12_F_distribution_Anova.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8727afd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "from scipy.stats import norm\n",
    "from scipy.stats import t\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from matplotlib import figure\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f8b88f5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ca63bb20",
   "metadata": {},
   "source": [
    "# Analysis of variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd2c891a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b6fc785e",
   "metadata": {},
   "source": [
    "- Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures (such as the \"variation\" among and between groups) used to analyze the differences among means. \n",
    "- ANOVA was developed by the statistician Ronald Fisher.\n",
    "- ANOVA is based on the law of total variance, where the observed variance in a particular variable is partitioned into components attributable to different sources of variation.\n",
    "- In its simplest form, ANOVA provides a statistical test of whether two or more population means are equal, and therefore generalizes the t-test beyond two means.\n",
    "- In other words, the ANOVA is used to test the difference between two or more means."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab63a31f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2ce81beb",
   "metadata": {},
   "source": [
    "### problem with multiple t-tests :\n",
    "\n",
    "- multiple t-test even at small significance level , can result in a high probability of false positive error ( incorrectly rejecting null hypothesis.  )\n",
    "- if the result of one hypothesis test is : mean1 = mean2 \n",
    "- and another hypothesis test result is mean2 = mean3.\n",
    "- we cannot conclude that mean1 = mean2 = mean3. \n",
    "- as the type 1 error (alfa)(false +ve) multiplies. \n",
    "              "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e36a05b",
   "metadata": {},
   "source": [
    "### Assumptions : \n",
    "\n",
    "- independence of observations \n",
    "- distibutions of the residuals are normal.\n",
    "- equality of variance : homoscedasticity - the variance of data in groups should be the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb31e0ec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3cdf3421",
   "metadata": {},
   "source": [
    "### Partitioning of the sum of squares : "
   ]
  },
  {
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    "-  The definitional equation of sample variance is\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    " - where the divisor is called the degrees of freedom (DF), the summation is called the sum of squares (SS), the result is called the mean square (MS) and the squared terms are deviations from the sample mean."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd235271",
   "metadata": {},
   "source": [
    "- ANOVA estimates 3 sample variances: \n",
    "    - a total variance based on all the observation deviations from the grand mean\n",
    "    - an error variance based on all the observation deviations from their appropriate treatment means a treatment variance. \n",
    "    - The treatment variance is based on the deviations of treatment means from the grand mean\n",
    "- the result being multiplied by the number of observations in each treatment to account for the difference between the variance of observations and the variance of means."
   ]
  },
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"
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"
    }
   },
   "cell_type": "markdown",
   "id": "3a62e0c7",
   "metadata": {},
   "source": [
    "> The fundamental technique is a partitioning of the total sum of squares SS into components related to the effects used in the model.\n",
    "For example, the model for a simplified ANOVA with one type of treatment at different levels.\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    ">The number of degrees of freedom DF can be partitioned in a similar way: one of these components (that for error) specifies a chi-squared distribution which describes the associated sum of squares, while the same is true for \"treatments\" if there is no treatment effect.\n",
    "\n",
    "![image-2.png](attachment:image-2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d0fda84",
   "metadata": {},
   "source": [
    " #### SST =  SSE + SSC\n",
    "\n",
    "    - SST : total/ overall sum of squares \n",
    "        > sum of squares between each data point and the overall mean\n",
    "        \n",
    "    - SSC : column/between/treatment sum of ssquares \n",
    "        > sum of squares  between each group mean and overall mean\n",
    "        \n",
    "    - SSE : Within/error sum of squares \n",
    "        > sum of squares between each data points and their own group mean\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7e06cb1",
   "metadata": {},
   "source": [
    "### The F-test :"
   ]
  },
  {
   "attachments": {
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LkiRJkkZnoHOTeeGFF9q55SM8a/bv39/O3R7MY54RWfmCmeBp3A4PtokpRD55WcZy8gk3btxYtn0OcrFdfjMy25SjxNjfJ598spQm8uJlN4FACqOhAuvJN/YZo7niO8iPW0L5ZHm+ZRTMx6i3/NKcMl3tTcgEksp04yIAUhsxR76xnKBWBKsCoygj2EXwhpGbWRwX6cq0fI9AIYHaOC4CpsNG31IfOb8yAJ9HCo6S11tvvdXMc7xPP/30bXXMMbDs3XffbZfcSnf69OlmnnV96aKOmGfK/Qe5v8Q2BAcD33MfGxUjDCkn2EfgjfUPPPBA++23fZbn0rB9RrCXbTjWfH6BfTLaMwfIc7sRiC7PJfpRlBnkwfmXf1+QR/VGGcmL868MhDPlc5h/erBtpOMzfwf7LcvG8eXAam43UO4XX3yxmWcd50Nej+gTca4wz1TuK+cd2+T+x/HU2qevzQ8cONB++w31VZ63iBGd0XclSZIkSStwU5vKhQsXeJV3M43ilVdeWdp+8WL75uHDh28uXng33/fu3dtsw2dsw3Tx4sVmysvKaWZmpkmbkXdtWybWBY6BssS62dnZpbTMI8oYE2mQl5UTeZw+fbq6LqaTJ082+YSFhYWb8/PzS+ujjmI+9lsi3dzc3NJ21CHpmO9LNwz5kVeUK8rDPHU+rFwh1zGf1Ge0c7R7DfUX6diOie+jOH78eJOO8pY4HtYxMT8KtsvHQFlGbRv6UZmO+ivTDesvbJv7R20a9XhCtCtpo36ZsjjOrok8ulCeXOY4v9gXdcAxlzgvYnvKwvZxDpKePEmft6nJ5Y7ty3Mu2qY2kQa1dTFx3tEutXUx0RezcfpSrovaxLbD2qdEmWP/HD/z5W9o/ObGeib2k9uSfCRJkiRJK7eNP4sXWOuGkSx5pM9KcCvmet+SvZ6oo8WL4NtGzXWJ0UjUK7ec7t69e9mIvljPZx7VxEjJ2q2V4NZYXkKTkQe3XHfdAh37C7QR+2E0F7d3Uq6jR4+2a28955K3PZOO4wVv7K7lT3koK9t1lZttZmdnl/LKYl/UEf3n+vXry0ZrdWHUF881jXSojcYaFXWSjze3Hflzy3VZ711Iy4hARoj98MMPTf0yCq/2tvOMdJSDdmE0XNluXUjHbbddacbttyDPmMZpm1o6lG0T29TQl+mPeRReKbYZF3kyff/9982ovtq51KWvH2ccV/SdOO/7+mbeHtQZbVX2RZ7l2nXMUZ+MwublWLkvh2HnMGk4H7vO4ZmZmaVRqzW0CfVT9vMoG1P0CX53ymOJbWpG6ROkrfVx0jB1tTnp6NdMzEdbRN/NbSFJkiRJWpl1D3RyGyHPLuMtv/likgs8Lny5wI1nnsV25YUpF4TcXixJkiRJkiRJWPdAZyBwyfMPw7BiEBTNz99bzag6SZIkSZIkSVvLhr2MKI/QrN3CWOJ2x3gZhrf3SZIkSZIkSco2LNCZb1vPbxrvE89F43Z2SZIkSZIkSQobFuiMF2Jg1JehBF5EIUmSJEmSJElhQwKd3LaeR3TWAp287bp8szpv5IW3rkuSJEmSJEnKNizQGbqez3n+/PnB66+/3n675ZdffhkcOnSo/SZJkiRJkiRJt2xIoHOU53OeOnVq8Pzzz7ffbuEZnWfOnGm/SZIkSZIkSdItGxLozM/nrHnuueeWjfqUJEmSJEmSpD7bbi5q59cFAcydO3e235Y/n5N1OcC5FkXjOZ9vvvlm+21tnDx5cuwXKEmSJEmSJEmanHUPdHLb+r59+5p5Xiq0Z8+ewbVr1wZ33nlnsyyCnaxbWFholq0GeeVb5dcCt9BLkiRJkiRJ2jzWPdD5xhtvLI2wnJ+fb75njMDk1vXauq0gj1iVJKkP//STJEmSJI1m3QOdjOaMEZa1YCaBQG5t34qBzjyaVZKkYbizwWCnJEmSJI1mXQOdEcQMtV1funRp8Nhjj63J8zk3GwKda/28UEnS1sUzoQ10SpIkSdJo1jXQGbelY8eOHYP//ve/zXxGMPTs2bODo0ePtkskSZIkSZIkqd+6BjqHPZ9zEj766KPBiRMn2m9r49ixY4NHH320/SZJkiRJkiRpo61roJPb1uNlPOsV6GR/jCRdS1vt2aGSJEmSJEnStFu3QOcoz+eUJEmSJEmSpJX4v/Zz4vKoypmZmXZOkiRJkiRJklZvoiM6jxw5Mvj2228HV69eHVy7dq1degtvkeWFRHfeeefg2WefHRw+fLhdI0mSJEmSJEnjmWigk2dZfv/99+232125cqUJdPK8zr1797ZLJUmSJEmSJGk86/oyIklb0xdffNE8h/enn34a/Pzzz80yX9olSZIkSZLWk4FOTRwBsDfffLOZZ/Qujy3Qxrp06dLggw8+aNrm5MmTq26Tbdu2tXO/WelPC0HTU6dONQHT48eP218kSZIkSdJI1u1lRPrf9fLLLzcvo4pJG+/8+fNNWxBUfO6559ql3QiI9rlw4UITMF0LlIeynT17dvDuu++2SzfWsOOfZlv52FbDepEkSZKk6WOgUxN3+fLldm7Q+8xWrZ/cDrwsrM8jjzyyNCK3C8/Y5YVia/1SsXvuuaed2zjcgr9v377229ZC2/rPh9tt5TaXJEmSpK3MQKcmLm6NJhjGG/a18fILwI4dO9Z8duGFYespHm8wicCplvvqq6/aOWXXr19v5yRJkiRJ08RApyaOgNrCwkJze7Nv198cCCTSHjxHsy+YyO273N6+nigP/WUtnh26Fr788st2bmvx1uxuX3/9dTsnSZIkSZomBjoldRr3tuZ44/pWwUub1jvQu142y/NPNxsCwI50lSRJkqTpZKBTUhVBPt5+/r/s9ddfb+e2FoJ57733XvtNGS9PkyRJkiRNp203uXdVmx6BiXyr6Y8//th87tixo7m9N27xLbcL5S3jr7766uBf//rX4Keffhr88ssvzbIDBw4M9u/fP3j00Ueb76WPPvqo2Tbnz0s7+M7IP15w8/jjjy+7Ffrtt98ebN++fSkNz77rGklGHnGbMNuzr127di09s7GG7Rhx98033yy9uIZjZYpyIcrG8lFuh460UR6OYffu3c2x9aU/cuRIUw7Ssd04+8zyKMI77rhjcO3ataW2BuWjD8zMzCytQ7RzpM/bzM7ONumjTUA+tTbh2AlyRj4cd/l81dhX9vTTTzdvS0f8tLAP8o/2oQxdt8tHe5EGd9111+Do0aPNPFhOmei3MXqUcjARmOVt8hi3vUvsgxcwxfGTR/lWeZZF3uW5EXXKd46pdm5ksd2nn37a1BP59m2PSHPu3Lml56iSjn6a66xE+3/22Wcjt215Dsd+8zzHe+PGjWqZSU9breYcjOPas2fPsrKB7TiWtfgNiDf+g2362jzEPqIdWM9+7r///iaYPDc31/mbKkmSJElaYwQ6tfnNz88TNapOrAuHDx+ubpMtXug3y/g8fvz4zcWL+WZ+8QK9WZ7zy2J9ni5evNh85v0yj1iXJ/ZTE2WanZ2tlmlubu7mwsJCu/Vvasd74cKFJj3z5ME2kQ+ftXyyqGu2ZZ7p0KFDS8tOnz7dbvkb8oxjYHv2H2UjDcc0KspPutpEPdSOmSnqtit9lKFcTvmyONa+qUwTclpQF2zL8lzuKGtWKzdpstqxc1y0CfP0H7Zhn3znk3zH0Vf/ecr9KPaXpyhTLjPtV4rtyKPWd2r9lWMu01DHHH9fulr9lVNuG/Io18/MzDTnNtuxH/LM+T744INNWo6LbcttosxdyJv1eVsm8mJZmbZ2TLThOL8BK2lzRLooZ+w39sXE8UiSJEmS1oeBzilC4CACAExc+HNhnS+k+Z6DTVx054vzHLgog00RcGGqBWTYD/lFwIFpx44dzbIcKOAiP5RlrgW48n7L9ZQ3ggZlgAOUifQR4GHi+EnD8tB33BnrattEYImJfWU5b+ohi3qhPOW6LuQXdR35Rt6sY2I+ysrEttEPWJ/biX3zneWgXkgf9cpnFvvP7UKgiGV5qsl9j/KRLst5UqYS5cqBqzJ9HHu5TZwLge3i+PkcVxxj1DF1lI+dKeM7xxN1GhPLKFd8L+s696tcfkRdlmmQ2zbaFcxHGWrpam3LeZWPiynE9hxHbE++tbbN28Q5X/6O5EBsebwh6pzPLB9b/i2gfBxP7nvsl21ZHkgf62t1E8c+apuD7cpygn3x28j6WjpJkiRJ0mQY6JxCcbHPxAV1KYIYtUBSDnDULvZzACkHCbIcuMxBpEhbBkAQZe4KCtTyCwQtYn3teJGDSUy1IEoud01eX6bP+RNQyboCM2HY+j6RttZWuUy1443AT5e+vEGAJvKvtWlNDjbV8iXPvr4QIpDXtd9cNqZh7V1bP4phdVTK+4w09NnauZH7fe0fC3l9jMYFy6NcTOU5kftF7TcAOW/KPIq8z1rb5TyZam2X2612zMPO9Qim1tojHzfTsD7RZdQ2j7KU//gIsS8DnZIkSZK0fnwZ0RR68cUX27nlz3IMvDF48SK9+ny/Z555ZrB4gd48r5HPEs/BC/HcxxLPTQzPP/98Ozdo8lvsU9V8+1DWKNOHH37YLv3Ngw8+2M4Nlp7/2Ifj3ls8x6/EMxUzvvNMRtTS8/348eNNGXP982y+aIMHHnig+SzxvD7wLD+mcczPzzef+VmDIedVe2nQ5cuXq30gxHM6JyXKnuVnFfbVBc8l7TMzM9PO3eo/tfZm+UaKfhL9uzw3or+BZ7uW8nHxTM0Q+dG2fJbHmb/HM2bXQu4v5TM9wX7zvvOxhtxu8TzNLF6QdOjQoduOC1Ef9J3ab18Y5TegL/04eA5u7bnDo5RBkiRJkrS2DHROIV40EkGAEydONJ8ZwYInn3yy/XY7LsD/8Y9/3HYRTvAgXvCCeOFRn1owYiW6ylSaVHAuXsiEu+++u51bjnovy8gLUAKBG+qwnKKO4vs4cv2WwcwcxCqDNnxnXzkQXcoB60no6hu//vprO7c2NmswaVhfLdss+keeIg/mM+o2gp3rJfeXSdQ5Ly0KvGyqrIuYwloFKlcq6oAyvfTSS4OHHnqoeRlXHAdtdOHCBV9EJEmSJEnryEDnlIqLbEZv5ov/mO96s3lGoIC3DO/bt2/w+9//frBz585lo8x4U/cw8bbntVKWadu2bc33cdx7773tXLdytGoOGuYRpMPwpulA3VGH5TRu+TOCJYwixdWrV5e1K28YzyM+c+Dn888/bz5XE2TJo+9WoivQudp8p0XX8Yd83tb6DVOMYM7bZix/4403lp0vpJu0Yb8NK2njHBimL5d1wcRxBt6i3mWU34DVon3zqGX+6UF7vfzyy01ZaRdJkiRJ0voy0DmlDhw40M7dun06MD8swEIQgYABE9sTrDx27NhgYWGhuT17HBGEWy0CNrlMBD3m5uaaMjEqahzXr19v50aXA0mMJhtVDi5STm5P7ptWMhLurbfeaj4JLkU5qSO+79+/fynPPOLzb3/72+CVV15pv21t6xHUWom+8zD3G7ar9ZVyyugHBNAJqBFg5zujd6MPTtqwAPr999/fzo0u/7OBAGJ5/OVUuzU+dP0GrOS3oQ/BTM6zsq1pD9rlkUceaZdIkiRJktaDgc4pxbM24+I6Ah1xcV17NmKIICefpCcw8ve//725Bba8WJ/UbeIlyk3AJpfpzJkzy27RD+MEIUt9QY6VHmtZvknI+/j666+bT4JCtBkBp3gGaATP+KRO//SnPzXfJyH2UZr07fCbBcdfPud1HLlNV3Ir/2OPPbb0Dw7+OcE/BAi6rcUt5eRba9tJywHrtQ5IrgXau9bmf/7zn5vfLNqA4CvPFw2MuF9NP5EkSZIkjcdA5xTLLwQh8EKAggBK13P7CF4Q5AxcnJeBkVrAryuotVZymXgZ0SjBmrUu0+9+97t2brDsOaXDRJARk6oj2pTRrSBwEm0d+472Zv+sY2QnI20nGYRlNCH7+l9FHX/88cftt/HRNtHPR3lEREbdRxoCa/xDYBj6xqj9k/xHeenXWsvn/bffftvObR60d7wsCfT/eIkU7Rm/vfyThqBn3L5fe46yJEmSJGkyDHROMUZwRTCLwMsHH3xQfRtyyAEVLsJHCYQRHCEQOakg3qVLl5bypjyjPlOSMhH0Wyv5eXrlS39KOciXg8rjpBvXU0891c7dygexb+otgkSM6GUfL7zwQvN9qxo3ODgJqx29mt/SH6Mza2jP/M+A3IeG/VPgypUrzSd9pm8fpY14jir9OB6FMez3hvN1nONZC7T3rl272m+32qEr2M2x8DgQ5BedSZIkSZImy0DnlItABxfdBH+6RnMi3yLb9Qy9HKzjNvEIKOU3sI8z4nGYMrBZC3AQvAuj7Ju3n9d0LQeBifxinxz4zCLAEvVepstBqIw0Od24SBdp2U/ZzhHgZv+sH2U/tbrOODamEumYauvWyrCyrZc4xrI8fO96O/+oGA0Y7ZT7eIl1ua7zfK2ect+Nl4XxIqss5/Hdd9+1c7/lx6MxSmt53qO8PZ0yxfNoKUfXOci6sk5Kfef6MJEv+8n1S9C4fB4sv4+8ab1PDo5KkiRJkibLQOeUyyM4uUDvu/gnqBIjtSIglnHBTkAk8iAAEsFRAh9x4Z9fGkLwrgwIhFge2+Rl8R05sFC+LZ5gB2XNZYpn3jH6K/LKAVrexpxHisY2udxx/LENCB7GiDLyKwMtbEuAhVv+M9IdPHiwWV8btcl3lpfpxpVvk9+9e3c7d0sObFKevn4Qxx3HzmetPhD55jZknvzzPiNtTh95BuZplxw0r21D/oE3y7Mston5fGv1uXPnhrY37RnLx5EDW2+//Xbzyb4od9y2jNo++84NUIfces45yTacf+W29BvOyfzc3TwStBxFTNlyEDD2f8cdd9w2AjW2uXz5cpMO0bZZ5JFH0eb+EOs5L/Oy8jtTbltuT8/tBvpUHGvXOUidcJ5G/4u833///eY7un4D8i3xXX0+n1tsA/JiBHntH0nUXy0oG23TN8pekiRJkrTGbmqqLSws3Lzvvvt4zfLN+fn5dmk3tp+ZmWm2Z9q7d+/NxYv3Jg+mCxcuNPnEeqaTJ082aWM/XdPp06eb7QLfa9vFFPIxMEWZopy1MsWxHjp0aNnyPJEn+spN+oyy5H2RNuqH71EXpTLd7Oxsk45j6Us3DvYR+deMsq+cR21ifcb3OPZoF+ZpkyznUU5zc3PN9rV1MR0/frwpd20dU7Rl7L9rQl97cwzj4PjzPqOOKW+Wz6nadPHixXbL27EP+gvbUfaoZ+aZyrpm+1ofjTJQj2wT65lqx122bZQh76/Mp5woR1+7MZHfKO2WvfPOO0vr4vii7stjmcRvwChtHm0QdR/n/MGDB5fSrsV5L0mSJEka3Tb+LF6QaYoxSoqRZIsX1e2SfjGKidFKjHziltQnnnhi2WilGO3ELe5xe3lttFdgVFP5UhTSM/Juz5497ZLf1LZnBBijpm7cuNFZJtLxVuMoB2l27NjRzGeMGmT53r17O8tNXpSNbUpRP9QrtyhzKyxl6Tr+kNOBEYFdt+CuBHlRhlwvgbrgmYG8DKUPIxNrbUKd1W5ZRozg69p3V57UMaPvZmZmmrYt24p9MlIwtunqL+yb/VK3NeTDsxDZZiXtPQz7/eSTT5q+OT8/f1v+fedGlL0PIwYZPc229J0YSdrXd2p9jf1EOWI937uOmW0oO78DjBguy8n6WptEu3Euoqtto01GabdSlP+bb74ZfP3114MDBw40t4GXfXRSvwGjtDkoe26LUdpOkiRJkjQZBjolSZIkSZIkTT2f0SlJkiRJkiRp6hnolCRJkiRJkjT1DHRKkiRJkiRJmnoGOiVJkiRJkiRNPQOdkiRJkiRJkqaegU5JkiRJkiRJU89ApyRJkiRJkqSpZ6BTkiRJkiRJ0tQz0ClJkiRJkiRp6hnolCRJkiRJkjT1DHRKkiRJkiRJmnoGOiVJkiRJkiRNPQOdkiRJkiRJkqaegU5JkiRJkiRJU89ApyRJkiRJkqSpZ6BTkiRJkiRJ0tQz0ClJkiRJkiRpyg0G/w/6bL3q37k0RwAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "id": "a3664134",
   "metadata": {},
   "source": [
    "- The F-test is used for comparing the factors of the total deviation. For example, in one-way, or single-factor ANOVA, statistical significance is tested for by comparing the F test statistic\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "![image-2.png](attachment:image-2.png)\n",
    "\n",
    "\n",
    "![image-3.png](attachment:image-3.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53c5050b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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    }
   },
   "cell_type": "markdown",
   "id": "1f2de18a",
   "metadata": {},
   "source": [
    "![image-2.png](attachment:image-2.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c190c660",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be5b98de",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e25cbf1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets say if we want to test the recovery time of all given 7 drugs are equal or not! \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "85b13ad0",
