSJXnjhBY+LBQAAocfjt2lmzpypmTNn9rr/unXrNHz4cBUWFkqSxowZo/379+vRRx/Vdddd5+nuAQBAiPH5MyN79+5Vdna2U9uMGTO0f/9+tbW1ud2mpaVF9fX1TgsAAAhNPg8jx44dU1JSklNbUlKSTp48qerqarfbrFy5UrGxsY4lLS3N12UCAACT+OVtmrCwMKfPhmG4bT+loKBAx48fdyzl5eU+rxEAAJjD5yOwJicn69ixY05tVVVVGjhwoOLj491uExkZqcjISF+XBgAAAoDPr4xMmjRJxcXFTm2vv/66JkyYIIvF4uvdAwCAAOfxlZGGhgYdPnzY8fnIkSMqLS3VkCFDNHz4cBUUFKiiokJPP/20JGnBggV6/PHHlZ+fr/nz52vv3r3auHGjtmzZ0ndHASC41ZVLTTXd97HFS3aeHwNCkcdhZP/+/brkkkscn/Pz8yVJc+fO1ebNm1VZWamysjLH+lGjRmnbtm2655579MQTTyglJUWPPfYYr/UCAaSirtll1tvDVQ3+2XldufTERKmtqft+Fpu0aB+BBAhBHoeRadOmOR5AdWfz5s0ubVOnTtV7773n6a4A+EFFXbOmr9qp5rZ2l3VWS7jioiN8W0BTTWcQuXaDlJDhvk/1IWnr/M6+hBEg5Pj8AVYAga22sVXNbe0qzBmv9MQYp3Vx0RFKtVv9U0hChpQy3j/7AhBQCCMAJEnpiTHKTI01uwwA/RCz9gIAAFMRRgAAgKkIIwAAwFSEEQAAYCrCCAAAMBVhBAAAmIpXewH4Vk9DvVcf8l8tAAISYQSA73gy1LvN/SzeAEIfYQSA7/RmqHeJSfCAfo4wAsC93syk25NTt2AY6h1ANwgjAFz19vZKb3ALBkAPCCMAXPX29kpvcAsGQA8IIwC6Fmi3V3p684bgAwQlwgiAwGeL77zds3V+9/0sNmnRPgIJEGQIIwACnz2tM2T0NF7J1vmdfQgjQFAhjAAIDvY0QgYQoggjAPqlw1UNLm1x0RFKtVtNqAbo3wgjAPqVuOgIWS3hyisqdVlntYRr+9KpBBLAzwgjAPqVVLtV25dOVW1jq1P74aoG5RWVqraxlTAC+BlhBEC/k2q3EjiAAEIYAfqRirpmt1cEAMBMhBGgn6ioa9b0VTvV3Nbuss5qCVdcdIQJVQEAYQToN2obW9Xc1q7CnPFKT4xxWsdbJADMRBgB+pn0xBhlpsaaXQYAOAwwuwAAANC/EUYAAICpCCMAAMBUhBEAAGAqHmAFgG9hzhrA/wgjACDmrAHMRBgBEFqqD3W/3hYv2dNcmpmzBjAPYQRAaLDFSxabtHV+9/0sNmnRvi4DCYED8D/CCIDQYE/rDBlNNV33qT7UGVaaatyGEQDmIIwACB32NEIGEIR4tRcAAJiKMAIAAExFGAEAAKYijAAAAFMRRgAAgKkIIwAAwFSEEQAAYCqvwsiaNWs0atQoRUVFKSsrS7t37+62/zPPPKNx48bJZrNp2LBhuu2221RT083ARAAAoN/wOIwUFRUpLy9Py5YtU0lJiaZMmaKZM2eqrKzMbf8333xTc+bM0bx58/Thhx/queee07vvvqs77rjjtIsHAADBz+Mwsnr1as2bN0933HGHxowZo8LCQqWlpWnt2rVu+7/99tsaOXKklixZolGjRuniiy/WnXfeqf3795928QAAIPh5FEZaW1t14MABZWdnO7VnZ2drz549breZPHmyvvjiC23btk2GYejLL7/U888/r6uuusr7qgEAQMjwKIxUV1ervb1dSUlJTu1JSUk6duyY220mT56sZ555Rjk5OYqIiFBycrLsdrv+8Ic/dLmflpYW1dfXOy0AACA0efUAa1hYmNNnwzBc2k45ePCglixZol/+8pc6cOCAXn31VR05ckQLFizo8vtXrlyp2NhYx5KWxsRXAACEKo/CSEJCgsLDw12uglRVVblcLTll5cqVuuiii/STn/xE5513nmbMmKE1a9Zo06ZNqqysdLtNQUGBjh8/7ljKy8s9KRMAAAQRj8JIRESEsrKyVFxc7NReXFysyZMnu92mqalJAwY47yY8PFxS5xUVdyIjIzV48GCnBQAAhKaBnm6Qn5+v3NxcTZgwQZMmTdL69etVVlbmuO1SUFCgiooKPf3005KkWbNmaf78+Vq7dq1mzJihyspK5eXlaeLEiUpJSenbowGA3qg+1P16W7xk5/Yw4C8eh5GcnBzV1NTowQcfVGVlpTIzM7Vt2zaNGDFCklRZWek05sitt96qEydO6PHHH9fSpUtlt9t16aWX6pFHHum7owCA3rDFSxabtHV+9/0sNmnRPgIJ4CcehxFJWrhwoRYuXOh23ebNm13aFi9erMWLF3uzKwDoO/a0zpDR1M0I0NWHOsNKUw1hBPATr8IIAAQtexohAwgwTJQHAABMRRgBAACmIowAAABTEUYAAICpCCMAAMBUhBEAAGAqXu0FgF44XNXg9DkuOkKpdqtJ1QChhTACAN2Ii46Q1RKuvKJSp3arJVzbl04lkAB9gDACAN1ItVu1felU1Ta2OtoOVzUor6hUtY2thBGgDxBGAKAHqXYroQPwIcII0B/Vlfc8PwsA+AlhBOhnLA0V0ubLpLamHjraOme5BQAfI4wA/Uz4N193BpFrN0gJGV13tMUzoRwAvyCMACGooq7Z6YFLyfXVVCVkSCnj/VcUAHSBMAKEmIq6Zk1ftVPNbe0u66yWcA22WkyoCgC6RhgBQkxtY6ua29pVmDNe6YkxTuvioiOU2PS/JlUGAO4RRoAQlZ4Yo8zUWNcVPTy3CgD+xtw0AADAVIQRAABgKsIIAAAwFWEEAACYijACAABMRRgBAACmIowAAABTEUYAAICpCCMAAMBUjMAKhBhLQ4XODTuiqOpYKSzGtUP1If8XBQDdIIwAoaSuXKOfu1QvRzZLL3bTz2KTbPF+KytUucyErM75f1LtVhOqAYIXYQQIJU01GnCyWf/dulCLc65S+lA3V0akziBiT/NvbSEkLjpCVku48opKXdZZLeHavnQqgQTwAGEECEGHjVR9kzBWSnEzUR5OW6rdqu1Lp6q2sdWp/XBVg/KKSlXb2EoYATxAGAEAL6TarQQOoI/wNg0AADAVYQQAAJiK2zQA4I4Xr0BHVTc4v1bNg8JArxBGAODbbPGdrz5vne/xpumSXo7Uf16rttikRfsIJEAPCCMA8G32tM4A0VTj8aaHv2rQfz9bqt//aLzSw452BpqmGsII0APCCAB8lz3NqwDxjXFcHxrHO1+rdjf6LQC3eIAVAACYijACAABMRRgBAACmIowAAABTEUYAAICpeJsGCGIVdc1Ok7VFVTco3cR6AMAbXl0ZWbNmjUaNGqWoqChlZWVp9+7d3fZvaWnRsmXLNGLECEVGRuqss87Spk2bvCoYQKeKumZNX7VTP/zDm47lv58tlSRFDRyguOgIcwsEgF7y+MpIUVGR8vLytGbNGl100UV68sknNXPmTB08eFDDhw93u80NN9ygL7/8Uhs3blR6erqqqqp08uTJ0y4e6M9qG1vV3NauwpzxSk/sHNMiqjpWelFam5ulRGaUBRAkPA4jq1ev1rx583THHXdIkgoLC/Xaa69p7dq1WrlypUv/V199VTt37tSnn36qIUOGSJJGjhx5elUDcEhPjFFmamznh/8baCsxJtLEigDAMx7dpmltbdWBAweUnZ3t1J6dna09e/a43eZ//ud/NGHCBP3mN79RamqqMjIydO+996q5ubnL/bS0tKi+vt5pAQAAocmjKyPV1dVqb29XUlKSU3tSUpKOHTvmdptPP/1Ub775pqKiovTiiy+qurpaCxcu1Ndff93lcyMrV67UihUrPCkNAAAEKa8eYA0LC3P6bBiGS9spHR0dCgsL0zPPPKOJEyfqyiuv1OrVq7V58+Yur44UFBTo+PHjjqW8vNybMgEAQBDw6MpIQkKCwsPDXa6CVFVVuVwtOWXYsGFKTU1VbGyso23MmDEyDENffPGFRo8e7bJNZGSkIiO55w0AQH/g0ZWRiIgIZWVlqbi42Km9uLhYkydPdrvNRRddpKNHj6qhocHRdujQIQ0YMEBnnHGGFyUDAIBQ4vFtmvz8fP3xj3/Upk2b9NFHH+mee+5RWVmZFixYIKnzFsucOXMc/W+66SbFx8frtttu08GDB7Vr1y795Cc/0e233y6rlVcPAY/UlUtHS6WjpYqq/kDnhh1RVPUHjjZVHzK5QADwnMev9ubk5KimpkYPPvigKisrlZmZqW3btmnEiBGSpMrKSpWVlTn6x8TEqLi4WIsXL9aECRMUHx+vG264Qb/61a/67iiA/qCuXHpiotTWJElKl/RypKQXv9PPYpNs8f6uDgC85tVw8AsXLtTChQvdrtu8ebNL2znnnONyaweAh5pqOoPItRukhAwd/qpB//1sqX7/o/FKHxrzn362eMmeZl6dAOAh5qYBgk1ChpQyXt8Yx/WhcVzfJIyVUmJ73g4AAhSz9gIAAFMRRgAAgKkIIwAAwFSEEQAAYCrCCAAAMBVhBAAAmIowAgAATEUYAQAApiKMAAAAUxFGAACAqQgjAADAVIQRAABgKsIIAAAwFWEEAACYaqDZBQBASKs+1P16W7xkT/NPLUCAIowAgC/Y4iWLTdo6v/t+Fpu0aB+BBP0aYQQAfMGe1hkymmq67lN9qDOsNNUQRtCvEUYAwFfsaYQMoBcIIwDQxw5XNbi0xUVHKNVuNaEaIPARRgCgj8RFR8hqCVdeUanLOqslXNuXTiWQAG4QRgCgj6Tardq+dKpqG1ud2g9XNSivqFS1ja2EEcANwggQKOrKe37YEQEv1W4lcAAeIowAgaCuXHpiotTW1H0/i63zlVEACCGEESAQNNV0BpFrN0gJGV33Y4AsACGIMAIEkoQMKWW82VUAgF8xNw0AADAVYQQAAJiKMAIAAExFGAEAAKYijAAAAFMRRgAAgKl4tRcIAhV1zW6HGAeAUEAYAQJcRV2zpq/aqea2dpd1Vku44qIjTKgKAPoOYQQIcLWNrWpua1dhznilJ8Y4rWNaegChgDACBIn0xBhlpsaaXQZ8oadJEJkGACGOMAIAZrHFd05+uHV+9/0sNmnRPgIJQhZhBADMYk/rDBlNNV33qT7UGVaaaggjCFmEEQAwkz2NkIF+j3FGAACAqQgjAADAVIQRAABgKsIIAAAwlVdhZM2aNRo1apSioqKUlZWl3bt392q7t956SwMHDtT48eO92S0AAAhBHoeRoqIi5eXladmyZSopKdGUKVM0c+ZMlZWVdbvd8ePHNWfOHF122WVeFwsAAEKPx2Fk9erVmjdvnu644w6NGTNGhYWFSktL09q1a7vd7s4779RNN92kSZMmeV0sAAAIPR6FkdbWVh04cEDZ2dlO7dnZ2dqzZ0+X2z311FP65JNP9MADD/RqPy0tLaqvr3daACDYHa5q0L8qjjstFXXNZpcFmM6jQc+qq6vV3t6upKQkp/akpCQdO3bM7TYff/yx7r//fu3evVsDB/ZudytXrtSKFSs8KQ0IbHXlPY+yiZAVFx0hqyVceUWlLuuslnBtXzqVCQ/Rr3k1AmtYWJjTZ8MwXNokqb29XTfddJNWrFihjIyMXn9/QUGB8vPzHZ/r6+uVlsYIhQhSdeXSExOltqbu+1lsnXOVIOSk2q3avnSqahtbndoPVzUor6hUtY2thBH0ax6FkYSEBIWHh7tcBamqqnK5WiJJJ06c0P79+1VSUqK7775bktTR0SHDMDRw4EC9/vrruvTSS122i4yMVGRkpCelAYGrqaYziFy7QUroJpQzM2tIS7VbCRxAFzwKIxEREcrKylJxcbGuueYaR3txcbFmz57t0n/w4MH64IMPnNrWrFmjf/7zn3r++ec1atQoL8sGglBChpQy3uwqACDgeHybJj8/X7m5uZowYYImTZqk9evXq6ysTAsWLJDUeYuloqJCTz/9tAYMGKDMzEyn7RMTExUVFeXSDgAA+iePw0hOTo5qamr04IMPqrKyUpmZmdq2bZtGjBghSaqsrOxxzBEAAIBTvHqAdeHChVq4cKHbdZs3b+522+XLl2v58uXe7BYAAIQg5qYBAACmIowAAABTEUYAAICpCCMAAMBUhBEAAGAqwggAADCVV6/2AvCdirpmpzlMDlc1mFgNAPgeYQQIIBV1zZq+aqea29qd2q2WcMVFR5hUFQD4FmEECCC1ja1qbmtXYc54pSfGONrjoiOYZK2/qz7U/XomWkQQI4wAASg9MUaZqbFml4FAYIuXLDZp6/zu+1ls0qJ9BBIEJcIIAAQye1pnyGiq6bpP9aHOsNJUQxhBUCKMAECgs6cRMhDSCCMAYDJ3b0zxnBD6E8IIAJgkLjpCVku48opKXdZZLeHavnQqgQT9AmEEAEySardq+9KpTuPKSJ1XSvKKSlXb2EoYQb9AGAEAE6XarQQO9HsMBw8AAExFGAEAAKbiNg1gku/OQSMxDw2A/okwApigqzloJOahAdD/EEYAE3Q1B43E+BIA+h/CCGAi5qABAMIIAAQsj0dmZWZfBCnCCHC66sp7nsQM8IDHI7Mysy+CHGEEOB115dITE6W2pu77WWydvzCAXvB4ZFZm9kWQI4wAp6OppjOIXLtBSsjouh+Xx+Ehj0dmZWZfBDHCCNAXEjKklPFmVwEAQYkRWAEAgKkIIwAAwFSEEQAAYCrCCAAAMBVhBAAAmIowAgAATMWrvYCPVdQ1ux28CgDQiTAC+FBFXbOmr9qp5rZ2l3VWS7jioiNMqAoAAgthBPCh2sZWNbe1qzBnvNITY5zWdTvhGQD0I4QRwA/SE2OUmRprdhkAEJAII0B3mJEXAHyOMAJ0hRl5AcAvCCNAVzyckZe3ZgDAO4QRoCe9mJGXt2YAwHuEEaAP8NYMAHiPMAL0Id6aAQDPeTUc/Jo1azRq1ChFRUUpKytLu3fv7rLv1q1bdfnll2vo0KEaPHiwJk2apNdee83rggEAQGjxOIwUFRUpLy9Py5YtU0lJiaZMmaKZM2eqrKzMbf9du3bp8ssv17Zt23TgwAFdcsklmjVrlkpKSk67eAAAEPw8DiOrV6/WvHnzdMcdd2jMmDEqLCxUWlqa1q5d67Z/YWGhfvrTn+p73/ueRo8erYceekijR4/WSy+9dNrFAwCA4OdRGGltbdWBAweUnZ3t1J6dna09e/b06js6Ojp04sQJDRkypMs+LS0tqq+vd1oAAEBo8iiMVFdXq729XUlJSU7tSUlJOnbsWK++Y9WqVWpsbNQNN9zQZZ+VK1cqNjbWsaSlpXlSJgAACCJePcAaFhbm9NkwDJc2d7Zs2aLly5erqKhIiYmJXfYrKCjQ8ePHHUt5ebk3ZQIAgCDg0au9CQkJCg8Pd7kKUlVV5XK15LuKioo0b948Pffcc5o+fXq3fSMjIxUZGelJaQAAIEh5FEYiIiKUlZWl4uJiXXPNNY724uJizZ49u8vttmzZottvv11btmzRVVdd5X21AIDT09Pkjv83vQHgTx4Pepafn6/c3FxNmDBBkyZN0vr161VWVqYFCxZI6rzFUlFRoaefflpSZxCZM2eOfv/73+vCCy90XFWxWq2KjWVwKADwC1t856SOW+d3389ikxbtI5DArzwOIzk5OaqpqdGDDz6oyspKZWZmatu2bRoxYoQkqbKy0mnMkSeffFInT57UokWLtGjRIkf73LlztXnz5tM/AsBbdeWdk+F1pad/QQImcTcBY4/TDtjTOkNGT//Pb53f2YcwAj/yajj4hQsXauHChW7XfTdg7Nixw5tdAL5VVy49MbFzVt7uWGyd/6IEAkBcdISslnDlFZW6rLNawrV96dSeAwkhAwGIuWnQPzXVdAaRazd0zsrbFe6fI4Ck2q3avnSqahtbndoPVzUor6hUtY2tTMqIoEQYQf+WkCGljDe7CqDXUu1WAgdCjlfjjAAAAPQVwggAADAVYQQAAJiKMAIAAEzFA6wITYwhgn7Iq/FH3GGUVvgZYQShhzFE0M+c9vgjpzBKK0xCGEHoYQwR9DN9Nv4Io7TCJIQRhC7GEEE/0mfjjzBKK0zAA6wAAMBUhBEAAGAqwggAADAVz4wAHqqoa3b7oCAAwDuEEcADFXXNmr5qp5rb2l3WWS3hiouOMKEqAAhuhBHAA7WNrWpua1dhznilJ8Y4rfNqcCkAAGEE8EZ6YowyU2PNLgMwD6O0og8RRgAAvccorfABwggAoPcYpRU+QBgBusBbM0AXGKUVfYwwArjBWzMA4D+EEcAN3poBAP8hjCD41JX3fL+6j/DWDAD4HmEEwaWuXHpiotTW1H0/i63zqX8Abp914gofAglhBMGlqaYziFy7QUrI6LofYxwAiouOkNUSrryiUpd1Vku4ti+dSiBBQCCMILD09hZMQoaUMt4vJQHBKtVu1falU92+FZZXVKraxlbCCAICYQSBw6RbMLzCi1CWarcSOBDwCCMIHCbcguEVXvRnPn+WhCHj0UuEEQQeP96C4RVe9Ec+f5aEIePhIcIIIF7hRf/i82dJPBkyvmxv9/24etIvEEYAoB/y+bMkPQ0Zz9UTfAthBADgf0y4h28hjAAAzMGEe/g/hBH4jx+HcQcABA/CCPyDYdwBAF0gjKBv9OaqB8O4A0GBuWzgb4QRnD5PrnoMn+TzsOFuRFWJv0yBnjCXDcxCGMHpC6DJ63oaUXVdbpbivzWqKsO+A//BXDYwC2EEPQuiyeu6GlG1prFVC/58QHM37XPZhmHfgf/obvwRbt/AVwgj6F6