{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Päivitetty 2025-08-26 / Aki Taanila\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "print(f'Päivitetty {datetime.now().date()} / Aki Taanila')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import norm\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Normaalijakauma\n",
    "\n",
    "Tähtitieteen tutkimusta haittasivat 1600-luvulla epätarkat mittaustulokset esimerkiksi taivaankappaleiden etäisyyksiä, sijaintia ja liikkeitä mitattaessa. Epätarkkuus johtui mm. mittauslaitteiden kehittymättömyydestä ja inhimillisistä tekijöistä. Jo Galileo Galilei tuli tulokseen, että mittausvirheet noudattavat symmetristä jakaumaa ja että pieniä virheitä esiintyy useammin kuin suuria. Gauss niminen saksalainen matemaatikko ja tähtitieteilijä esitti vuonna 1809 lausekkeen jakaumalle, jota mittausvirheet noudattivat. Nykyään kyseistä jakaumaa kutsutaan normaalijakaumaksi.\n",
    "\n",
    "\n",
    "Normaalijakauman lauseke määrittelee funktion, jolla on seuraavia ominaisuuksia:\n",
    "\n",
    "\n",
    "\n",
    "- se on satunnaismuuttujan arvon funktio\n",
    "- funktion arvojen laskemiseksi täytyy tietää jakauman keskiarvo (odotusarvo) ja keskihajonta\n",
    "- funktion kuvaaja on symmetrinen ns. Gaussin kellokäyrä, jonka huippu on odotusarvon kohdalla\n",
    "- funktion alle jäävä pinta-ala voidaan samaistaa todennäköisyyteen.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Satunnaismuuttuja x')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7R40axahRo4r8Wmxs7GWf+/zzz/P8888X2j9gwAAGDBhQkjgi4uzS9sOCe+HYT+DhDX2mQPuHHH+5x78a9H4JOoyARUPMszJz+5n7Oo7U5SURN6PRbyJStvZ8BbN7mAVMpWB4cAV0GF62BUW1MHhoJbQcCEYOfDkOlj4CmWfL7nuKyFWnIkZEys73b8JHgyDjJIR0gke+g5Drrs739vGHv82GW14BmydsWwjv9YbTR6/O9xeRMqciRkTKxvr/QNyzgGFe3hm2HCoHX90MNht0ehSGLgP/QEjZBnP7w9njVzeHiJQJFTEi4nib3oWV483tHhPgttfBy8KJ6MK6wfA4qBQER3fCvLvgXLp1eUTEIVTEiIhjbV0An//T3O46Fro/aW2ePIENYMinUKEaHN5iXubSGBkRl6YiRkQcZ9cy8zZqDLjuYXP+F2e6IyioGQxZAr5VIGk9LLwPsjOu/DwRcUoqYkTEMfZ+DZ8MByMX2twPt0xxrgImT+22cN/H4O1vzvb78YOQk211KhEpARUxIlJ6x/bAx8MgNwua3wl3THPu9YvqdYLB88HTF37+HL6ZZHUiESkBJ/4pIyIuIeO0ObFc5mkI7Qp3zgYPT6tTXdm1N8Df3ja310+DXZ9ZGkdE7KciRkRKzjBg+T8uTGQ34D1r70KyV/M7IfIxc/vTUZC6z9o8ImIXFTEiUnI/zoYdi8HDC+6OhcpBViey383PQ73OkHnq/BmlM1YnEpFiUhEjIiXz24/w1TPmds8XITTS2jwl5ekNd885P4fMLlg+xjzDJCJOT0WMiNjv9DFYNAxys81LMp0etTpR6VQONs8k2Txh+yLY9F+rE4lIMaiIERH7GAYs+TucOgzVG8MdbznnrdT2Cu0MPV8wt7+MhsMJ1uYRkStSESMi9omPhV9WgVcFGPgB+Fa2OpHjRI6Gpn3NW8U/HQ3ZmVYnEpHLUBEjIsV34jdYOdHcvulZqBlubR5Hs9ng9hjwr26usbT2dasTichlqIgRkeIxDFj+hHkXT0hH6PiI1YnKRsXqcOur5vba1yBlu7V5ROSSVMSISPEkfgj7vzFnub3jP64xoV1JNb8Twm83By5/OgpysqxOJCJFUBEjIleWngxfnr+dusczUKOxtXnKms0Gt70BFa6BlG3w/ZtWJxKRIqiIEZHLMwz431jIOAm1212Y4dbdVQ4yF7EE+G4KHN1tbR4RKURFjIhc3vaPYc8X4OEN/WeAp5fVia6eVgOh8S2QkwnLRmu1axEnoyJGRC7tzz/gy3Hm9vVPQ82m1ua52mw2uH0q+AbAoXjY/J7ViUTkIipiROTSvvs3nE2DGuHQdYzVaaxRpTbc/Ky5veolOHvc2jwikk9FjIgU7djP5gKPALdMNtcYKq8iHoSgFnDuBKx62eo0InKeihgRKcwwzKn3c7Ohya3Q4EarE1nLw9Ms5AA2vwtHdlqbR0QAFTEiUpS9K805YTy8ode/rE7jHMK6Q9M7wMg1xwlppWsRy6mIEZGCsjPNszAAkaMgsIG1eZxJrxfNyf4OrIGfPrc6jUi5pyJGRAr68W04vh8q1oRuT1qdxrlcUx86P25urxwPWecsjSNS3qmIEZELTh8170gCuPk58KtibR5n1HUsVK4FfxyEjTOsTiNSrqmIEZELvn0RMtKhdltofa/VaZyTbyW4eZK5veY1c0kGEbGEihgRMR37GRLmmdu3TAEP/Xi4pJZ3Q532kHUG1rxqdRqRcks/pUTEtOpl886b8NuhXker0zg3Dw/oef5szJb34fgBa/OIlFMqYkQEkrfCrk8BG/QYb3Ua11C/K1zbw5xL57spVqcRKZdUxIgIfHt+LpiWAyCombVZXMmNE83P2xaal+NE5KpSESNS3iVtNCe3s3nCDdFWp3EtdSPMy29GrrmukohcVSUqYmbMmEFYWBh+fn5ERESwdu3aS7Zdt24dXbp0ITAwkAoVKhAeHs7UqVMLtImNjcVmsxX6OHdOczCIlCnDgG9eNLfb3qeJ7Uqix3jABruWweFEq9OIlCte9j5h4cKFjBkzhhkzZtClSxfefvtt+vTpw65du6hXr16h9hUrVuSxxx6jVatWVKxYkXXr1vHII49QsWJFHn744fx2VapU4eefC56O9fPzK8FLEpFi+2UV/LoOPH3g+qetTuOagpqZl+G2f2xelrv/E6sTiZQbdp+JeeONNxg+fDgjRoygadOmxMTEEBISwsyZM4ts37ZtWwYPHkzz5s2pX78+999/P7179y509sZmsxEcHFzgQ0TK0MVnYdoPh4C61uZxZTdEm5fj9sWZl+dE5Kqw60xMZmYm8fHxjBs3rsD+Xr16sX79+mIdIyEhgfXr1/OvfxVcVO706dOEhoaSk5NDmzZtePHFF2nbtu0lj5ORkUFGRkb+4/T0dACysrLIysoq7kuSv8jrO/Vh6Tl7X9p+XoHX4S0Y3v5kd3ocnDQnOH9fUqUenq3vxSPxA3K/fp6c+z8Dm83qVIU4fT+6EPWl45SmD+0qYlJTU8nJySEoKKjA/qCgIFJSUi773Lp163Ls2DGys7N5/vnnGTFiRP7XwsPDiY2NpWXLlqSnp/Pmm2/SpUsXtm7dSqNGjYo83uTJk5k0aVKh/atWrcLf39+elyVFiIuLszqC23DKvjRyueGniQQAe6vdxO41m61OVCxO2Zfn+WW142bbfDyTNrBx4ascq9LC6kiX5Mz96GrUl6V39uzZEj/X7jExYF76uZhhGIX2/dXatWs5ffo0GzduZNy4cTRs2JDBgwcD0KlTJzp16pTftkuXLrRr14633nqLadOmFXm86OhooqKi8h+np6cTEhJCjx49CAwMLMnLEsyKOC4ujp49e+Lt7W11HJfmzH1p2/MFXom/YfhUIuy+NwircI3VkS7LmfuygAq7YPM7dMr+npxbn7I6TSEu048uQH3pOGlpaSV+rl1FTPXq1fH09Cx01uXo0aOFzs78VVhYGAAtW7bkyJEjPP/88/lFzF95eHjQoUMH9u7de8nj+fr64uvrW2i/t7e33lAOoH50HKfrS8OA9TEA2DqMwLtKTWvz2MHp+vKvuo2BLbF4/Po9HslbnHbmY6fvRxeiviy90vSfXQN7fXx8iIiIKHT6LC4ujs6dOxf7OIZhFBjPUtTXExMTqVWrlj3xRKQ4DqyBQ/Hg5QeRo61O414C6kLre8ztdW9Ym0WkHLD7clJUVBRDhgyhffv2REZGMnv2bJKSkhg5ciRgXuY5dOgQc+fOBWD69OnUq1eP8PBwwJw35rXXXuPxxx/PP+akSZPo1KkTjRo1Ij09nWnTppGYmMj06dMd8RpF5GJrXzc/txsKlVznLIzL6DoWEj+EPV9CynYIbml1IhG3ZXcRM2jQINLS0njhhRdITk6mRYsWrFixgtDQUACSk5NJSkrKb5+bm0t0dDQHDhzAy8uLBg0a8Morr/DII4/ktzlx4gQPP/wwKSkpBAQE0LZtW9asWcN1113ngJcoIvl+3wwHvgMPL+j8+JXbi/0CG0Cz/rBzCax9A+6eY3UiEbdVooG9o0aNYtSoUUV+LTY2tsDjxx9/vMBZl6JMnTq10Cy+IlIG8s7CtBoEVQtPTikO0i3KLGJ2LjVn9K3e0OpEIm5JayeJlBdHdsLPKwAbdBljdRr3FtwSGvUGDPg+xuo0Im5LRYxIebHu/NnOZndAjcbWZikPuv3T/Lx1AZz83dosIm5KRYxIeXD8F9ix2NzO++UqZateR6jfDXKzYP1/rE4j4pZUxIiUB9+/CUYuNOwJtVpbnab86HZ+Qs74WDh9zNIoIu5IRYyIuzt9FBI/Mre7RV2+rTjWtT2gdlvI/hM2/dfqNCJuR0WMiLvb9F/IyYS6HSC0+JNSigPYbND5H+b2pv9C1p/W5hFxMypiRNxZ1kVnADoVPS2ClLGmd0BACJxNhW2LrE4j4lZUxIi4s20L4Wya+Uu06R1WpymfPL2g4/nJPTdMN9euEhGHUBEj4q4MAzbMMLc7jjR/mYo12g0Fn0qQ+jPs+8bqNCJuQ0WMiLva9435S9OnMrQbYnWa8s0vwCxkADZqTTgRR1ERI+KuNpyfm6TdUPOXqFir4yNg84D938KRXVanEXELKmJE3NGRnfDLKvOXZsdHrtxeyt419aFpX3NbZ2NEHEJFjIg7yhsL0/QOuCbU2ixyQeRj5udti8z5e0SkVFTEiLibU0dg+/lbeSNHW5tFCgq5Duq0N+ft0eR3IqWmIkbE3Vw8uV3IdVankb/KKyw1+Z1IqamIEXEnWedg87vmts7COKf8ye/SNPmdSCmpiBFxJzuXmr8cq9SB8L5Wp5GieHrBdX83tze9o8nvREpBRYyIO9n0jvm5/UOa3M6ZtR0CXn6Qsh1++9HqNCIuS0WMiLv4PR4OxYOnD7QbZnUauRz/atBygLn942xrs4i4MBUxIu4i7yxM8zuhUg1rs8iVdTh/SWnXMvOOMhGxm4oYEXdwJhV2LDG3r3vY2ixSPLXbQN3rIDcLtrxvdRoRl6QiRsQdbJkLORlQqw3UibA6jRRX3gDfze9BTpa1WURckIoYEVeXm2P+EgTzLIzNZm0eKb5m/aBiDTiVDD99bnUaEZejIkbE1e35Ek7+BhWqQYu/WZ1G7OHlCxEPmNs/vmNpFBFXpCJGxNXl3d3Sbgh4V7A2i9gv4kGwecKv67S6tYidVMSIuLJje+CX1YAN2g+3Oo2UREAdCL/N3N6kszEi9lARI+LK8hYRbNJHq1W7srw7yrYuhHMnrc0i4kJUxIi4qozTkPiRud1hhLVZpHTqd4UaTSHrDGxdYHUaEZehIkbEVe1cApmn4JowuLaH1WmkNGw2c6kIgM1ztJ6SSDGpiBFxVZvnmJ8jHgAP/Vd2ea0GglcFOLZb6ymJFJN+8om4ouStcHgLeHhDm/usTiOOUKEqtLjL3I6fY2kUEVehIkbEFeWdhWnaV+skuZP2D5qfdy6FP/+wNouIC1ARI+JqMk7B9o/N7bxfeuIe6kRAUEvIPqcBviLFoCJGxNXsWAyZp6FaA6jfzeo04kg2G0QMM7c1wFfkilTEiLiaiwf0ap0k99NqIHj7Q+rPkLTB6jQiTq1ERcyMGTMICwvDz8+PiIgI1q5de8m269ato0uXLgQGBlKhQgXCw8OZOnVqoXaLFy+mWbNm+Pr60qxZM5YuXVqSaCLu7XACJCeCp48G9Lorv4ALA3w3a4CvyOXYXcQsXLiQMWPGMH78eBISEujWrRt9+vQhKSmpyPYVK1bkscceY82aNezevZsJEyYwYcIEZs+end9mw4YNDBo0iCFDhrB161aGDBnCwIED+eGHH0r+ykTcUf6A3jugYqC1WaTs5I112rUMzh63NouIE7O7iHnjjTcYPnw4I0aMoGnTpsTExBASEsLMmTOLbN+2bVsGDx5M8+bNqV+/Pvfffz+9e/cucPYmJiaGnj17Eh0dTXh4ONHR0dx0003ExMSU+IWJuJ1z6bD9E3NbA3rdW+12ENwKcjJg63yr04g4LbuKmMzMTOLj4+nVq1eB/b169WL9+vXFOkZCQgLr16/n+uuvz9+3YcOGQsfs3bt3sY8pUi7s+MSclr56YwjtYnUaKUs224VCVQN8RS7Jy57Gqamp5OTkEBQUVGB/UFAQKSkpl31u3bp1OXbsGNnZ2Tz//POMGHFhrZeUlBS7j5mRkUFGRkb+4/T0dACysrLIysoq9muSgvL6Tn1