   "metadata": {},
   "outputs": [],
   "source": [
    "drug1 = np.random.normal(4.0,0.2,50)\n",
    "drug2 = np.random.normal(4.0,0.2,50)\n",
    "drug3 = np.random.normal(4.0,0.2,50)\n",
    "drug4 = np.random.normal(4.0,0.2,50)\n",
    "drug5 = np.random.normal(4.0,0.2,50)\n",
    "drug6 = np.random.normal(4.0,0.2,50) \n",
    "drug7 = np.random.normal(4.0,0.2,50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5d8267ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame([drug1,drug2,drug3,drug4,drug5,drug6,drug7]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7e81f077",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.columns = [\"drug1\",\"drug2\",\"drug3\",\"drug4\",\"drug5\",\"drug6\",\"drug7\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e3842ab4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>drug1</th>\n",
       "      <th>drug2</th>\n",
       "      <th>drug3</th>\n",
       "      <th>drug4</th>\n",
       "      <th>drug5</th>\n",
       "      <th>drug6</th>\n",
       "      <th>drug7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.901537</td>\n",
       "      <td>4.295903</td>\n",
       "      <td>3.901227</td>\n",
       "      <td>4.021824</td>\n",
       "      <td>4.430117</td>\n",
       "      <td>3.794902</td>\n",
       "      <td>4.127478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.140125</td>\n",
       "      <td>3.900369</td>\n",
       "      <td>3.569446</td>\n",
       "      <td>4.030061</td>\n",
       "      <td>4.361243</td>\n",
       "      <td>3.827261</td>\n",
       "      <td>3.983712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.158193</td>\n",
       "      <td>4.127469</td>\n",
       "      <td>4.246845</td>\n",
       "      <td>4.141104</td>\n",
       "      <td>3.768614</td>\n",
       "      <td>3.670279</td>\n",
       "      <td>3.893244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.733953</td>\n",
       "      <td>3.775608</td>\n",
       "      <td>3.896946</td>\n",
       "      <td>4.635956</td>\n",
       "      <td>3.883147</td>\n",
       "      <td>3.905008</td>\n",
       "      <td>3.968151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.925810</td>\n",
       "      <td>3.892121</td>\n",
       "      <td>3.790849</td>\n",
       "      <td>3.995478</td>\n",
       "      <td>4.008429</td>\n",
       "      <td>3.840468</td>\n",
       "      <td>4.288380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.128361</td>\n",
       "      <td>4.033271</td>\n",
       "      <td>3.855636</td>\n",
       "      <td>3.697431</td>\n",
       "      <td>4.135255</td>\n",
       "      <td>4.072602</td>\n",
       "      <td>3.927466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.127237</td>\n",
       "      <td>3.962498</td>\n",
       "      <td>3.967129</td>\n",
       "      <td>4.072554</td>\n",
       "      <td>4.491441</td>\n",
       "      <td>3.780391</td>\n",
       "      <td>4.345378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.012842</td>\n",
       "      <td>3.628802</td>\n",
       "      <td>4.052504</td>\n",
       "      <td>3.926250</td>\n",
       "      <td>4.034204</td>\n",
       "      <td>4.086574</td>\n",
       "      <td>4.224207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.902635</td>\n",
       "      <td>3.944068</td>\n",
       "      <td>3.966492</td>\n",
       "      <td>4.008424</td>\n",
       "      <td>4.064658</td>\n",
       "      <td>4.190458</td>\n",
       "      <td>3.637151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.836433</td>\n",
       "      <td>3.700822</td>\n",
       "      <td>3.813226</td>\n",
       "      <td>3.776604</td>\n",
       "      <td>3.819343</td>\n",
       "      <td>3.569541</td>\n",
       "      <td>3.730848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.294699</td>\n",
       "      <td>4.199871</td>\n",
       "      <td>4.012213</td>\n",
       "      <td>4.025107</td>\n",
       "      <td>4.442772</td>\n",
       "      <td>4.057380</td>\n",
       "      <td>3.674864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.107701</td>\n",
       "      <td>4.116407</td>\n",
       "      <td>4.021463</td>\n",
       "      <td>4.137157</td>\n",
       "      <td>3.775299</td>\n",
       "      <td>4.417892</td>\n",
       "      <td>4.384300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3.982826</td>\n",
       "      <td>3.824696</td>\n",
       "      <td>4.119235</td>\n",
       "      <td>4.442812</td>\n",
       "      <td>3.884025</td>\n",
       "      <td>4.017676</td>\n",
       "      <td>3.840451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.713727</td>\n",
       "      <td>3.741908</td>\n",
       "      <td>4.177190</td>\n",
       "      <td>4.162970</td>\n",
       "      <td>3.777764</td>\n",
       "      <td>4.179323</td>\n",
       "      <td>3.778626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3.833069</td>\n",
       "      <td>3.612462</td>\n",
       "      <td>4.287545</td>\n",
       "      <td>3.874668</td>\n",
       "      <td>3.991215</td>\n",
       "      <td>3.986342</td>\n",
       "      <td>4.002048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4.288348</td>\n",
       "      <td>4.399167</td>\n",
       "      <td>3.901233</td>\n",
       "      <td>3.896736</td>\n",
       "      <td>3.980184</td>\n",
       "      <td>4.100126</td>\n",
       "      <td>3.913111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4.410710</td>\n",
       "      <td>4.149187</td>\n",
       "      <td>3.775328</td>\n",
       "      <td>3.875963</td>\n",
       "      <td>4.092970</td>\n",
       "      <td>3.909532</td>\n",
       "      <td>4.448642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>4.171925</td>\n",
       "      <td>3.877779</td>\n",
       "      <td>4.216417</td>\n",
       "      <td>4.645918</td>\n",
       "      <td>3.894121</td>\n",
       "      <td>3.855900</td>\n",
       "      <td>3.868027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>4.134809</td>\n",
       "      <td>3.837619</td>\n",
       "      <td>3.738498</td>\n",
       "      <td>3.696935</td>\n",
       "      <td>4.091563</td>\n",
       "      <td>4.003680</td>\n",
       "      <td>4.042204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3.760730</td>\n",
       "      <td>3.844229</td>\n",
       "      <td>4.215499</td>\n",
       "      <td>3.972365</td>\n",
       "      <td>3.946261</td>\n",
       "      <td>3.929867</td>\n",
       "      <td>3.914966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3.911198</td>\n",
       "      <td>3.999019</td>\n",
       "      <td>4.176591</td>\n",
       "      <td>3.930874</td>\n",
       "      <td>3.895453</td>\n",
       "      <td>4.190086</td>\n",
       "      <td>3.853120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3.931958</td>\n",
       "      <td>4.271919</td>\n",
       "      <td>3.886801</td>\n",
       "      <td>3.989046</td>\n",
       "      <td>3.859771</td>\n",
       "      <td>4.119364</td>\n",
       "      <td>4.231521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3.619749</td>\n",
       "      <td>3.840472</td>\n",
       "      <td>4.068316</td>\n",
       "      <td>4.184864</td>\n",
       "      <td>4.305690</td>\n",
       "      <td>3.998420</td>\n",
       "      <td>3.913645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4.140906</td>\n",
       "      <td>3.788738</td>\n",
       "      <td>3.844013</td>\n",
       "      <td>4.222487</td>\n",
       "      <td>4.094565</td>\n",
       "      <td>3.941809</td>\n",
       "      <td>4.084862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3.802649</td>\n",
       "      <td>3.907703</td>\n",
       "      <td>3.901617</td>\n",
       "      <td>3.837230</td>\n",
       "      <td>3.776289</td>\n",
       "      <td>3.935229</td>\n",
       "      <td>4.146574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3.862654</td>\n",
       "      <td>4.217732</td>\n",
       "      <td>4.125821</td>\n",
       "      <td>3.909794</td>\n",
       "      <td>3.964997</td>\n",
       "      <td>3.781737</td>\n",
       "      <td>3.797452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4.026802</td>\n",
       "      <td>3.932671</td>\n",
       "      <td>3.877703</td>\n",
       "      <td>3.901528</td>\n",
       "      <td>4.279114</td>\n",
       "      <td>4.161743</td>\n",
       "      <td>4.236865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3.819025</td>\n",
       "      <td>3.669970</td>\n",
       "      <td>3.944974</td>\n",
       "      <td>3.954996</td>\n",
       "      <td>3.915604</td>\n",
       "      <td>4.175591</td>\n",
       "      <td>3.985353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4.021772</td>\n",
       "      <td>3.878075</td>\n",
       "      <td>4.047354</td>\n",
       "      <td>4.045108</td>\n",
       "      <td>3.647389</td>\n",
       "      <td>3.939164</td>\n",
       "      <td>3.917025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.099327</td>\n",
       "      <td>3.966849</td>\n",
       "      <td>4.106955</td>\n",
       "      <td>3.877851</td>\n",
       "      <td>3.750075</td>\n",
       "      <td>3.964317</td>\n",
       "      <td>4.064036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>3.893277</td>\n",
       "      <td>3.930381</td>\n",
       "      <td>3.557469</td>\n",
       "      <td>4.052865</td>\n",
       "      <td>4.173467</td>\n",
       "      <td>4.321823</td>\n",
       "      <td>4.171878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>4.144223</td>\n",
       "      <td>3.884230</td>\n",
       "      <td>3.589871</td>\n",
       "      <td>4.077316</td>\n",
       "      <td>3.875001</td>\n",
       "      <td>3.933120</td>\n",
       "      <td>4.004920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>4.131725</td>\n",
       "      <td>4.008353</td>\n",
       "      <td>3.849325</td>\n",
       "      <td>4.627740</td>\n",
       "      <td>3.865015</td>\n",
       "      <td>3.935392</td>\n",
       "      <td>3.761423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>3.638411</td>\n",
       "      <td>3.788603</td>\n",
       "      <td>3.762648</td>\n",
       "      <td>3.955439</td>\n",
       "      <td>4.403098</td>\n",
       "      <td>4.000220</td>\n",
       "      <td>3.754467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>4.111743</td>\n",
       "      <td>4.076635</td>\n",
       "      <td>4.069233</td>\n",
       "      <td>4.053559</td>\n",
       "      <td>3.813954</td>\n",
       "      <td>3.717224</td>\n",
       "      <td>3.924053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>3.946347</td>\n",
       "      <td>4.203533</td>\n",
       "      <td>3.882780</td>\n",
       "      <td>4.203149</td>\n",
       "      <td>3.871762</td>\n",
       "      <td>3.985283</td>\n",
       "      <td>4.069321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>3.957723</td>\n",
       "      <td>3.778014</td>\n",
       "      <td>4.029532</td>\n",
       "      <td>4.170947</td>\n",
       "      <td>4.040704</td>\n",
       "      <td>3.868295</td>\n",
       "      <td>4.032376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>3.868260</td>\n",
       "      <td>4.125096</td>\n",
       "      <td>4.054972</td>\n",
       "      <td>4.105787</td>\n",
       "      <td>4.467859</td>\n",
       "      <td>4.288397</td>\n",
       "      <td>3.795281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>4.197501</td>\n",
       "      <td>4.052141</td>\n",
       "      <td>3.814943</td>\n",
       "      <td>3.768934</td>\n",
       "      <td>3.584141</td>\n",
       "      <td>4.134030</td>\n",
       "      <td>3.893302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>4.097794</td>\n",
       "      <td>4.083152</td>\n",
       "      <td>4.343155</td>\n",
       "      <td>4.216498</td>\n",
       "      <td>4.229254</td>\n",
       "      <td>4.076749</td>\n",
       "      <td>3.893464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>4.208350</td>\n",
       "      <td>3.869683</td>\n",
       "      <td>4.406533</td>\n",
       "      <td>3.957044</td>\n",
       "      <td>3.899390</td>\n",
       "      <td>4.352796</td>\n",
       "      <td>3.806243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>4.015863</td>\n",
       "      <td>3.690551</td>\n",
       "      <td>4.057274</td>\n",
       "      <td>3.666163</td>\n",
       "      <td>4.041198</td>\n",
       "      <td>3.997369</td>\n",
       "      <td>4.209606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>3.858276</td>\n",
       "      <td>4.501480</td>\n",
       "      <td>4.228134</td>\n",
       "      <td>4.248210</td>\n",
       "      <td>4.021552</td>\n",
       "      <td>4.031306</td>\n",
       "      <td>4.223574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>4.103631</td>\n",
       "      <td>3.657778</td>\n",
       "      <td>4.211005</td>\n",
       "      <td>4.056709</td>\n",
       "      <td>4.394398</td>\n",
       "      <td>3.772808</td>\n",
       "      <td>3.975740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>4.051494</td>\n",
       "      <td>4.068664</td>\n",
       "      <td>4.085195</td>\n",
       "      <td>3.895248</td>\n",
       "      <td>4.285226</td>\n",
       "      <td>4.184135</td>\n",
       "      <td>4.165192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>3.945318</td>\n",
       "      <td>3.741517</td>\n",
       "      <td>3.966480</td>\n",
       "      <td>3.905016</td>\n",
       "      <td>3.857691</td>\n",
       "      <td>3.949075</td>\n",
       "      <td>4.222751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>4.020489</td>\n",
       "      <td>3.874124</td>\n",
       "      <td>4.018192</td>\n",
       "      <td>3.923221</td>\n",
       "      <td>4.034624</td>\n",
       "      <td>4.291500</td>\n",
       "      <td>3.926390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>4.423241</td>\n",
       "      <td>3.761989</td>\n",
       "      <td>4.141624</td>\n",
       "      <td>4.151702</td>\n",
       "      <td>4.270818</td>\n",
       "      <td>4.008943</td>\n",
       "      <td>3.883139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>3.872209</td>\n",
       "      <td>3.898451</td>\n",
       "      <td>4.042505</td>\n",
       "      <td>4.197345</td>\n",
       "      <td>4.296334</td>\n",
       "      <td>3.544156</td>\n",
       "      <td>3.852684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>4.276953</td>\n",
       "      <td>4.037174</td>\n",
       "      <td>3.679434</td>\n",
       "      <td>4.099553</td>\n",
       "      <td>3.785141</td>\n",
       "      <td>3.654726</td>\n",
       "      <td>4.077668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       drug1     drug2     drug3     drug4     drug5     drug6     drug7\n",
       "0   3.901537  4.295903  3.901227  4.021824  4.430117  3.794902  4.127478\n",
       "1   4.140125  3.900369  3.569446  4.030061  4.361243  3.827261  3.983712\n",
       "2   4.158193  4.127469  4.246845  4.141104  3.768614  3.670279  3.893244\n",
       "3   3.733953  3.775608  3.896946  4.635956  3.883147  3.905008  3.968151\n",
       "4   3.925810  3.892121  3.790849  3.995478  4.008429  3.840468  4.288380\n",
       "5   4.128361  4.033271  3.855636  3.697431  4.135255  4.072602  3.927466\n",
       "6   4.127237  3.962498  3.967129  4.072554  4.491441  3.780391  4.345378\n",
       "7   4.012842  3.628802  4.052504  3.926250  4.034204  4.086574  4.224207\n",
       "8   3.902635  3.944068  3.966492  4.008424  4.064658  4.190458  3.637151\n",
       "9   3.836433  3.700822  3.813226  3.776604  3.819343  3.569541  3.730848\n",
       "10  4.294699  4.199871  4.012213  4.025107  4.442772  4.057380  3.674864\n",
       "11  4.107701  4.116407  4.021463  4.137157  3.775299  4.417892  4.384300\n",
       "12  3.982826  3.824696  4.119235  4.442812  3.884025  4.017676  3.840451\n",
       "13  3.713727  3.741908  4.177190  4.162970  3.777764  4.179323  3.778626\n",
       "14  3.833069  3.612462  4.287545  3.874668  3.991215  3.986342  4.002048\n",
       "15  4.288348  4.399167  3.901233  3.896736  3.980184  4.100126  3.913111\n",
       "16  4.410710  4.149187  3.775328  3.875963  4.092970  3.909532  4.448642\n",
       "17  4.171925  3.877779  4.216417  4.645918  3.894121  3.855900  3.868027\n",
       "18  4.134809  3.837619  3.738498  3.696935  4.091563  4.003680  4.042204\n",
       "19  3.760730  3.844229  4.215499  3.972365  3.946261  3.929867  3.914966\n",
       "20  3.911198  3.999019  4.176591  3.930874  3.895453  4.190086  3.853120\n",
       "21  3.931958  4.271919  3.886801  3.989046  3.859771  4.119364  4.231521\n",
       "22  3.619749  3.840472  4.068316  4.184864  4.305690  3.998420  3.913645\n",
       "23  4.140906  3.788738  3.844013  4.222487  4.094565  3.941809  4.084862\n",
       "24  3.802649  3.907703  3.901617  3.837230  3.776289  3.935229  4.146574\n",
       "25  3.862654  4.217732  4.125821  3.909794  3.964997  3.781737  3.797452\n",
       "26  4.026802  3.932671  3.877703  3.901528  4.279114  4.161743  4.236865\n",
       "27  3.819025  3.669970  3.944974  3.954996  3.915604  4.175591  3.985353\n",
       "28  4.021772  3.878075  4.047354  4.045108  3.647389  3.939164  3.917025\n",
       "29  4.099327  3.966849  4.106955  3.877851  3.750075  3.964317  4.064036\n",
       "30  3.893277  3.930381  3.557469  4.052865  4.173467  4.321823  4.171878\n",
       "31  4.144223  3.884230  3.589871  4.077316  3.875001  3.933120  4.004920\n",
       "32  4.131725  4.008353  3.849325  4.627740  3.865015  3.935392  3.761423\n",
       "33  3.638411  3.788603  3.762648  3.955439  4.403098  4.000220  3.754467\n",
       "34  4.111743  4.076635  4.069233  4.053559  3.813954  3.717224  3.924053\n",
       "35  3.946347  4.203533  3.882780  4.203149  3.871762  3.985283  4.069321\n",
       "36  3.957723  3.778014  4.029532  4.170947  4.040704  3.868295  4.032376\n",
       "37  3.868260  4.125096  4.054972  4.105787  4.467859  4.288397  3.795281\n",
       "38  4.197501  4.052141  3.814943  3.768934  3.584141  4.134030  3.893302\n",
       "39  4.097794  4.083152  4.343155  4.216498  4.229254  4.076749  3.893464\n",
       "40  4.208350  3.869683  4.406533  3.957044  3.899390  4.352796  3.806243\n",
       "41  4.015863  3.690551  4.057274  3.666163  4.041198  3.997369  4.209606\n",
       "42  3.858276  4.501480  4.228134  4.248210  4.021552  4.031306  4.223574\n",
       "43  4.103631  3.657778  4.211005  4.056709  4.394398  3.772808  3.975740\n",
       "44  4.051494  4.068664  4.085195  3.895248  4.285226  4.184135  4.165192\n",
       "45  3.945318  3.741517  3.966480  3.905016  3.857691  3.949075  4.222751\n",
       "46  4.020489  3.874124  4.018192  3.923221  4.034624  4.291500  3.926390\n",
       "47  4.423241  3.761989  4.141624  4.151702  4.270818  4.008943  3.883139\n",
       "48  3.872209  3.898451  4.042505  4.197345  4.296334  3.544156  3.852684\n",
       "49  4.276953  4.037174  3.679434  4.099553  3.785141  3.654726  4.077668"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b0f6fd75",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0009736600101515"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(np.mean(data,axis = 0)) # overall mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72fdcd07",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {
    "image.png": {
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HMHPUKeTIDGH/2TpMrCOuCfIXuFgOfdg1GY9Fw7ugooNGSn09PavyaD5yDXZsXI2v25eDrXH+9WKMFS1FogHNyr40bIuVhOLULmzafx63I1OQ/ZorT0NTK5QsXQHquxdxcsdeHH2UIC4UC14MSjIyEHP8MPbCGB6l6qJ783LSCPa2XLzrwUSciDJ+m4WFZ8MRm5a3AehDSsb1pT1Qqsn3+CPCSffFRFsLNf7cMhcf9xiAaYu+w6B23fDDSSVcavugYvkEBJ+cgI4jluJ2SNJbXKznIC3+PBaP74Fun8/GqWtq2NZvgTbt2sHZORR3141F14blMGX/bSRnix3LrBhcfZugjbcnSqplgMIIPrXroq3I365eJRjIX929PCfzMQ7MG4Oe/eZgu7EzGn/yCUaPHo2u5ZRIuxWJ4OwXS5qTGo5z68bho86TsP9OKqr4+KB6dXsE7/kWnwycgvXXnyLlzSv2Rhn3F6NFw2YYv/oq0uXl4S3WoU4dX+hnh2DRN8Mw8offcS9O6s6fR05mFA5+2xrtJ59DgqMznExMoY6PR6K2LBMu4udve6P78Pk4lVUOvQZ9LtazIz6KjcDWntXg1ukH3JK6/edkhuDPJePQs8cMbFSVRL3Bg3Vl0s3LCFl3ohCSmfOG+tPW3UUsndgL3YZ9jxNXVbCt1xytdXUXjvsbxqFLgzKYvO+WVHc2cK3dGG0qlYeTVHdVa9XJrbv6lV9bd9rfyQg/J36nB1qNW4Ld6Yao1LAh2rXzhEdGLI7NHYThK4Jzc2bF4MaJ79G3QxfMWncP2dY+qCHmX7WqJdLvrsU3nSqi79w1CIhL+xsbktLxaP9MtOkyBguOZqFqnWbwcCmJB5fXo7NPQ0zbswNjm3ph4E+PYejdAL61FEh9/AvGfTsV644GSI2y+WnrfN+ERvDqNgtBYltp0KAeHBVRuPJ9b3w+ahaOPXrRMJqVdAij3Oui86aHiG/fXlenwz7uAR9FJJ76JYA/NMqKquy7N3SPOIWgLD7v3hDWZgbSmPenUBuixceT8FHVV72anDH2JnIjU1TsMghtsp4i6NIJXAzKkMa8kJV8FHu3JSBNVh+9W7y6McvMpgQcirtAefEwdu89jsthyeJ8+eozOcXHIejiaZyANWpVrIumNd//QwEZafGIighFaGhhQxgio+KR8XLI9pxzBV+YyhTIWT0Nc0+HFRq327lUgJWpJWS7V2DRn/cRmvhymT2T8+ghLvo/wH24oFerBnBxtJDG/DWUnY6b+3fitqUpHNrVhZeU/jpZRw5gTU42lIramDDQV0r9a4qXrYnOXXrAq6SplMIYK9Kknmj/csF0eNZgqmFuSHI4Ud3hC+nA8WsUEBrziseuYunW3u+pvb05KVGMKrT/ilbtPUf3H4VTQkZWvq7EWSmxtHe0O0FmS9XaL33HF1qyXBdpVmVnMoQ+acx96ZPle+naDX8Ki0umTCnHC5fo+9qeZI6KNPVsdL5u30TxdOfQNGoIK6rVfTadiZKS6Total2NiolNGRZOVHHCfgrUdsXOzqKYc1tpoK2BNmohuYEZVezyNe2QXnwa/WAbdWngSiqZCw3Z9oCSRXRTqNTH9PvkjlTGTE3W9T+m1aefUOrzFQiig990IjsDFdl5dqXlt1502Nc+wtnT1/atH+GMvbmTxrSyJ2WNfvTzyRAp9RVyMij8+hrqUlpBho4NaOiKc9LjyHHkf3EJ9W1VhozUMrHuXjTpWBhl6PrVZ1Nc6FrqBAMqXfUzermX+01a2rE22cOTxuz2owRp/TIC1tGgYcNp+dWw3ASdJLrz+1Rq4y7Kt8ZntPHikzxd94/QELW2H6geyQ1dqeG4/XQrgiglJYViYmKI0oJo14xuVM7CmOx7zqO9QfGUIU2p3ZdXtbMihaktdVpym9JFStzdg/RNOwdSVu1OPxwp+OLdtyB+b8fUzlTWXNRdvX606lQwpTyvO3HsmNiZ7A2UZOvRmZbdzFN32kc467zDI5wZEXRixRdUo5gh2bQeR6vESr+YJpTOzxtC07VvAM7JpIib66mbi4w0DhWpx5xdFPD8WfIYur/je2pVwY6U+q708aY7Ylm12+brHuF8RDsmdKWycKY+C/+gx8lSsiQrYxf1goJsXbrT5mApUazzoTkDyFu7zxjbUOm+C2jvo7jcOgi9RN+5W+v2GT2ZnByqtqJZf+S+Gjnp6WWaObwRmShtqdW4DXQ7Ju/DGkdoqNj+oadHihKVqfXEfZT70FkKhV9aTF80MiOlXSOasOkyPZvM75cOZG2iIKMhB3ITGPuPCD08i1p5mxM0bWnxxUCxl3x4IWdX8yOcjL2F3Ec4DeijFY8o6ckNWtjFjOBQjfotvVDgvJ9B937pRaWs1WQ5dDdlZOVQ/NaXH+EkCqNTy0ZTAytjcU1SgmoMnEG//3mZHgZHUUr+wJZSwm7Tsv6lCHI3ajdyCwVI6e/j9uGZ1LtNBapQobChOnXoO4cuhksTvdJlmlvTg4xEDKcyqk4Dlu2hq9cfUmhsUp5Y7Zn7tH5QS3LRKEkPHtRq5ga6ePEuPY6I18VveUWeX00D69mI8mpAE7ddEdHOh5EZf5pGlNEn69J1aNktKbEQ9xY1IpVcrJv16L/towyXFrchT36Ek7Eiq2h8RADF0WjwEAz9tCfqu2Xj0k+j0KxuRwwaNx8bDp7HvehUKZ+WGTzrd8PwqcPQqrwZwg8tQL+WHdBzyBT8vO0IrobEPu+9lpOTjbDg+4CRBoZlnfCWnYlZPlUwZvd8fFyrBlzU17F0YAc0bD4Ak35ch4NX/BCR/KHecmwE5z5z8cfXDVFS2xVbXwaTsjXx0ccVALkhnHy7Y8zXX6Ft+dy7Qxau9dGvpiMcjB/i4Kl7SC/sdpyQcu8k9p26hLv6VfDJkM/Ruoo91M+/j10Cdb/8FqNrqxD29DK27r2G19+DK1x6ajKSE2NhKs+BXg4h/TWLRekpCD26EZtCbFG5YX+M7F9NbNlapnCu8gnmTfwMjd2s8SE+nK0o1R3LfpiPARVtpRQtQ5SpXBPelTyhfByG0LhEFHh6EPr6VvBpOhYLpzWDpzWg0Whgbm6OzEdXceTsNdyiuhjRsxl8S5jkWc7i6DS4KzQpabj5x1lonzrMSEvRlYmJnKBPOXjXTowp90/jwKmLuKNXCYM+/RxtqhaH5nndFUedMVPwpa8a4ZGXsWXvVbzNA6Gvkh58G6eOHMbZ1Mro/VFrtBYr/eJzA3bwGbEIX9cUx5WMFATsXYYNQeYoW7snRvRvjVK5lSeYw63tAIzsWAkOpgFYse4o4vI9tvshqWHb8FMs/qoXWjiZ5taBXWX0HysWEnrQlPDBoJkrMKahjXYMDG080LxmddR0CMflO3cR8jT3s/x56cltUG3MCvwyuTlyHzrToFiZBqhZrSHsw/zw8OETREsbSlxMGEgcY0toshFVcONhrAiLiw5FclIsUKYkSor44kMcpxljf52hmQ18GnaAQ9xj+J08jWtxeWLUhDvYffgq4sgOg1vXgOz5s4kF2aJWtwEYNqo/mnjIcWftRHwk5tn/y1lYvfc0bkcmS/lEfCNiuYjwR4CVGcyd7WElpb8PI4UJilnawc6usMEWFlYm0FNKE71SJYzcNgcDateCu8ld/DK4Axo07Y9vFq7FgUsPEJ6cN8J1Q7fpX+PTTq1RwykG+8d1h0+1Thg+bRm2H7+GwLgXr7xIjI9EQnwEUNoe9lam+Ov9bnNFnPoVex4YwrRUJ9TxlBILERp0V9t5BPrVy+L9+/sxxv7LikgDmmBaHt3HzMKyOZPxaf+OaFRDiVtrp6N/s14YtXAVTgW8OGFBUxy+Xb/C4sVzMeaznmhe3xZR4gA8Vpzwhk5ZiL03C3zxSi6D3FAjLgXZe7Frg/mbNmLx+CHo1N4Xzgb3sfGbkRj88Rj8sPMUAmPet7kpr1Lo0qwyNKoXlyIymQLWNnYiqigBj2qt0cgrb9dqc5QoZQONgQIPQ58iO+c1j/HqZOLRw9sIDgyEcY0GqFuuBMwLBB8KZXHUqO8NJKUi2e8xXnq92FsysXeCm3tFZN05ic2/LcPmg2fhF56MzAINadnZYpn8b0NhZQ7HOt5i7fMzr1oLvta2+JCdyXNyMsWF3yP437qACxfE8DAY2t76mtBoxCSkvNRoKFeVR/+5/VEwnomJCkNMdDjgYgGKDsFd7bzyDFcSANfMDKT5+SEoFTC2LQ63spVA989imyiTTQfO4H5YUqGPILyQicCHdxD06BGMq9VHnfKOsChYd4riqFm/EpCchuQH71t3WQgP9ceDO3dgUK403N0cYSmNKSgrIxbnThyH3KoY3Jo0R2VzacRz5qjo4w0TUyPgph/8st5qRd+DDXwrVYRz8fyPUdjZO+oaoA3L90PfBrmNZ7kMYGFjC0sbA4RFxyIhJe+NiVxyWVn0beUl5pyHgQhsjU1e2hZL1WwCF5Ua93bMxrzlG3Hsqj8iuSGN/ZcYGegeCX9zIJaMKP9rOHf4MA4XGO4G86c2GftgDCzhXKM1ulRMwc2bR3HsTMjz2Cbm3FZsuRwM+AxDj2qGL97t9SrG7mg3ZCqWLJiGzwd2QZNaxvDfsRBD2vTFsNnLcNQvScooUSqgMFBDJf35PhzrforvVx7AgQOFDb/j59kDXxF3FGDbEvO2bMbSCUPRuWM9uJoGYMu3ozC43ygs2HYcAdF5Ij7LWhg5bxl+mTYGvXq0RZ2KqTix8GsM6DAYk1Zux7UnBWIFjRoahQLP72P+JWHYMfsXBBo5oHKfxvCQUt+KmQlElPUWohB45gSOFDj2Hjt5DsFv8YFkxljRU3Qa0LRMTVGqdT/MXrJEnLRmYHy/j1DH7iEOzPwSY2bvRL7zlYEBbH1bYtSsBVi+eA4mD+mFlu6JuLZqMj6fsBrX46V8WumZSI+KRd4k9m70HYrDd8h3+G31MsydPAYDm3hA82gHFk/5Hr8dvosP0ob2jswsHaBUvugj9HopSIxPQorYfmwtTGGoejnE0ZfJYevsAcu0DKSHRiNOSn9XGocyaNp1MPpWt8Wt3+dh9IjhGD9tLhas34e74WnPX9afnZ2NqKeBugZDe8s3RUJ/EeXg6f1DWP/DQsyaMQkTx47EyJFi+GY+Np178I77RQaiI0MRExkLPDqDlfMm584rz/DV/D2IAIEoTfsaQqht3dCw8yfoX7sE/Pb+gC9HfI7xU+dgwdq9uBWqfd+hNOtXSkFSQm7dFdPWnfrlutPTl6FYaQ9YaevuSdR71l0G0lLikSAmtjQxgpmRoZT+spycSESGiboWAaSD5fOuZ/mYO5aCrVoD2aNwRBTauPu/ZWhkDiMxfAiWtXpi7Cd9UE/jh5kje+Kz0RMxfeFybDhyjRvS2H9DZIw492XizU3kUbi5+xdMGTwYgwsMv13gnYWxD0cO82KuqFzTGwgMwI2rtxCm7ZaeEozDB08hPMICrTr7ooSm0C5cuYyN4di0G6YsXIRlP87CxIGd0MAxGMdmj8LnUzbjbt5O3CmpSIlNQIFmtf9XerZ2qPHpFF3cPn/KV/ikuSdMQvZg6dSZWHngJqLyxu2WlijTfRRW/bYci2dPweguDeGhfwGbp0/EjJXHEJz3MBUbj7hUEd9Jf/4lQbuw9rg+TJyaYXBLdynxLT0JR6T0z8L5Y++UrzGswLF39IRpOOIvZWGM/acUrQa058zg4tMGQ2fNwqyRzWCXmYZ7u5fgz8fS6HyM4FCmHnp9PRHfje2McmYGCN49H1vui5OHnj5MLezEtbE4sYUGITxPJzb2PvShNioF344D8fX0L9G7YVnQ/TM4ceY8AotC66RM/wPcUTODe/0uGDF3Fr6bMgYfVTHHvQ1zMeHL0Rj3zVbcTf+7Hud7vcz7uzBx3JcYN3EaDgQpYVulERo1EkMtH3gVs8Drm4pehZCTox0AC9uSqF6zTu688gxNmvZB/0njMeKz9vDUdVsygUvtjvhizixMFUFch+o28N+2ABPHjMK4CZtxMyldzPUv0BP/037BVPrzH0FfBrleUf+KU2m0/W46Zs6YignjBqNs9k1smPQFRn45Dcs3XUaElIuxosbAyAwqlQEQFYTgyNS36E1rCnuv+mjZuzd6Fxh8PN7iQp4x9tYUZg4oX7MJqpsF4sL5M7jsH4fkh39i75lHeGrXEt18S8FA+S6XT6Zw8m6OQVOnYebo1nDU14PfjrnYHwDI5UqYmFoDKfGIDw9DvjfOvKPI2wew+sfJmDy5sGEmflp9CP5vfT2jD5VBSdRsPwBjp3+Nfk3LQ+/hRZw+eQb+sVKWfKzg2aAbRs2dga/71IZJdAiuHd6Ki+GA2sBYDCZAbBjCI+KR8gE6z/rvWoNzIl5yatIald7y3p6Zpb32Ag85wXfx6K3umDrAs3VHdCtw7O3UviXcHKQsjLH/lCLagCYxd0b1gb1QWft1vMwQ+AUWcqQ0tke5ju1RzdQEcjzBjbuRkCmUcNe+uycjFqEPTuLcvZff+cPeg8IAlhWqo1rt6iiOBIRFhCE2/sV7Ev551DA0NoTGUAQo2nd9pb983ywnOxsR/vfxVKWEspg5RIjwF6hRwr02+g8Zg6nTZ2DO/BFooYzC/l+mYtvN/OWUlpGJp7F/b+vj7W3jsWL/IxTvMRGzp3+HKd9+i2+1w9hBaFXF+S27wD+jEuWo0ZVltqMPunwyOndeLw3j8cUndUTY8owKDq410PfT0Zg6bQZmzxuF1kbx+GPVdGy7loTXfHRX0Ii6M9D9XlRcAlJfUXekq7t7ou4UUNpavGfdKaHSGMNIGxsmpSAh+fVRsJ6+JSxsRN1lZiI8NkFKzS8+OABP0lKRVcIalvpF+TBtBp+W/URQPxUzZs3ApJGtYHllPzZuXIer7/scNGP/cDYu5VC6WHEYRF7AHxceISntTS1o2hsrHTDkFcfKjzz55RKMfVAKU5QuVwN1ahaD/7nzOHfxFA7vO44bfkD17k1R3srk/W64mZZE5T7d4aNtvMkOwT3/GKiNzeAq4mGblFD43b6IW4/fv0dp5OPz2LdjNVavLmxYh137LyHiXbu6ydUw96yKavVqwRGJCI8KQ3Tc6+IcPSjsy6Jap7Yoi3TEJz1BcFgiLIq7wMXRFcaxN3HmioiX875f7r34Yefqy9CXOaNZ6ypv/cqS0jWawFnEVdlB+7D7wtt877s46g0ZgQkFjr1fffEJanADGmP/SUW7AU0rLQ3aQ7yeOGGpVW+4U5uRgbQc0vVm0ajV0BMnjOLVe6J95UxEPryE/Rv34HVtaFEnfsTMXy9Jf7E3ys5BVlYWtP2pFHI5ZDJtOGIOq+IKyJWPER6Zna9XUWZCHPwvncFt6e//LRWcSnvAwbE44i7ewr2n0S+9MD8rMxQXTl6D2sIErjW8PtAHJzSwLumNJr2GoVcLOxipH+C09jaeoNA27npWQ0Z0PALP3ECwLvWF+OuXcCE6UoQ5eelBJreBTcl0pGeEiOBHSpYk3L6Gy+GhyH9TMUKs1x3IlAZwqdUBjVxs37HH2cuK2ZaETTFHxF+6i3uhkXi3jp0aWDl6oVGPIejVyhFmBn44ezns+aOtL1PC0dkDxUuWQNylO7gbFvXS72VlheH8iatQm5nATdSdvZT+buQwMbNFMXtrJN0JgJ9/cIFyFCKO4OD1p6LuLOFTqzoyYxLw+Mw1BEmjX4jHjUvXdY8NOzWuAXdFYf0aDWBiaQRD0wjExCWKepWSJeGHduOU9O9/Mj19C7hWa4bOAwehacVkRMb4we/RqxsXGfu30zj5onkddzhaxuD0qpU48CAWma94Ujs98iFO/S7Gn3sipTDG/hcMHEqifMXqcI68hpMbfsL8zWfxUOWFjvU9YWH0Fz77kZ6uuybRdi9Xq1SQGdrAo2YbNPJMxeNLx7B/70m8rg3t6cEp+H7tLemvlzlU7oKRk5dgifY1Nq8dFmDiyA7weJ8X5ObkIDszE9pmL7mI2+WyQmITEtdSGenQ3vKV6cugVCigsquIOr6V4ekQj6vbNuHA1cdIeMVDFVlJkbi2azE2/RkopbzGnX349VY2ZG6t0cr77W99mnj3xcD6crEuIdg3fwnOvOY9Zgm3duG39QfwmB8/YowVUAQa0CJwccOvmDzqJ2w5exPxaS966IRf24/RXefggkwF4/I90KYK4fHl3Zjx8Sws23cWoYkvmhdi/M5izvAfcTg8FlnufTGggbE4wclgUqoqeowahArxITj22zxMmvgz/rwf+vwiPDr6AdbOHog+I6djzslXPiP6H3YbK3pMwOiFv+NK2IvuJOkJkTi2bi0WLz+EICtv+FTzgZudNiBxQCkPDVSaWPz+zc+4GKK9oiCkp9zD3rUTMX7pceQ2H/3vGZfxQtXSHrAPP4oFCzdj7+VYvHiaMhRnFvfHwhPpsLVthPZt3PJ8ffFdpCHo0h9Y8/N6/Bma+KKhJ/o+rgck637P1Di3v5e+Sg3nRm1RPTUc109vwqI/HiG3uSERj4/+hokTluCA39MCX5TUg1xRGqU9sxEZcBu7F+9FbqfMJITc2YRpsxfh9+vBucHdc0YwNhMBTXY27odH4HmckfIYRzevx8b9115uKHoDI7caaFirGjwyTmDxj5ux60I0UvMEUTlZQTi+cxnmHbsj/krHk+vHsHbJbzj0JM/7QWL8cCMgUfcIgIkok8Lf5VsBVUqXgcPTY/jhB1F3l2LyfMkzFGcX9cWC42mwKdYIHdq5v2fdieVwdoOPT124xFzB+l37ceB+9PPyj7j5G8b3GYXlFxOhr1ShVOO2qJH2FDf+WI3vV1xG8PNOhPEIODgTi7adwJN4H/TpWx2WqsLudZvDxkHboy0JZ1bvxIkzQdJjEU9xde/X6DZ6Cx7p8v3TROPonBn46aQfQp6/2C8DySG3cDM0t1FdexODsSJJVQx1+vdHu7JuwN1dmDr4ayzYclocjXJlZaXh1uUdmDKqF4aI8+exx/wGVsb+pzQO8K5RH3V9FLh+4hjO3X4E95atUMPFHupCr5