QPnjqbkRVd//ik/jLFOgJt2/ga4QRdC+AbsGcLmYvBbxj+u0bJtwLeYSR/i6IbsF8l7sHVXkGBPANU8I8Q8b3G4SR/sykWzB98bZLTw+q8gwIYK4+eauNCff6Da/CyJo1a/Tb3/5WlZWVOvfcc1VYWKgpU6Z02X/nzp3Kz8/Xhx9+qJSUFP30pz/VggULvC4afcSEWzA9hQh39567ugLi7kFViWdAAH/77hXJUw+M9/atNqmbn9u+nHAv58+SLaH7fj0h1PiEx2GkqKhIeXl5WrNmjS666CI9+eSTmjlzpg4ePKjhw4e79D9y5IiuvPJKzZ8/X3/5y1/01ltvaeHChRo6dKiuu+66PjmIkNLTbROp738Y/HgLpqu3Xbq699xTePneqCEED8AkPT3Y+qfbJzqFjp7eavPqQdjeXD1pqpaKcqW/9MHvnN6EGgKLxzwOI6tXr9a8efN0xx13SJIKCwv12muvae3atVq5cqVL/3Xr1mn48OEqLCyUJI0ZM0b79+/Xo48+Shj5Lk9um/RFwu+juWC6uhzrzql/Qbl72+Xb67/9mSsgQGDq6sFWqeufz+4ehH33yNeq9ebn/FtXT9z9fRSXcLZSewosvdHbUMMzLB7zKIy0trbqwIEDuv/++53as7OztWfPHrfb7N27V9nZ2U5tM2bM0MaNG9XW1iaLxeKyTUtLi1paWhyfjx8/Lkmqr6/3pNzeOfGl1PBl33+vN2oOSw2N0qzHpPh0932aaqStP5Y2Xts3+xxolU5GSF7+tz1a16z/evxNfdPW0ettoiwDNLD9G9XXh/2njPZvFNHxjZY87fr/UZRlgM6JH6iUQWHfWdOm+vo2r+oG0DcGDZAGufxsSl39fLrrP7B9YLc//4U/Ol9DbK6/K77r66Y25T1b4vL3UZRlgP7n7ouVYj+zx+/oVsyZUu4/pOavu+5Tc1h6aYl08B9d/z0eiGKSpEFJff61p35vG4bRfUfDAxUVFYYk46233nJq//Wvf21kZGS43Wb06NHGr3/9a6e2t956y5BkHD161O02DzzwgCGJhYWFhYWFJQSW8vLybvOFVw+whoU5p1rDMFzaeurvrv2UgoIC5efnOz53dHTo66+/Vnx8fLf7CSX19fVKS0tTeXm5Bg8ebHY5fsWxc+z96dj763FLHHt/OHbDMHTixAmlpKR028+jMJKQkKDw8HAdO3bMqb2qqkpJSe4v7yQnJ7vtP3DgQMXHu39dNDIyUpGRkU5tdrvdk1JDxuDBg0P6f9TucOwce3/SX49b4thD/dhjY2N77DPAky+MiIhQVlaWiouLndqLi4s1efJkt9tMmjTJpf/rr7+uCRMmuH1eBAAA9C8ehRFJys/P1x//+Edt2rRJH330ke655x6VlZU5xg0pKCjQnDlzHP0XLFigzz//XPn5+froo4+0adMmbdy4Uffee2/fHQUAAAhaHj8zkpOTo5qaGj344IOqrKxUZmamtm3bphEjRkiSKisrVVZW5ug/atQobdu2Tffcc4+eeOIJpaSk6LHHHuO13h5ERkbqgQcecLld1R9w7Bx7f9Jfj1vi2PvrsbsTZhg9vW8DAADgOx7fpgEAAOhLhBEAAGAqwggAADAVYQQAAJiKMBJAfv3rX2vy5Mmy2Wy9HuTt1ltvVVhYmNNy4YUX+rZQH/Dm2A3D0PLly5WSkiKr1app06bpww8/9G2hfay2tla5ubmKjY1VbGyscnNzVVdX1+02wXrO16xZo1GjRikqKkpZWVnavXt3t/137typrKwsRUVF6cwzz9S6dev8VGnf8+TYd+zY4XJ+w8LC9O9//9uPFfeNXbt2adasWUpJSVFYWJj++te/9rhNKJx3T487lM65twgjAaS1tVXXX3+97rrrLo+2u+KKK1RZWelYtm3b5qMKfcebY//Nb36j1atX6/HHH9e7776r5ORkXX755Tpx4oQPK+1bN910k0pLS/Xqq6/q1VdfVWlpqXJzc3vcLtjOeVFRkfLy8rRs2TKVlJRoypQpmjlzptMwAN925MgRXXnllZoyZYpKSkr0s5/9TEuWLNELL7zg58pPn6fHfsr//u//Op3j0aNH+6nivtPY2Khx48bp8ccf71X/UDnvnh73KaFwzr3Wqxny4FdPPfWUERsb26u+c+fONWbPnu3Tevypt8fe0dFhJCcnGw8//LCj7ZtvvjFiY2ONdevW+bDCvnPw4EFDkvH222872vbu3WtIMv797393uV0wnvOJEycaCxYscGo755xzjPvvv99t/5/+9KfGOeec49R25513GhdeeKHPavQVT4/9jTfeMCQZtbW1fqjOfyQZL774Yrd9Qum8n9Kb4w7Vc+4JroyEgB07digxMVEZGRmaP3++qqqqzC7J544cOaJjx44pOzvb0RYZGampU6dqzx7XacgD0d69exUbG6vvf//7jrYLL7xQsbGxPR5DMJ3z1tZWHThwwOlcSVJ2dnaXx7l3716X/jNmzND+/fvV1uY6LX2g8ubYTzn//PM1bNgwXXbZZXrjjTd8WWbACJXz7q3+eM5PIYwEuZkzZ+qZZ57RP//5T61atUrvvvuuLr30UrW0tJhdmk+dmnzxuxM0JiUluUzMGKiOHTumxMREl/bExMRujyHYznl1dbXa29s9OlfHjh1z2//kyZOqrq72Wa19zZtjHzZsmNavX68XXnhBW7du1dlnn63LLrtMu3bt8kfJpgqV8+6p/nzOT/F4OHh4Zvny5VqxYkW3fd59911NmDDBq+/Pyclx/DkzM1MTJkzQiBEj9PLLL+vaa6/16jv7iq+PXZLCwsKcPhuG4dLmb709bsm1fqnnYwjkc94dT8+Vu/7u2oOBJ8d+9tln6+yzz3Z8njRpksrLy/Xoo4/qBz/4gU/rDAShdN57q7+fc4kw4nN33323fvSjH3XbZ+TIkX22v2HDhmnEiBH6+OOP++w7veXLY09OTpbU+S+pYcOGOdqrqqpc/mXlb7097vfff19ffvmly7qvvvrKo2MIpHPuTkJCgsLDw12uBHR3rpKTk932HzhwoOLj431Wa1/z5tjdufDCC/WXv/ylr8sLOKFy3vtCfznnpxBGfCwhIUEJCQl+219NTY3Ky8udfkGbxZfHPmrUKCUnJ6u4uFjnn3++pM778zt37tQjjzzik332Vm+Pe9KkSTp+/Lj27duniRMnSpLeeecdHT9+XJMnT+71/gLpnLsTERGhrKwsFRcX65prrnG0FxcXa/bs2W63mTRpkl566SWnttdff10TJkyQxWLxab19yZtjd6ekpCRgz29fCpXz3hf6yzl3MPPpWTj7/PPPjZKSEmPFihVGTEyMUVJSYpSUlBgnTpxw9Dn77LONrVu3GoZhGCdOnDCWLl1q7Nmzxzhy5IjxxhtvGJMmTTJSU1ON+vp6sw7DK54eu2EYxsMPP2zExsYaW7duNT744APjxhtvNIYNGxZUx37FFVcY5513nrF3715j7969xtixY40f/vCHTn1C4Zw/++yzhsViMTZu3GgcPHjQyMvLM6Kjo43PPvvMMAzDuP/++43c3FxH/08//dSw2WzGPffcYxw8eNDYuHGjYbFYjOeff96sQ/Cap8f+u9/9znjxxReNQ4cOGf/617+M+++/35BkvPDCC2YdgtdOnDjh+FmWZKxevdooKSkxPv/8c8MwQve8e3rcoXTOvUUYCSBz5841JLksb7zxhqOPJOOpp54yDMMwmpqajOzsbGPo0KGGxWIxhg8fbsydO9coKysz5wBOg6fHbhidr/c+8MADRnJyshEZGWn84Ac/MD744AP/F38aampqjJtvvtkYNGiQMWjQIOPmm292eb0vVM75E088YYwYMcKIiIgwLrjgAmPnzp2OdXPnzjWmTp3q1H/Hjh3G+eefb0RERBgjR4401q5d6+eK+44nx/7II48YZ511lhEVFWXExcUZF198sfHyyy+bUPXpO/XK6neXuXPnGoYRuufd0+MOpXPurTDD+L+ngwAAAEzAq70AAMBUhBEAAGAqwggAADAVYQQAAJiKMAIAAExFGAEAAKYijAAAAFMRRgAAgKkIIwAAwFSEEQAAYCrCCAAAMBVhBAAAmOr/Axv9d1dW6hmLAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.99511636269197 0.99546465884015 0.31326753831096515 0.42925857926355127\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9991353105600731 0.9985336770389502 0.18102220486999085 0.12274409194104928\n"
+     ]
+    }
+   ],
    "source": [
     "import scipy\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "xs = scipy.stats.norm.rvs(1,1,size=(1000,10))\n",
-    "ys = scipy.stats.uniform.rvs(0,2,size=(1000,10))"
+    "xs = scipy.stats.norm.rvs(1,1,size=(10000,10))\n",
+    "ys = scipy.stats.uniform.rvs(0,2,size=(10000,10))\n",
+    "\n",
+    "mean_1 = np.mean(xs, axis=1)\n",
+    "mean_2 = 0.5*(np.max(xs, axis=1) + np.min(xs, axis=1))\n",
+    "\n",
+    "plt.hist(mean_1 - 1, bins=50, density=True, histtype=\"step\", label=\"mean\")\n",
+    "plt.hist(mean_2 - 1, bins=50, density=True, histtype=\"step\", label=\"min+max\")\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "print(np.mean(mean_1), np.mean(mean_2), np.std(mean_1), np.std(mean_2))\n",
+    "\n",
+    "\n",
+    "mean_1 = np.mean(ys, axis=1) \n",
+    "mean_2 = 0.5*(np.max(ys, axis=1) + np.min(ys, axis=1))\n",
+    "\n",
+    "plt.hist(mean_1 - 1, bins=50, density=True, histtype=\"step\", label=\"mean\")\n",
+    "plt.hist(mean_2 - 1, bins=50, density=True, histtype=\"step\", label=\"min+max\")\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "print(np.mean(mean_1), np.mean(mean_2), np.std(mean_1), np.std(mean_2))\n"
    ]
   },
   {
@@ -153,12 +207,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 242,
    "id": "4489a6f3",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.09173979084057217 -0.03219645250800269 0.43094513719995864 0.09625986700443462\n"
+     ]
+    }
+   ],
    "source": [
-    "#try again with `np.var`"
+    "#try again with `np.var` \n",
+    "n_sample = 10\n",
+    "xs = scipy.stats.norm.rvs(1,1,size=(10000,n_sample))\n",
+    "ys = scipy.stats.uniform.rvs(0,2,size=(10000,n_sample))\n",
+    "\n",
+    "var_1 = np.var(xs, axis=1)\n",
+    "var_2 = np.var(ys, axis=1)\n",
+    "\n",
+    "plt.hist(var_1 - 1, bins=50, density=True, histtype=\"step\",  label=\"Gaussian\")\n",
+    "plt.hist(var_2 - 4/12, bins=50, density=True, histtype=\"step\", label=\"uniform\")\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "print(np.mean(var_1-1), np.mean(var_2-4/12), np.std(var_1), np.std(var_2))\n",
+    "\n"
    ]
   },
   {
@@ -205,11 +290,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "bdca5aab",
+   "execution_count": 243,
+   "id": "a5693ddf",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.008192951318145484 -0.0020165491132805398 0.46306611838448253 0.10639942106612825\n"
+     ]
+    }
+   ],
+   "source": [
+    "#try again with `np.var` \n",
+    "n_sample = 10\n",
+    "xs = scipy.stats.norm.rvs(1,1,size=(10000,n_sample))\n",
+    "ys = scipy.stats.uniform.rvs(0,2,size=(10000,n_sample))\n",
+    "\n",
+    "var_1 = np.var(xs, axis=1, ddof=1)\n",
+    "var_2 = np.var(ys, axis=1, ddof=1)\n",
+    "\n",
+    "plt.hist(var_1 - 1, bins=50, density=True, histtype=\"step\", label=\"Gaussian\")\n",
+    "plt.hist(var_2 - 4/12, bins=50, density=True, histtype=\"step\", label=\"uniform\")\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "print(np.mean(var_1-1), np.mean(var_2-4/12), np.std(var_1), np.std(var_2))\n"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -246,9 +363,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 244,
    "id": "55bf0cad-8a50-4831-8a37-daf9bcb39b71",
    "metadata": {
+    "scrolled": true,
     "slideshow": {
      "slide_type": ""
     },
@@ -285,13 +403,12 @@
     "plt.plot(x_axis, f(x_axis),'--')\n",
     "plt.xlabel(\"x\")\n",
     "plt.ylabel(\"y\")\n",
-    "plt.savefig(\"line.png\")\n",
-    "plt.show()"
+    "plt.show() "
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7bcf9382",
+   "id": "b443d6f9",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -324,7 +441,7 @@
   {
    "cell_type": "code",
    "execution_count": 92,
-   "id": "a5bf2c61",
+   "id": "534de9a6",
    "metadata": {
     "cell_style": "split"
    },
@@ -351,7 +468,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "719d7eb2",
+   "id": "ae253750",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -363,8 +480,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
-   "id": "24f5aa80",
+   "execution_count": 247,
+   "id": "a5d44fca",
    "metadata": {},
    "outputs": [
     {
@@ -393,7 +510,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c60129df",
+   "id": "3a3b54ea",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -405,8 +522,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