Yeo7uS69N72EDctoMITc72yHHdBXl8n0Z3h+vryZgS9tL9v7vMBxQuJbLfiwj6kvHKU0f2lXE5LH95Y4IwzAK7furtWvXcvr0aTZu3Mi4ceNo2LAhgwcPLvExJ0+ezKRJkwrtX7VqFf7+/sV5GXIZcXFxVkdwG47oy4CzB7nhyHZybF58daQaWStWOCCZ6ylv78vWVTpQP201yZ9PYUv9kQ47bnnrx7Kkviy9s2fPlvi5dhUx1atXx9PTs9AZkqNHjxY6k/JXYWFhALRs2ZIjR47w/PPP5xcxwcHBdh8zOjqaqKio/Mfp6emEhITQo0cPAgM14LGksrKyiIuLo2fPnnh7e1sdx6U5si89vvg/AGxN+9LzjkGOiOdSyuv70nYoGGJXU/fUFoJv7GLeuVQK5bUfy4L60nHS0tJK/Fy7ihgfHx8iIiKIi4vjzjvvzN8fFxdHv379in0cwzAKXAqKjIwkLi6OsWPH5u9buXIlnTt3vuQxfH198fX1LbTf29tbbygHUD86Tqn7MvOsuWI14BExDI9y/O9S7t6XoddBzWbYju7Ce/dSuO7vDjlsuevHMqS+LL3S9J/dl5OioqIYMmQI7du3JzIyktmzZ5OUlMTIkeapzujoaA4dOsTcuXMBmD59OvXq1SM8PBww54157bXXePzxx/OP+cQTT9C9e3emTJlCv379WLZsGV9//TXr1q0r8QsTcRu7P4OMk1C1HoRdf+X24j5sNmg3FL4cB1vmOqyIEXEXdhcxgwYNIi0tjRdeeIHk5GRatGjBihUrCA0NBSA5ObnAnDG5ublER0dz4MABvLy8aNCgAa+88gqPPPJIfpvOnTuzYMECJkyYwMSJE2nQoAELFy6kY8eODniJIi5uywfm57ZDwEOTbJc7rQZB3LOQss1cvbxWa6sTiTiNEg3sHTVqFKNGjSrya7GxsQUeP/744wXOulzKgAEDGDBgQEniiLivtP3w6zqweUCbe61OI1bwrwbht5uXFLd8ALepiBHJoz/rRJxZwvmzMA1ugoC61mYR67QbYn7etgiy/rQ2i4gTUREj4qxysiHxI3O73VBrs4i1wm6AgHrm2Kjdy61OI+I0VMSIOKu9K+H0EfCvDo1vsTqNWMnDA9reb25vmWttFhEnoiJGxFnlXUpqMxi8fKzNItZrex9gg4NrzbFSIqIiRsQpnUqBPV+Z2211KUkwx0Q1vMncTphnbRYRJ6EiRsQZJX4ERg6EdIQaja1OI86i7fkBvokfmWOmRMo5FTEizsYwLlxK0oBeuViTW8E/EE6nwD6t2SOiIkbE2SRtgOO/gE8laNbf6jTiTLx8oPX5hXN1SUlERYyI00n40PzcvD/4VrI0ijihNveZn/d8CWdSrc0iYjEVMSLOJOM07Fxqbre539os4pyCmkHttpCbbU5+J1KOqYgRcSa7lkHWGajWAOp1sjqNOKu8szGJH5pjqETKKRUxIs4k8fylpDb3misYixSl5QDw9IUjO8xFIUXKKRUxIs7i+C/w6/fmYo95gzdFilLhGgi/zdzOK3xFyiEVMSLOIm+dpGt7QEAda7OI82t7/pLS9o8hO8PaLCIWUREj4gxycyBxvrmd98tJ5HKu7QFV6sCff8DPK6xOI2IJFTEizuDAd5D+O/gFQJPbrE4jrsDDE1rfY24n6JKSlE8qYkScQd4voZZ3g7eftVnEdeTdpbT/G0g/bG0WEQuoiBGx2p8n4Kf/mdttdClJ7BDYAOpFgpELWxdYnUbkqlMRI2K1HYsh+xzUPD+JmYg9NGeMlGMqYkSslj83zH2aG0bs17w/ePtD2j