xicHPPBkwdthDrjl9FdMqL1rCIW3/im77zcCYnB0rP/uhcwxCQqeFQqT669umEUhF38PuPs/DdrHU4Fxyta3zSTRdxC8smdESXEQuw5PLro2DTYh6oVrspmjYtbGiImj7uMH/t6fUuVvefhC/nbcOl0GdHJCAjKQanNq3HD4v3I9CiPKr4VEcZh1AcmPkDJkz6FYfuPES6iBO1iHLw8Pg2fDtiJW4b2KKkd0vUKSN+UGmByu27on2tqjAIOIKFX07E3FWH8TA1Q3fDXPtlzAC/o5g1qhMGzFiH3Q8K6x0WiYM/r8GDrCw0+2wIvN7lUQgjF/SeNwmNc7IQcHQpvho8FRtP338eyyYlheHgxmkYNvJLTNx+EU8S3/fb7IyxIkscsP7lAmnPlI+pilExKuFWhipVrUrVqlXTDRU9SpGxWEVNje604kEEpVMc3do/nZqoLcm2lBt5Va78PK93OTeyUqtI370RTTvvT6nS3ImyKS36Fu2d0Y4ql5CTxtiWSnt6UZVn03l7koOVmmRlGtP0m0+kaViuE/SlXQkytXYkDy+v52VdtbI3udhbk8qiFDX6Zimdi0qiLGmKxGs/UGsnS5LDitwq+FD//v2pahVP8ijjRPWadqCWjlZUq/tsOhMlTUDXaVHralQM5enrg4GU/GxGWsmRdHHhRwQzD2o1cT9FSMnPPNkxmrydjAldllNkYrqU+jqpFHVtAw319SBjjQ2V9KhEVXv1pn4Daop18iIXKxmZ2TWluUeeUJI0hVb02eXU09eW4NaLVp4LpDQp/dWS6M6+edS9UnEq7VWZ2nz0EQ0Y0IQa+rqStUZJ+k2+oRthz5YzhzKi79DmYb4ElRFZOJenpi1bUuvWzal8aQfybN+Wmjo6kim8aNKxMMrIzp0qOz2erq7uTc5QkJGpE9Vt2IzatWtFXmVLkGft5tSjhiuVMPKkMbv9KCEzd5qnf3xOZfTlpHapRI2HDaMZEybQJ+2aU+kKbqS0thZxTx2asO0KRedmF47QELWSlJoG9MsjKSmfTEq4v52+bFqOTDTW5OjuTVV69KK+AwbQgOrVycenArl4lKFas0+JvCn04M+fqHclB3IWZdKqXTtRJs2ocV03sjEU+2vDsXThcbIojcKkUvT1TfRFvbLi92xyf69nr+d152otI9NijWn2nwXq7txK6lPHjuDak5afDshzTHiddIq/uoWmN/UgS2PxO57e1KlLFxpQrx5VK+NAprJyNPGidi6i7hL9af/0ruQk15CJnQdVaNKS2gxoTS1aVKLypczIUFmKus86RI+SMqV1y6GEiA3UGRoqVWkw7QzVJeqkPtpHE9t5ibo2oRLOFahr9x5Uu7YPeTiZkFfbL2hwGQXZunSnzcHSBBRMh+YMIG84Uqcpv9ODRCn5mYPDCKK+rVsvp8dS0jMxl9bR4IYOBN9htOVymJSqdYSGGqhIoa7/ijoPogMz+5EXSlLXabvIT/d7IbSmlzc5lylHFXyqiTrtT316VyEvNzvSWLtS8+/2UGSKbmLGiqhkCj/1E33R3JaMVIZkVcKVvKRzpI9PVSrrUZLMbIpR5WGz6GjUm3eG7LREurpSHEO1x1ExtGlal2yMxHWpoRXVbNRSSv+Gdp0PozTp2M4YIzrwmYpkKgP6aEX+k1dWxAVa8lkDstLeyUUlGrnuPEUU2Hfit/YVMZgFefZdT7lXAKF0bPFwqmVoTQ4uHuRdpcrz2Ldi2dIiDpARyrelxXfD88SDmZQUdJJ++6o+lSmmJCNzBxH/elPVZ9NVLEO2ptrp2tFPgXHSNH+X0zTOuTRZiLjdvUDc7upgQ2pzR6r71Y90OiJRxO23aHnXpuRqZk/OZctRVR+f3Pziv+VcipOByphKdRpFvz+OoQxp7tp4LPLKWvq2qxtZG2nIws6ZylWpSj663/Gh8uXEb1sYU+muX9Gu8LwRWQHhO6mviwXJ0Jo2vleRxNH9DcOoqYceyZXmVNzVkypJ61q5sheVKm5B6lLe1H/DEQpJffMBM/HhMVo/48Xxt2aFkqRRiO1GXOf06NVHl/bZkLl0OU/sxhj79yoCDWjixBNwgbZ+N4Za13MiU9PcGxm6oUIF6jL+Fzr2JFJchmtlU3rUQzq57Dvq2awS2dnlyeviQo2HTqPttwIoPltqbXgmJ4vSEwPp/oWl9O3nTcV04qJams7FpS4Nnb6BrjwIpsTswi/j/3tSKOrkOprUvbsIAPKUtbExlWjRib5d/wfdjUvKc2IVMhMp/MxmGlCnFunr6ZHawIhM6w6gBVuO0eVDS6mP2/9XA5qQlULRN67QztHdqLmlIalUKlKr9cQ6VaLa/X6jk4/CKLHAefbdGtDE9vk0mC4umkSDPG3JTqEQ85eRvrsLNZmxmi4GxlB2Tp5tLDuLUsOu0e4fOlO1kiC5XE5yR29qPuJHOnZzD82oW0EEfvkb0LS/kRH3mI4vn0K1nZ1JJoI5uUM5qv/ZXDp08RxtGd2IXE3zN6DlpEXT7e0LqVnZsiQ3MCATo+JU3L0vjVz2K03/rBU5v3MDmiDKMka77D9/Ra0aWZPaVEUqtZrU0AaNPWnGupMUFCM1NkU+oStLv6PPKtiTg1hHXZm4lqaGU3+hM/5RlPU2+532925epZ1f9qAWVoakFnWn0tWdN9Xsu/rVdffODWhCZgrF3ztJPw7rQyUtLEipVJJavwyVazmXdt94RLHPNvYc7bEohK6sWkRjqjtRcW3dqeWiPkqQU42v6Kf91+hJUrqobyl/IQ1olJVKsXeO0ox+nclW7Fva7VKvfBv6csl+Cgq9RFOr/hMb0DIp4epZWvdxPaoq0xd1qiaVsZLMGrekcTsuUlhc4XsKY0VCZhLFhJ+lkxsGUe0aVZ6fI1XiwrNB82G0ZOd5ComOp7c4O1FGYhT92kscQ7XHUTEoxPnj2fy0/85N96avd9yj+HwnXcb+217XgEbZUXRr2zBq4Q4q2XoiHbwTKyKo/F5uQMuklOBrtGf2eGrfwJ0sLPLEviKGajdmER0OCqdkXd48sjMoJe4B3Tw2mz7v20BMZ/F8urJlW9KYRXvIL/jpy9N9cKkUdXojTendk7y98yy7oSHZN/uIvvntIN2OTZTi9nSKv3mYlo36jOrXMBfxjpRXG7tXr0WfzNlE5yNiXo57RTwWH3mZzv0+klo1qaGLk7TT6enpU43a/WjOxlP0OCK20Hg5dNsn5GSuJnRc+95lkpMeTSF+O2nNrI/I1dX1+bra2pah7kPn0f5z9yg6Je2lOn+V4JPLaWDtF8dffX395/PLvVZQk5lVS/rtdTExY+xfRU/7f2IH/3ejbGRlZCItIx2ZWdnaR+9zyeVQKTXQqBUvnlUVI7OzMpCeno6MrCzd1wB1ZDIolWqo1Urd1+9eRqCcTGSkZyA1XTtd7oQymQJKtQZqpVycM3RJLC9RZmmpGcjIToco7lyioPQVSmhUaigVspcfv8vJQkpyqu4l61p6CjUMNSrIkYXU5BRkyzQwMFSLetKOzUZaYrLIK2IYY2OoFfov5ifqOis9GQmp2VCoDWCkUeT7rZyMFCSmpCNbbgBzQ9Xb1Z+o96wMsWxpGciU3penff+VQmwDhgZ5tjMJZaUjJSUV6aQQ8YcGSnme5XsVscw5meli/mli+8yBbivTbpti+Q2VYvkLTkzad1KkIiVVbPva3vP6cihFuWpUt/FDk/6Y/idhyLEDGO9rC1E0ErEPZGaI5UoT04iJ9GVQqESZqmTISU9BSloOFEZG0OSpGxL7TLKokwxdF31RfzIVVKI89cX6pYn8SiNDsQ88y5+JlNhEpEGss6kxVK993EGUpViOtHSxHJk50n4rh1wh9lu1GqpnC6wrE7HPpolyLFAmBqJM9N+m3rTeu+7kou4M3lx3z4jjUUZaOlLFIAIvkSCDXByHDA2VkOWdgXa9ssQxRaxXmqi83COKPmRie9cYqKAQmfNnF3UQm4wsuRKGorxf1KcgfjM9NU33kYQc7W+KPAa64xKQmhAv1kH8bWyE3A+H5SBT/GZKSiZkGgNxfCywXJkpiElME/uoIUyMVfnKRbsdpKSm6OZnZCD2X1EmubR1niTqXJTVS3We9/cMdcdj3e+J+shMSxbllKl7F6KWnojANaJe1c/ny1hRJ45pWWlITs0Sx8HcPUH7zla5OA6o1Ko8+9gbiP0+PTkWyS9/JyUPGdTaY/vzYzVjLDM5BonpelAamsIo38lLG3toz11pIu4UMZg4L8sLBByUkYxYsdPJlIYwFud43dQiLsvKEDGL9ppExEx5rzNUItbSXme8+sUMIi7J1sZE2iFL7NK5UYpMpv1IkfZ8/l6fLnh3IgZPF3FSujguFYzb1SK+VOWN23NEvCOWNzevtlNGbrKeQiGWVyPWV/5SbJVLe9wTMVZaFjLE9dszCnHcU2rEbxR63AvD5gFNMHjtLbT8LQ5rO7/PC90kInbKzEjTLUe29AiqvhQTq1UiVnnLAFO7Lqkpua9aeR09PQUMCo2JGWP/FkWjAY0xVsBVzG/0ugY0xhhjjDHG/mWebEOfRp9j07362Ji8Eu0M3vCBOMYY+8D4kpoxxhhjjDHG2D9YDs6uX4STERGoNG0Imiv/whdRGWPsPXEDGmOMMcYYY4yxf7BoJKRZw6FEC3Rv6g5ZvvdPMMbY/wY/wslYkfQAGz+fhtWXCB0Wz0ffipbSO+MYY4wxxhhjjDH2rrgBjTHGGGOMMcYYY4yxQvAjnIwxxhhjjDHGGGOMFYIb0BhjjDHGGGOMMcYYKwQ3oDHGGGOMMcYYY4wxVghuQGOMMcYYY4wxxhhjrBDcgMYYY4wxxhhjjDHGWCG4AY0xxhhjjDHGGGOMsUJwAxpjjDHGGGOMMcYYY4XgBjTGGGOMMcYYY4wxxgrBDWiMMcYYY4wxxhhjjBWCG9AYY4wxxhhjjDHGGHst4P8ApwCnn6kNalUAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "id": "496b9e39",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "74af0930",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "drug1    4.011291\n",
       "drug2    3.947379\n",
       "drug3    3.985907\n",
       "drug4    4.044451\n",
       "drug5    4.031444\n",
       "drug6    3.989000\n",
       "drug7    3.997344\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_means = np.mean(data,axis = 0)\n",
    "column_means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1e9d76c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.000973660010151"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "overall_mean = np.sum(np.sum(data,axis = 0))/(len(data)*len(data.columns))\n",
    "overall_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d845b5df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.30905267316893076"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SSC = np.sum((column_means-overall_mean)**2) * len(data)\n",
    "SSC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69096949",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24c79d6d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "b8d03a5c",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "408d5ce8",
   "metadata": {},
   "source": [
    "    each data points with its own mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7f0ccfb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "drug1    4.011291\n",
       "drug2    3.947379\n",
       "drug3    3.985907\n",
       "drug4    4.044451\n",
       "drug5    4.031444\n",
       "drug6    3.989000\n",
       "drug7    3.997344\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "183d1cff",
   "metadata": {},
   "outputs": [],
   "source": [
    "SSE = 0\n",
    "for group in data.columns:\n",
    "    SSE += np.sum((data[group]-np.mean(data[group]))**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "630fbfb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.009364156398236"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "deef05cb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "bc1a1e79",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f02f241e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.000973660010151"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "overall_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8ce9daa5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.318416829567173"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SST = 0\n",
    "\n",
    "for grp in data.columns:\n",
    "    for ele in data[grp]:\n",
    "        SST += np.sum((ele-overall_mean)**2)\n",
    "SST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f84b8637",
   "metadata": {},
   "outputs": [],
   "source": [
    "SST = SSC+SSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "863e964d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.318416829567168"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SST"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "42ce93ed",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b43fbcb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "350"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = len(data)* len(data.columns)\n",
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "84157d25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = len(data.columns)\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8b588f4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "MSC = SSC/(c-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a7328b0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "MSE = SSE/ (n-c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "af40daff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.05150877886148846, 0.04084362727812897)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MSC,MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ea084201",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2611215578561135"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F_ratio = MSC/MSE\n",
    "F_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "25aa1d81",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cdf(x, dfn, dfd, loc=0, scale=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9dd3144a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from scipy import stats'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.27476493428063553"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_value = 1-stats.f.cdf(F_ratio,c-1,n-c)\n",
    "p_value "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "742a4ad5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from scipy import stats'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "2.1250365781660565"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F_critical = stats.f.ppf(0.95,c-1,n-c)\n",
    "F_critical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "beb21826",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ad05caa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from scipy import stats'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=1.2611215578561454, pvalue=0.27476493428062365)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(data[\"drug1\"],\n",
    "              data[\"drug2\"],\n",
    "              data[\"drug3\"],\n",
    "              data[\"drug4\"],\n",
    "              data[\"drug5\"],\n",
    "              data[\"drug6\"],\n",
    "               data[\"drug7\"],\n",
    "              )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3236508e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all population means are same\n",
      "all population means are same\n"
     ]
    }
   ],
   "source": [
    "if F_ratio > F_critical:\n",
    "    print(\"reject null, all populations means are different \")\n",
    "else:\n",
    "    print(\"all population means are same\")\n",
    "    \n",
    "if p_value < 0.05:\n",
    "    print(\"reject null, all populations means are different \")\n",
    "else:\n",
    "    print(\"all population means are same\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "92cb45b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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F28QHxpwxZ+K1u6j81U6GVnVg6Gp3+pdotFrGzlqMZ+UyXtj9HBOiJ5AVlQVAyM03c+KRR7Bu3kzA7NknrxkeE8jUlDD+u7OM+2eloOkhICrKYNKfbxwvA4vPdlJK+bvPS8oCPwY2SikbT2kyp/O8ChqXku3PQkgypF9+8lBHXhNoBcaU4G4vrcw/Soe1jfDSClzjJrD0X3vYUdzATy7PYOvjcwiMW8Hyxg9ZbIjkP8cLSRCiy4l2jVmPaUQY7fvrkO6uV2F92chZ8xBeSWZDDE9sfYJ2t682R8C8uegiImh+480zrrllUgInmtvZXNS7zLyKMlj0Z83xTUBjjw19bgbe6K++KBeIsp1QsRumPOjLOdWpo6AR49AgNIazF2wCKMrZiUarJbSiin+4Ygky6Vn20DTunJ7AL3b+lNePvs5tI27j14v+hZ/H7duRfhaW7Gi8djftRxt61fWIxGRCY+OZ3JJMWVsZ/8j9BwBCryf4xhuxbtqEs6LitGsWjogmzGLgzV1quEq5sAz4HIcQwozvzeS9Uw5LYLUQIkcIcW8P198rhNgjhNhTV9e7Sm7KILXj7+AXDGO/yGLrburAXduO37Ced1oX5+wizByExgtVGVm89Z0pJIeb+cnmn/Bx8cd8d9x3+WH2D9GEpfhSmOS8DM3lXd7LmBqMNtCAfU9Nr7ouhCBj6kxaisu4LmYprx55leOtxwEIvvEG0Ghofuut064x6DRcOXYIa/NqaWl39epzFGUwGPDAAVwBbP3SMNU0KWUWcBnwoBBi5tkullI+L6XMllJmR0RE9Hdflf5iq4e85TDuVjB8UTOjI78JAL+M7uc3GisraKo6ga6slqKIJJ59cB7h/gae2vEUK0tX8uj4R7l3zL1fDE3N/CFICdv+0uX9hEZgzoqio7Cp14kP06fOBCmZYx+FXqvnz3v/DIA+KoqAuXNpfuddvA7HaddcNTYWp9vLqsO9y5GlKIPBYAgcN/GlYSopZWXnn7XAB8DEAeiXcj7tfxO8Lhh322mHO/Ib0Yb6oQs3dXt56f69AKRXnyB+ySJig008s+cZ3it8j3tG38O3R3379AuC42HMN3zVAq1dv6masyLBC/bc3r3Jhg6JJTI5hYrdOXx71LdZc3wN+2r3ARBy8014mpuxrl172jWZcUEkhplZltv7lO6KMtAGNHAIIYKAWcBHpxyzCCECPv87sBA4NDA9VM4LKX3/gMdNhMiMLw67vTiONeM3LKTH3eK523fiwoDZ6WbkDUt5p+AdXjnyCjel38R3x32364umPwJuB+x8rsvT+kgz+jh/7Ht7N1wFkDF1JtVFBVwdvohIUyS/3/17pJSYJ09GFxND8wcfntZeCMHSMTFsL25QezqUC0a/BQ4hxBvAdiBdCFEhhLhLCHGfEOK+U5pdA6yWUtpOORYFbBFC7Ad2AcullCv7q5/KIFCxG+rzIev0tw1HaatvGe6w7oepHA4HdYVHCGl3o0tI4Ih/C7/a+SumDZnG4xMfP3vQCU+D4VfArn9BR2uXTSzjInFV2XBV27o8/2XpU2YAcHzXbh4a9xAH6g+wtmwtQqMh6KorsW3diqvm9Lrk84dH4fFKNhaoOTrlwtCfq6pullLGSCn1Uso4KeWLUsrnpJTPndLmZSnlTV+6rlhKmdn5NVJK+csz765cVPa+AnoLjLzmtMOOY82gAePQoG4vf+OTzei8LobV1aCdMYlHNzzGEMsQfjPzN2g13a/EYvqj4GiBPS91edqUGQEagW1vbZfnvywwIpIhw4aTt20TV6ZcSUJAAi8cfAEpJUFXXQVeL60fLzvtmsy4YML9DXx2tHefoSgDbTDMcSiXMkcbHPoARl3rS0Z46qljzRjiAtD4nX2faofLw5aN25AIwlraeCXkMO3udp6Z8kfM+PdccS82C4bO9q3ocp2ZdFDrb8AvPQR7bi3S27vqfelTZ1JfVkrTiQruHHUnhxsOs6NqB8bkZEzjxtH84Yen9UujEcxJj2Rjfi0uT+/2jSjKQFKBQxlYR5aBywZZt5922NvhxlnR1uOmv7d2lxPaVEKw3o+mqEysJ6byrd2/ZO3PK3j+4Y28+IPNrPjHAY7trcVztn+Upz/my8Sb+3qXp81ZkXhbnb43oF5InzIdITTkb9/MlSlXEmGK4MWDLwIQdPXVOIuO0XHo8GnXzBseRWuHmz2lTb36DEUZSCpwKAPr4Du+neJxE0477ChpAa9vP8XZuD1eXll/mEhnHW5vMofS7yHBPpz0rFimXJPClGtSGJoZQV1ZGyufP8Sb/28Xxw91saEveSYMyYKtfwbPmQWWTBlhCD8d9l4OV1mCQ4gfOZr8bZvQa/TcPuJ2dlbv5GDdQQIvW4wwGGj54IPTrpmRFo5Bq2Ht0d5PxCvKQFGBQxk4bTVQshFG3wBfmsB2FDWDToMxIfCsl684VM2QimMIQBrSaDa+x82/nMC824eTtSiRrEWJzL19OLf9ciqLv+NLl/bJs/vZ8N983M5Tkg4KATMe8+XIOvzBGZ8j9BrMY8JpP1SP1+E543xX0qfOpKmqktqSY9yQfgOBhkBePPQi2sBAAubPp3X5crzOL1ZRWYw6JqeEsS5PzXMog58KHMrAOfwBSO/JCn+nchxrwZgUiNCf/Ud0+SdFjLdWIoSBifteYsI3JhPpf+YmUI1GkDIukpuemMi4BQkc3nSCj/60j3brKctf05dA+DBfHZAu5kXMWZFIl5f2Q73LK5U2aSoarY68bZuw6C18I/0brCtbxwnrCQKvvAJPSwu2rVtPu2ZeRiTF9TaK66y9+gxFGSgqcCgD5+A7ED0GItJPO+yxOnFV27qd39i0qYyRZU683uMY3a04AtpZMOXWbj9Oq9Mw9bpUFt87iroyKx/8fi+2ls6d3BoNTHsEag5B0WdnXGtIDEQb6od9X+/eCEz+ASRljiN/22ak18uN6TcihODt/LfxnzoVTVAQrZ9+eto1czMifc+mluUqg5wKHMrAaCz2lXIdfcMZpxzFLQAYU7pehttcY2f/28dophHcbSTWthIyY3aPmwQ/l5IVyZUPZ9LW2MFHf8r94s1j9A0QGAtb/nTGNUIIzOMicRxrxt3iOON8VzKmzqStoY7KgjyiLdHMiZ/D+4Xv49RKAubPw7p2Hd6OL1ZyxYeaiQ81sb24d4kVFWWgqMChDIyD7wECRl13xinHsWaEUYsh9sxqfx63l0+fP4jD48UR4yu9GtPcTty8pef08UPSQlj6YCatde2s+PtB35yHzuDLzHt8C5TvOuMaS1YkSHr91pGSPQmd3kDetk0A3JRxE82OZlaWrCTwssvx2mxYN2067ZopQ8PYUdyIp5dLfxVlIKjAoZx/UvqGqRKnQVDsGacdJa2++Q3tmW8Quz4pofGEjZVmJ7rWHKRwYfZIzJMmnXM3YtNDmH/nCKqLW1j/Wp5vb0XWt3wZert469CFmTAkBmLfW9vz/hDAYDKTnJVN4c6teL0eJkVPIjkomTfz3sQyeRLakJAzhqumpoTT0u7iaFXXO9kVZTBQgUM5/+ryfClGRl1zximv3YW71o4h6czVVPUVbexbdZy6CB22yFpM1TZiHBJTZibagO5rkZ9N6vhIJl6RTMGuGo5sqQSjP0z6DuQvh9q8M9qbsyJx19pxVfY2BclMbM1NVBw5jBCCm9Jv4lDDIQ435xGwcCHWDRvx2u0n209JCQNg+zE1XKUMXipwKOffkY8AARlXnHHKcdz3m7Yx8fT5DSklm94owGDW8Y7LSoplPXqPhtjKRizTpn6t7oy/LIn44SFsfquQ+oo2mPgd0Jl8+zq+xDw6HLSi14kPh2Zlozf6kb/dNyR1ZcqVmHQm3i14l8DLLkO2t2PdsOFk+6hAP4ZGWNh2TFUFVAYvFTiU8+/IR5A4FQKizjjlLG0FrcAQ73/a8cLdNVQda0GOCabdUIO28hgAoW12LFO/XuDQaATz7xyJ0aJj1b8O49QGwfhvwcG3zyj09HlZWXtu78rK6o1+DB0/kcKd2/B6PPgb/FmQuICVpSth7Ai0EeFdDFeFsaukUaUfUQYtFTiU86uuAGqPwIirujztKG3FEOuP0H+RnNDj9rJzWTHh8f580tpCRPwW4hrMhJnMGP1MmEaP/trdMgcaWHjXSFpq7Wx9p9A3SQ6w/W9nth0fhdfmov1o7yojp0+ZTntbK2WH9gNwbdq12Fw2PqtYR+DCRVg3bsJr+2Loa2pKODanhwMVLV/7uRSlP6jAoZxfRztLrww/c5hKurw4K9owJJ0+THVkSyWt9R0kzRnCgdpSvOwlpFlPRIsdU/Z4hO7sSRDPReywEMbOT+DI1ioqagJ8y3P3vgL20wOE37AQtIEGbLt7V7UveWw2BpOJ/O1bAMiKzCIxMJH3C98nYMECpNOJ9ZTNgJOH+uY5dqhlucogpQKHcn4d+QjiJ0HgkDNOOU+0gUdiTPxiYtzj8pLzaSkxqUHscbSjD95JTIMfAgg5XoF5woQz7vN1TLgimcAIE+tfy8M14XvgssOu509rIzQCc3YUjsIm3M1nZtT9Mp3BQEr2ZIp2bcPjdiGE4OrUq9lbu5fatHA0QUGnVQYMtRjIiA5QE+TKoKUCh3L+NByD6oMw/MouTztKfRPjhsQvVkjl7ajC1uJkwpJkVh2pwBS2h+z2ZAwGI0F2B5aJfVtVWG/QMufWDFrr2tm90wDpl/sqBDpPX0VlyY4GwLa7d5Pk6VNm0GGzcvxgLuCbJNcIDR+VfkzA7Nm0bdiIdLlOtp+QFMq+sibcap5DGYT6swLgS0KIWiFEl2VfhRCzhRAtQojczq8nTzm3WAiRL4QoEkI83l99VM6zo50FjEZ0HTicpa3oIkxo/Q0AeD1e9q4uIzIxAP0QE0daN+HFRmg1RBnN6Mxm/EaM6PNuxqWHMGJaDLmflVGX8j1ob/KVtj2FLtQPY2ow9j01varTkZQ5DqPFQv62zQBEmiOZETuDZceWYZ43B29LC/acnJPts5NCsDk95FW39e3DKUof6M83jpeBxT202SylHNv59f8AhBBa4G/AZcAI4GYhRN//66Ccf3nLIWYsBCeccUp6JY7SVoynzG+U7K+nta6drMWJrDlSgyF0G+kyCUdzK2ENTZjGj0fo9f3S1anXpWK06Nm8yYCMnwrbngX36TXBLROi8bQ46CjouYaGVqcndcIUinbvwN2ZFfeKlCuoa6/jaKoR4edH25ovcmRlJ4UCsKe0dxPwinI+9Wfp2E3AV/mpnwgUdZaQdQJvAl0vwVEuHG3VvtriGV2nBnHX2pEd7tM2/u1fV05guB/JmRF8eHQbWr9KLhPZAAQfO97n8xunMpr1TLpyKFVFLRyLegxaK+DQu6e1MY0IQ+Ovx7ajqlf3zJgyA2e7ndL9ewGYFTcLf70/yys/wzJtGm1r157ckR4bbCImyI89x1VhJ2XwGeg5jilCiP1CiE+FECM7j8UCpy6er+g81iUhxL1CiD1CiD11dSqr6KCV37lXIePyLk9/Pr9h7AwcdWVtVBW1MHp2HFanm6O2leiFiaBqD4GBQZidbiwT+y9wAIyYPoSwWH+2bbPgDs/0pSHxfjHnIHQaLJNi6MhvxN3Q3uP94kdl4hcQSP5233CVn86PBYkL+KzsM4xzZuCurqbj8JGT7ccnhpCjAocyCA1k4NgLJEopM4G/Ah92Hu8qxelZB5GllM9LKbOllNkREWfWYlAGifwVEJwIkV2POjqPt6IJ0KMN9QPg4IYKdEYtw6fGsPJIKRr/Q0yLmEflkSNE6fwQZjN+I0d2ea++otEIpt+YRltjB7nm//GlScn75LQ2/pOiQQis23t+69DqdKRNnMKxPTtxOXyrsZYOXYrNZSM3VQsaDW1rTxmuSgyhqqWDE809ByVFOZ8GLHBIKVullNbOv68A9EKIcHxvGPGnNI0DKgegi0pfcbRB8UbfMNVZUp87SlswJgUhhMDZ4aYwp5a07EiMZj1vH1mO0Li4OmAiLkcHoVW1mMeN67f5jVPFpYcwdGwEObn+2ALGwsbfnvbWoQ00YhoVhm1PDV5nz9UB06fMwOXooCTXNxGeHZ1NpDmSZQ0bMI8fj3XtupNt1TyHMlgNWOAQQkSLzgIKQoiJnX1pAHYDaUKIZCGEAbgJWDZQ/VT6QNFa8DjOOkzlbnHgaXJg6Ny/UZRTi9vhYfjUIXi8knzbeswiGmNFG0KjIbCgGHMfL8PtzpRrU/C4JTnGH0LNQV8CxFP4Tx2C7HD3Kt16/IjRmIOCT66u0ggNS5KXsPXEVrTTJ+EoKMBV6fs9KSM6AItBy55SNVylDC79uRz3DWA7kC6EqBBC3CWEuE8IcV9nk+uBQ0KI/cBfgJukjxt4CFgFHAXellIe7q9+KudB/gowhUD85C5PO780v5G3rYrgKDPRQwP5rPAI+BUzNWoRxw/sIzIiCr3Xi7mf5zdOFRxpZvi0GA4ftdDqnw0bf3NaeVlDYiD6GAvWrSd6XJqr0WpJmzSN4r27cXb4hqCWDF2CW7rZPdT3JmPd5AsqOq2GcQkhaoJcGXT6c1XVzVLKGCmlXkoZJ6V8UUr5nJTyuc7zz0opR0opM6WUk6WU2065doWUcpiUMkVK+cv+6qNyHnhcULAKhl0G2q5TgzhKWxAGDfoYf5qqbVQda2H41BiEELx++AOkFNw57DKqi4uIklqEyYRp1Kjz+hgTLk9GCMFuzWO+TYx5X7x1CCEImBmHu7adjl7kr8qYMgO300Fxjq9YVHpoOqnBqbzn3IE+Nhbrxo0n245PDCG/upW2DtfZbqco591Ar6pSLnZl26Gj+azDVOB74zAk+Ao35W2vQmgE6ZOjkVJysGUtZm86xupGkJKQssrzNr9xKv8QI6Nnx5JfaKLRMgU2/vq0tw7TmAi0IUbaNpT3WOQpNmME/iGhJ1dXgW+SfH/9AZg6HtuOHXgdvvK0WYkheCUq4aEyqKjAofSvvOWg84OUuV2e9na4cVXbMCQG4vV4ydteTeKoMCxBRraU5+DW1DMhfAHHD+zDYDJjyi88r/Mbp8panIjOoGWn93u+t478FSfPCa3vrcNZ3nayZvrZCI2GYZOnU5Kbg6OziNOSoUsAyEkB2d6OfdduAMbGBQOQW97c9w+kKF+RChxK/5ES8lbA0DlgsHTZxFnWBtI3v1F2uBF7q5PhU2MAeO3gMqRXxzdHXU7pgX0MiYpBAwMWOEz+BjLnx1Nc4keDaSpsOP2tw5IdhcZfT9uG8m7u4pM+dQYel4tje3YAEG2JZnzUeN62HEEYjSdrkQeZ9QwNt7CvrLlfnklRvgoVOJT+U3MIWsq6HaZylLaABgwJARTsrsHPoidxdBhe6SWnfgOajnTSDJK2+joiXRLh54dpVP/u3+hO5tx49EYtOZqHoPrAFxsbAaHX4j8tFkdhM87y7nNMxaSmExAWcdpw1eKkxRTYS5HjR2HduPHkkNfY+GByy5t7VedcUc4HFTiU/pP/KSBg2NlTljlLW9HH+OMVgpID9QwdF4FWqyG3Zj8OmhgeOIPyg/sACC4+jjlrHMJgOE8PcCY/i57Rs2MpLDbRZJ4IG54+7a3Df0oMGrOOllWl3d5HaDQMmzKd0v376LBaAZifOB+N0JCXbsFVVoaz1HePsQnB1FsdVLb0nMJdUc4HFTiU/lOwEuKywT+yy9PS48VZ3oYxKZDjhxpwOzykZvvavnX0Y6RXxxWp8zl+YB/BkdHoBnB+41SZ8xLQ6TTs1T18xluHxk9HwJwEHEXNdBR2v4w2Y8oMvB43Rbu3AxBuCmdC1ATejygFOLm6amx8MAC5arhKGSRU4FD6h7UWTuRA2qKzNnFV2pAuL4akQAr31GIK0BObFoxXetl0Yh1u2zBmDo2h7PABhoRHgZSDInCYAw2MmDGE/CIzrZYs2PCr0986JsegDTbSsrK0230dUSlpBEVFnzZctSh5EbnaSkiKw9a5nyMjOhCDTkNuudrPoQwOKnAo/aNwje/PYQvP2sRR6lt9JKItHD9YT0pWJBqthv11+7F6GgjxjoeaEtwOBxF2R+f8xvndv3E24xYkIDSCvcbvd+7r+CKHldBrCFyQiOuElfZD9We9hxCC9MnTOX4wF3ur77/F/IT5aIWW48NDsOfk4O3owKDTMGpIoFpZpQwaKnAo/aNwFQTEQPSYszZxlLaiDfOjorQNt8tLWucw1cqSVeDVMS12FqUH9qHR6gjML8I0buyAzm+cyj/Ej+FTYjhaYMEamAXrnz4th5V5XCT6aDMtK0q6zWGVPnUm0uulaJdvuCrEL4RJMZNYHVGDdDhOFncaGx/CwRMtuFRFQGUQUIFD6XtuJxxbD2kLz5rUUEqJs7QVY2IghXtqMAcZiEkJRkrJqpLPcNvSmJUaT+n+vcSkpOLJL+jzMrFfV9aiRKQXck0/hNrDcOTDk+eERhB8ZSqeZgdt68rOeo+IxGRCYmLJ377p5LFFSYvYFNGA1OmwbfMlVBibEEyHy0u+qgioDAIqcCh9r2w7OFph2NnnN9z17XhtLrRD/Ck73EhqViRCI8hvyqfBUYO7bQSZYVrqSosZEhTmm9/ox8JNX0VguIlhE6M4fMRMe8h4374O7xdvF8ahQZjHR9G26QSuGluX9xBCkD5lOuWHD2Fr9s1hzEuYh8eopzE1Ats235vIuM8nyNVwlTIIqMCh9L3C1aA1QPKsszb5PLFhncODx+0ldbxvmGp92XqQggTTeKzFvtyW4S1WhNGI35izD3sNlPGLE3G7vRzw/4GvXseh9087H3RZEho/LU0fFJ11ojx9ygyk9FKwc6vvGmMQk4dMZkecHcfRo7gbGogLMRFmMajAoQwKKnAofa9gFSTNAKP/WZs4SlvRmHUcK27BFKAnaqiv1vjasnV4OxKYMXQopQf2YQoMwnj4CKZx49AMkvmNU4VEW0geE87Bw/44w7N8Oaw87pPntf4Ggi5LxlnainVr12VlwhOSCItLOJlqHXzDVVtiffs7bNu2I4Q4uRFQUQaaChxK32oshobCboepwFfxT58QyPHDjSSPCUejEVRZq8hvysPVOoLJQ0M4fmAfCRkjcR7NP69p1M9V1qJEHHY3R0J+BA1FcPDt086bs6PwGxFGy8oSnJXWLu+RPmUGJ/KP0NboW4U1N2EuZUN0OC2GL+Y54oM5VmelVWXKVQaYChxK3ypY7fsz7ezLcD1tTtz17bSbdbg6PCRn+kr+ri9fD4DbNoI0bRv2lmZiLIEgJZZBNr9xquihQQxJCyb3YACeyHG+eh2eL/5xF0IQcl0aGrOOxjfzka4zV1mlT50BUlK4wzdcFWgIZGrcdA4nabFt24aUkrEJwUgJB8pVplxlYKnAofStgpUQPgxCk8/axHncN79R2eJCZ9QSlxECwIbyDRi8UYwMT6Wh4CAAobWNg3Z+41RZixOxNTsoiP4JNJXC/jdOO6+16Am9MR13rZ2mD4+dkXcqdEgcEYnJ5G0/fbhqV7wDd00NzmPHGHMyU67aCKgMrP6sAPiSEKJWCHHoLOe/KYQ40Pm1TQiRecq5UiHEQSFErhBiT3/1UeljDisc39rt2wb45jfQacg/1kzCiFB0Bi1tzjZ21+zG3jycqSlhlO7fS0RCEuTuxzR2LBqj8fw8w1eUMCKUsDh/9h0IRA4ZDxt/51uWfAq/tBAC5sZjz6npcr4jfcoMqgryaK33laCdEz+HvBTfvI5t2zaCTHpSIixqnkMZcP35xvEycPbsdlACzJJSjgGeAp7/0vk5UsqxUsrsfuqf0teKN4DH2W1SQwDH8VZEhAlrs5OhmeEAbKvchtvrxtmawcQ4CyfyjpAwfBQdR48OumW4XRFCkLUogaZqOyUJP/FlBd736hntAucn+uY7lhefkcsqfcoMAPK3bwHA3+DPsJEzqA3VYt36+TxHiMqUqwy4/iwduwk4ax1NKeU2KeXn/8/ZAcT1V1+U86RwFRiDIKHr2uIAXqcH1wkrLRqB0AgSR/sCx5YTWzAIC8KRQJS1HK/HTbTe1JmfavAHDoDUrEgCw/3Yuz8YGTcJNj8DrtMz2gqNIPQb6egizTS8fhRn1Rf7O4KjY4gamnra6qoFiQvYl+jFumsH0unszJTrpKKp/bw9l6J82WCZ47gL+PSU7yWwWgiRI4S4d4D6pJwLKX0T4ylzQHv2sq7O8jbwSsrqOxiSFoSfRY+Ukq0ntqJ3ZpAZH0blob0YTCYCyysRBgOmzMyz3m8w0Wg1jJ2fQE1JK1VpP4bWE7D3P2e2M2oJv2MkGoOW+pcO4m74IgikT5lBTXEhzdVVAMyKn8XhoTpEuwN7bq7aCKgMCgMeOIQQc/AFjh+dcnialDILuAx4UAgxs5vr7xVC7BFC7Kmrq+vn3ipnVbUfrNU9L8Pt3Ph3vLad5DG+1VT5TfnUtdfRWJ/M1KGhlOzbQ+KYcThyci6I+Y1TDZ8agylAT86BEEic1vnWcebbgS7Ej/C7RoFHUvfiITxtvvmQL4arfG8dgYZAzJMm4tGAbes20qMDMOo0KnAoA2pAA4cQYgzwAnCVlLLh8+NSysrOP2uBD4CzJimSUj4vpcyWUmZHRET0d5eVsylcDQhIXdBtM0dpC25/PW4JiaPCAN8wFYCrLZ0xZhvWxgaSLqD5jVPpDFrGzImn7HAj9SMf9wXTPf/usq0+ykLYHSPxWp3Uv3gIb7ubwIhIYtLST0u1Pmv45RTFQP3mdei1GkbFBqnAoQyoXgUOIcR7QoglQog+CzRCiATgfeA2KWXBKcctQoiAz/8OLAS6XJmlDCIFqyB2PPifPXhLj8R5vI1mCUERJoKjzABsrthMsDYJvQjCVOP7UYjyCPB6MU8aXIkNe2PUrFj0Ri17D4b50q5s+QM4u85VZUwIJOy2Ebjq7NS/fBiv00P6lJnUHS+hsbIC8K2uOpSshaNFeFpbGRcfzKETLTjdKlOuMjB6Gwj+AdwCFAohfi2EyOjpAiHEG8B2IF0IUSGEuEsIcZ8Q4r7OJk8CYcDfv7TsNgrYIoTYD+wClkspV57LQynnmbXOV7Sph2EqV7UN6fRQ3uAgcbTvbaPV2cr+uv14bBlkJ4ZQtj+HqKFpcPAQwmjENHbseXiAvuVn0TNyxhCKcmppHfc42Opg9wtnb58WQuhNGTjLWml49QhpE6aAECcnyUP8QnCPHY6QEtvu3YxLCMHh9pJX3Xq+HklRTtOrwCGl/ExK+U0gCygF1nTuvbhTCNHlTKiU8mYpZYyUUi+ljJNSviilfE5K+Vzn+bullCGdS25PLruVUhZLKTM7v0ZKKX/ZN4+q9JuiNYDscf+Gs7NwU53Dc3KYanvldjzSQ011ElOGGKkqzGdoVja2nbsGbX6q3sicl4AQkHs4DFLmwZY/gePsKdHNo8MJuS4NR2EzrlX1xKWPIm/rxpPLbkfMvAqnDqo2rWZcQjAA+1QpWWWA9HroSQgRBtwB3A3sA/6ML5Cs6ZeeKReOglXgHw0x3a9+chxvxa3X4NJrGJIWDPjmN0xafzzt8Qx1ngApSUjNwJGXh+UCHKb6nH+IkfRJ0RzZVoV94o+hvRF2fXmr0uks2dEELR1K++EGxocuoLGygtrSYgDmpi0mP1bQun0bMUF+RAUa2VemdpArA6O3cxzvA5sBM3CFlPJKKeVbUsrvAmdPgapc/DwuOLbOVyL2LEWbwFe4yVHaSoNbEpcRik6vPbkMN1QzCovBgPf4YcxBwQTU+hL9mSdNOl9P0S/GLUzA4/ZyMD/Mtyly61+6fesACJgeS+D8BPyq9IwOnUne1o0AhJvCaRoxBP+yejzNzSpTrjKgevvG8YKUcoSU8mkpZRWAEMIIoHZ2X+LKdviKNqV1P7/haXLgbXVSbXOfHKb6fBlua2MKExKDOX5gL8ljs2nftRthMg2a+uJfVUi0haGZERzcUIFz0g+hoxn2vNTjdQHzErBMiGZE0BTadlUiO0vSRk6fC8DxjSsYlxBCaYOdRpuzu1spSr/obeD4RRfHtvdlR5QLVMFKX9GmobO7beboTGzY6PaSMDIU+GIZblVVEhMtrThsNoZmZWPftRNzVtagqS/+dYxblOBLuV4S6ftvtO3ZM3aTf5kQguCrU3CHS0abplOxIReASXNvpUMPx9d/cspGQDVcpZx/3QYOIUS0EGI8YBJCjBNCZHV+zcY3bKVc6gpX+za6dVO0CXwT424BuigzgWEmwLcMN8YvBekJILqlBI1WS2xcIo7Cogt+mOpz0clBxA4LJvezcjxTvw+22i5zWH2Z0GqIuSsLu7sFz9pmPG1OhoQkUDE0AG3uUUbHBaHVCDVBrgyInt44FgG/x5dH6g/AM51fjwE/6d+uKYNeYwnUF/SY1BCgo7iFRpeXxFG+3FSfL8M1e0YSZNLTWniA2IyReA76tuxcyBPjX5a1qDPlek0qxE30zXV4ei7GZAzxpzLuBLih4Y2jSK9Enz2OyBoHDVV5ZEQHqMChDIhuA4eU8hUp5RzgDinlnFO+rpRSvt/dtcoloLCzaNOw7pfheu0uPHXtNLjkGctwq6uTmREFDeXHSRk/EdvOnWgsFvxGjuzv3p838SNCCY/3Z9+aMuSMH/gy5x58p1fXDp09kb31a3AWt9K2royM+dcDkLvqNcYl+CbIPWepZa4o/aWnoapbO/+aJIR47Mtf56F/ymBWsArC0iB0aLfNPp/faNEIolN9tcW3nNiCvz6A6tpIRrrLAUidMAX7zl2YsscjdLr+7ft5JIQga2GiL+W6fRxEjYLNfwDvmZUAvyxh9FiqxXEa/WppXVtGTHQ2HUYNbdu2MjY+BKvDzbG6rsvRKkp/6WmoytL5pz8Q0MWXcqlyWKF0c4+7xcFXuMkLBAwLQavVnFyGm2AaC2jxO3GEiKShmKXAWVKCZeLFMb9xqpSsCF/K9dVlyOmP+eqyH/24x+u0Oh3pU2ew+dg7aAL0NH1YjG1kMlH59SRE+lZUqf0cyvnW01DVPzv//HlXX+eni8qgVLLRV7Sph93iALaCJprdXuLH+OY3Pl+GK+0ZxBndNJYUkDZhCvZdu4ALf/9GVzRaDeMW+FKuV+pnQ2iKL3NuLwoyZUybTYfTSluaHXeNnSGjv8mQJigtXU2QSa/2cyjnXW83AP5WCBEohNALIdYKIepPGcZSLkUFq8AYCAlTum0mXR681TYa3JLEkadnwy0pi2OGXw1ISerEKdh37UQTGIjf8B5ToV2QMqb4Uq7vXVMB0x+F6gNQ9FmP1w0ZlkFgRBSHCjdgGhuBoS0OTUAMZRuXMzY+WE2QK+ddb/dxLJRStgJLgQpgGPDDfuuVMrhJ6ZsYT5kDuu73WjjL2xASnMFGLMG+uhqbKzYzNHAYdS1GYpuLCI6KITw+EdvOXZizsxFa7fl4ivNOZ9AyZm48ZYcbqA9dAoFxvreOHgghGD59FscP5mKYGYYw6jCMvQXzwWIyYgX5NW1YHe7z8ASK4tPbwPF5IsPLgTeklGctCatcAqoPQFtVj7vFAeyFvvrYQSNPz4YbqcvE4HXgLM8ndeIU3NXVuMrKLqpluF0ZNbMz5fpnlTDte1C2HY73vJc2Y9ospNdL0YEdBM1PQB+WTrZ1JNJ8CCnhgBquUs6j3gaOj4UQeUA2sFYIEQF0v/1VuXgVdC7DTeu+aBNA25EGWr0Q31nt7/NluNamVLJEFdLjIXXCFGzbdwBgnnz2euUXAz+LntGzYynMqaUh6jowhcK2v/R4XXh8IhEJSRzdsgHL5BiEwUlU0vU0HN8AwD4VOJTzqLdp1R8HpgDZUkoXYAOu6s+OKYNY4SoYkgX+kd02kx4JdXaaJESnfLEMN8AQwKHiYEa7y7EEhzAkLR3btm1ow8MxDht2Pp5gQI1bmIjRpGPHikqYeA/kr4D6wh6vy5g+m6rCfFrqa/GfEYE2IIbR+/1JihRqZZVyXp1LRb/hwDeEELcD1+OrzKdcamz1ULGnV7vFnSfa0HhBRJvR6r5YhjsieAJ2uwtTTSEp2b4VVLZt27BMnYLoJsPuxcLPomfcwgRKD9RTGX4LaI2w/dker8uYNhOAvK0bCZg7EndTETPk5SRElLCvrPlk7Q5F6W+9XVX1Kr7UI9OBCZ1fKivupajoM0D2uFscoPmgLz16cOcw1efLcE3ukcR3VCBdDtImTMGRn4+nsRHL1Kn92fNBZczceMxBBnasbEBm3gy5b4C1tttrAsMjic0YydEtGxAaDTpzOUZdIBOaHTTYnFQ0tZ+n3iuXut6+cWQD06SUD0gpv9v59b3uLhBCvCSEqBVCdFkvXPj8RQhRJIQ4IITIOuXcYiFEfue5x3v/OEq/K1gJ/lEQ3X3RJgBrXiNWjyR+vG9I6/NluNXViYyXFRjNFuJHjcG2dSsAlimXTuDQG7RMWJJM1bEWjofe5dsTs+tfPV43fPpsGk+UU1tyDMvENNw1h5lXlY5RdLBXDVcp50lvA8chIPoc7/0y0N14xmVAWufXvfjqmiOE0AJ/6zw/ArhZCDHiHD9b6Q8eFxSt802Ka7r/0ZFeiba+HatBc1o23PSQDA6WuIhpLmJo1gS0Oj22bdswpqWhj+p+zuRiM3xaDEGRJrZ9ZseTthR2/wuc9m6vSZ8yA61ez+GNazFPmoQz/xMCvYHc6GdV+zmU86a3gSMcOCKEWCWEWPb5V3cXSCk3Ad0t270K+I/02QEECyFigIlAUWftcSfwJmoifnAo3wmOlt5lwz3Rhk6CLs6XmabF0cL+uv0kW8YTZS1DONtJnzoTb0cH9j05l9Qw1ee0Wg3Tb0ijqdrOfv13oL0Jcl/v9ho/f39SsidzdOtGtPFxoGmh0Z7PN5zR7C9Rq+SV86O3geNnwNXAr/gitXrPO5e6FwuUn/J9Reexsx3vkhDiXiHEHiHEnrq6uq/ZJaVbBatAo++xaBNA7a4aAMKyooAvluF6bMNJtx/DYLaQlDkO+54cpNOJZfq0/uz5oJU0OpykMeHs2erFGjnHN0neQ/LDkbPm0tHWSsm+PVgmT0aTt5Jgj5mUaqvaCKicF71djrsRKAX0nX/fDez9mp/d1fIZ2c3xs/XteSlltpQyOyIi4mt2SelW4WpImgbGnvNbthc20eGVRI/z5afafGIzQcYgCkv8SbWXkDZxim+YautWhF6POfvSXWsx/YY0vB7Jto77oam0x+SHSWOysASHcHjjOsyTJqItO8wxXTE3oGWveutQzoPerqq6B3gX+GfnoVjgw6/52RVA/CnfxwGV3RxXBlJTKdTl9Wq3uJQSfbODdosevUGHV3rZcmILEyKnYDt2FK3HScaUGYBvGa5p/Hg0JlM/P8DgFRRhYtyiBAoL9FQYFvg2BHaztFaj1TJ8xhxK9u1GM9I3/XesfROx0kRVTvX56rZyCevtUNWDwDSgFUBKWQh83ZnMZcDtnaurJgMtUsoqfG8zaUKIZCGEAbips60ykD7fLd6LNOpNhc0YAUOSb9Pf4frDNHY0Eq4dS5q1CL0lgPhRmbhqa3Hk51+S8xtflrUokcAIE+sa78FZfsSXiqQbI2fOxevxUFRShC4mhriacqr1DcQUNp+fDiuXtN4GDkfnRDUAQggd3QwfdbZ5A9gOpAshKoQQdwkh7hNC3NfZZAVQDBQB/wIeAJBSuoGHgFXAUeBtKeXhc3gmpT8UrvKlAg9L6bFp7S7fb73h2b75jc0nNiMQNFXHkWw/TvrkaWh1OmybNwPgP2N6//X7AqE3aJl/xwisNj2b7Q/A9r912z48IYmooakc2bQOy8SJxBY28VHIWoZ2CGylLeep18qlqreBY6MQ4ieASQixAHgH6HYgVkp5s5QyRkqpl1LGSSlflFI+J6V8rvO8lFI+KKVMkVKOllLuOeXaFVLKYZ3nfvlVH07pI04blGzu1WoqAGdxCy4gdHgo4FuGOyZiDCdyj6KXLoZP9e2Atm7YiC4qCmPGxZlG/VzFpASRtSiRPOt0inNroeFYt+1HzJxHbekxOtLT0LRYKfLmYhUOqj47fp56rFyqehs4HgfqgIPAd/C9Lfxff3VKGWSKN4LH0athKpfTjcnqxBFkRGgE9e31HGo4xJjQyYRVHURYgogbMRLpdGLbtg3/mTMviTQjvTVhaTLhsX5saH0A24YXu22bMW0mGq2O0o42ABa0RLMqeDuGYy14Wp3dXqsoX0dvV1V58U2GPyClvF5K+S+pEuNcOgpWgiGgx6JNANX76jFpBKZhwQBsPeHbFU5DIgnt5aRNmYlGo8Wek4PXZsN/9qx+7PiFR6vTMP+uMbiwsGpTHJ62hrO2NQcGMTRrAvm5e9DGxzPuhJYVoevRSLDtVpPkSv/pNnB0Tlz/TAhRD+QB+UKIOiHEk+ene8qA+7xoU+rcHos2ATTs9e3fiJwUA/jmNyJMEdTsOY4WL5MX+d5arBs2IvR6LBd5GvWvImyIP3OuDaXKkcGW5z/rNnnh6HkLsbc00zRiGOZDxdTqWtlrrMS6q8qXnVhR+kFPbxyP4FtNNUFKGSalDAUmAdOEEI/2d+eUQeDzok29nN/wVlhxaQR+sf64vC62ndjG1CHT0RXvxRMcQ0RCEgDWjRsxT5yIxmLpx85fuIbNzyYrdg+HCiPY+2nxWdslZWYREBZBiXDjbW1jtn0EH4euwdvipCNP7elQ+kdPgeN24GYpZcnnB6SUxcCtneeUi13BKkBAas9Fm1ob2gl0efBEmBBCsL92P22uNiJak4jsqCVhom/vhvP4cZylpfjPUsNU3Zl8UxZpfpvZsew4BzdUdNlGo9Eyeu5CTlRVYDfoWNoaxa6g3diMHqw7q85zj5VLRU+BQy+lrP/yQSllHV+Uk1UuZgWrIHY8+Pe8K//Ermr8NILAUb7d4ptObEKn0WHNqceLYP4VlwG+tw1AzW/0QKTOY17KKpIC89j0ZgG5n5V12W7UnAUIoaFyaAIpJc140bI6IA9HQRPuBpVqXel7PQWO7pZmqGUbFztrHZzI6dVqKoDWg76J3JBxviCzvmw9EyKz8RbkYg1LJjzSd9y6YSOG5GQMCQn90++LhRBop93HYtMTpAyDre8WsfmtAjwe72nNAsLCSc7KpsxsoD1nH0O0mbwX8CFowLpTTZIrfa+nwJEphGjt4qsNGH0+OqgMoKI1+Io29Rw4PG4vmjo7Lr0GXZiJ4pZiSltLGW1Lw+xsJTrbN0zlsdqw796thql6a/SNaP1DWBj5HJnz4jmwvoKP/riP1vrT3yQy519Gh8dFtRaucGXQ4HeChliJfU810uU9y80V5avpNnBIKbVSysAuvgKklGqo6mJXsBICYiB6TI9Nq4qaCRWgifVHCMH6svUAuPc20qExMnfRPABsmzYiXS4C5s/r165fNPR+MOEeNEWrmD7by8K7RlJfbuWNp3axb00Zns6gkDQ2C//gUMrDAlnUakR6dXxg2Y3X7sZ+6IzRZkX5Ws6l5rhyKXE74dh6SFsIvdigV7mrGqNGEDy2c5iqfD2j/YfjPnaUqvARDBsSAkDrmjVow8IwjRvXr92/qEy4C3R+sP1vpE2I4uafTiJ2WDDb3ivitSe3k7OylA6rh9HzF1MXYMa9Pxc/dwbL5DK0YX7Y1CS50sdU4FC6VrYdHK29nt+w5/vKlloyQqlvr+dA3QEmNiah8XoYMnEWQgi8DgfWjZsImDcPodX2Z+8vLpZwyLwJ9r8JtnoCQv1Y+mAmVz4ylqAIEzs+LOaVH2+l/kQ8CDhcVcMYyxRcmkZahkucpa24aruvLKgo50IFDqVrBatAa4TknucimmvsWDrceEw6dMF+bCjfgJQSub+eGkME86b53i5sW7ch7XYCFi7s585fhCY/6Ev7svuFk4fiM0K5+rEsbvnZJEbPiqOpRoNGP5TjwQFkbxvGFYe+y6eHi5ECKpeX0Fhpw+3svkiUovSGbqA7oAxShasgaToY/XtsWnqgjjCtwC81GIB1ZesY7orDW1dLScwcxif6hqna1qxBExiIZeKE/uz5xSlimK8Wyq5/wbRHfHMfnUKiLUy/MY1pN6RyaGUHq1/+I0FyP5WeOCgLpNLoJeJoA2/trMELWIIMBEaYCAo3ERbnT/TQICLiA9Dq1e+RSu+onxTlTA3HoKGo17vF6/bVYtAIAkaFYXPZ2Fm1kwk1sbiFjoTsaei0GqTLhXXdOgLmzEYYek5donRhyoNgr4eDb3d5WgjBqMVzCfAKOhq2sT29gpcm/AjzdQEYNIKF8+OYeEUy8SN8WYvLjjay9d0i3vttDv96dBOf/G0/eTuqcLSr8rNK99Qbh3KmglW+P4f1PKTksLsQlTaknxZjaggry1eDw4Umr56jljSuz0wEwL5nD56WFgIW9LwDXTmL5JkQNdpXq2PcbV0uWhBCMDxhKLsqjnGZfigvo+VD1nB32FwCGtpJ+c6w09rbWhxUF7dQWdhM8b46jh9sQKvLJ2NKNOMWJhIUcelWZlTOTr1xKGcqWAkRGRCS1GPTssONROgEIsKE1qJnVekqxtZGg8vN0ZDRzBzmW2XVtmYNwmTCMm1aP3f+IiYETH3IV8K3aO1Zm41auAS920PU0W14rGmsPL4S84QonCVnTpJbgoykjItkxo3DuP1XU7nuf8aTPiWao9ureP3J7Xz27yPYWhz9/WTKBaZfA4cQYrEQIl8IUSSEeLyL8z8UQuR2fh0SQniEEKGd50qFEAc7z+058+5Kv+hohePber2aqiy3lhCtBv+R4bQ529hSsYXhZUE0WGIYNiIDf6MO6fHQumYN/jNmXNK1xfvEyGt9e2u2//WsTQKnTiG+qY26siJCWkfS6qqnJLEONALbrrPvJBdCED00iDnfzOD2X0wlc148RTm1/PdnOzm8+QTSq7LtKj79FjiEEFrgb8BlwAjgZiHEiFPbSCl/J6UcK6UcC/wY2CilPDWl55zO89n91U/lS4rXg9flm4jtgdfjxZ7XhEaAKT2EDeUbiKjVIpra2WMeweWjowGw79qFp66ewMsv7+fOXwJ0Bph4LxRvgOpDXTbRBgSQHjEEkMyz68CrY0XNakwjw7DvrenVTnJLsJFp16dx0xMTiYj3Z8Pr+Xz051za21SmIaV/3zgmAkVSyuLOeuVvAld10/5m4I1+7I/SGwWrwC8I4if12LS6uIUQrxep02BICGBl6UrGnohA+lkoCUhl4Qhf4Gj5+BM0FotKathXxt8BejPs+PtZm0RMm05Ui43Qsv3IlmGsKFmJKTsSr91N++He7yQPjjJz1aPjmP3NdKqPtfDO03uor2jrg4dQLmT9GThigfJTvq/oPHYGIYQZWAy8d8phCawWQuQIIe4924cIIe4VQuwRQuypq6vrg25fwrzezqJN80Hb87qJ0v31ROo1GFODaHW3sb9oJxFVGgqDRzE5NZIQiwGvw0Hb6tUELFyIxs+vx3sqvWAOhbHfhANvQ1vXQ0+WqVNJqmvG62hnaHk0Lc5GDgUUoQ3163a4qitCCEbOiOWaH2Th9Xh577c5FOeq/69dyvozcHSVp+Jsg6RXAFu/NEw1TUqZhW+o60EhxMyuLpRSPi+lzJZSZkdE9Jz6W+lG5T6w1fVqmAqg5kA9Zo3AnBHG+vL1pBebEULDFv0wloz2VQC0btyI12olcOmS/uz5pWfy/eB1+/Z1dMGUmUmY1BDqZ2ZCfQMar5FPj6/EMjEaR3ELrrpz30kelRTIDT+ZQOgQf1Y+f4iinNqv+xTKBao/A0cFEH/K93FA5Vna3sSXhqmklJWdf9YCH+Ab+lL6U/5yEFpI63nJbHOtHb9m32obv2EhfHr0Y4ZVBOBNHkeHIYCFI33DVK0ff4I2PBzLpJ6HvpRzEJYCGUtgz4vgtJ1xWuj1WCZOJKWhFXNHM7HFQ1ldugbDuFDfJPlXrEluCTJy1SNjiUoKZPWLh1XwuET1Z+DYDaQJIZKFEAZ8wWHZlxsJIYKAWcBHpxyzCCECPv87sBDoeiZQ6Tt5KyBxqm8opAelB+qJ0As0wUbqDE207c5H5xZsMo5gakoYoRYDntZWrBs3Enj5ZQid2jLU56Y8CO1NsL/rqUHL1KmEFx3HPyySzDJBq6OFPdZ9mEaEYs+pQbq/Wrp1g5+OK76XeTJ4qGGrS0+/BQ4ppRt4CFgFHAXellIeFkLcJ4S475Sm1wCrpZSn/toUBWwRQuwHdgHLpZQr+6uvCtBYDHVHIb13K59K9tUSoddgGh7KssKPyCj1xz8ljYPt/lzeOUzVtmYN0ukkaOnS/uz5pSthCgzJgu1/981PfYll6hQEMC45jfAOG/G1QXxa8imWiTF4bec2Sf5lnwePiIQA1rx0mLoyNWF+KenXfRxSyhVSymFSyhQp5S87jz0npXzulDYvSylv+tJ1xVLKzM6vkZ9fq/Sj/E99f6Zf1mNTW4sD5/E2tIAxLZhd6z7G4tBRmzADg1bD5aN8gaNl2cfoExLwG61qfvULIXxvHY3HfJs2v8SQkoIuMpLoihqkfwijCsJZU/oZ3iQj2hAjtq9ZHdDgp+Py+0fj569n+d/2Y21SGwUvFWrnuOKTtwIiR0Boco9NS/bXE6kToBHkWUqIOeJCHxnCh7X+zBseSZBZj7O8HPvOnQRfczWiF/U8lK9oxNUQFO9LQ/IlQggsU6fSsWMHw+dfQVSbm8BaD+sr1n+tSfJTWYKMLHkgE2eHhxX/OIDLobLvXgpU4FDA3uirv9GLtw2AY3triTFpMSQFsnzDq4RYDUROv4oGu4trs+IAaH7vPdBoCLrmmv7suaLVwaTvwPEtvlVxX2KZNhVPSwsz0lPp0JkZeyycj4s/xjI+GjRg213ztbsQHufPwrtHUlfexua3Cr72/ZTBTwUOxbd3Q3ogveclsx1WF42FzfhL0Kb549xSgCfYyGZ3EiFmPbOGRSDdblre/wDLjOnoo6PPwwNc4rJuB0MAbP3LGacsU6cC4Ny1C83oWcTU6yg4socmfSt+w8Ow51R/5UnyUyWNDmf84kSObqui4Bz3iSgXHhU4FMhbDv7RMKTncq4lB+qI0vqGntbWrCC4VUfa4sWsOVrHFZlDMOg0WLdswV1bS/D11/d3zxXw7fSf8G048qEvJf4pdGFh+I0ejXXjJuZcfTUdGj1jCgNZXrwc/4nRvknyIw190o2JS5OJSQliw3/zaVYVBy9qKnBc6lwdvkyr6YtB0/OPw7F9dcSatWjDTRzZuIoOf0F79Dycbu8Xw1Tvvos2LIyA2bP7ufPKSZMfBI0etv7pjFP+M2fSvn8/2ZFmiqMnEl9rZsPOZRjTQtAGG895J/nZaLQaFtw1Eo1GsPqFw3j64E1GGZxU4LjUlW4Gl61Xw1TOdjeVRxsJFVBtriagURI3fxpv760iNdKfzLgg3HV1WDdsJOjqqxB6/Xl4AAWAgCjIug1y34CWitNO+c+aCV4vtq1bGTF/CXadjtA9zRS2FGKZEI2jqBl3fXvfdCPUj7m3DaeurI2clcf75J7K4KMCx6UubznoLb4iQT0oOVBPuAAhYe+RVVgtHkZP+Ra55c3cPDEBIQQtH30EbjfB16lhqvNu2sOAhG3PnnbYb9QotKGhWDdt4tpJKeQEj2VIg4mP1/0Hy4SozknyvpuXGDougrQJUeSsKKW+wtpn91UGDxU4LmVer2/9f+rc02pYn03hnhriLDo8Oi8tdaUEzhvLB7kNGHQarsuKRXo8NL3xJubsbIxDe17Wq/Sx4AQYfSPkvAy2Lzb3CY0G/xnTsW3eTFKIH5rhc7AZBU2f7UVatPhlhGH7GjvJuzLzG8MwWnSs+89RvB41ZHWxUYHjUla5D9qqejVM1W51Un64kSidoMJWSH2gg6sW38+H+06wdHQMwWYD1g0bcJ04Qchtt52Hzitdmv4ouDtgxz9OO2yZORNPczMdBw9yzfhEdocPJaRRw6p1r+M/KRqv1YX94FffSf5lfv56Zt6UTl1ZG/vWlPXZfZXBQQWOS9mRD0Gj802M9+DY3jqCBWjdksqWfDSzUtldDG0ONzdPSgCg8bXX0MXEEDBvbj93XDmriGEw4kpf1tyOlpOH/adNA40G66ZNLB0zhALTbNrMHg5+sAxDShC6SBPWTRVI2XdV/lLHR5KSFcHuT0rVKquLjAoclyop4egySJ4FppAemxfsqiYpSINHutnrd5g7l36f/+4sIy3Sn+zEEByFhdi37yDk5ptVQsOBNv0xcLSclnJdGxyMaexYrBs3EWIxsGBkIvtiI9A3ONi3ZTUB0+NwVdlwFLd0c+NzN+PGYWh0gs1vFfZpUFIGlgocl6rqA9BU6vvttAdtjR1UFbUQ7nVS3V4K81Jobgllf0ULt05ORAhB42uvI4xGgm9Qk+IDbshYX02VbX89/a1j5kw6Dh/GVVvL7VOSOKSZQWOAk01vvIxhVAgaix7r5hN92hVLsJGJS5MpO9xASW7fDYUpA0sFjkvVkWUgNJDRc+bawt01BNCMCT/2+B3grrkP89KWEgL9dFw/Pg5PSwsty5YRuHQJupCe316U82DOT6Cj+bS5Dv85swGwrt/AhKQQUkPSyR1mwNtsI/ezT/CfEkNHXiOuPh5WGjMnjrBYC5vfLlC5rC4SKnBciqSEIx9B4jSwhPfYPH9XNbH6WrzSS80MI34M4dNDVdw8KQGLUUfzu+8i29sJvfXW89B5pVeGjIXhV/iSH9p9hTWNw4ahj4+n7bPPEEJw25RECrzjqYhoZ9v7b6Ad5Q86gXVL3751aLQaZt6cjrXJwZ4VpX16b2VgqMBxKarLg4ZCGHFVj00bTlipK9lLjD6aIm0xd8y4l1e2lSKE4FtTkvA6nTS+8h/MkybhN3z4eei80muzfwKONt+QFb5suQELFmDbsQNPaytXj4vF2JHFvgwbro4Odq18D0tWFLa9tXiszj7typDUYNInR5O7tozWPtpsqAwcFTguRUeWAcL3G2kPDm4owuTKJdAQRstoDZGmBN7cVc7lo2MYEmyiddky3LW1hN1zT//3Wzk3USNg1LWw858n93UELJgPLhfWjRvxN+q4PiuVGu8IiuPb2bd6Od50Hbi92HZU9Xl3Jl+VgkYj2P7BsZ4bK4OaChyXoiMfQcJkCOg+c63H7eXg2reIMycCMHfBFbyxs4w2h5u7picjvV4aXnwJ44jhWKZNPR89V87V7B+Dux22/BEAU2YmuogI2tZ8BsBd05NxNU1mT1o9aARbV72BX0Yo1u2VePt4PsI/xMi4hYkU5dRSdaxvV28p51e/Bg4hxGIhRL4QokgI8XgX52cLIVqEELmdX0/29lrlK6ovgtrDvRqm2rVsM07bQWKCRtAW7cIYFMTzm4uZlhrG2Phg2tauxVlSQvjdd6tiTYNVeBqM+QbsfgHaqn27yOfPw7p5M96ODuJDzVw1fBI2GUfRMBeFO7fRkezCa3Nj29n3bx3jFiRgCTKw5Z1CpFctz71Q9VvgEEJogb8BlwEjgJuFECO6aLpZSjm28+v/neO1yrk6+pHvzx6GqVwdHez68AWC/JII14QRNymdd/aUU9fm4ME5qUgpafjXC+gTEghYuPA8dFz5ymb9D3jdsPE3AAQuWIBsb8e2dSsAD8xJwdk4hW1x5RiCA1m3/EWMqUG0barA6+zbtw69Ucvkq1OoLW2lcM/XLyKlDIz+fOOYCBR11g93Am8CPf+a+/WvVbpzZBnEZkNQXLfN1rzwT9yOJkJiMpECDCPDeW5jMeMTQ5gyNAz7rt10HDhA2LfvVBv+BrvQoZD9bch5BeryMU+YgCYoiLbVawBIifBnfvwCPJg5nmWk7ngJtUFVeK2uPku5fqr0SdFEJASw/YNjuPs4MCnnR38Gjlig/JTvKzqPfdkUIcR+IcSnQoiR53gtQoh7hRB7hBB76urq+qLfF6/GEqjK7XHTX/G+3RzdvAatMZtMcxqmYSF8VFDLieZ2HpqTihCC+mefRRcRQdDVV5+Xritf06wfgcECa36K0OsJmD2btvXrkS4XAA/NzcDRNJFVxhwi04exYeXL6BL9adtYjnT17T/uQiOYdn0q1iYHuWvLe75AGXT6M3B0Nej95UHNvUCilDIT+Cvw4Tlc6zso5fNSymwpZXZERMRX7eul4dC7vj9HXnvWJvbWFpb/7Rm8uhAC40fiZ9eiHxXOX9cVMnJIILPTI7Dt2Il9927C7rkHjV/PWXWVQcASDjMeg4JPoWQTAYsX4W1txbplCwAjhwQxO+ZKJILS8f44bDaKPQfxtrmw9sMKq9hhIQwdG8Helcext/bt0l+l//Vn4KgA4k/5Pg6oPLWBlLJVSmnt/PsKQC+ECO/Ntco5khIOvAMJUyA4vusmXi/Lnv0tHVYrfqalzEgYCjoNn7TbKW9s5weL0gF8bxuRkQR/48bz+QTK1zXpPgiKh9X/h//UqWiDg2lZtuzk6ScWT8XbNpr3m1cyYv4Cdmx5FxFvpG19Od4Od593Z8o1KXhcXnZ9UtLn91b6V38Gjt1AmhAiWQhhAG4Clp3aQAgRLTqX4wghJnb2p6E31yrnqOYQ1OfD6LPnktr+8Tuc2L+f9tBRmMOisdS60KcH88dNxUxMDmX2sAjsO3di37OHsHvvRWM0nscHUL42vQnmPQlV+xGH3iTw8suxrl2Hp60NgPhQM5cl3ISbdvanSIz+/uyr/Qyv3U3bxooebn7ugqPMjJwZy5EtlTRV2/r8/kr/6bfAIaV0Aw8Bq4CjwNtSysNCiPuEEPd1NrseOCSE2A/8BbhJ+nR5bX/19ZJw8F1fCvUR13R5uizvINveeJXKcA+hngVMGh2J1+Zis0FSb3Xwo8W+t426vz6LLipKJTO8UI2+wffW+dnPCFo0G+l00rZ69cnTTy5YBO2pvHX8HabccDP5edtwx4J1ywk8bX0/pDRhSRI6g0ZtCrzA9Os+DinlCinlMCllipTyl53HnpNSPtf592ellCOllJlSyslSym3dXat8RV4vHHoPUuaCJeyM023Njbz5uydoM7lIH3YXOoOWCKcHYdHz1JETzB8eyfjEUGxbt9Gek0PYvfeot40LlRBw+e+howW/2vcwJCbS8tEXL/NBZj3XpXwTl2jmI20DEUlD2ZL3DtIjaf2s72uImwIMZC1KpGR/PZWFzX1+f6V/qJ3jl4LyndBS7vtt80tcLid/+/n9SLuTIdcuQh4LZ3hWBK6iZg6F6Gh1uvnBonSk10vtH55BHxtL8A1n3ke5gESPgon3IHJeJnB2NvZdu3BVfjGF+MTcazF4h/B+yX/J/sad1NQX0xrajG1XNc6qvh9SypwXjyXYyNb3ilTNjguEChyXgoPvgM4E6ZefdtjlcfHrX9+FvtKGeWkWE43X43Z6SQ/3A6/kmap6rh4bS0Z0IK2fforjyFEiHv4eGoNhgB5E6TOzfwyWCII0GwBo+WT5yVNarYZ7xtwJhip+k7+PETPnsm7fq2DU0PLxsT7/x11v0DLpyqHUlrZSlFPbp/dW+ocKHBc7V4dvGW7GEjD6nzxsd9n5yd/vxHyoCePkVO6/8eccWF/OkNQgKGqm0qyhVEgeWzAM6XRS96c/Y0xPJ3Bpz/U7lAuAKRgu+w2G9oOYUqNp+eij0wLC3eOux18Txe6Wt9FMugJhFBzzHMBR3EL7ob4vyJQ+OZqwWH92fHgMj8vb5/dX+pYKHBe7/OW+KnDjvnnyUFNHE9979Q4itjZhSo3lgYefoWBnDdYmB+MnROGusfO63cY9M5KJDzXT9M47uMrLiXzsUYRG/chcNEZeAxlLCQ4rwnnsGO179pw8pdPoeHTC/WhNJ/jxxhWMu/ab5BSswBMgaVle0uepSDQawdTrUmit7+DQpr6tB6L0PfWvwMVu3+sQGOerLQ5UWiu5953bSNrQhjk0hG//+BmQgpyVpUQmBmCpteEQcCBAwwOzU/FYbdT//R+YJ0zAMnPmAD+M0qeEgCV/IDBNg8bPV/73VNcMu5IIvxgc/qt4qT6KqNRh7DzxMZ5mB21ry/q8OwkjwogfHsLuFSU47K4+v7/Sd1TguJi1nIBj62DszaDRUtRUxJ0f3caIDR4CNBZu/snT+Pn7U7inltb6DsbPi8eaW8cq6eR7l2dgMepo+Ne/8DQ0EPmD76sMuBejgCg0V/yG4KQ22tasxlX9RW4qvUbPA+PuReNXzvryLTgnXUNFYz7NAY20ba7ol4nyqdel4rC7yfm071dwKX1HBY6L2f43AAljbyG3Npc7l3+L8dsMBHcYuPZ/fkpYXDzSK8n5tJSwWAsBVgdaj+RItJGrMmNxVpyg8d//JvDKKzBlZg700yj9Zcw3CFk0Bbxemv71p9NOXZVyFUMsQwiLX8vTO1uInbaA9YdfA4Og+f2+T40eHhdAxqRoDqyvoLVBVQocrFTguFhJCbmvQ+I0tnfUcO/qe5l8IISwei2L73+U+BGjATi2r46majvjFydSt7mCA7i557pRaDSC2md+DxoNkY89NsAPo/QrITDc8U/8EwXN73+Et/WLyW+9Vs9D4x6iXZQREnWYvzanoPX340jHDpzlbVi3930moElXDQUBOz8q7vN7K31DBY6LVdl2aCxmbfIEHlz7INOKY4g+DtNvup3h02cD4PV42fVxMSHRZoTHQ2C7l8qkADLjg7Hn5ND26UrC7r4bfXT3lQKVi4AphJDv/ABPO7T99k7fLx6dlgxdQkZoBpaoz6hzejgQN5uDJRtwhLpoXVmKq87ep13xD/Ejc148BbtqqCtr69N7K31DBY6L1e4X+Tg4jO+XfcT0+hTiDnsYPW8RE6/+YvPeka1VNFXbmXTVUApXFdOM5JqbRyE9Hmp+9TS66GjC7vr2AD6Ecj5Zrr4TQ1QQjeuOILf99eRxjdDwaNaj1HVUceWMUlbaonDHpLEu71XQCpreLkB6+nbIKmtRIn7+erUpcJBSgeNiZK3lg+Or+d8QCzNdo0ja5SBp7Hjm3/XAyQluZ4ebXZ+UEJMaxKHGVka2S9oyggkL8qP5nXfpOHyYyO9/H43JNMAPo5wvQqMh9P5H6Gg0YHv1l1C45uS5qbFTmRwzmR2Nb3PLlAj+q82mtaOJYuMRnOVttG3s27oaRpOOCUuSOJHfxPFDDX16b+XrU4HjIvTpxp/x07AgZmtGkbqpg/CEJK545EdotNqTbXLXlNHe6mTkZYnUrSvHg2DCdem4Gxup/eMfMU+cSODSJQP4FMpACLr2WnSRkTQURsK734a6/JPnfpD9A1qdrVii1zJ2ZBo7A7PYlfshnngNrZ+V4Txh7dO+jJwRS1CEie0fHMPrUZsCBxMVOC4y60s/4yd1m5jYYWHEVh1Giz/X/uinGEzmk21sLQ72rSkjJSuSV/eVMdejRYwJQxdgpPaZZ/DabEQ/+YRafnsJ0hgMhN19N/YTbmy1fvDaddDiS6meHprOjcNu5O2Ct3h0iT/W9Jk0GkJZeeDfaMw6Gt/OR/bhrm+tTsOUa1JorLSRt73vS9gqX50KHBeR7ZXb+f6mHzDS7mbaoTRcHQ6uffxn+IeenhF32/tFeD0SY1YI5kON6BHELkzGvncfLe+9T+i3bseYmjpAT6EMtOAbb0AXHU1tSTqyvQX+cxVYfWWZHxr3EEGGIP649ze8eOck9iUuorW5hny/o7hr7DQv79uVUEPHRRA9NIgdy4pxtvd9MSnlq1GB4yJxuP4wD69/mKEeDUtyY2lubOPK7/+EiISk09qVH22kYGcNo+fH86u1+VwnDBhHhKIL0lH985+ji44m4oEHBuYhlEFB4+dHxHcfoiPvGG2JP/JtJH3tGrA3EmQM4uGsh9lbu5ecxrU88+AVHAkdy97d7+HM0GPbUYV9f12f9UUIwfQb02hvc7JruaoUOFiowHERqLZV89C6hwjV+nPrDi2VLSYWfud7JI4ee1o7t9PDxv/mExRh4jM6mNzkwSIFwXMSaHjxJRz5+UT/3/+isVgG5kGUQSPoqqswpqVS+/IyvNf82zfX8fISaK3imrRrGB0+mt/t/h3RIR6+9dB3aNEF8uaGf6CLs9D0fiHu+r7bvBeVFMiIaUM4sK6Chsq+nUdRvpp+DRxCiMVCiHwhRJEQ4vEuzn9TCHGg82ubECLzlHOlQoiDQohcIcSeL1+r+Nhddr677ru0u9v5bn4wx5qjmHLVNYycNe+Mtjkrj9NS10703CG8s/M439KZ8MsIRbrqqf/73wlYtIiA+fMH4CmUwUbodET95Ce4ystp2FAM33wHmsvgpUVomkr5+dSf0+ps5Te7f8PskXGkXXsnxvYGXqncABpBw3+P9ul8x+Srh2Lw07L5zQK1PHcQ6LfAIYTQAn8DLgNGADcLIUZ8qVkJMEtKOQZ4Cnj+S+fnSCnHSimz+6ufFzKP18OPNv+IgqYCfqS5mrwjTkamhTLl5jP3XjScsLJ31XFSJkTxiz3F3ONnwc8tCZgXT9UTTyBMJqL/738H4CmUwcoyZQqBl19Gwz+fxyES4VvLwNEGLywgraWGe0bfw/Li5Wyq2MRt1y1AN2Iq2mPrWBPUjKvSRvMnfVcO1uRvYPJVQzlR0EzRHlWzY6D15xvHRKBISlkspXQCbwJXndpASrlNStnU+e0OIK4f+3PR+dPeP7GhfAMPB99GyYdbSLC0sOD7vzpjNZTb6WH1i4cxWvTkBEsaGuxc69HjNzIM+5bltOfkEPU//4MuImJgHkQZtCJ/9DjCZKLq8R8jo8fCXavBFAL/uYp7OgSpwan8fLvv7eO+/3kEGRBGye7/UJBgxLazGtvuvlsNNWJGLBEJAWx5p5AO25nZc50VFTS9+RZVT/6U43fcSfEVV1C0cBHFV1zJ8TvupOrnP6f5gw9x1dT0WZ8uVf0ZOGKBU3cFVXQeO5u7gE9P+V4Cq4UQOUKIe892kRDiXiHEHiHEnrq6vpuUG+zeK3iPlw+/zC0hV2B9dzvhRhtXLhmLNuTM2Lv9g2M0VtoYsjCWF/Yc55cxEWjcXswj9dT89ndYpk4l6NprBuAplMFOHxVJ9BNP0L5/Pw3PPw/haXDPWkidj37l4/yiw0BjewNPbX8Kg5+JW374OAFuK//NeYumSD+aPizCcby1T/qi0Qjm3JpBu9XFtveKAHBVVlL7xz9RfMUVHJu/gOqf/YzWVauQHR0YkpIwjR6NPjEBb7ud1k+WU/XjH1M0azalt95K68pVSE/f1hW5VOj68d5dbQLocnBSCDEHX+CYfsrhaVLKSiFEJLBGCJEnpdx0xg2lfJ7OIa7s7OxLYvBzZ9VOfrHjF8wKmETQx8fR6yXXxhzGOPuFM9oeP9TAgfUVpM8YwpO7i8kOtpBV58Q0Opya3/wfQq8n5le/VHs2lLMKXHI51g0bqPvrs5jGjsUyZQrc9AZs+QMjNzzNA+FR/KV0JdNjp3NV+lVMvOZGxAdv8b+lu/ljVBYNrx0h6qFxaIOMX7svEQkBjFsQz95VZYTu/wTjqv+AlJizs4l8/Dr8Z83CkJTU5c+z9HpxFBZiXbeO5g8/5MQjj2BITCT07rsIvvZaxCkbZJXu9ecbRwUQf8r3ccAZqTSFEGOAF4CrpJQncwtIKSs7/6wFPsA39HXJK20p5dENj5JqTCRzA0iPm2tjcvDPuhrCUk5ra21ysPaVI4QOsfCRtFHb5uDXoSEIIfDUb6Fj/wGif/qkSmKodEsIQczPf4ZhaDInHn0MZ2kpaDQw8wdw1xq+7TGR3d7Br7b+lPKaA0y7/mYiklKY2rCeR9uqcHd4qH+tbybL23NziVnzF0z2WvY2JBLwjW+SunoVia+8TNgdd2BMTj7rL0FCo8EvPZ3w++8nZcUKYv/0JzQBAVQ/8SSlN36D9oOHvnb/LhX9GTh2A2lCiGQhhAG4CVh2agMhRALwPnCblLLglOMWIUTA538HFgKX/P+qzR3NPLj2Qfy8ei7fF4e9sZGrpwUSZrTDnNMntt1OD58+dwC304tmajgfH6rmqaxEjMda8RtuoOGffyZw6VKClqi0IkrPNBYL8c8+C0JQdvc9uGo7J6hjs9B+ZzNPJ12N1uPi+x/fhHvfyyx58FH8hIeRDWv4pbThKm+j8d2Cr1S/Q0qJdfNmjt92O6U33YwjZxeThtto9wunZNi16GO7GwHvmtBqCVy8iKR33ib2j3/AVVtD6Y03Uv3LX+Ht6Djn+11q+i1wSCndwEPAKuAo8LaU8rAQ4j4hxH2dzZ4EwoC/f2nZbRSwRQixH9gFLJdSruyvvl4IXB4Xj254lJq2am4/lk1TaRmX33ELsRXvQvZdEJJ4sq2UkvWv51F7vI2xN6Tw1OYiJiYGM+eEA02gnqaXn0QXEUH0E/83gE+kXGgMSUnE//M53I2NlN12O67KzgEEg5noxb/jVxMe56heyy92/pLQj29k3uXTCW8tw9W6i1cNbtr319G6uveV/aTbTcvy5ZRccy3l99yLs6yMyMd/RNq6tYz88V2MnhPHgfUVHD/81ZMgCiEIvOwyUlasIOTmm2l69VVKb7gRR1HRV77npUBcTGuis7Oz5Z49F9+WDyklP932Uz4o/ICH6xbRsieP+Xc/QGbD61C8ER7OBUv4yfb71pSx7b0isi5P5OmyKkrqbKyYOgyxvgLZvg3r2tdIfPU/mMeNG7iHUi5Y9r37KP/Od9CYzcQ9+1dMo0efPPfsvmf554F/8kS7hhuqSlnZOp0jlYLVCddwiyWVeR0agq9JxX9SzFnv7+3ooOXDD2l48SVc5eUYhg4l7O67CVq6BGEwnGzndnp459d7aLe6uPmJiZgCDGe9Z29ZN2+m8keP47XbiX7iCYKvu/Zr33OwE0LknOuWB7Vz/ALw78P/5oPCD7ircTYte/KYdM03yEwLgrxPYNrDpwWNgt3VbHu/iJRxESxz2ThQ0cKfFw9Hs6USjcWKddXLRP3wBypoKF+ZOWscia/+B6HVcvybt9Lw4ksnVyfdn3k/02Kn8bRFw945jzI/Mp9Qg50rqz9gdcduDhqh+cMi2vMaz7ivq6aW2j/9iaI5c6n+2c/RhoQQ9+xfGfrJxwRfe81pQQNAZ9Cy4NsjcdhdrHs1r082BvrPmEHyBx9gysyk6n//l+pf/BLpVjmyvky9cQxya46v4fsbvs+19eMJ2FXH2EVLmHvbnYjnpoPHAQ/sAIMvRUjpwXo+/cdBolOCcE4L40cfHuK+mUO5q8KFs6yFtk8fx39aNrF//pNaRaV8be6mJqr+7wmsa9dizMgg4uHv4T9rFq2uNm5dcStNjiZenfdPAre9x3/f3E6wwU50nJkwzfeJFCYi7hqNPtqIdcNGWj/5mLb1G8DjwX/OHEK/9S3MEyf06ud0/9pytrxTyLTrUxk7P6FPnk263dT+7vc0vvIK5imTifvjH9EGB/fJvQebr/LGoQLHIJZTk8O9q+9lZu1Q4na1M2LGHBY/8Chi029hw9PwzfcgzZcipLKomY//nEtIjIVhN6Vw40s7yU4M4R8j4mn7uBhH/rvQXkDSO2+jDQgY4CdTLhZSStpWraL2mT/gKi9Hn5BA0NIl2MalcW/Jr9Ba/Hl9yes05R7gwz/+ntSAJuaZaqg3/hatJoiO3X/FXZWPNjycoCVLCPnmLRgSzu0ff+mVrPzXIUr213Pl9zKJywjts+drfu99qn/2M3QxMcT//W8XZdZoFTguosBR3FzMbZ/exojKEDJ2QeqEKVzx6ONoGo/Bc9Ng+JVw/YsAlOc1suIfB/EPNjLn/lHc+Mou3B7JshvH4fz3EbxNRXTkPEfS229hSEzs4ZMV5dxJl4vWlStpfudd7Hv2gNe39LbVDC6zgShTJAVeB3nhgaRVNzKsFSwzf4gwmAicZCfgmqu+1j4KZ4ebd3+TQ3urkxt+nE1geN9VrrTv3UfF976HbG9nyDO/J2D27D6792CgAsdFEjjq7HXcuuJWooo8ZOaaSBw9lqv/50l0GgGvLIXaI/DQHvCPpORAPaueP0RQpIlFD47h7rf2cqiylbfvmEDU+8W46xqxrft/JPzzL5izVcovpf95mpux792LIz+f48f2kVu8lSC/YCYNX0ROYw3HKo8TNmE+sqmARZ6FgIaI0RvRL37gjL1I56K5xs47v95DQJgf1/4gC4Nf3+1vdlVVUf7ggziO5hH5gx8Q+u07L5rhXjU5fhFodbbywNoHCD/aQeY+P5LHjufqHz6BTq/3DU+VbYfLfgf+keRtr2LlcwcJi7Vw1aPj+L+VR9hX3syfbswkfkctnoZ22rf+jZgnf6SChnLeaIODCZg7l/D772f8758n9g/P8NRiKz+fVMaCp39D8rhsGvesoyNtAffhwqkxUXdoJq6/XA8fPgiNX63uRnCUmYV3j6Sx0sanzx3E04fZefUxMSS99hoBCxdS+7vfUfXjn+B1Ovvs/hcaFTgGEavTyn1r7sOQU03WoQBSJ0zhqh/8LzqDAQo/g82/h3G34R19I1vfK2LtK0cZMiyYKx8Zy2/XF7LiYDU/uWw40yodtB9qoOPw+4Tdex1BV13V84crSj9ZnLSYp6Y9xa6qXTyw/kFmP/Ag0alpONa8wsLhRu7wumnVhlDreQbH/gPwbDYs+54vjfs5ShwZxtzbM6jIa2L1S4f7tFa5xmwm9o9/IPyhh2j58EPKvnUH7vr6Prv/hUQFjkHC5rJx/5r7MGyrIOtoEOlTZ7L0kR+h1el9FdjevwciR+KY/TQr/n6A3DVljJ4Vy5KHxvCH9UW8vK2Uu6Ync5PeSNuGCpylmwm6chRhd9wx0I+mKFyZciW/nflbDtQd4L6NDzL74e8RFp+Ia9VL3DTCyR1uK7VCT53raTqG/gj2vwF/yYJPHvP9/J+DjMkxTL8hjeJ9dax/LQ/vV9itfjZCoyHioQeJ/dMf6Th6lJIbbqTj6NE+u/+FQgWOQaC5o5nvrLyHoM8qGV0UyKg5C7n8u99Hq9NBexO8fj14nFROfJ43f3OA8iONzLolnRk3DePP64r456Zibp2cwGOxwbR8WIS75hD+04OIuPeegX40RTlpcfJi/jL3L5S0lHDXpvvI/u5dRCWn0L7yJe4f5eAet5VyIak/MhXb7E2QdRvs/Q/8ZSys+B9o632K9sx58UxYmkze9mpWv3CoT4etAAIXLybx9ddASkpvvoXm997v0/sPdmpyfIBVWat46ON7Sd3UQWSjkek33c7Eq2/wTbw5bfDqNXhOHCAn6b/s2Q4B4SYWfnsk4YkB/Pzjw/xn+3G+kR3P/w0JpPmjUrxNx/GfLAj79u0D/WiK0qX9dft5ZP0j2F12fjXx/1H32jpO5B0meu61/P54NE9r/RnlEvhPG0LQVC1i6zOw73XQ6mHC3b5Nr/6Rvfqs3M/K2PpuEbHpIVx+32gMpr5NCO6ur+fED36IfccOgq6+mugnn0BjNvfpZ/Q3tarqAgsch+oP8bO3H2XsDj0Wr4HLHniMjKkzfSdd7fDWrVQdPcEG+RSNjVrSJ0Uz8+ZheLWC77+9n+UHq7h35lDu9zqwbm7B21RM8JVDCFq6aGAfTFF6UG2r5uH1D3O04Sh3ZNzOsB1e8rdtIjJrGs+2j+VWl5FrPHoMyUGE3pSOznMCNv4ODrwJOj+YeA9MfRgsYT1+Vt6OKtb9J4+QaDOL7x1FSLSlT59FejzU/+3v1P/jHxiSkhjy66cxZWb2fOEgoQLHBRQ43j36Dh+98VfGFAQQEBHBdT/4KRGJyb6Ttnpsr9zFrsIRHGlfgH+IkZk3p5M8JpzyRjv3v57DoROt/N+idK7YW4CrLgBPcxHhd47GMkmtnlIuDB3uDn6969e8V/geI0KHc0fbHI58tJygIfGsjFhAeKs/P9KaMBh0hF6XhmlUONQXwcbfwMF3fBkTJt0HUx4Ec/eb/sqONLDmxSN43F7m3JZBWnZUnz+PbccOKn/8E9w1NYTd9W3CH3oIjfHr1yDpbypwXACBo8XRwu9X/Az3ikOEtxpJnjSZJfc9hrHz9dZRnse+515hf+MMvMLI6LkJTFyajMFPx2dHavj+O/vxSsmz02JIWZ0P2mhkex7R/3sN+sjwHj5dUQafz45/xs+2/4x2Vzt3mq9Es7IAZ7ud5hELWduSwK8NgSS6wDQmnOClKWgDDVCbBxt/DYc/AGOgbwhr4j0QOOSsn9PW2MHqFw5TXdxCxuRopl6X2ieJEU/laWuj9re/pfmdd9EnJhD1o8fxnzN7UO/5UIFjEAcOKSWr85az7LW/knhMh9ZkZMm9j5AxxTc0ZW2wc+CNFRw+7IdTmkkboWPiTdkER5ppsDr4f58c4aPcSkZFmPmLqxp9XQhoDBhiGoh85DqERq1zUC5ctfZafr/793xa+ikp+gQuz0+mLa8UY2Qcy0yTmK6L51sY0em1BM5PwH/yEIReAzWHYcOv4ejHoNHCyGtg8v0QO77Lz/F4vOz+uIR9q8vQm7RMvSaV4VNjEJq+/YfdumUrNb/6Fc7iYizTphH5wx/gl5HRp5/RV1TgGKSBY1/5Ht544/cE72/B4NGSMGUiS+98GD//QCryGjm6Lp/iwzakhJTwYrK+uYCI4UPpcHl4bcdx/ra+iA57B38w1jOiToc2MBnpriPs9rGYxwwd6MdTlD6zvXI7v971a4qbi5lqTWPkAQOuFiv2mOHs1ozlO5Y4Ml0aNEEGAuclYBkX5QsgjSWw61++VVjONogZC2NvgVHXdzkP0lBpZdMbBVQWNhMSbWb8ZUmkZUei0fbdL2DS5aLpv/+l7tm/4W1rw3/2bMLv+w6msWP77DP6ggocgyhwSClZt/8TNn7yBqajzRjcGgypMVx39/+AO4Li/XUc212JtcWDUbSR7r+NzCVjCJx1Cy0dbt7LqeCFzcU4q6r5oauGiW5/9GHDkW475jF+hN46s89/S1KUwcDj9bCiZAX/2P8PqpoqmFIRR+oxI9LhoiE4mRa/8dzgn0yqR4Ow6PCfFINlYjS6YD/oaIXc/0Lua1B9EDR6GLYIRlwFqfNPmwuRUlKUU8ueFaU0VtoIijAxYvoQ0idHY+mD+ugnn6elhcbXX6fplf/gaWnBb/Rogq+7jsClS9D6+/fZ53xVKnAMcODwer0cKNjJpk0f0LDvCIGN4BUS/dAEMsd8A0drGCfym+iwudEIN/GGXDL8t5E8MwvPlAfYVuVlxcEqdm87yPTaMq4QgqjAFLQBMUhvB6bhJkJunoTWTz9gz6go54vb62Z9+Xr+e/S/7K/IYXhZIKPLQtG3e3HoTbgCJpAVMIqxmgAEoE8MxDI2Ar9hIejCTFB9yLeR8OA7YK0BoYG4CZC2EJJmQEwm6P2QXknJ/npyPyuj6lgLQkD88FCSxoSTMDKMoIi+SZjotdlofu99mt99F0dBAcLPD8vUqfjPmY3/rFnoI3u3xLivDbrAIYRYDPwZ0AIvSCl//aXzovP85YAduENKubc313blfAcOa1szhwt3k3dkNycK8/CWNWGy+/57ekyBWIIz0XlH4nb6Jr4DDC0M0eaSaNhFXFQTDalL2SxmkH+gClPBcdI7HKQZ/AkISkDj71v1IQw2/KcnEDgnHaH/6tlDFeVCVtxczKeln7Lq2Ke4jtWSWmEhvt6M1iMwGoIJD8oizTScKK3vN3hXgB5jUiABSUEYhljQy0I0pauhcBVU7ffdVKOHmDEQmw1RIyB8GM3eBI7mdlC0t47WunYAAsP9iEoOIiopkLA4f4IjTViCjF/5jV9KScehQ7R8tAzrunUnS/DqExMwjx2HX+YYjENTMAxNRhcR0e8T64MqcAghtEABsACoAHYDN0spj5zS5nLgu/gCxyTgz1LKSb25titfNXC4nB7cTg/WpiYctnYammtpaKnHamul3WrFbm2lo60VR6sVl9WK12pF025D43Gd8sAmNLohaHSJGAzJWHR+BIsaQrzVhMgqgjy1uL0m2ryROLzB6LAQoDVisIQjzGEI4RtblV4n2kA35swh+M9I871+K4oC+P7RPd56nO1V29lVvp2avHxM5e1ENhkJbTUQqAsl2pRMlDmJEGMMFu0XQ0F24cCmd+EwOtFo2zDQgMVTian9GBZvPTrRjlHTjsbPiAgIo1WfSFnHcCqtQ6hpCsJm/+JNX6eDwFAtgSF6TAF6TAFGTIFGTIEm9GYTOqMGvUGLzqhFb9BitOgw+Z+5gktKiaOgANuWLdj37aN97z48jV9UR9RYLBiSk9FFRqILD0cXHoY2PBxtYBAaixmNxYLGbEEb4P+VSyYMtsAxBfiZlHJR5/c/BpBSPn1Km38CG6SUb3R+nw/MBpJ6urYrXzVwPPfdDbidHhzNf+yhpRY0FoQmALT+aPQmjDoNoUY3cbpmTB3N2HMd/P/2zv+1qjqM46+320zdzDm/NZ2GgQYaJf0gpv2w+slSk6AfjCCIQAqiICj6Seg/kH6IkAiKsAjCkKGlEFG41LLUNBPWnGXTxE1nitPu9vTDOcuz671354x77zn38rzgsnM+n+ezz/s+e7bnfs45n2cLV25k6pzi//DFbJSR3A2s4V8amqcwfWEz0+5fxF3L22ma3+z3LhwnAZeHL3Nq8BSnL/zK+bM9DPzVz/DfgzQN3WTOcAuLrYN5WkBLUyvNjbNoaWxlWsMMGqdM/Chu28wdzMh1gY1yfWQ2g7kOhkYWciXXztBIO/+MzGN49G5ujM5ilOK70pesaGPTq6smnM/MyF24wK0zZ7jZe4Zbvb3c6usjd+kSuYGBIKkU+Jvd0NbG8u4DE37/QkwmcZR3//14FgF/Rs7PEawqJrJZFHMsAJK2AlvD02th8onLXKD85S2/r2jdmsporjy1qNs1V48a0H0VOBttSKb5tTLLySf+Ja183YmXKpVMHIXeRX6qLGYTZ2zQaLYD2JFMWji59GPSTJs2tagZalO3a64etai7FjVDeXRXMnGcAxZHzjuA/pg2U2OMdRzHcVKgktuNfwCWSVoqaSqwBdidZ7MbeF4Ba4AhMzsfc6zjOI6TAhVbcZhZTtIrwFcEj9R+YGYnJb0U9r8H7CF4oqqH4HHcF0qNrYDMSV3iSpla1Ay1qds1V49a1F2LmqEMuutqA6DjOI5TebwynuM4jpMITxyO4zhOIuoucUiaJumwpGOSTkp6u4DNc5KOh69uSQ9F+vok/SLpqKSq1S+JqbtT0lCo7aikbZG+9ZJOS+qR9FaGNL8R0XtC0oiktrAvFV+HczdI+llSV4E+SXon9OVxSQ9H+qru5zxtpXRnLq5jaM5UTMfUnNWYLjl3WePazOrqRbAHpCU8bgIOAWvybNYCs8PjJ4BDkb4+YG5GdXcCXQXGNgC/A/cRPMp8DFiRBc159puAr9P2dTj368DOIv58Etgbvr81Y/GRlp8T6M5cXMfQnKmYjqM5zy5LMV1y7nLGdd2tOCzgWnjaFL4sz6bbzC6HpwcJ9omkShzdJVgN9JhZr5ndAj4FNldA5jgmoflZ4JNK65oISR3ABuD9IiabgY/C93cQaJXUTkp+HmMi3VmM6xi+LkZqvk6oORMxHZOyxXXdJQ74f5l5FLgI7DezQyXMXyTIwmMYsE/SEQXlTKpGTN2PhJeG9kpaGbYVK91SceL6WtIMYD3weaQ5LV9vB94ERov0JymFUxU/h2yntO4oWYnr7UysOVMxTUw/Zyym48xdtriu5M7x1DCzEWCVpFZgl6QHzOxEvp2kxwh+wR6NNK8zs35J84H9kn4zs28zovsn4F4zu6agsvAXwDISlGgpN3F9TbCkP2Bmg5G2qvta0kbgopkdkdRZzKxAW6JSOOUmpu4x20zEdUzNmYrpJH4mIzGdYO6yxXVdrjjGMLMrwDcEnwrGIelBgqXoZjMbiIzpD79eBHYRLOOqSjHdZnZ17NKQme0BmiTNJV55l4pSytchW8hb0qfk63XAU5L6CJbkj0v6OM+mmD/T9HMc3VmL6wk1ZzCmY/k5JCsxHXfu8sV1NW7aVPMFzANaw+PpwHfAxjybJQS71dfmtTcDMyPH3cD6DOm+h9ubNlcDfxB8WmgEeoGl3L65tTILmsO+WcAg0JwFX0c0dFL4xuwGxt9EPBy2p+LnBLozF9cxNGcqpuNoDvsyFdNx5i5nXNfjpap24EMF/wxqCvCZmXVpfKmTbcAc4F0FpYhzFlSLXEBwuQUCZ+40sy8zpPsZ4GVJOeAGsMWCn3y1SrRMRjPA08A+M7seGZumr+9A2SqFE5saiOs7yHhMx9EM2YvpgnNXKq695IjjOI6TiLq+x+E4juOUH08cjuM4TiI8cTiO4ziJ8MThOI7jJMITh+M4jpMITxyO4zhOIjxxOI7jOIn4D1V2V1dtIocKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(drug1)\n",
    "sns.kdeplot(drug2)\n",
    "sns.kdeplot(drug3)\n",
    "sns.kdeplot(drug4)\n",
    "sns.kdeplot(drug5)\n",
    "sns.kdeplot(drug6)\n",
    "sns.kdeplot(drug7)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a9a2b45",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5cd3d4b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19d52357",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc80ceb7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0491a3d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3286675e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a16791cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# one-way anova with same sample sizes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1d3386d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def one_way_ANOVA(*X):\n",
    "    data = pd.DataFrame(X).T\n",
    "    print(data)\n",
    "    column_means = np.mean(data,axis = 0)\n",
    "    print()\n",
    "    \n",
    "    print(column_means)\n",
    "    print()\n",
    "    overall_mean = np.sum(np.sum(data,axis = 0))/(len(data)*len(data.columns))\n",
    "    print(overall_mean)\n",
    "    SSC = 0\n",
    "    SSC = np.sum((column_means-overall_mean)**2) * len(data)\n",
    "    \n",
    "    SSE = 0\n",
    "    for group in data.columns:\n",
    "        SSE += np.sum((data[group]-np.mean(data[group]))**2)\n",
    "        \n",
    "    SST = 0\n",
    "    for grp in data.columns:\n",
    "        for ele in data[grp]:\n",
    "            SST += np.sum((ele-overall_mean)**2)\n",
    "    \n",
    "    n = len(data)* len(data.columns)\n",
    "    c = len(data.columns)\n",
    "    print(n,c)\n",
    "    MSC = SSC/(c-1)\n",
    "    MSE = SSE/ (n-c)\n",
    "    F_ratio = MSC/MSE\n",
    "    \n",
    "    print()\n",
    "    print(\"SSC:\",SSC)\n",
    "    print(\"MSC:\",MSC)\n",
    "    print(\"SSE:\",SSE)\n",
    "    print(\"MSE:\",MSE)\n",
    "    print()\n",
    "    \n",
    "    print(\"F-Ratio: \",F_ratio)\n",
    "    p_value = 1-stats.f.cdf(F_ratio,c-1,n-c)\n",
    "    F_critical = stats.f.ppf(0.95,c-1,n-c)\n",
    "    print(\"F_critical : \",F_critical)\n",
    "    \n",
    "    print(\"Results : \")\n",
    "    if F_ratio > F_critical:\n",
    "        print(\"reject null, all populations means are different \")\n",
    "    else:\n",
    "        print(\"all population means are same\")\n",
    "    \n",
    "    \n",
    "    if p_value < 0.05:\n",
    "        print(\"reject null, all populations means are different \")\n",
    "    else:\n",
    "        print(\"all population means are same\")\n",
    "    return F_ratio,p_value\n",
    "              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b06d7358",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6c1e46c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    0   1   2   3\n",
      "0   8  12  18  13\n",
      "1  10  11  12   9\n",
      "2  12   9  16  12\n",
      "3   8  14   6  16\n",
      "4   7   4   8  15\n",
      "\n",
      "0     9.0\n",
      "1    10.0\n",
      "2    12.0\n",
      "3    13.0\n",
      "dtype: float64\n",
      "\n",
      "11.0\n",
      "20 4\n",
      "\n",
      "SSC: 50.0\n",
      "MSC: 16.666666666666668\n",
      "SSE: 208.0\n",
      "MSE: 13.0\n",
      "\n",
      "F-Ratio:  1.2820512820512822\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from scipy import stats'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from scipy import stats'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F_critical :  3.238871517453585\n",
      "Results : \n",
      "all population means are same\n",
      "all population means are same\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1.2820512820512822, 0.3143594700488932)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_way_ANOVA([8,10,12,8,7],[12,11,9,14,4],[18,12,16,6,8],[13,9,12,16,15])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02ac5b21",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "68bdb8dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from scipy import stats'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=1.2820512820512822, pvalue=0.3143594700488932)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway([8,10,12,8,7],[12,11,9,14,4],[18,12,16,6,8],[13,9,12,16,15])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2564293a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7af25937",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from scipy import stats'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=25.221574344023324, pvalue=1.8124793267561276e-05)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway([8,9,11,12],[6,8,10,4],[14,12,18,9],[20,22,25,23])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79f3f349",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8b35026",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1337112e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1e3d6f7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2bf6362",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1c9f0bbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# anova one way works with different sample sizes : \n",
    "\n",
    "G1 = norm.rvs(loc= 10.0,scale = 2.0,size = 400)\n",
    "G2 = norm.rvs(loc= 10.3,scale = 2.1,size = 400)\n",
    "G3 = norm.rvs(loc= 9.8, scale = 2.3,size = 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fee6f203",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(G1)\n",
    "sns.distplot(G2)\n",
    "sns.distplot(G3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7f20ccf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from scipy import stats'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=5.175751027394441, pvalue=0.005769290641501678)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(G1,G2,G3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "950363bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from scipy import stats'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "3.002662301977574"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f.ppf(0.95,3-1,1300-3) # T-critical value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c7d694b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from scipy import stats'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.002225558713822906"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1-stats.f.cdf(6.1366,3-1,1300-3)  # p-value "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d87ad77",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d5e06db9",
   "metadata": {},
   "source": [
    "    as sample sizes increases , ANOVA can detect even very small differences in mean1 mean2 mean3.\n",
    "    variance in p-value depends on sample size of sample data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0f42844",
   "metadata": {},
   "outputs": [],
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "749f4591",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6d96e42",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c876507d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca442517",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38a01d6c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e316c636",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "dd760f80",
   "metadata": {},
   "source": [
    "    under null hypothesis , \n",
    "    \n",
    "    Test Statistic F-Ratio (f) ~ F-distribution(c-1,n-c)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "947de29d",
   "metadata": {},
   "source": [
    "## F-distribution\n",
    "\n",
    "- the F-distribution or F-ratio, also known as Snedecor's F distribution or the Fisher–Snedecor distribution is a continuous probability distribution that arises frequently as the null distribution of a test statistic, most notably in the analysis of variance (ANOVA) and other F-tests."