-   "id": "c99a5bfe",
+   "execution_count": 248,
+   "id": "db7c5046",
    "metadata": {
     "cell_style": "split"
    },
@@ -430,8 +547,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
-   "id": "3ac8c350",
+   "execution_count": 249,
+   "id": "bb95cb56",
    "metadata": {
     "cell_style": "split"
    },
@@ -524,7 +641,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4a662c3a",
+   "id": "9ca39402",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -536,8 +653,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
-   "id": "b261a378",
+   "execution_count": 262,
+   "id": "03e795a4",
    "metadata": {},
    "outputs": [
     {
@@ -553,10 +670,13 @@
     "C = np.eye(n)*sigma_y*sigma_y\n",
     "W = np.linalg.inv(C)\n",
     "J = np.ones((len(ys),1))\n",
+    "#print(C)\n",
+    "#print(W)\n",
+    "#print(J.T@W@J)\n",
     "sum_xy = (np.linalg.inv(J.T@W@J)@(J.T@W@(xs*ys)))[0]\n",
     "sum_y = (np.linalg.inv(J.T@W@J)@(J.T@W@(ys)))[0]\n",
     "sum_x = (np.linalg.inv(J.T@W@J)@(J.T@W@(xs)))[0]\n",
-    "sum_x2 = (np.linalg.inv(J.T@W@J)@(J.T@W@(xs*xs)))[0]\n",
+    "sum_x2 = (np.linalg.inv(J.T@W@J)@(J.T@W@(xs*xs)))[0 ]\n",
     "print(sum_xy, sum_y, sum_x, sum_x2)\n",
     "mhat = (sum_xy - sum_x*sum_y)/(sum_x2-sum_x*sum_x)\n",
     "ahat = (sum_y*sum_x2 - sum_x*sum_xy)/(sum_x2-sum_x*sum_x)\n",
@@ -565,7 +685,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8326e6aa",
+   "id": "cf8b9180",
    "metadata": {},
    "source": [
     "### Result plot"
@@ -574,7 +694,7 @@
   {
    "cell_type": "code",
    "execution_count": 82,
-   "id": "69f7f23f",
+   "id": "b25411d4",
    "metadata": {},
    "outputs": [
     {
@@ -601,7 +721,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "060476bf",
+   "id": "d75224bd",
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -616,7 +736,7 @@
    "id": "b08ade05-e5eb-4a7e-9315-31a45c51aadb",
    "metadata": {
     "slideshow": {
-     "slide_type": ""
+     "slide_type": "slide"
     },
     "tags": []
    },
@@ -625,8 +745,8 @@
     " \n",
     "\n",
     "$$\\begin{aligned}\n",
-    "V(\\hat m) = \\sum_i \\left(\\frac{d\\hat m}{y_i}\\sigma_i\\right)^2\\text{; }\\frac{d\\hat m}{y_i} & = & \\frac{1}{\\sum_i 1/\\sigma_i^2} \\frac{x_i - \\langle x \\rangle}{\\sigma_i^2(\\langle x^2  \\rangle - \\langle x  \\rangle^2)} \\\\\n",
-    "V(\\hat a) = \\sum_i \\left(\\frac{d\\hat a}{y_i}\\sigma_i\\right)^2\\text{; }\\frac{d\\hat a}{y_i} & = &  \\frac{1}{\\sum_i 1/\\sigma_i^2} \\frac{\\langle x^2 \\rangle - \\langle x \\rangle x_i}{\\sigma_i^2(\\langle x^2  \\rangle - \\langle x  \\rangle^2)}\n",
+    "V(\\hat m) = \\sum_i \\left(\\frac{d\\hat m}{dy_i}\\sigma_i\\right)^2\\text{; }\\frac{d\\hat m}{y_i} & = & \\frac{1}{\\sum_i 1/\\sigma_i^2} \\frac{x_i - \\langle x \\rangle}{\\sigma_i^2(\\langle x^2  \\rangle - \\langle x  \\rangle^2)} \\\\\n",
+    "V(\\hat a) = \\sum_i \\left(\\frac{d\\hat a}{dy_i}\\sigma_i\\right)^2\\text{; }\\frac{d\\hat a}{y_i} & = &  \\frac{1}{\\sum_i 1/\\sigma_i^2} \\frac{\\langle x^2 \\rangle - \\langle x \\rangle x_i}{\\sigma_i^2(\\langle x^2  \\rangle - \\langle x  \\rangle^2)}\n",
     "\\end{aligned}$$ $$\\begin{aligned}\n",
     "V(\\hat m) &=&  \\left(\\frac{1}{\\sum_i 1/\\sigma_i^2}\\right)^2 \\sum_i \\left(\\frac{x_i - \\langle x \\rangle}{\\sigma_i^2(\\langle x^2  \\rangle - \\langle x  \\rangle^2)}\\right)^2 \\sigma_i^2 \\\\\n",
     "&=& \\frac{1}{\\sum_i 1/\\sigma_i^2} \\frac{\\langle x^2 \\rangle - 2\\langle x \\rangle \\langle x \\rangle + \\langle x \\rangle^2}{(\\langle x^2  \\rangle - \\langle x  \\rangle^2)^2} \n",
@@ -638,10 +758,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "27fb63ff",
+   "id": "7f22c648",
    "metadata": {
     "slideshow": {
-     "slide_type": ""
+     "slide_type": "slide"
     },
     "tags": []
    },
@@ -672,8 +792,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
-   "id": "3fb345e7",
+   "execution_count": 250,
+   "id": "d90bfbf6",
    "metadata": {},
    "outputs": [
     {
@@ -688,15 +808,15 @@
    ],
    "source": [
     "sum_siginv = sigma_y*sigma_y/len(ys)\n",
-    "V_am = np.array([[sum_siginv/(sum_x2-sum_x*sum_x), -sum_siginv*sum_x/(sum_x2-sum_x*sum_x)],\n",
+    "V_ma = np.array([[sum_siginv/(sum_x2-sum_x*sum_x), -sum_siginv*sum_x/(sum_x2-sum_x*sum_x)],\n",
     "                 [-sum_siginv*sum_x/(sum_x2-sum_x*sum_x), sum_siginv*sum_x2/(sum_x2-sum_x*sum_x)]])\n",
-    "print(V_am)\n",
-    "print(np.sqrt(V_am[0,0]), np.sqrt(V_am[1,1]), V_am[1,0]/(np.sqrt(V_am[0,0])*np.sqrt(V_am[1,1])))"
+    "print(V_ma)\n",
+    "print(np.sqrt(V_ma[0,0]), np.sqrt(V_ma[1,1]), V_ma[1,0]/(np.sqrt(V_ma[0,0])*np.sqrt(V_ma[1,1])))"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "1f411636",
+   "id": "f8719bfa",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -711,8 +831,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
-   "id": "c3428ec5",
+   "execution_count": 263,
+   "id": "2cd1c5d6",
    "metadata": {
     "cell_style": "split"
    },
@@ -748,8 +868,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
-   "id": "c60a8d76",
+   "execution_count": 264,
+   "id": "66570dd4",
    "metadata": {
     "cell_style": "split"
    },
@@ -764,13 +884,13 @@
     }
    ],
    "source": [
-    "C_am = A@C@A.T\n",
-    "print(C_am)"
+    "C_ma = A@C@A.T\n",
+    "print(C_ma)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "645c90a0",
+   "id": "51fba90d",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -786,10 +906,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
-   "id": "71d74760",
+   "execution_count": 267,
+   "id": "ef4d6988",
    "metadata": {
-    "cell_style": "split"
+    "cell_style": "center"
    },
    "outputs": [
     {
@@ -805,7 +925,7 @@
        "np.float64(0.12649110640673517)"
       ]
      },
-     "execution_count": 88,
+     "execution_count": 267,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -815,17 +935,17 @@
     "    A = np.array([x, 1])\n",
     "    return np.sqrt(A.T@V@A)\n",
     "\n",
-    "print(np.sqrt(V_am[0,0]), np.sqrt(V_am[1,1]))\n",
-    "err(0, V_am)\n",
-    "err(2, V_am)"
+    "print(np.sqrt(V_ma[0,0]), np.sqrt(V_ma[1,1]))\n",
+    "err(0, V_ma)\n",
+    "err(2, V_ma)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
-   "id": "4d49f5f2",
+   "execution_count": 255,
+   "id": "0379201b",
    "metadata": {
-    "cell_style": "split",
+    "cell_style": "center",
     "scrolled": true,
     "slideshow": {
      "slide_type": "slide"
@@ -848,8 +968,8 @@
     "plt.errorbar(xs,ys,yerr=sigma_y,fmt=\".\")\n",
     "plt.plot(x_axis, f(x_axis),'--')\n",
     "plt.plot(x_axis, x_axis*mhat + ahat,'-g')\n",
-    "plt.plot(x_axis, x_axis*mhat + ahat - np.vectorize(err,excluded=[1])(x_axis, V_am),'--g')\n",
-    "plt.plot(x_axis, x_axis*mhat + ahat + np.vectorize(err,excluded=[1])(x_axis, V_am),'--g')\n",
+    "plt.plot(x_axis, x_axis*mhat + ahat - np.vectorize(err,excluded=[1])(x_axis, V_ma),'--g')\n",
+    "plt.plot(x_axis, x_axis*mhat + ahat + np.vectorize(err,excluded=[1])(x_axis, V_ma),'--g')\n",
     "\n",
     "plt.xlabel(\"x\")\n",
     "plt.ylabel(\"y\")\n",
@@ -857,104 +977,6 @@
     "plt.show()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "dbeeaf78",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Can we avoid the large correlation between $\\hat m$ and $\\hat a$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0936b292",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fit2(ys):\n",
-    "    p, V =  opti.curve_fit(lambda x, m, a: m * (x - np.mean(xs)) + a, xs, ys, sigma=[sigma_y]*len(ys), absolute_sigma=True)\n",
-    "    return p, V"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 210,
-   "id": "ccf42805",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([1.99378966, 5.17922444]),\n",
-       " array([[ 9.81818187e-03, -5.48506188e-11],\n",
-       "        [-5.48506188e-11,  1.60000000e-02]]))"
-      ]
-     },
-     "execution_count": 210,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fit2(ys[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b4f07663",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Is this true?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 215,
-   "id": "62222c23",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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FVC9JbcaQtrrB9pdSNN0WypdRagsF+78j3zTVS9LO5gJT/YsfjTKP8dDZ5ab6W39/ran+mkn2/V65ZaypPrw9wzxGxJihmbPT9vpuzQnbBpAUzYh9qJs1lM8/WBujmXzOHPxnfK+V7IF58Qi/S5aAvXjgzA0AAHAKzQ0AAHAKzQ0AAHAKzQ0AAHAKzQ0AAHAKzQ0AAHAKzQ0AAHAKzQ0AAHAKIX4xEiQsKZxtC7OzilZ/at7GGvwXarLvdzTTFnZVPzjNVB855JvqJSliywlUNMAryStuNtWnRGwBiZkh2+NL0qF2W+Dhd0ZsMo9xWuoBU/1rkx4y1a9uGGGql6RvjHzfVP9S2qnmMSLG5293Vh9Tff6H9uM8bb/t59vwIXuYXdsg23tIPL4pWYMC/QDv59bvAckY+teTceYGAAA4heYGAAA4heYGAAA4heYGAAA4heYGAAA4heYGAAA4heYGAAA4heYGAAA4hRC/nizVFrgWKj7JPkaLLbTLa7IHx4WN2+R9FDbV1w/JMNVLUt1gz1TfmmcPUIu22cawuv/Fb5q3GTvhQ1N9Rtge6vat928w1f9k5CpT/aaGQaZ6Sfp/Nf1N9e1bc81jHOpvC13LsuXMqTnffjzl/Nm8iVnokG2//aYmU72XYnsflOyhfPEIzIvHGL0pKJAzNwAAwCk0NwAAwCk0NwAAwCk0NwAAwCk0NwAAwCk0NwAAwCk0NwAAwCk0NwAAwCk0NwAAwCkkFCcRa2qmjPXt+w/YHl9SODvbVO9lZZrH8Iv6mOrbMm2HbTQOR3mk0Z4O29xu+9kiJdJuqv/G+W+Z6iVpQ/UQU/2m3bZkX0maMOgTU33Z9ktM9XsO5JnqJSk9zZi0PKLBPEZ4Z5Z5G4uoLbhbknTglHRTfckr+81j+AdtUcteUaFtgIN1tnpJnjGpN8hZAOsY7Q32Y8qqJycOWyX0zM3ChQvleV6nW9++fU+4zdq1a3X22WcrPT1dw4YN0+OPPx6n2QIAgJ4g4WduRo0apVdeeaXj63D4+D9+7NixQ5dccoluuukm/epXv9Kbb76pW2+9VUVFRbrsssviMV0AAJDkEt7cRCKRLzxbc8Tjjz+uQYMG6ec//7kk6dRTT9XGjRv1wAMPHLe5aW5uVnPz5x/MWFdnP4UJAAB6joRfULxt2zaVlpZq6NChuuqqq7R9+/bj1q5fv14XXXRRp/umTp2qjRs3qrX12L8zLysrU15eXsdt4MCB3Tp/AACQXBLa3IwbN07Lly/XmjVrtGTJElVVVWnixImqqak5Zn1VVZVKSko63VdSUqK2tjbt27fvmNssWLBAtbW1HbeKiopu3w8AAJA8EvprqYsvvrjj32PGjNGECRN08skna9myZbrjjjuOuY3ndf6rFN/3j3n/EWlpaUpLS+umGQMAgGT3pZqbLVu2aOfOnWr5mz8v+8Y3vhHo8bKysjRmzBht27btmP/ft29fVVVVdbqvurpakUhEhYXGPx8EAABOCtTcbN++XZdeeqk2b94sz/OOOnvS3m7L4ziiublZW7du1aRJk475/xMmTNALL7zQ6b6XX35ZY8eOVUpKSqAxAQCAWwI1N3PnztXQoUP1yiuvaNiwYXrrrbdUU1OjO++8Uw888ECXH2f+/PmaPn26Bg0apOrqat13332qq6vTddddJ+mz62V2796t5cuXS5JuueUWPfzww7rjjjt00003af369Vq6dKnKy8uD7IZJyBjIFCQsycs2hny12ILHIn3ybY8vScamMVr9qXmIljGDzNtYtKXbA/bCh41jBMhnC6fZfgg4sK3AVL89xxaeJknVfyg21Q8/e6d5jPU7h5jq/3Tuf5jq76z6O1O9JP36T6ebtzErtR1UkZ0Zpvq8HW2meklKqTNuk2L/luHlG0MVjaF8fmOT7fHjJNahfNbvSVJyhvhZ9iPke1IXdyFQc7N+/Xq9+uqrKioqUigUUigU0rnnnquysjLNmTNHmzZt6tLj7Nq1S1dffbX27dunoqIijR8/Xhs2bNDgwYMlSZWVldq58/M3z6FDh2r16tX6h3/4Bz3yyCMqLS3VokWLyLgBAAAdAjU37e3tyv5LLP9JJ52kPXv26JRTTtHgwYP1wQcfdPlxVq5cecL/f/LJJ4+6b/LkyXr33XdN8wUAAL1HoOZm9OjRev/99zVs2DCNGzdO999/v1JTU7V48WINGzasu+cIAADQZYGam7vvvluNjY2SpPvuu09f//rXNWnSJBUWFuqpp57q1gkCAABYBGpupk6d2vHvYcOGacuWLdq/f7/69Olz3LwZAACAeOi2EL+CAttfcgAAAMRCwj9bCgAAoDvR3AAAAKck9LOlEimUmqqQ1/WAumQMP1KqLWCvbW+1eYhwQR9TfWhAqXmMUGvUVB8+ZAsvjEbsYVft6bb6DPvSqq3WFtKWft6xPxz2eG7t/6qpXpIyv/2Sqf4HH15uHuPOMa+Y6m/dPc5Uv6PB/lEs7c1hU31qVoCQzq3ZpnprkGRDP/vbedEuW9BcS99c8xjW12s4CUP54vH+H4+g2GRk2Y+o3/VjiTM3AADAKTQ3AADAKTQ3AADAKTQ3AADAKTQ3AADAKTQ3AADAKTQ3AADAKTQ3AADAKb02xC/a0qKo58fs8a2BTJLkNzSa6q0hTkHmZOUfrDVvk5KZZqpvGppnqi96zx4KVj/EFrBX39/+gbGNp9mev+E5trW99ffXmuolKT/PdgzmpDabxzgtbbepfvH2c031QeZk1bI307xNli0vT1Hju3P6Qfv72aEBtmDBtnT7z8O5v9thqvdbbKF/Xr7t/UCS2nbZjsEgwtm2tW1vMB4gOCHO3AAAAKfQ3AAAAKfQ3AAAAKfQ3AAAAKfQ3AAAAKfQ3AAAAKfQ3AAAAKfQ3AAAAKfQ3AAAAKf02oTiWLOmB0v2BGFrvReHhGKVFps38VrbTfXmlNRC+34fzrclDqfYgn0lSZkf2ub1fri/qb5vsT0tujjTlpL6h12l5jF+0H65qf5gbZapvqa2j6lektRme779dNsxK0l9PrIlCFeNC5vqi947ZKoPIqvGnqLbPnyAqd577wPbAE32BPK4pLUH+B6A7sOZGwAA4BSaGwAA4BSaGwAA4BSaGwAA4BSaGwAA4BSaGwAA4BSaGwAA4BSaGwAA4BRC/JKINfgvUmIPzLPyW1tN9d5Be8iXUlNM5dl/tiXmteammeolKfPTqKk+cthWL0l7BtlefiUv2faj8kJ7mF1Nli0wr7igzjxGVXWeqT68J91Unx4gUDHFeNjWnWYP8YtGbEGBw56tN9UfLskw1UtSxi7bjkcz7eF3oS07TPVen3zbACm29w9J8htsB0mQUFarcHa2qT5ISGA89iNZcOYGAAA4heYGAAA4heYGAAA4heYGAAA4heYGAAA4heYGAAA4heYGAAA4heYGAAA4pdeG+IVSUxXyuh7+ZA0/CqXaw66sogcOmupDxUX2QRqbTOW+bPWSpHxbGGF4z35bfa093OyQca1SqtvMY+Rut7382mxZdsp5334MHiqxbVNVan8LSd1h25HwYdvjpwQI8Tt0ri3MLn2rLXBNkg4Ot25hC1TM3XrAOoCahtoCFdP3HjKPEerf11Qf3bHTPEasBXk/t37PaG8IEICK4+LMDQAAcArNDQAAcArNDQAAcArNDQAAcArNDQAAcArNDQAAcArNDQAAcArNDQAAcEqvDfGLtrQo6vkxffxYC2fbgsTadu2O+RheSteDETs0NdvqW1uN9fY55XxiCys7VJJmH2OnbT8ajYF5oXZTuSQprcZYv92YLCipqcRWX/An24605Nh/Zsv7D9t+NBXZ3zusz3dqjS0Q02u1P+GZO2ptY3xqDwqU8T3By7aFF/oNAVIb48Aa/BeP7xm9CWduAACAU2huAACAU2huAACAU2huAACAU2huAACAU2huAACAU2huAACAU2huAACAU2huAACAU3ptQrEL/DgkWlrH8IpPso9Rvc+2QaoxcTjTnh4crrMlFGeYR5CaC20JpocKPVN93idRU70ktWXYxkg/YE/FTT9g+5kqbb8t2Td8KGyql6TmAttbYdaeNvMYKXW2JO7DJbak3oxd9uciSKqxlX/QloIcD6QBx06yJDNz5gYAADiF5gYAADiF5gYAADiF5gYAADiF5gYAADiF5gYAADiF5gYAADglaZqbsrIyeZ6nefPmnbBuxYoVOuOMM5SZmal+/fpp1qxZqqmpic8kAQBA0kuKEL+3335bixcv1umnn37CunXr1mnmzJn62c9+punTp2v37t265ZZb9L3vfU+rVq2K02yThzX8KJydHaOZfC5QYJcxlM9vaDTVhz49YKqXpPbBJab6SE2DeQzJ9nz0X2sLFmzNtYcXhtpsAXgtOfbAvMghW7hga67tbSoasQURSlLWblvAXjTF/nOhH7GtVfpe23HuNdn2QZKUYltbv9UWqCjJ/Ppu329/vcaaNZguCFeCBZNlPxJ+5qahoUHXXHONlixZoj59+pywdsOGDRoyZIjmzJmjoUOH6txzz9XNN9+sjRs3xmm2AAAg2SW8uZk9e7amTZumKVOmfGHtxIkTtWvXLq1evVq+72vv3r165plnNG3atONu09zcrLq6uk43AADgroQ2NytXrtS7776rsrKyLtVPnDhRK1as0JVXXqnU1FT17dtX+fn5euihh467TVlZmfLy8jpuAwcO7K7pAwCAJJSw5qaiokJz587Vr371K6Wnp3dpmy1btmjOnDn68Y9/rHfeeUcvvfSSduzYoVtuueW42yxYsEC1tbUdt4qKiu7aBQAAkIQ83/f9RAz83HPP6dJLL1U4/PlFdu3t7fI8T6FQSM3NzZ3+T5K++93v6vDhw3r66ac77lu3bp0mTZqkPXv2qF+/fl84bl1dnfLy8nSevqmIZ/x06R4uHhcUmz+xOwDzBcV98s1jWC8oDtfaLvaVpLZC2/Phtdk+wTnIBcWtxguE2zLsPx9ZLygOtdneooJcUGz95PEgFxRHmmyfJB46ZLswM1TbZKqXZL+g+NPY/2UqFxTjeNr8Vr2m51VbW6vc3NwT1ibsr6UuuOACbd68udN9s2bN0siRI/XDH/7wqMZGkpqamhSJdJ7ykboE9WgAACDJJKy5ycnJ0ejRozvdl5WVpcLCwo77FyxYoN27d2v58uWSpOnTp+umm27SY489pqlTp6qyslLz5s3TV7/6VZWWlsZ9HwAAQPJJipyb46msrNTOnTs7vr7++utVX1+vhx9+WHfeeafy8/N1/vnn61/+5V8SOEsAAJBMEnbNTaIEvebG+jvXZPz9aZDfG3vGbfw47HdogO0sXXTXnpiPESi8sLTYVN6em2Efw6gl13bNVOaHn5rHaBpRZKpPrbNdD2O9NkmSmgu69kcNR1gD9qQA18S0BAjMM4pW256/INevWYP/rNfUxeO9Nsh7ZzJ+D+jpLNfcJDznBgAAoDvR3AAAAKfQ3AAAAKfQ3AAAAKfQ3AAAAKfQ3AAAAKfQ3AAAAKfQ3AAAAKckdUJxMknGQKZ4fJhbe0NDzMcIF/Qx1VtD+bzsLFO9JPMHCnop9g8MjabYPqQystMWuHZ45Bd/kOzfChJOF+sxQk2xf+2lt9qC/wLNKcahfH5TgA/OtI7RaB/DGuxpfa91JZwU3YszNwAAwCk0NwAAwCk0NwAAwCk0NwAAwCk0NwAAwCk0NwAAwCk0NwAAwCk0NwAAwCk0NwAAwCkkFPdgvTU12ZouGiQZNrpjp6nePCdJ3p8rTfW+8fFT6pqNW9jnpADJzCHr85FqG8Ov3md7fEmecQwVFZrHUKttv31jfZDjvLcm9cYjeR2JxZkbAADgFJobAADgFJobAADgFJobAADgFJobAADgFJobAADgFJobAADgFJobAADgFEL8cELxCOVr33/AVG+dU5CAvVCffFO939hkHsOzBuAZ68N79tseX/agwCD8Jttaea3GdbIG8knyGxrN28R6DGtIZ5DXajIGgVq5sA+S/flzZb9jhTM3AADAKTQ3AADAKTQ3AADAKTQ3AADAKTQ3AADAKTQ3AADAKTQ3AADAKTQ3AADAKYT4oVslY7CUH2BO7Q0NpvpwdrZ5DL+11baBNSgwQJidVaDwwvw8U320+lNbfYDnO1JSbKo3P3cBxCOsUgQFJg3WqXtx5gYAADiF5gYAADiF5gYAADiF5gYAADiF5gYAADiF5gYAADiF5gYAADiF5gYAADiFED+cUDyCpazBYMkYdhUkKDAa66DAFnvQnDm8sKCPeYxYh/IFCpo7cNC8jXmMGO+H9bkLIshrz4XXN3oeztwAAACn0NwAAACn0NwAAACn0NwAAACn0NwAAACn0NwAAACn0NwAAACn0NwAAACn0NwAAACnkFCMhIt1cqsrqarxSKC17rff0BijmXwuGZ+LeGA/gOA4cwMAAJxCcwMAAJxCcwMAAJxCcwMAAJxCcwMAAJxCcwMAAJxCcwMAAJxCcwMAAJxCiB96nFiH/gURZIxkDDeLx5zi8XzEWjg727xNrEMY43EMunKcw32cuQEAAE6huQEAAE6huQEAAE6huQEAAE6huQEAAE6huQEAAE5JmuamrKxMnudp3rx5J6xrbm7Wj370Iw0ePFhpaWk6+eST9ctf/jI+kwQAAEkvKXJu3n77bS1evFinn376F9ZeccUV2rt3r5YuXarhw4erurpabW1tcZglAADoCRLe3DQ0NOiaa67RkiVLdN99952w9qWXXtLatWu1fft2FRQUSJKGDBlywm2am5vV3Nzc8XVdXd2XnjO6lzUYzBoKRohYcol1cFw8gub8JDym4nGc81pCT5HwX0vNnj1b06ZN05QpU76w9te//rXGjh2r+++/X/3799eIESM0f/58HTp06LjblJWVKS8vr+M2cODA7pw+AABIMgk9c7Ny5Uq9++67evvtt7tUv337dq1bt07p6elatWqV9u3bp1tvvVX79+8/7nU3CxYs0B133NHxdV1dHQ0OAAAOS1hzU1FRoblz5+rll19Wenp6l7aJRqPyPE8rVqxQXl6eJOmnP/2pLr/8cj3yyCPKyMg4apu0tDSlpaV169wBAEDyStivpd555x1VV1fr7LPPViQSUSQS0dq1a7Vo0SJFIhG1t7cftU2/fv3Uv3//jsZGkk499VT5vq9du3bFc/oAACBJJezMzQUXXKDNmzd3um/WrFkaOXKkfvjDHyocDh+1zTnnnKOnn35aDQ0Nyv7Lp/J++OGHCoVCGjBgQFzmDQAAklvCztzk5ORo9OjRnW5ZWVkqLCzU6NGjJX12vczMmTM7tpkxY4YKCws1a9YsbdmyRa+//rq+//3v64Ybbjjmr6QAAEDvk/C/ljqRyspK7dy5s+Pr7Oxs/fa3v9XBgwc1duxYXXPNNZo+fboWLVqUwFkCAIBk4vm+7yd6EvFUV1envLw8nadvKuKlJHo6UOxzbtCzBcmtsYp19k6QMQB01ua36jU9r9raWuXm5p6wNuEhfkBvfdOnqeuaZNzvZJwTgM8l9a+lAAAArGhuAACAU2huAACAU2huAACAU2huAACAU2huAACAU2huAACAU2huAACAUwjxAxKEIDh0J1KTgc9x5gYAADiF5gYAADiF5gYAADiF5gYAADiF5gYAADiF5gYAADiF5gYAADiF5gYAADil14X4+b4vSWpTq+QneDIA0E1CvmfeJuq3xmAmQGy06bPj9cj38RPpdc1NfX29JGmdVid4JgDQjQgbRi9RX1+vvLy8E9Z4fldaIIdEo1Ht2bNHOTk58jz7TzpWdXV1GjhwoCoqKpSbmxvz8ZIRa/AZ1oE1kFiDI1gH1uCIrq6D7/uqr69XaWmpQqETX1XT687chEIhDRgwIO7j5ubm9uqDV2INjmAdWAOJNTiCdWANjujKOnzRGZsjuKAYAAA4heYGAAA4heYmxtLS0nTPPfcoLS0t0VNJGNbgM6wDayCxBkewDqzBEbFYh153QTEAAHAbZ24AAIBTaG4AAIBTaG4AAIBTaG4AAIBTaG6+hNdff13Tp09XaWmpPM/Tc889d8L666+/Xp7nHXUbNWpUfCYcI9Z1kKQVK1bojDPOUGZmpvr166dZs2appqYm9pONkSBr8Mgjj+jUU09VRkaGTjnlFC1fvjz2E42hsrIyfeUrX1FOTo6Ki4v1rW99Sx988MEXbrd27VqdffbZSk9P17Bhw/T444/HYbaxEWQNKisrNWPGDJ1yyikKhUKaN29efCYbQ0HW4dlnn9WFF16ooqIi5ebmasKECVqzZk2cZtz9gqzBunXrdM4556iwsFAZGRkaOXKkfvazn8VpxrER9H3hiDfffFORSERnnnmmaVyamy+hsbFRZ5xxhh5++OEu1f/iF79QZWVlx62iokIFBQX6zne+E+OZxpZ1HdatW6eZM2fqxhtv1B//+Ec9/fTTevvtt/W9730vxjONHesaPPbYY1qwYIEWLlyoP/7xj7r33ns1e/ZsvfDCCzGeaeysXbtWs2fP1oYNG/Tb3/5WbW1tuuiii9TY2HjcbXbs2KFLLrlEkyZN0qZNm/SP//iPmjNnjv7rv/4rjjPvPkHWoLm5WUVFRfrRj36kM844I46zjZ0g6/D666/rwgsv1OrVq/XOO+/oa1/7mqZPn65NmzbFcebdJ8gaZGVl6bbbbtPrr7+urVu36u6779bdd9+txYsXx3Hm3SvIOhxRW1urmTNn6oILLrAP7KNbSPJXrVpl2mbVqlW+53n+J598EptJJUBX1uFf//Vf/WHDhnW6b9GiRf6AAQNiOLP46coaTJgwwZ8/f36n++bOneufc845MZxZfFVXV/uS/LVr1x635gc/+IE/cuTITvfdfPPN/vjx42M9vbjoyhr8tcmTJ/tz586N7aQSwLoOR5x22mn+vffeG6NZxVfQNbj00kv9a6+9Nkazij/LOlx55ZX+3Xff7d9zzz3+GWecYRqHMzcJtHTpUk2ZMkWDBw9O9FTiauLEidq1a5dWr14t3/e1d+9ePfPMM5o2bVqipxY3zc3NSk9P73RfRkaG3nrrLbW2tiZoVt2rtrZWklRQUHDcmvXr1+uiiy7qdN/UqVO1ceNGJ9ahK2vQGwRZh2g0qvr6emfWLsgabNq0Sf/7v/+ryZMnx2pacdfVdXjiiSf08ccf65577gk0Ds1NglRWVuo3v/lNj/5VTFATJ07UihUrdOWVVyo1NVV9+/ZVfn6+HnrooURPLW6mTp2qf//3f9c777wj3/e1ceNG/fKXv1Rra6v27duX6Ol9ab7v64477