   ]
  },
  {
   "attachments": {
    "image-4.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "325ff1d4",
   "metadata": {},
   "source": [
    "![image-4.png](attachment:image-4.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "1ebb0515",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77988707",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e1f15fa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "272b39ef",
   "metadata": {},
   "source": [
    "# two-way Anova (w/o replication)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10861c9f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "efda1ead",
   "metadata": {},
   "source": [
    "Lest assume that starbucksuses secret shoppers who appear to be customers to enter a store and document their experience in terms of customer service and quality.\n",
    "the secrete shopeers service standardize training by stabucks to ensure consistency and objectivity in their store reviews. \n",
    "\n",
    "we would like to know if a difference in secrete shoppers ratigs exists among the cities. \n",
    "are they all same ? \n",
    "is one significantly higher than other two ? \n",
    "are all three different from each other ? \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40216b7c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2ecf4fe0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Starbucks.csv\",index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "57ead2c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = ['shopper1', 'shopper2', 'shopper3', 'shopper4', 'shopper5',\n",
    "       'shopper6']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f15a0d0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sydney</th>\n",
       "      <th>brisbane</th>\n",
       "      <th>melborne</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>shopper1</th>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shopper2</th>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shopper3</th>\n",
       "      <td>50</td>\n",
       "      <td>55</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shopper4</th>\n",
       "      <td>65</td>\n",
       "      <td>60</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shopper5</th>\n",
       "      <td>80</td>\n",
       "      <td>65</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shopper6</th>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sydney  brisbane  melborne\n",
       "shopper1      75        75        90\n",
       "shopper2      70        70        70\n",
       "shopper3      50        55        75\n",
       "shopper4      65        60        85\n",
       "shopper5      80        65        80\n",
       "shopper6      65        65        65"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5c6f959",
   "metadata": {},
   "source": [
    "SST = SSC + SSB + SSE\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "738fdc76",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "daed5367",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "overall_mean = np.sum(np.sum(df,axis = 0))/(len(df)*len(df.columns))\n",
    "overall_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "46a60a30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sydney      67.5\n",
       "brisbane    65.0\n",
       "melborne    77.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_means = np.mean(df,axis = 0)\n",
    "column_means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a1285f28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "shopper1    80.0\n",
       "shopper2    70.0\n",
       "shopper3    60.0\n",
       "shopper4    70.0\n",
       "shopper5    75.0\n",
       "shopper6    65.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "block_means = np.mean(df,axis = 1)\n",
    "block_means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "41d559fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1750.0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SST = 0\n",
    "for col in df.columns:\n",
    "    for r in df[col]:\n",
    "        SST += ((r-overall_mean)**2)\n",
    "SST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "25e73fe4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "525.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SSC = np.sum((column_means-overall_mean)**2)*len(df)\n",
    "SSC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2fed9dcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "750.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SSB = np.sum((block_means-overall_mean)**2)*len(df.columns)\n",
    "SSB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "9d57e74e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "475.0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SSE = SST-SSC-SSB\n",
    "SSE"
   ]
  },
  {
   "cell_type": "raw",
   "id": "24e0944b",
   "metadata": {},
   "source": [
    "MSB/MSE\n",
    "\n",
    "MSC/MSB\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "78cd6959",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfc = len(df.columns)-1\n",
    "dfc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "82d4c065",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfe = (len(df.columns)-1)*(len(df)-1)\n",
    "dfe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6e06d601",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dft = len(df)*len(df.columns)-1\n",
    "dft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "bebb55d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfb = len(df)-1\n",
    "dfb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "cf9599a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "262.5"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MSC = SSC/dfc\n",
    "MSC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "1260db49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "150.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MSB = SSB/dfb\n",
    "MSB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "cd9ac78d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47.5"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MSE = SSE/dfe\n",
    "MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e8af1990",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.526315789473684"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F_column = MSC/MSE\n",
    "F_column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d6eef189",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.1578947368421053"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F_block = MSB/MSE\n",
    "F_block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "27336695",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from scipy import stats'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.024180654296874993"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_value_col = 1-stats.f.cdf(F_column,dfc,dfe)\n",
    "p_value_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c92b1c6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from scipy import stats'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.05739916157799785"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_value_block = 1-stats.f.cdf(F_block,dfb,dfe)\n",
    "p_value_block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c58cf985",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from scipy import stats'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "3.3258345304130112"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F_crtical_block = stats.f.ppf(0.95,dfb,dfe)\n",
    "F_crtical_block\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c4e64080",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from scipy import stats'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "4.1028210151304005"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F_crtical_col = stats.f.ppf(0.95,dfc,dfe)\n",
    "F_crtical_col\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3f9804d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "50be925b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sydney', 'brisbane', 'melborne'], dtype='object')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a2be3696",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sydney      67.5\n",
       "brisbane    65.0\n",
       "melborne    77.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "91edf4b8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>sydney</th>\n",
       "      <th>brisbane</th>\n",
       "      <th>melborne</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>shopper1</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>shopper2</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>shopper3</td>\n",
       "      <td>50</td>\n",
       "      <td>55</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>shopper4</td>\n",
       "      <td>65</td>\n",
       "      <td>60</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>shopper5</td>\n",
       "      <td>80</td>\n",
       "      <td>65</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>shopper6</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      index  sydney  brisbane  melborne\n",
       "0  shopper1      75        75        90\n",
       "1  shopper2      70        70        70\n",
       "2  shopper3      50        55        75\n",
       "3  shopper4      65        60        85\n",
       "4  shopper5      80        65        80\n",
       "5  shopper6      65        65        65"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "eafb6d20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind = \"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1efc99aa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12632319",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "4d005d38",
   "metadata": {},
   "outputs": [],
   "source": [
    "# using library "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "a424df5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9fa2e9f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "ce391dde",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.melt(id_vars=\"index\",value_name=\"rating\",var_name=\"city\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "d722b0f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>city</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>shopper1</td>\n",
       "      <td>sydney</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>shopper2</td>\n",
       "      <td>sydney</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>shopper3</td>\n",
       "      <td>sydney</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>shopper4</td>\n",
       "      <td>sydney</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>shopper5</td>\n",
       "      <td>sydney</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>shopper6</td>\n",
       "      <td>sydney</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>shopper1</td>\n",
       "      <td>brisbane</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>shopper2</td>\n",
       "      <td>brisbane</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>shopper3</td>\n",
       "      <td>brisbane</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>shopper4</td>\n",
       "      <td>brisbane</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>shopper5</td>\n",
       "      <td>brisbane</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>shopper6</td>\n",
       "      <td>brisbane</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>shopper1</td>\n",
       "      <td>melborne</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>shopper2</td>\n",
       "      <td>melborne</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>shopper3</td>\n",
       "      <td>melborne</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>shopper4</td>\n",
       "      <td>melborne</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>shopper5</td>\n",
       "      <td>melborne</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>shopper6</td>\n",
       "      <td>melborne</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       index      city  rating\n",
       "0   shopper1    sydney      75\n",
       "1   shopper2    sydney      70\n",
       "2   shopper3    sydney      50\n",
       "3   shopper4    sydney      65\n",
       "4   shopper5    sydney      80\n",
       "5   shopper6    sydney      65\n",
       "6   shopper1  brisbane      75\n",
       "7   shopper2  brisbane      70\n",
       "8   shopper3  brisbane      55\n",
       "9   shopper4  brisbane      60\n",
       "10  shopper5  brisbane      65\n",
       "11  shopper6  brisbane      65\n",
       "12  shopper1  melborne      90\n",
       "13  shopper2  melborne      70\n",
       "14  shopper3  melborne      75\n",
       "15  shopper4  melborne      85\n",
       "16  shopper5  melborne      80\n",
       "17  shopper6  melborne      65"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38a82f4f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2bd87e79",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c5977b16",
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.formula.api import ols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "21a50938",
   "metadata": {},
   "outputs": [],
   "source": [
    "ano2 = ols(\"rating~city+index\",data = df).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "8f034ba0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x25d6a28cd00>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ano2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "1f6eec86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <td>2.0</td>\n",
       "      <td>525.0</td>\n",
       "      <td>262.5</td>\n",
       "      <td>5.526316</td>\n",
       "      <td>0.024181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <td>5.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3.157895</td>\n",
       "      <td>0.057399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>10.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>47.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            df  sum_sq  mean_sq         F    PR(>F)\n",
       "city       2.0   525.0    262.5  5.526316  0.024181\n",
       "index      5.0   750.0    150.0  3.157895  0.057399\n",
       "Residual  10.0   475.0     47.5       NaN       NaN"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.stats.anova_lm(ano2,type=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd03099c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "069bad92",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "414c8af6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "568c844d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afa6f11e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12b84011",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af876e23",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67a04a42",
   "metadata": {},
   "source": [
    "## sm.statsmodel eexample "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "00cc0495",
   "metadata": {},
   "outputs": [],
   "source": [
    ">>> import statsmodels.api as sm\n",
    ">>> from statsmodels.formula.api import ols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a6576e9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "b30a1763",
   "metadata": {},
   "outputs": [],
   "source": [
    ">>> moore = sm.datasets.get_rdataset(\"Moore\", \"carData\", cache=True) # load\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "972cfe99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'statsmodels.datasets.utils.Dataset'>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "13d1c3e6",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>partner.status</th>\n",
       "      <th>conformity</th>\n",
       "      <th>fcategory</th>\n",
       "      <th>fscore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>low</td>\n",
       "      <td>8</td>\n",
       "      <td>low</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>low</td>\n",
       "      <td>4</td>\n",
       "      <td>high</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>low</td>\n",
       "      <td>8</td>\n",
       "      <td>high</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>low</td>\n",
       "      <td>7</td>\n",
       "      <td>low</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>low</td>\n",
       "      <td>10</td>\n",
       "      <td>low</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>low</td>\n",
       "      <td>6</td>\n",
       "      <td>low</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>low</td>\n",
       "      <td>12</td>\n",
       "      <td>medium</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>low</td>\n",
       "      <td>4</td>\n",
       "      <td>medium</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>low</td>\n",
       "      <td>13</td>\n",
       "      <td>low</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>low</td>\n",
       "      <td>12</td>\n",
       "      <td>low</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>low</td>\n",
       "      <td>4</td>\n",
       "      <td>medium</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>low</td>\n",
       "      <td>13</td>\n",
       "      <td>high</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>low</td>\n",
       "      <td>7</td>\n",
       "      <td>low</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>low</td>\n",
       "      <td>9</td>\n",
       "      <td>medium</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>low</td>\n",
       "      <td>9</td>\n",
       "      <td>high</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>low</td>\n",
       "      <td>24</td>\n",
       "      <td>high</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>low</td>\n",
       "      <td>6</td>\n",
       "      <td>low</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>low</td>\n",
       "      <td>7</td>\n",
       "      <td>high</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>low</td>\n",
       "      <td>23</td>\n",
       "      <td>high</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>low</td>\n",
       "      <td>13</td>\n",
       "      <td>high</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>low</td>\n",
       "      <td>8</td>\n",
       "      <td>low</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>low</td>\n",
       "      <td>12</td>\n",
       "      <td>low</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>high</td>\n",
       "      <td>19</td>\n",
       "      <td>high</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>high</td>\n",
       "      <td>12</td>\n",
       "      <td>medium</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>high</td>\n",
       "      <td>21</td>\n",
       "      <td>low</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>high</td>\n",
       "      <td>9</td>\n",
       "      <td>high</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>high</td>\n",
       "      <td>23</td>\n",
       "      <td>low</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>high</td>\n",
       "      <td>7</td>\n",
       "      <td>high</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>high</td>\n",
       "      <td>17</td>\n",
       "      <td>medium</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>high</td>\n",
       "      <td>14</td>\n",
       "      <td>medium</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>high</td>\n",
       "      <td>11</td>\n",
       "      <td>high</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>high</td>\n",
       "      <td>16</td>\n",
       "      <td>medium</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>high</td>\n",
       "      <td>15</td>\n",
       "      <td>low</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>high</td>\n",
       "      <td>20</td>\n",
       "      <td>medium</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>high</td>\n",
       "      <td>8</td>\n",
       "      <td>medium</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>high</td>\n",
       "      <td>12</td>\n",
       "      <td>low</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>high</td>\n",
       "      <td>14</td>\n",
       "      <td>high</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>high</td>\n",
       "      <td>14</td>\n",
       "      <td>medium</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>high</td>\n",
       "      <td>17</td>\n",
       "      <td>medium</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>high</td>\n",
       "      <td>7</td>\n",
       "      <td>medium</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>high</td>\n",
       "      <td>17</td>\n",
       "      <td>medium</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>high</td>\n",
       "      <td>13</td>\n",
       "      <td>high</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>high</td>\n",
       "      <td>16</td>\n",
       "      <td>low</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>high</td>\n",
       "      <td>10</td>\n",
       "      <td>high</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>high</td>\n",
       "      <td>15</td>\n",
       "      <td>medium</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   partner.status  conformity fcategory  fscore\n",
       "0             low           8       low      37\n",
       "1             low           4      high      57\n",
       "2             low           8      high      65\n",
       "3             low           7       low      20\n",
       "4             low          10       low      36\n",
       "5             low           6       low      18\n",
       "6             low          12    medium      51\n",
       "7             low           4    medium      44\n",
       "8             low          13       low      31\n",
       "9             low          12       low      36\n",
       "10            low           4    medium      42\n",
       "11            low          13      high      56\n",
       "12            low           7       low      28\n",
       "13            low           9    medium      43\n",
       "14            low           9      high      65\n",
       "15            low          24      high      57\n",
       "16            low           6       low      28\n",
       "17            low           7      high      61\n",
       "18            low          23      high      57\n",
       "19            low          13      high      55\n",
       "20            low           8       low      17\n",
       "21            low          12       low      35\n",
       "22           high          19      high      68\n",
       "23           high          12    medium      41\n",
       "24           high          21       low      16\n",
       "25           high           9      high      63\n",
       "26           high          23       low      15\n",
       "27           high           7      high      54\n",
       "28           high          17    medium      48\n",
       "29           high          14    medium      42\n",
       "30           high          11      high      59\n",
       "31           high          16    medium      45\n",
       "32           high          15       low      30\n",
       "33           high          20    medium      44\n",
       "34           high           8    medium      42\n",
       "35           high          12       low      22\n",
       "36           high          14      high      52\n",
       "37           high          14    medium      42\n",
       "38           high          17    medium      41\n",
       "39           high           7    medium      50\n",
       "40           high          17    medium      39\n",
       "41           high          13      high      57\n",
       "42           high          16       low      35\n",
       "43           high          10      high      52\n",
       "44           high          15    medium      44"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = moore.data\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "5f080024",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.rename(columns={\"partner.status\" :\n",
    "                             \"partner_status\"}) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "d4325c57",
   "metadata": {},
   "outputs": [],
   "source": [
    " moore_lm = ols('conformity ~ C(fcategory, Sum)*C(partner_status, Sum)',\n",
    "                 data=data).fit()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "9dbc636e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                              sum_sq    df          F  \\\n",
      "C(fcategory, Sum)                          11.614700   2.0   0.276958   \n",
      "C(partner_status, Sum)                    212.213778   1.0  10.120692   \n",
      "C(fcategory, Sum):C(partner_status, Sum)  175.488928   2.0   4.184623   \n",
      "Residual                                  817.763961  39.0        NaN   \n",
      "\n",
      "                                            PR(>F)  \n",
      "C(fcategory, Sum)                         0.759564  \n",
      "C(partner_status, Sum)                    0.002874  \n",
      "C(fcategory, Sum):C(partner_status, Sum)  0.022572  \n",
      "Residual                                       NaN  \n"
     ]
    }
   ],
   "source": [
    ">>> table = sm.stats.anova_lm(moore_lm, typ=2) # Type 2 Anova DataFrame\n",
    ">>> print(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "94779632",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>df</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C(fcategory, Sum)</th>\n",
       "      <td>11.614700</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.276958</td>\n",
       "      <td>0.759564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(partner_status, Sum)</th>\n",
       "      <td>212.213778</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.120692</td>\n",
       "      <td>0.002874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(fcategory, Sum):C(partner_status, Sum)</th>\n",
       "      <td>175.488928</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.184623</td>\n",
       "      <td>0.022572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>817.763961</td>\n",
       "      <td>39.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              sum_sq    df          F  \\\n",
       "C(fcategory, Sum)                          11.614700   2.0   0.276958   \n",
       "C(partner_status, Sum)                    212.213778   1.0  10.120692   \n",
       "C(fcategory, Sum):C(partner_status, Sum)  175.488928   2.0   4.184623   \n",
       "Residual                                  817.763961  39.0        NaN   \n",
       "\n",
       "                                            PR(>F)  \n",
       "C(fcategory, Sum)                         0.759564  \n",
       "C(partner_status, Sum)                    0.002874  \n",
       "C(fcategory, Sum):C(partner_status, Sum)  0.022572  \n",
       "Residual                                       NaN  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74ae184c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73a7cdda",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e291df4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa111619",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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