tC5556r0aNHH7euqqpKJSUlne4rKSlRW1tbj1+Hrq6B64Kuw4MPPqjGxkZdccUVMZxdfFjXYMCAAUpLS9PYsWM1e/ZsZ75PdHUdtm3bprvuuksrVqxQJBLs87173aeCJ4snn3xS+fn5+ta3vpXoqcTdli1bNGfOHP34xz/W1KlTVVlZqe9///u65ZZbtHTp0kRPLy7+6Z/+SVVVVRo/frx831dJSYmuv/563X///QqHw4me3pd222236f3339e6deu+sNbzvE5f+38JTf/b+3sayxq4LMg6lJeXa+HChXr++edVXFwcw9nFh3UN3njjDTU0NGjDhg266667NHz4cF199dUxnmXsdWUd2tvbNWPGDN17770aMWJE8MFsvy3D8chwzU00GvWHDx/uz5s3L7aTSoCurMO1117rX3755Z3ue+ONN3xJ/p49e2I4u/iwHAstLS1+RUWF39bW5j/66KN+Tk6O397eHtsJxthtt93mDxgwwN++ffsX1k6aNMmfM2dOp/ueffZZPxKJ+C0tLbGaYsxZ1uCvuXbNTZB1WLlypZ+RkeH/93//dwxnFj9Bj4Uj/vmf/9kfMWJEN88q/rq6DgcOHPAl+eFwuOPmeV7Hfb/73e+6NB5nbhJg7dq1+uijj3TjjTcmeioJ0dTUdNSpxiNnK/xe9lFnKSkpGjBggCRp5cqV+vrXv65QqGf+ttj3fd1+++1atWqVXnvtNQ0dOvQLt5kwYcJRfyH28ssva+zYsUpJSYnVVGMmyBq4KOg6lJeX64YbblB5eXmPvwavu44F3/fV3NzczbOLH+s65ObmavPmzZ3ue/TRR/Xqq6/qmWee6fI60tx8CQ0NDfroo486vt6xY4fee+89FRQUaNCgQVqwYIF27959VH7J0qVLNW7cOGd+D29dh+nTp+umm27SY4891vFrqXnz5umrX/2qSktLE7UbX4p1DT788EO99dZbGjdunA4cOKCf/vSn+sMf/qBly5Ylahe+tNmzZ+s///M/9fzzzysnJ0dVVVWSpLy8PGVkZEjSUetwyy236OGHH9Ydd9yhm266SevXr9fSpUtVXl6esP34MoKsgSS99957kj47jj799FO99957Sk1N1WmnnRb3fegOQdahvLxcM2fO1C9+8QuNHz++Y5uMjAzl5eUlZke+hCBr8Mgjj2jQoEEaOXKkpM9iMx544AHdfvvtidmJbmBdh1AodNT3xuLiYqWnp9u+Z36p80y93P/8z//4ko66XXfddb7v+/51113nT548udM2Bw8e9DMyMvzFixfHf8IxEmQdFi1a5J922ml+RkaG369fP/+aa67xd+3aFf/JdxPrGmzZssU/88wz/YyMDD83N9f/5je/6f/pT39KzOS7ybH2X5L/xBNPdNQc61h47bXX/LPOOstPTU31hwwZ4j/22GPxnXg3CroGx9pm8ODBcZ17dwqyDpMnTz7ha6inCbIGixYt8keNGuVnZmb6ubm5/llnneU/+uijPfpX1UFfE38tyJ+Ce38ZHAAAwAk985f7AAAAx0FzAwAAnEJzAwAAnEJzAwAAnEJzAwAAnEJzAwAAnEJzAwAAnEJzAwAAnEJzAwAAnEJzAwAAnEJzAwAAnEJzA6DHOu+883T77bdr3rx56tOnj0pKSrR48WI1NjZq1qxZysnJ0cknn6zf/OY3iZ4qgDiiuQHQoy1btkwnnXSS3nrrLd1+++36+7//e33nO9/RxIkT9e6772rq1Kn67ne/q6ampkRPFUCc8KngAHqs8847T+3t7XrjjTckSe3t7crLy9O3v/1tLV++XJJUVVWlfv36af369Ro/fnwipwsgTjhzA6BHO/300zv+HQ6HVVhYqDFjxnTcV1JSIkmqrq6O+9wAJAbNDYAeLSUlpdPXnud1us/zPElSNBqN67wAJA7NDQAAcArNDQAAcArNDQAAcAp/LQUAAJzCmRsAAOAUmhsAAOAUmhsAAOAUmhsAAOAUmhsAAOAUmhsAAOAUmhsAAOAUmhsAAOAUmhsAAOAUmhsAAOAUmhsAAOCU/w+ISCmRfzZRZQAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[ 0.00981114 -0.00027401]\n",
-      " [-0.00027401  0.01656427]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "p = np.zeros((len(ys),2))\n",
-    "chi2s = np.zeros(len(ys))\n",
-    "for i in range(len(ys)):\n",
-    "    p[i] = fit2(ys[i])[0]\n",
-    "    chi2s[i] = np.sum(((ys[i] - p[i,0] * xs - p[i,1])/sigma_y)**2)\n",
-    "    \n",
-    "plt.hist2d(p[:,0], p[:,1],bins=(40,40))\n",
-    "plt.xlabel(\"m\")\n",
-    "plt.ylabel(\"a\")\n",
-    "plt.show()\n",
-    "print(np.cov(p, rowvar=False))"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "424bdd1f-53bf-422b-bc4a-0702231b976d",
@@ -975,6 +997,14 @@
     "\\end{aligned}$$"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a8551020",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "id": "952ccb17-cf14-4561-ad21-acc50b72cf73",
@@ -1038,7 +1068,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "38237122",
+   "id": "0d95c4b4",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1052,8 +1082,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
-   "id": "4ce59d00",
+   "execution_count": 268,
+   "id": "e1ad8248",
    "metadata": {
     "cell_style": "center"
    },
@@ -1082,8 +1112,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
-   "id": "ad83e252",
+   "execution_count": 270,
+   "id": "b9f40b7d",
    "metadata": {
     "cell_style": "center"
    },
@@ -1106,7 +1136,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e1bdf074",
+   "id": "220a99d2",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1118,17 +1148,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 202,
-   "id": "a5225373",
+   "execution_count": 271,
+   "id": "102298b9",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x14d774c40>]"
+       "[<matplotlib.lines.Line2D at 0x14dad28b0>]"
       ]
      },
-     "execution_count": 202,
+     "execution_count": 271,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1152,7 +1182,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c9f4436b",
+   "id": "c3ba1c25",
    "metadata": {},
    "source": [
     "### 2-D multivariate normal distribution\n",
@@ -1168,7 +1198,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a31ff98e",
+   "id": "2e49b964",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1180,8 +1210,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
-   "id": "11909eb4",
+   "execution_count": 280,
+   "id": "dcd29ad8",
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1216,7 +1246,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "73e034e9",
+   "id": "94f35366",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1228,8 +1258,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
-   "id": "46dc2732",
+   "execution_count": 281,
+   "id": "c1ba8cc9",
    "metadata": {},
    "outputs": [
     {
@@ -1258,7 +1288,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8bc47787",
+   "id": "6aeb8f97",
    "metadata": {},
    "source": [
     "with $v = (m, a)$\n",
@@ -1267,8 +1297,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
-   "id": "ca2b732f",
+   "execution_count": 282,
+   "id": "e95555b4",
    "metadata": {},
    "outputs": [
     {
@@ -1309,16 +1339,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
-   "id": "e5ce8ad4",
+   "execution_count": 283,
+   "id": "d6a11740",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1.99378966 1.19164511] [[ 0.00576909 -0.01153818]\n",
-      " [-0.01153818  0.03247785]]\n"
+      "[1.99378966 1.19164511] [[ 0.00981818 -0.01963636]\n",
+      " [-0.01963636  0.05527273]]\n"
      ]
     }
    ],
@@ -1329,14 +1359,14 @@
     "    return m*x + a\n",
     "\n",
     "pfit, Vfit = opti.curve_fit(fitf , xs, ys, \n",
-    "     sigma=[sigma_y]*len(ys))\n",
+    "     sigma=[sigma_y]*len(ys), absolute_sigma=True)\n",
     "\n",
     "print(pfit, Vfit)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0a1a32c1",
+   "id": "6f29e4a6",
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1346,6 +1376,111 @@
     "Achtung! Wrong uncertainties without `absolute_sigma=True`"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "0f8110c6",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Can we avoid the large correlation between $\\hat m$ and $\\hat a$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 284,
+   "id": "32acec21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fit2(ys):\n",
+    "    p, V =  opti.curve_fit(lambda x, m, a: m * (x - np.mean(xs)) + a, xs, ys, sigma=[sigma_y]*len(ys), absolute_sigma=True)\n",
+    "    return p, V"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 286,
+   "id": "61c5726d",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([1.99378966, 5.17922444]),\n",
+       " array([[ 9.81818187e-03, -5.48506188e-11],\n",
+       "        [-5.48506188e-11,  1.60000000e-02]]))"
+      ]
+     },
+     "execution_count": 286,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fit2(ys) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f18ee09b",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Is this true?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 287,
+   "id": "7751e351",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.0097912  -0.00067184]\n",
+      " [-0.00067184  0.01569472]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "n = 10 \n",
+    "xs = np.linspace(0,4,n)\n",
+    "sigma_y=0.4\n",
+    "ys = stats.multivariate_normal.rvs(f(xs), np.eye(n)*sigma_y**2, 2000, random_state=42)\n",
+    "\n",
+    "p = np.zeros((len(ys),2))\n",
+    "chi2s = np.zeros(len(ys))\n",
+    "for i in range(len(ys)):\n",
+    "    p[i] = fit2(ys[i])[0]\n",
+    "    chi2s[i] = np.sum(((ys[i] - p[i,0] * xs - p[i,1])/sigma_y)**2)\n",
+    "    \n",
+    "plt.hist2d(p[:,0], p[:,1],bins=(40,40))\n",
+    "plt.xlabel(\"m\") \n",
+    "plt.ylabel(\"a\")\n",
+    "plt.show()\n",
+    "print(np.cov(p, rowvar=False))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "a5fec52e-bd3a-4437-bb43-620de44939b2",
@@ -1363,8 +1498,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
-   "id": "e16145b5",
+   "execution_count": 288,
+   "id": "1987f58c",
    "metadata": {},
    "outputs": [
     {
@@ -1382,13 +1517,13 @@
     "    r = (y - my)/sy\n",
     "    return np.sum(r**2)\n",
     "    \n",
-    "res = opti.minimize( lambda p: chi2(xs, ys, sigma_y, p[1], p[0]),x0=np.zeros(2))\n",
+    "res = opti.minimize( lambda p: chi2(xs, ys[0], sigma_y, p[1], p[0]),x0=np.zeros(2))\n",
     "print(res.x, res.hess_inv * 2)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0b7f3f5d",
+   "id": "75697090",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1430,7 +1565,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "322bf66d",
+   "id": "1fd7eba9",
    "metadata": {},
    "source": [
     "### Goodness of fit"
@@ -1438,7 +1573,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "98381e4e",
+   "id": "06297a50",
    "metadata": {},
    "source": [
     "$\\chi^2$ test:\n",
@@ -1454,7 +1589,7 @@
   {
    "cell_type": "code",
    "execution_count": 153,
-   "id": "fb5a00ab",
+   "id": "9d7b25cd",
    "metadata": {},
    "outputs": [
     {
@@ -1493,7 +1628,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "76486014",
+   "id": "2b62f203",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1505,13 +1640,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
-   "id": "3ba80ec4",
+   "execution_count": 290,
+   "id": "209c08d1",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1523,7 +1658,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "7.983673395154516\n"
+      "8.06715894456369\n"
      ]
     }
    ],
@@ -1541,6 +1676,7 @@
     "chi_axis = np.linspace(0,30,100)    \n",
     "plt.hist(chi2s,bins=50, density=True)\n",
     "# draw Chi2 distribution\n",
+    "plt.plot(chi_axis, stats.chi2.pdf(chi_axis,8))\n",
     "plt.xlabel(\"$\\chi^2$\")\n",
     "plt.show()\n",
     "print(np.mean(chi2s))"
@@ -1548,7 +1684,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1ea7723c",
+   "id": "065a85db",
    "metadata": {},
    "source": [
     "### Compute p-value for goodness of fit"
@@ -1557,7 +1693,7 @@
   {
    "cell_type": "code",
    "execution_count": 146,
-   "id": "78ced062",
+   "id": "a2f71629",
    "metadata": {},
    "outputs": [
     {
@@ -1580,8 +1716,12 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cbe8c412",
-   "metadata": {},
+   "id": "904fa103",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "source": [
     "### What if the model does not fit"
    ]
@@ -1589,7 +1729,7 @@
   {
    "cell_type": "code",
    "execution_count": 172,
-   "id": "882205bd",
+   "id": "38d684d9",
    "metadata": {},
    "outputs": [
     {
@@ -1628,7 +1768,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4bf58838",
+   "id": "92a09b71",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1640,8 +1780,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 173,
-   "id": "c599579b",
+   "execution_count": 291,
+   "id": "2bd90520",
    "metadata": {},
    "outputs": [
     {
@@ -1665,7 +1805,7 @@
   {
    "cell_type": "code",
    "execution_count": 174,
-   "id": "17dca38c",
+   "id": "cb6e7ba1",
    "metadata": {},
    "outputs": [
     {
@@ -1691,7 +1831,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4ee549ea",
+   "id": "f2937642",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1703,13 +1843,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 175,
-   "id": "a6afc5c7",
+   "execution_count": 294,
+   "id": "94531253",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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7O1uzZ89WSUlJh+fv3btXF198sbKzs1VUVKS7775bd9xxhzZs2OA75w9/+IMWLVqk++67T8XFxVqzZo3Wr1+vxYsXd/+TRYJjhzzbAcnW1tFd3qc2V38p1VZaWwsAIKz5HVgef/xx3XzzzZo7d67Gjx+v5cuXKy0tTatWrerw/NWrV2vEiBFavny5xo8fr7lz5+qmm27SY4895junoKBA55xzjq677jqNGjVKOTk5uvbaa7V9+/buf7JI4L2luX+YBpa4eGnwaM9+GdNCAIDu8yuwNDY2qrCwUDk5OW2O5+TkaOvWrR3+TEFBQbvzZ82ape3bt6upqUmSNHPmTBUWFuq9996TJO3Zs0d5eXm65JJLOq2loaFB1dXVbb4ijjewhOsIi0QfCwAgIPwKLBUVFXK5XEpKSmpzPCkpSWVlZR3+TFlZWYfnNzc3q6KiQpJ0zTXX6MEHH9TMmTMVHR2tMWPG6Lvf/a4WLVrUaS3Lli1TQkKC7ystLc2fjxIewn1KSKKPBQAQEN1qujUMo82fTdNsd+xk57c+/uabb+qhhx7SypUr9f777+v555/X3/72Nz344IOdvubixYtVVVXl+zpw4EB3Pop9NdZ5nnYsSf2TTnyunXFrMwAgAKL8OTkxMVFOp7PdaEp5eXm7URSv5OTkDs+PiorSkCFDJEn33nuvbrjhBs2dO1eSNGnSJNXW1uqnP/2plixZIkcHi6bFxsYqNjbWn/LDy7GWaxbdV4odYG0tPZHcMiVU+bnUUBPenwUAYBm/RlhiYmKUkZGh/Pz8Nsfz8/OVlZXV4c9kZma2O3/jxo2aPn26oqOjJUl1dXXtQonT6ZRpmr7RmF7H13CbJJ1g9Mr2+g+VBrQ8uLFsp7W1AADClt9TQrm5uXrmmWe0du1aFRcXa+HChSopKdG8efMkeaZqbrzxRt/58+bN0/79+5Wbm6vi4mKtXbtWa9as0Z133uk759JLL9WqVau0bt067d27V/n5+br33nt12WWXyel0BuBjhiFfw22YrsHSGk9uBgD0kF9TQpI0Z84cVVZWaunSpSotLdXEiROVl5enkSNHSpJKS0vbrMmSnp6uvLw8LVy4UE8++aRSU1P1xBNP6KqrrvKdc88998gwDN1zzz06ePCghg4dqksvvVQPPfRQAD5imPI13IZx/4pXymTp05fpYwEAdJthRsicS3V1tRISElRVVaX4+Hiry+m5/Pukt5dLM26VZj9idTU9U/w3af31UtIk6dYtVlcDALCRrv7+5llCduWbEoqEEZaWKaHDxVJzg7W1AADCEoHFro6F+Sq3rSUMl/oMktzNUvnHVlcDAAhDBBa7qomAReO8DIPGWwBAjxBY7OpYBCzL35pvxVuW6AcA+I/AYkdN9dI3X3v2w3mV29a8IyzcKQQA6AYCix15b2l2xnp6PyKBL7B8JLld1tYCAAg7BBY78gaWcF/ltrXBY6ToflLzN1LFZ1ZXAwAIMwQWO6qJsP4VSXI4pOSJnn2mhQAAfiKw2FEkrXLbmu9OIRpvAQD+IbDYUU2pZxsJa7C0xp1CAIBuIrDYUU2Ej7CUfShFxhMhAAAhQmCxo2MR9KTm1oaOkxzRUn2VdHS/1dUAAMIIgcWOvCMskTYlFBUjJU3w7LPiLQDADwQWO/L2sETalJBE4y0AoFsILHbjapLqKjz7kTbCItF4CwDoFgKL3dQe9mwNp9R3iLW1BEPKVM+WtVgAAH4gsNjNsXLPtt9Qz2JrkSbpDMlweNaa8S6QBwDASUTgb8Qw5w0s/YdaW0ewxPSVEk/37DMtBADoIgKL3dR6R1hOsbaOYPI13jItBADoGgKL3fhGWCLwDiEvX2DZYWkZAIDwQWCxG2/TbaROCUnH7xSi8RYA0EUEFrs51gumhJInebZHS6S6I9bWAgAICwQWu/H2sPSP4MDSZ6A0KN2zzygLAKALCCx20/q25kiWwgJyAICuI7DYTW9oupVYoh8A4BcCi524mqRvWno6InlKSDq+4u1XO6ysAgAQJggsdlLb8gwhwyn1GWxtLcHmDSxHvpDqqy0tBQBgfwQWO/EtGpcYmcvyt9ZviJSQ5tmn8RYAcBIR/lsxzPSGW5pbo48FANBFBBY7OdYLbmluzRtY6GMBAJwEgcVOesMaLK15+1gYYQEAnASBxU6OtSzLH+lrsHh5R1gqPpUajllbCwDA1ggsdtLbRlgGJEkDUiSZ0qGPrK4GAGBjBBY7OXbIs+0tTbcSfSwAgC4hsNiJd0qot4ywSNwpBADoEgKLnfS2KSGpVePtDiurAADYHIHFLlzNUl3Lsvy9cUro8CdSY521tQAAbIvAYhd1FZJMyXBIfSN8Wf7W4lM9d0WZbhpvAQCdIrDYhXfRuL6JksNpbS2hZBhS6pmefRpvAQCdILDYhW+V2yRr67CC78nNRZaWAQCwLwKLXfgabnvJonGteUdYaLwFAHSCwGIXve3Bh615A8vhT6TGWmtrAQDYEoHFLmq9a7D0whGW+BTPVJjplspovAUAtEdgsYvePMIiMS0EADihKKsLQIvaXvbgw29LmSp9+krIGm9HLfr7Sc/Z98glIagEANAVjLDYRW2FZ9sbp4Qkbm0GAJwQgcUuevsIS+pUz7ZiN423AIB2CCx24Ha3rHSr3htYBiRLA1JaGm93Wl0NAMBmCCx2UH9Ucjd79vsmWlqKpXzTQiwgBwBoi6ZbO/D2r8QlSFEx1tZipZSp0u68EwYWmmUBoHdihMUOenv/ipd3hOXg+9bWAQCwHQKLHRBYPIZN82wrP5Pqq6ytBQBgK90KLCtXrlR6erri4uKUkZGhzZs3n/D8TZs2KSMjQ3FxcRo9erRWr17d7pyjR4/q9ttvV0pKiuLi4jR+/Hjl5eV1p7zw4wssvbh/RfJ8/oQRnn1ubwYAtOJ3YFm/fr0WLFigJUuWqKioSNnZ2Zo9e7ZKSko6PH/v3r26+OKLlZ2draKiIt1999264447tGHDBt85jY2NuvDCC7Vv3z79+c9/1u7du/X0009r2LBh3f9k4aS2l98h1Nowb+Mt00IAgOP8brp9/PHHdfPNN2vu3LmSpOXLl+vVV1/VqlWrtGzZsnbnr169WiNGjNDy5cslSePHj9f27dv12GOP6aqrrpIkrV27VkeOHNHWrVsVHR0tSRo5cmR3P1P4YUrouNRp0scv0scCAGjDrxGWxsZGFRYWKicnp83xnJwcbd26tcOfKSgoaHf+rFmztH37djU1NUmSXnrpJWVmZur2229XUlKSJk6cqIcfflgul6vTWhoaGlRdXd3mK2wRWI7z9rFwazMAoBW/RlgqKirkcrmUlJTU5nhSUpLKyso6/JmysrIOz29ublZFRYVSUlK0Z88evf7667r++uuVl5enzz77TLfffruam5v1H//xHx2+7rJly/TAAw/4U759+aaEenkPi+S5tVmGVHVAOna4W48q6MqtzwCA8NKtplvDMNr82TTNdsdOdn7r4263W6eccoqeeuopZWRk6JprrtGSJUu0atWqTl9z8eLFqqqq8n0dOHCgOx/FHhhhOS4uXko8zbNPHwsAoIVfIyyJiYlyOp3tRlPKy8vbjaJ4JScnd3h+VFSUhgwZIklKSUlRdHS0nE6n75zx48errKxMjY2Niolpv5habGysYmNj/SnfvggsbaVOkyo+9fSxnD7LsjJONlLDAnUAEDp+jbDExMQoIyND+fn5bY7n5+crKyurw5/JzMxsd/7GjRs1ffp0X4PtOeeco88//1xut9t3zqeffqqUlJQOw0pEaW70LM0vEVi8fH0sjLAAADz8nhLKzc3VM888o7Vr16q4uFgLFy5USUmJ5s2bJ8kzVXPjjTf6zp83b57279+v3NxcFRcXa+3atVqzZo3uvPNO3zm33nqrKisrNX/+fH366af6+9//rocffli33357AD6izdVVeraGU4obaGkptpHaElgOvi+1TB8CAHo3v29rnjNnjiorK7V06VKVlpZq4sSJysvL892GXFpa2mZNlvT0dOXl5WnhwoV68sknlZqaqieeeMJ3S7MkpaWlaePGjVq4cKEmT56sYcOGaf78+brrrrsC8BFtrvWicQ4WHpYkJU+SHFGeJ1hXHZAGjrC6IgCAxQzTjIy/wlZXVyshIUFVVVWKj4+3upyu+/wf0u9/KCVNlG592+pq7OM350qlH0j/9FvpjCt9h+10BxA9LADQc139/c1f6a3GLc0dG5bh2X653do6AAC2QGCxGncIdWzYdM/2YKG1dQAAbIHAYjUCS8eGtwSWr3ZIriZLSwEAWI/AYjWmhDo25DQpNkFq/kYq/9jqagAAFiOwWI0Rlo45HMef3My0EAD0egQWqxFYOuftY/mSwAIAvR2BxWq+KSECSzvePpaD3CkEAL2d3wvHIYBMs+3Ccb3ct9dYGaIqFcZJ7vLdmrzoTzqmvhZVBgCwGiMsVmqs9TSVSoywdKBSCTrgHiqHYWqyY4/V5QAALERgsZJ3dCW6rxTTz9pabGqHOUaSNNX43OJKAABWIrBYiVuaT2qH+1RJ0pmOLyyuBABgJQKLlbwjLH0JLJ0pagksUx2fS4qIx14BALqBwGIlbmk+qV3mKDWaTg01qjTcOGx1OQAAixBYrFTHLc0n06AYfWyOkiRlGJ9aWwwAwDIEFiv5eliGWFuHzb3vPk2SNM3xmcWVAACswjosVmLRuC55332abtIrtgss3143piP7HrkkBJUAQORjhMVKNN12SaH7dEnSeKNEfVRvcTUAACsQWKxED0uXlGqISs3BijLcmmzstbocAIAFCCxWooely95vub3ZbtNCAIDQILBYxTTpYfEDjbcA0LvRdGuV+irJ3eTZ7yU9LF1pUu1MUUtgOdPxmTwLyBmBKQoAEBYILFapq/RsYwZI0XHW1hIGPjLT1WBGKdGo1gijXCVmktUlBQx3GwHAyTElZBXfKre9Y3SlpxoVrV0sIAcAvRaBxSo8+NBv3tubMxwEFgDobQgsVuE5Qn7bTmABgF6LHhareNdg6RsZtzT3pKG2qwrdYyVJY40vFa9jqlb/oL9nT4XiugBAb8AIi1W4pdlvFUrQHneyHIbJ7c0A0MsQWKxCD0u3ePtYpjMtBAC9CoHFKvSwdMs20zMtdJZjt8WVAABCicBiFe86LBHSwxIq21v6WKYYXyhazRZXAwAIFQKLVRhh6ZY9ZooqzQGKM5o0kQchAkCvQWCxgtt9fISFHhY/Ga36WJgWAoDegsBihfqjkrtlOqOXPEcokLa1TAvReAsAvQeBxQreO4RiE6SoGGtrCUNtV7w1rS0GABASBBYr1HFLc0/sNEer3oxWolGtMcZXVpcDAAgBAosVePBhjzQpSkXu0yRJZzs+sbgaAEAoEFiswCq3PfauOU6SNMNRbHElAIBQILBYoTayniNkhXfd4yVJMxyfiD4WAIh8BBYr1DHC0lNF7lPVaDqVYhxRmlFudTkAgCAjsFiBHpYeq1esPjTHSJK+w7QQAEQ8AosV6GEJiHfdnj6Wsw0abwEg0hFYrEAPS0Ac72NhhAUAIh2BxQqswxIQhe7T1Ww6NMJxWCmqtLocAEAQRVldQK/jdrV6jtAp1tYS5mrVRx+ZozTV2KOzHcV60T3T6pKCZtSiv5/0nH2PXBKCSgDAGoywhNo3X0um27PPlFCPvce0EAD0CgSWUPPeIdRnsORkgKun3mkJLNwpBACRjcASatzSHFDvucep2XRotKOMPhYAiGAEllDjluaAOqa+2mmOliRlOnZZXA0AIFgILKFWyx1CgVbgniBJynJ+bHElAIBgIbCEmm9KiBGWQNnqPkOSd4SF5woBQCQisIQagSXgtrtPV6Pp1DCjUiONQ1aXAwAIgm4FlpUrVyo9PV1xcXHKyMjQ5s2bT3j+pk2blJGRobi4OI0ePVqrV6/u9Nx169bJMAxdccUV3SnN/mi6Dbh6xarIPE2SlEUfCwBEJL8Dy/r167VgwQItWbJERUVFys7O1uzZs1VSUtLh+Xv37tXFF1+s7OxsFRUV6e6779Ydd9yhDRs2tDt3//79uvPOO5Wdne3/JwkXvmX5CSyBtNXlnRaijwUAIpHfgeXxxx/XzTffrLlz52r8+PFavny50tLStGrVqg7PX716tUaMGKHly5dr/Pjxmjt3rm666SY99thjbc5zuVy6/vrr9cADD2j06NHd+zThgCmhoKCPBQAim1+BpbGxUYWFhcrJyWlzPCcnR1u3bu3wZwoKCtqdP2vWLG3fvl1NTU2+Y0uXLtXQoUN18803d6mWhoYGVVdXt/kKC3Xc1hwMO8xT9Y0Zo6FGtU43vrS6HABAgPkVWCoqKuRyuZSUlNTmeFJSksrKyjr8mbKysg7Pb25uVkWF55f322+/rTVr1ujpp5/uci3Lli1TQkKC7ystLc2fj2KN5kapvsqzTw9LQDUpStvcYyVJ5zg+srgaAECgdavp1jCMNn82TbPdsZOd7z1eU1Ojf/7nf9bTTz+txMSu/xJfvHixqqqqfF8HDhzw4xNYxDu64oiS4gZaWkok2uyeJEnKduy0uBIAQKD59TCbxMREOZ3OdqMp5eXl7UZRvJKTkzs8PyoqSkOGDNGuXbu0b98+XXrppb7vu92ehwNGRUVp9+7dGjNmTLvXjY2NVWxsrD/lW8/bv9I3UXJwR3mgbXZPlvRHfcdRrBg1qVHRVpcEAAgQv35rxsTEKCMjQ/n5+W2O5+fnKysrq8OfyczMbHf+xo0bNX36dEVHR2vcuHHauXOnduzY4fu67LLL9N3vflc7duwIj6meruKW5qD6xEzTYTNBfY0GZTg+tbocAEAA+f244NzcXN1www2aPn26MjMz9dRTT6mkpETz5s2T5JmqOXjwoJ599llJ0rx587RixQrl5ubqlltuUUFBgdasWaPnnntOkhQXF6eJEye2eY+BAwdKUrvjYY9l+YPM0Gb3JP3QuUUzHTtV0HLnEAAg/PkdWObMmaPKykotXbpUpaWlmjhxovLy8jRy5EhJUmlpaZs1WdLT05WXl6eFCxfqySefVGpqqp544gldddVVgfsU4SJMb2ketejvVpfQZZtdnsCS7dipX+kaq8sBAASI34FFkm677TbddtttHX7vt7/9bbtj5513nt5///0uv35HrxERwjSwhJMtbs+o3ERjnwarWkcUb3FFAIBA6FZgQTfVVnq2TAkFzWENUrF7hMY7SnSO4yP91d1xb1UkOtlI2L5HLglRJQAQeNyqEkqt7xJC0HB7MwBEHgJLKDElFBK+wOLcKZbpB4DIQGAJpVqW5Q+F99zjVG9GK8U4orFGGCwoCAA4KQJLqJgm67CESINifA9D/K5jh7XFAAACgsASKo21UvM3nn1GWILuDfdUSdJ3nTssrQMAEBgEllDxjq5E9ZFi+llbSy/wpnuKJCnD+FTxqrW4GgBATxFYQqWu1S3NJ3hQJALjgJmkz92pijLcmsndQgAQ9ggsoUL/Ssj5poXoYwGAsEdgCRVuaQ4577TQ+c4PZMhtcTUAgJ4gsIQKgSXktrnHqdaM1VCjShOM/VaXAwDoAQJLqBxjSijUGhWtt1ueLfQ9R5HF1QAAeoLAEiq15Z5tv1OsraOXed19piTpAmfXH74JALAfHn4YKsdaAkt/Akso/cM1Te4oQ1Mce5SkIzqkwVaXZJmTPRxR4gGJAOyLEZZQoYfFEoc1UDvMMZIYZQGAcEZgCZVjhzzb/knW1tELvebKkCRd4Ci0uBIAQHcRWELB1SR987VnnymhkNvo9gSWLMcu9dM3FlcDAOgOAksoeKeDDKfUp/f2UFjlc3OY9rqTFGs0K5tVbwEgLNF0Gwrehtt+iZKDjBh6hl5zZ+gWR54udG7XK+6zrS7ItmjMBWBX/PYMBV/DLdNBVslv6WP5nmOHnHJZXA0AwF8EllDw3dLMHUJWKTRP1xGzvwYZx3S24xOrywEA+InAEgosGmc5l5za6JouSZrteM/iagAA/iKwhIJ3WX5GWCz1snuGJOki5zY5eBgiAIQVAksoeEdYWIPFUlvdZ6jK7KtTjKPKMD61uhwAgB8ILKHgXTSOKSFLNSlK+W7PtNDFznctrgYA4A8CSygwJWQbeS7PLc0XObfJYFoIAMIGgSUUaLq1jS3uSaox+yjFOKIzjc+tLgcA0EUElmBzNUt1Rzz7LMtvuUZF6x/uMyVJs53cLQQA4YLAEmx1FZJMyXBIfYdYXQ0kvezy3C10ifMdpoUAIEwQWILNu2hc3yGSw2ltLZAkvemeomqzj1KNI5rO3UIAEBYILMHGLc2206AYvdLSfHu5822LqwEAdAUPPww27x1C/ex5h1BXHnYXiV5yZ+lqbdLFznd1f/OP1cx/CgBga/xfOth8Iyw03NpJgXuCDpsJGmpUaaZjp95sacTFyfFEZwBWYEoo2Lw9LDYdYemtXHLqb67vSJIud261uBoAwMkQWILtGCMsdvWi6xxJUo5ju/qo3uJqAAAnQmAJNhaNs60d5hjtd5+ifkaDLnS8b3U5AIATILAEG8vy25ihv7g9oyxXOd+yuBYAwIkQWIKNERZb2+A6V5KU7dipZFVaXA0AoDMElmByu6S6ll+CrMNiSyVmkt51j5PDMPVD5xarywEAdILAEkx1lZLplmSwLL+N/blllMUzLWRaWwwAoEMElmCqKfNs+w2VnCx5Y1d5rhmqM2M1xlGqacZnVpcDAOgAgSWYvIFlANNBdlarPnrZ7Vmq/0c03wKALRFYgqmm1LMdkGJtHTipP7nOkyT9wFnAmiwAYEMElmA6dsizHZBsbR04qXfd47TPnaR44xv9wPmO1eUAAL6FwBJMjLCEDVMOPef6niTpeudrFlcDAPg2Aksw+XpYGGEJB392natG06mpjj06w9hrdTkAgFa4dSWYvCMs/Qks4aBSCXrFfbYucxboeuc/dHfzXKtLCls80RlAoDHCEkw19LCEmz+6vi9Jusy5Vf30jcXVAAC8CCzB4na1arqlhyVcvOMery/cKepv1OsK59tWlwMAaEFgCZbaCsl0SYbDs3AcwoShP7gukCTd6NwoVr4FAHsgsASLt3+l3ymschtm/uQ6T8fMOI11fKlzHB9ZXQ4AQASW4GGV27BVo76+5wvd5HzF4moAAFI3A8vKlSuVnp6uuLg4ZWRkaPPmzSc8f9OmTcrIyFBcXJxGjx6t1atXt/n+008/rezsbA0aNEiDBg3SBRdcoPfee687pdnHMW9goX8lHP3WNUuS9H1nkUYZpRZXAwDwO7CsX79eCxYs0JIlS1RUVKTs7GzNnj1bJSUlHZ6/d+9eXXzxxcrOzlZRUZHuvvtu3XHHHdqwYYPvnDfffFPXXnut3njjDRUUFGjEiBHKycnRwYMHu//JrMYaLGFtn5mif7jOlCT92LnR4moAAIZpmn51Fc6YMUPTpk3TqlWrfMfGjx+vK664QsuWLWt3/l133aWXXnpJxcXFvmPz5s3TBx98oIKCgg7fw+VyadCgQVqxYoVuvPHGLtVVXV2thIQEVVVVKT4+3p+PFBx/nS8V/lY6f7F0/iJLSujKWhjo3DmOnfpDzDIdM+OU2bBCNeprdUkRhXVYAEhd//3t1whLY2OjCgsLlZOT0+Z4Tk6Otm7d2uHPFBQUtDt/1qxZ2r59u5qamjr8mbq6OjU1NWnw4MGd1tLQ0KDq6uo2X7biHWHpTw9LuHrbPVG73cPV36hnuX4AsJhfgaWiokIul0tJSW1/CSclJamsrKzDnykrK+vw/ObmZlVUVHT4M4sWLdKwYcN0wQUXdFrLsmXLlJCQ4PtKS0vz56MEXw09LOHP0G+afyBJujnqZcWq0eJ6AKD36lbTrWEYbf5smma7Yyc7v6PjkvToo4/queee0/PPP6+4uLhOX3Px4sWqqqryfR04cMCfjxB89LBEhJfcWfrSTNRQo0o/cr5ldTkA0Gv5FVgSExPldDrbjaaUl5e3G0XxSk5O7vD8qKgoDRkypM3xxx57TA8//LA2btyoyZMnn7CW2NhYxcfHt/myDVezVFvu2WeEJaw1K0pPN3t6LX7q/JuccllcEQD0Tn4FlpiYGGVkZCg/P7/N8fz8fGVlZXX4M5mZme3O37hxo6ZPn67o6GjfsV/96ld68MEH9corr2j69On+lGU/tYcl0y0ZTqlfotXVoIfWu85XpTlAIx3lusTxrtXlAECv5PeUUG5urp555hmtXbtWxcXFWrhwoUpKSjRv3jxJnqma1nf2zJs3T/v371dubq6Ki4u1du1arVmzRnfeeafvnEcffVT33HOP1q5dq1GjRqmsrExlZWU6duxYAD6iBXxPaT5FcjitrQU9Vq9Y/W/zRZKk26JelCG3xRUBQO/jd2CZM2eOli9frqVLl2rq1Kl66623lJeXp5EjR0qSSktL26zJkp6erry8PL355puaOnWqHnzwQT3xxBO66qqrfOesXLlSjY2N+tGPfqSUlBTf12OPPRaAj2iBYzylOdI867pQ1WYfjXMc0GxHmC9qCABhyO91WOzKVuuwbF8r/W2hNPZi6drnLCuDdVgCa75zgxZGb9Cn7mG6qPGXcvNkix5hHRYAUpDWYUEXcYdQRFrrmq0qs69OdxzUDxzvWF0OAPQqBJZg8PWwEFgiSY366qmWdVnmR23gjiEACKEoqwuISDypOWL91jVLc6PyNMZRqiudW/Rn13lWlxS2TjZlyZQRgNYILMFQ9aVnmzA8qG9Dj0ro1aqPVjVfqrujn1Nu1J/0V1emGhRjdVkAEPGYEgo005SOtqy6mzDC2loQFP/nmqUvzUSlGkd0s/Nlq8sBgF6BwBJo9UelxhrPfpBHWGCNBsXosaarJUm3Rr2kwbLZgzcBIAIxJRRo3tGVvolSTF9ra0HQvOjO0s3uPE1y7NMdUc/r/uafWF1SxOnKlCd9LkDvwQhLoFW1BJaBNnt6NALKlEMPN18vSbre+Q+danxpcUUAENkILIHm618hsES6AvcZ2ujKULTh0tKo30qKiDUYAcCWmBIKNN8ICw23vcHS5ht1ruNDZTk/1g9c7+hv7kyrS+pVmDYCeg9GWALtaMtzlBhh6RW+NIdqZfPlkqQl0X9QX9VbXBEARCYCS6DRw9Lr/Mb1A+13n6IU44gWRG2wuhwAiEgElkCjh6XXaVCM7m/+sSTpZmeeJhl7LK4IACIPgSWQGuukugrPPiMsvcob7jP1oitLTsPUo9G/UbSarS4JACIKgSWQvEvyxwyQ4gZaWgpC74GmG1VpDtB4xwH9q/OvVpcDABGFwBJIVd6G2+GSYVhbC0LuiOL1QJNnaujnUS9orFFicUUAEDkILIF0lIbb3u4ld6byXdMUazRrefSTilWj1SUBQEQgsARSFQ23MLS46RZVmPEa7zigf49ab3VBABARCCyBxAgLJFUoQb9o+qkkaW7Uy5rp2GlxRQAQ/ggsgcQIC1q87p6m3zVfIEl6PHqVhupriysCgPBGYAmkoyzLj+Mear5en7jTdIpxVCti/kdOuawuCQDCFoElUFxNUs1Xnn1GWCCpXrG6rWm+asw+muH4hH4WAOgBHn4YKNVfSaZbcsZI/ZOsrgY2scdM1S+afqpVMb/WvKi/6QP3GL3snmF1Wb0KD0gEIgMjLIHi7V+JHyY5uKw47mX3DD3dfLEkTz/LRJbuBwC/8Zs1UI7s9WzpX0EHHmm+Vm+4pqiP0ahnYv5LSTpidUkAEFYILIFSsduzHTrW2jpgSy459fOmn2u3e7iSja+1JuYx9Ved1WUBQNggsATK4U8928TTra0DtnVMfXVz07/rsBmviY59+k30f7MSLgB0EU23geIbYRlnbR2wtS/NofqXxl/ouZiHdI5zl36tJ3V70x1yyWl1ab0ajbmA/THCEghN30hf7/fsMyWEk/jIHK2fNuWqwYzSRc5t+mX003LIbXVZAGBrBJZAqPhMkinFDZT6DbW6GoSBAvcZuqPp52o2HfqR8y39Kno1oQUAToDAEggVLf0rQ8dJhmFtLQgbr7rP0h1NP1Oz6dBVzi36r+hVrIYLAJ2ghyUQDnv7VwLXcNuVOXWEvzz3d+Rucuh/ov9HVzrfVn/V6+dNP1O9Yq0uDd9ysv8m6XEBgosRlkDwNtwm0r8C/73iPlu3Nc1XgxmtC52F+l3MMiXomNVlAYCtEFgC4TB3CKFn8t3TdUPjIlWbfXWW41NtiLlfI40yq8sCANsgsPSUq1mq/MKzH8ApIfQ+75nj9U+N/6FSc7BOdXylF2PuVZbjI6vLAgBbILD01Nd7JXeTFN1Xih9udTUIc7vNEbqs4UEVuU/VQKNWz0Y/olucf5NkWl0aAFiKwNJT3umgxNN56CEC4rAG6ZrGe7TBNVNRhltLov+op6P/i74WAL0av2F76vAnni0LxiGAGhSjf2u6VUuabmppxn1febGLmSIC0GsRWHqqgmcIIVgM/cF1gX7Y+ID2upM0zKjUH2Me1v1Rv1Uf1VtdHACEFIGlp7hDCEG2yxylSxqX6XfNF0iSfhK1URtj7tL5jiKLKwOA0GHhuJ5obmwVWJgSQvDUKU73Nt+kV91n6ZfRTynNcVi/jfmV8lxn6+Hm6/WlySMhrMYDFIHgIrD0xFfvS83fSH2HSIPHdPnHWMUW3bXFPUkXNvxKd0Q9r7nOPF3sfE/fdxRpresirWy+XDXqa3WJCAHCEXojAktP7H3Lsx01kzuEEDJ1itMjzdfpBddM/UfU73SOc5dujfqr5jjf0NPNP9D/uXJUpziry0Q38RcaoGP8lu0Jb2BJP9faOtAr7TZH6Pqmu3VT45363J2qwcYx3RW9Tptj5+s2518Uz23QACIIIyzd1VQvHXjPsz+KwAKrGHrdPU1vNk7V5Y63NT/qeY1yHNIvov+fbo96Uetd39Wzrgu1z0yxulCI0ROgJxhh6a4v35NcDVL/ZCnxNKurQS/nlkMvuLP1/cbHtLDxVhW709TPaNBNUa/ozdh/07PRyzTL8Z6i1Wx1qQDQLYywdNfezZ5terZkGNbWArRwyakX3Nl6oXGmznV8qJ84X9X5jg90rnOnznXu1BGzv150naO/ujL1vnmaJP7dBRAeCCzdRf8KbM3QW+4pess9RWnGIV3nfF0/dG5WknFU/xL1qv4l6lV9aSbqZdfZyndlqNA8XS45rS4aADplmKYZEU9Vq66uVkJCgqqqqhQfHx/cN2uslR4Z6Xno4R07pMHpfv0489iwglMuzXR8pCucW3Sho1D9jeOr5X5t9tdm9yS95Z6sza5JOqTBFlaKUOC2Z9hFV39/M8LSHSXveMJKQpo0aJTV1QBd4pJTm9xTtMk9RbFq1PmOHcpxFup7jiINMo7pMmeBLnMWSNHSHney3nGP1zb3OL1vnqb9ZpKYPoosrOWCcENg6Y7ilzzbUfSvIDw1KEavus/Wq+6z5ZRL04zPlO38UOc6PtQkY69GO8o02lGm6/SGJKnSHKCd7tH60EzXLne6is0ROmAOlUnfPoAQIbD4q/wT6f3fefanXtfu20z3INy45NQ2c5y2NY/T47pa8arVdMdufcdRrAzHp5po7NUQo0bnOz/Q+frA93PHzDh9bg7zfLlTtddM0V4zWfvNJDUoxsJPBCASdSuwrFy5Ur/61a9UWlqqM844Q8uXL1d2dnan52/atEm5ubnatWuXUlNT9Ytf/ELz5s1rc86GDRt077336osvvtCYMWP00EMP6corr+xOecG18R7JdEljL/HcIQREmGr10+vuaXrdPU2SFKMmTTD2a6JjryYbezTesV+nGwfV36jXVOMLTdUX+na/7mEzQQfMoTpoJuqgmahSc4jKzEE6ZA5WuTlQhzVQTfx9yfZC+Rcwpp9wMn7/H2P9+vVasGCBVq5cqXPOOUe/+c1vNHv2bH388ccaMWJEu/P37t2riy++WLfccot+//vf6+2339Ztt92moUOH6qqrrpIkFRQUaM6cOXrwwQd15ZVX6oUXXtDVV1+tLVu2aMaMGT3/lIHy2WvS5/mSI1rKedDqaoCQaFS0dpinaofrVN8xp1xKN0p1mnFQpxoHNcbxlUYZZRptlCneqNNQo0pDjSpN0+edvu5Rs58qzXhVKEFfmwP0tdlfR9VfR83+qlI/VZn9VKO+qjH7+La16qM6xYp+mshzsnBEoIHfdwnNmDFD06ZN06pVq3zHxo8fryuuuELLli1rd/5dd92ll156ScXFxb5j8+bN0wcffKCCggJJ0pw5c1RdXa2XX37Zd85FF12kQYMG6bnnnutSXUG9S6i+Svr8Nen1/5SO7JEyfybNeqjDU5kSQu9mKkG1SjPKNcIoV4pRqWFGpZKNI0o2jijJ+FpDdVQxhqvb7+A2DdUpVt8oVnVmrOoUq3rFqt6MUb2iVa8Y1StGDWa0GhStRkWrUVFqNL37TjUp6viX6VSzotQsp5rlaNl3yCWnmk3P1iWHXHKoRn1VYiYF8HqhqwgskSsodwk1NjaqsLBQixYtanM8JydHW7du7fBnCgoKlJOT0+bYrFmztGbNGjU1NSk6OloFBQVauHBhu3OWL1/eaS0NDQ1qaGjw/bmqqkqS54MHjGlK/+/HnjVXzJYVQvsMkab+q9TJ+7gb6gL3/kAY+loOfa1kfajkTs4wFa9aJRpVGqxjGmxUa5BRowTVapBxTPFGrQaoTvFGnQaoTv2NevVXnfqrXk7DlGRK+qYlskiDQvfR9LZrgv616d9C+I7wGrHwT1aX4JePHphldQlhw/t7+2TjJ34FloqKCrlcLiUltf0bRlJSksrKyjr8mbKysg7Pb25uVkVFhVJSUjo9p7PXlKRly5bpgQceaHc8LS2tqx+nm2qk+0YG+T0A2NO7kq62ugiEgYTlVlcQfmpqapSQkNDp97vV9WZ861Ze0zTbHTvZ+d8+7u9rLl68WLm5ub4/u91uHTlyREOGDDnhz/mrurpaaWlpOnDgQPAXpOtFuK7Bw7UNDq5rcHBdgyOcrqtpmqqpqVFqauoJz/MrsCQmJsrpdLYb+SgvL283QuKVnJzc4flRUVEaMmTICc/p7DUlKTY2VrGxsW2ODRw4sKsfxW/x8fG2/4cejriuwcO1DQ6ua3BwXYMjXK7riUZWvPxa9SkmJkYZGRnKz89vczw/P19ZWVkd/kxmZma78zdu3Kjp06crOjr6hOd09poAAKB38XtKKDc3VzfccIOmT5+uzMxMPfXUUyopKfGtq7J48WIdPHhQzz77rCTPHUErVqxQbm6ubrnlFhUUFGjNmjVt7v6ZP3++zj33XP3yl7/U5ZdfrhdffFGvvfaatmzZEqCPCQAAwpnfgWXOnDmqrKzU0qVLVVpaqokTJyovL08jR3oaUUtLS1VSUuI7Pz09XXl5eVq4cKGefPJJpaam6oknnvCtwSJJWVlZWrdune655x7de++9GjNmjNavX2+LNVhiY2N13333tZt+Qs9wXYOHaxscXNfg4LoGRyRe14h5WjMAAIhcPLkMAADYHoEFAADYHoEFAADYHoEFAADYHoHlJFauXKn09HTFxcUpIyNDmzdvtrqksPLWW2/p0ksvVWpqqgzD0F/+8pc23zdNU/fff79SU1PVp08fnX/++dq1a5c1xYaRZcuW6ayzztKAAQN0yimn6IorrtDu3bvbnMO19d+qVas0efJk32JbmZmZbR7KyjUNjGXLlskwDC1YsMB3jGvrv/vvv1+GYbT5Sk4+/gyvSLumBJYTWL9+vRYsWKAlS5aoqKhI2dnZmj17dpvbtnFitbW1mjJlilasWNHh9x999FE9/vjjWrFihbZt26bk5GRdeOGFqqmpCXGl4WXTpk26/fbb9c477yg/P1/Nzc3KyclRbW2t7xyurf+GDx+uRx55RNu3b9f27dv1ve99T5dffrnvf/Jc057btm2bnnrqKU2ePLnNca5t95xxxhkqLS31fe3cudP3vYi7piY6dfbZZ5vz5s1rc2zcuHHmokWLLKoovEkyX3jhBd+f3W63mZycbD7yyCO+Y/X19WZCQoK5evVqCyoMX+Xl5aYkc9OmTaZpcm0DadCgQeYzzzzDNQ2Ampoa87TTTjPz8/PN8847z5w/f75pmvz72l333XefOWXKlA6/F4nXlBGWTjQ2NqqwsFA5OTltjufk5Gjr1q0WVRVZ9u7dq7KysjbXODY2Vueddx7X2E9VVVWSpMGDB0vi2gaCy+XSunXrVFtbq8zMTK5pANx+++265JJLdMEFF7Q5zrXtvs8++0ypqalKT0/XNddcoz179kiKzGvarac19wYVFRVyuVztHsCYlJTU7kGN6B7vdezoGu/fv9+KksKSaZrKzc3VzJkzNXHiRElc257YuXOnMjMzVV9fr/79++uFF17QhAkTfP+T55p2z7p16/T+++9r27Zt7b7Hv6/dM2PGDD377LM6/fTTdejQIf3nf/6nsrKytGvXroi8pgSWkzAMo82fTdNsdww9wzXumZ/97Gf68MMPO3z2FtfWf2PHjtWOHTt09OhRbdiwQT/+8Y+1adMm3/e5pv47cOCA5s+fr40bNyouLq7T87i2/pk9e7Zvf9KkScrMzNSYMWP0f//3f/rOd74jKbKuKVNCnUhMTJTT6Ww3mlJeXt4usaJ7vN3sXOPu+/nPf66XXnpJb7zxhoYPH+47zrXtvpiYGJ166qmaPn26li1bpilTpujXv/4117QHCgsLVV5eroyMDEVFRSkqKkqbNm3SE088oaioKN/149r2TL9+/TRp0iR99tlnEfnvK4GlEzExMcrIyFB+fn6b4/n5+crKyrKoqsiSnp6u5OTkNte4sbFRmzZt4hqfhGma+tnPfqbnn39er7/+utLT09t8n2sbOKZpqqGhgWvaA9///ve1c+dO7dixw/c1ffp0XX/99dqxY4dGjx7NtQ2AhoYGFRcXKyUlJTL/fbWs3TcMrFu3zoyOjjbXrFljfvzxx+aCBQvMfv36mfv27bO6tLBRU1NjFhUVmUVFRaYk8/HHHzeLiorM/fv3m6Zpmo888oiZkJBgPv/88+bOnTvNa6+91kxJSTGrq6strtzebr31VjMhIcF88803zdLSUt9XXV2d7xyurf8WL15svvXWW+bevXvNDz/80Lz77rtNh8Nhbty40TRNrmkgtb5LyDS5tt3xb//2b+abb75p7tmzx3znnXfMH/zgB+aAAQN8v6Mi7ZoSWE7iySefNEeOHGnGxMSY06ZN8902iq554403TEntvn784x+bpum59e6+++4zk5OTzdjYWPPcc881d+7caW3RYaCjayrJ/N///V/fOVxb/910002+/96HDh1qfv/73/eFFdPkmgbStwML19Z/c+bMMVNSUszo6GgzNTXV/OEPf2ju2rXL9/1Iu6aGaZqmNWM7AAAAXUMPCwAAsD0CCwAAsD0CCwAAsD0CCwAAsD0CCwAAsD0CCwAAsD0CCwAAsD0CCwAAsD0CCwAAsD0CC4CwceDAAZ1//vmaMGGCJk+erD/96U9WlwQgRFiaH0DYKC0t1aFDhzR16lSVl5dr2rRp2r17t/r162d1aQCCLMrqAgCgq1JSUpSSkiJJOuWUUzR48GAdOXKEwAL0AkwJAQhL27dvl9vtVlpamtWlAAgBRlgAhJ3KykrdeOONeuaZZ6wuBUCIMMICwDZ+9KMfKTExUatWrfId2759u2JjY5WXlydJamho0JVXXqnFixcrKyvLqlIBhBhNtwBso7S0VA888ID+8pe/qKysTHV1dTrzzDN14YUXasWKFTJNU9ddd53Gjh2r+++/3+pyAYQQgQWArXzyyScaP368ysrKdO+992rLli0qLCxUnz59tGXLFp177rmaPHmy7/zf/e53mjRpkoUVAwgFAgsAW3G73RowYIB+8pOf6JlnntE777yjM8880+qyAFiMplsAtuJwODRx4kStXLlSjz76KGEFgCRGWADYjGmayszMVFNTk7Zt2yaHg3sDAHCXEACbWb58ud5991253W7CCgAfRlgA2MbOnTt11lln6eabb9Yzzzyj2tpaRUUxcw2AwALAJurr63XWWWdp2rRp+vWvf61BgwZp586dmjhxotWlAbABxlsB2MKiRYtUW1urFStWaODAgRo5cqT++7//WwcPHrS6NAA2QGABYLmNGzfqySef1O9//3sNGDBAknTPPffoxRdf1Lx58yyuDoAdMCUEAABsjxEWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABgewQWAABge/8frWZ/apYbYEAAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1726,7 +1866,7 @@
     }
    ],
    "source": [
-    "p = np.zeros((len(ys),2))\n",
+    "p = np.zeros((len(y2s),2))\n",
     "chi2s = np.zeros(len(y2s))\n",
     "for i in range(len(y2s)):\n",
     "    p[i] = fit(y2s[i])[0]\n",
@@ -1734,7 +1874,7 @@
     "\n",
     "chi_axis = np.linspace(0,30,100)    \n",
     "plt.hist(chi2s,bins=50, density=True)\n",
-    "# draw Chi2 distribution\n",
+    "plt.plot(chi_axis, stats.chi2.pdf(chi_axis,8))\n",
     "plt.xlabel(\"$\\chi^2$\")\n",
     "plt.show()\n",
     "print(np.mean(chi2s))"
@@ -1742,7 +1882,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b6900f2c",
+   "id": "885a8c6d",
    "metadata": {},
    "source": [
     "### Compute p-value for goodness of fit"
@@ -1751,7 +1891,7 @@
   {
    "cell_type": "code",
    "execution_count": 176,
-   "id": "b05f7c60",
+   "id": "9f929217",
    "metadata": {},
    "outputs": [
     {
@@ -1797,7 +1937,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dbc80ced",
+   "id": "079da48a",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1819,8 +1959,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
-   "id": "7ce7209b",
+   "execution_count": 296,
+   "id": "cb71a85e",
    "metadata": {
     "cell_style": "split"
    },
@@ -1839,6 +1979,7 @@
    "source": [
     "import scipy.stats as stats\n",
     "\n",
+    "\n",
     "def like(m, a, xs, ys, sigma_y):\n",
     "    return np.prod(stats.norm.pdf(ys,xs*m+a,sigma_y))\n",
     "\n",
@@ -1853,8 +1994,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 156,
-   "id": "3865ec3c",
+   "execution_count": 297,
+   "id": "e7840c7a",
    "metadata": {
     "cell_style": "split"
    },
@@ -1880,7 +2021,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "556b0e57",
+   "id": "79943c7a",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1892,8 +2033,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
-   "id": "199578d3",
+   "execution_count": 295,
+   "id": "6fb5b592",
    "metadata": {
     "cell_style": "split",
     "slideshow": {
@@ -1927,7 +2068,7 @@
   {
    "cell_type": "code",
    "execution_count": 133,
-   "id": "591cb218",
+   "id": "6ca9738c",
    "metadata": {
     "cell_style": "split"
    },
@@ -1953,7 +2094,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "952ea74d",
+   "id": "6b816fd9",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1965,8 +2106,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
-   "id": "1b35afbb",
+   "execution_count": 298,
+   "id": "0316845d",
    "metadata": {},
    "outputs": [
     {
@@ -2000,7 +2141,7 @@
   {
    "cell_type": "code",
    "execution_count": 158,
-   "id": "a66199ac",
+   "id": "ec27bdef",
    "metadata": {
     "cell_style": "split"
    },
@@ -2046,10 +2187,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2b0c38b2",
+   "id": "5d503546",
    "metadata": {
     "slideshow": {
-     "slide_type": ""
+     "slide_type": "slide"
     },
     "tags": []
    },
@@ -2075,10 +2216,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "44dde589",
+   "id": "f050d6ca",
    "metadata": {
     "slideshow": {
-     "slide_type": ""
+     "slide_type": "slide"
     },
     "tags": []
    },
@@ -2101,7 +2242,7 @@
    "id": "d9571970-772e-4e95-8474-82e0afb1dd68",
    "metadata": {
     "slideshow": {
-     "slide_type": ""
+     "slide_type": "slide"
     },
     "tags": []
    },
@@ -2121,7 +2262,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c751f13f",
+   "id": "3ec8f522",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2133,7 +2274,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "288f14a1",
+   "id": "75c89f79",
    "metadata": {},
    "source": [
     "Needs likelihood ratio: \n",
@@ -2142,8 +2283,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 183,
-   "id": "2cf6b3b2",
+   "execution_count": 301,
+   "id": "7a0b7218",
    "metadata": {},
    "outputs": [
     {
@@ -2163,8 +2304,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 189,
-   "id": "35c623ea",
+   "execution_count": 302,
+   "id": "e69c328d",
    "metadata": {},
    "outputs": [
     {
@@ -2176,7 +2317,7 @@
        " np.float64(4.700741203958106))"
       ]
      },
-     "execution_count": 189,
+     "execution_count": 302,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2192,7 +2333,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7bec9f14",
+   "id": "3d800699",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2205,7 +2346,7 @@
   {
    "cell_type": "code",
    "execution_count": 198,
-   "id": "04bfdf08",
+   "id": "197c6ea9",
    "metadata": {},
    "outputs": [
     {
@@ -2243,7 +2384,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6baae520",
+   "id": "a5ba5b83",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2256,7 +2397,7 @@
   {
    "cell_type": "code",
    "execution_count": 201,
-   "id": "5c35df31",
+   "id": "7cee3c8a",
    "metadata": {},
    "outputs": [
     {
@@ -2277,12 +2418,16 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "50c1ba94",
-   "metadata": {},
-   "outputs": [],
-   "source": []
+   "cell_type": "markdown",
+   "id": "a7f8fddf",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Thanks a lot"
+   ]
   }
  ],
  "metadata": {