747Uer04hcVSpiRKx07Gc4FA8eXtBqoNVpxBX5Vr6wUKjmjJFyRkWMiJXy5oZp1Asq1bQ2i7iuNvean3cuhaw/rc0ichWpiBGxSm4ObFtobmuGXimN0C4QUA8y0uGnz61OI3LVqIgRscovq+BUMlSoBo1vsTqNuDIPD2hzvhDOO7snUg6oiBGxSt4vm5YDwMvH2izi+vImvvtlleaMkXJDRYyIFf48ceG0f954BpHSqHYt1OtszhmTd5lSxM2piBGxws6l5twwNZpCrTZWpxF3cfElJc0ZI+WAihgRK2w9v9hjm3s1N4w4TrP+4FUBUvfAoS1WpxEpcypiRK621H3w2w9g89DcMOJYflWgaV9zW3PGSDmgIkbkass7C9PwZqgcbG0WcT95Y6x2LIasc9ZmESljKmJErqbcixbq09wwUhbCukOVOnDuBOz5wuo0ImVKRYzI1XRwDaT/Dn4B0ORWq9OIO/LwvHC7deJ8a7OIlDEVMSJXU97cMC3uAm8/a7OI+8o7y7fvazh1xNosImVIRYzI1ZJxCnYvN7dba24YKUPVG0HdDmDkwPZFVqcRKTMqYkSull3LIOssBDaEuu2tTiPuLu9sTOJ8zRkjbqtERcyMGTMICwvDz8+PiIgI1q5de8m2S5YsoWfPntSoUYMqVaoQGRnJV199VaBNbGwsNput0Me5cxpZL24kb3xC68GaG0bKXou/gacvHN0JKdusTiNSJuwuYhYuXMiYMWMYP348CQkJdOvWjT59+pCUlFRk+zVr1tCzZ09WrFhBfHw8PXr0oG/fviQkJBRoV6VKFZKTkwt8+PlpzIC4iT9+hV/XAbYLgy5FylKFa6BJH3M77444ETdjdxHzxhtvMHz4cEaMGEHTpk2JiYkhJCSEmTNnFtk+JiaGp556ig4dOtCoUSNefvllGjVqxPLlywu0s9lsBAcHF/gQcRt5a9mEdYeAutZmkfIjb86YbYsgJ8vaLCJlwK4iJjMzk/j4eHr16lVgf69evVi/fn2xjpGbm8upU6eoVq1agf2nT58mNDSUunXrcvvttxc6UyPisgyj4DIDIldLgxuhYg04m2reqSTiZrzsaZyamkpOTg5BQUEF9gcFBZGSklKsY7z++uucOXOGgQMvTLceHh5ObGwsLVu2JD09nTfffJMuXbqwdetWGjVqVORxMjIyyMjIyH+cnp4OQFZWFllZ+oujpPL6Tn1Yenl9mHNwPd7Hf8Hwrkh2w96gvrWb3pcl59H8Ljx/nEVuwodkhVwPqB8dQe9JxylNH9pVxOSx/WVQomEYhfYVZf78+Tz//PMsW7aMmjVr5u/v1KkTnTp1yn/cpUsX2rVrx1tvvcW0adOKPNbkyZOZNGlSof2rVq3C39+/uC9FLiEuLs7qCG4j5asY6gO/VW5LwtdrrI7j0vS+tF+Vs3XpARg/f8HqL5aCVyX1owOpL0vv7NmzJX6uXUVM9erV8fT0LHTW5ejRo4XOzvzVwoULGT58OB9//DE333zzZdt6eHjQoUMH9u7de8k20dHRREVF5T9OT08nJCSEHj16EBgYWIxXI0XJysoiLi6Onj174u3tbXUcl5aVlcU3X31O6Ol4AGr3+Se16nezOJVr0vuydIx35uN5dCc3B6fzRWol9aMD6D3pOGlpaSV+rl1FjI+PDxEREcTFxXHnnXfm74+Li6Nfv36XfN78+fN56KGHmD9/PrfddtsVv49hGCQmJtKyZctLtvH19cXX17fQfm9vb72hHED96BjBJ7dgy0iHgHp4NbgBPDQ1U2nofVlCbe6FlePx2vkxBD2hfnQg9WXplab/7L6cFBUVxZAhQ2jfvj2RkZHMnj2bpKQkRo4cCZhnSA4dOsTcuXMBs4AZOnQob775Jp06dco/i1OhQgUCAgIAmDRpEp06daJRo0akp6czbdo0EhMTmT59eolfmIgzqHd8nbnRepAKGLFOq4EQ9yweh+OpFJBsdRoRh7G7iBk0aBBpaWm88MILJCcn06JFC1asWEFoaCgAycnJBeaMefvtt8nOzmb06NGMHj06f/+wYcOIjY0F4MSJEzz88MOkpKQQEBBA27ZtWbNmDdddd10pX56IhU6lUDN9u7ndSnPDiIUq1YSGN8Perwg5vg4YbnUiEYco0cDeUaNGMWrUqCK/lleY5Fm9evUVjzd16lSmTp1akigiTstj52JsGOTW6YBH9YZWx5HyrvU954uY78HItTqNiEPo/LZIWTAMPLabE9wZrQZZHEYEaHIrhl8AFbKOYzu4zuo0Ig6hIkakLKRsw3Z0Fzk2b3Kb9rc6jQh4++W/Fz22axkCcQ8qYkTKwvnFHlMC2kKFqtZmETnPOD82y/bT/yDjlMVpREpPRYyIo+VkwfaPAfitWleLw4hcYNRpz2nfIGxZZ2H38is/QcTJqYgRcbR9X8PZVIyKNTla5dJzHYlcdTbbhcI68SNrs4g4gIoYEUc7/8sht8VdGDZPi8OIFPRbtS7mxsG1cCLp8o1FnJyKGBFHOnsc9nwJQG5L3ZUkzudPn+rkhp4/G7N1obVhREpJRYyII+1cAjmZENQSglpYnUakSPkF9tb5YBjWhhEpBRUxIo50/q4k2gy2NofIZRjht4O3PxzfD79vsjqOSImpiBFxlNS9cGgz2Dyh5d1WpxG5NN/K0PQOc1sDfMWFqYgRcZS8XwYNbzbXqhFxZnlnC3cugaxz1mYRKSEVMSKOkJsL284PktSlJHEF9btDlbpw7iTs+cLqNCIloiJGxBEOroH0Q+AXAI37WJ1G5Mo8PKDVQHM7byyXiItRESPiCHm/BJr/Dbz9rM0iUlxt7jU/7/saTh+1NotICaiIESmtjNOw+zNzO++XgogrqN4I6rQHIwe2LbI6jYjdVMSIlNauZZB1Fqo1gLodrE4jYp+8MVyJH2nOGHE5KmJESivvrqQ294LNZm0WEXu1uAs8feDoTkjZZnUaEbuoiBEpjT8Owq/rABu0vsfqNCL2q3ANNLnV3NYAX3ExKmJESmPrAvPztddDQF1rs4iUVJv7zM/bF0F2prVZROygIkakpHJzL1xKaq0BveLCGtwIlYLgbBrsi7M6jUixqYgRKamkDXDiV/CpBE1vtzqNSMl5el1YKkPLEIgLUREjUlJbz/+wb94ffCpaGkWk1PKmB9jzFZxJszaLSDGpiBEpicwzsPNTc1uXksQdBDWHWq0hNwt2fGJ1GpFiUREjUhK7/weZp+Ga+lAv0uo0Io6RN8A38UNrc4gUk4oYkZLI+yHferC5Bo2IO2gxADy8IXkrHNlldRqRK9JPXxF7nfgNDqwxtzU3jLiTioHQuLe5vVUDfMX5qYgRsde2BYABoV3Ny0ki7iRvgO/WhZCTbW0WkStQESNiD8O4MKupFnsUd9SoF/hXhzNHYf83VqcRuSwVMSL2+O0HOL4fvP2h2R1WpxFxPE/vi+aM0QBfcW4qYkTskTDP/NysP/hWtjSKSJlpe/4upZ+/gLPHrc0ichkqYkSK6+K5YfJ+yIu4o+CWENwKcjJhu+aMEeelIkakuHYvh8xTUDUU6nW2Oo1I2cqfM2aetTlELkNFjEhx5V1KanOf5oYR99fy7gtzxqTssDqNSJH0k1ikOP44CAfXAjZoM9jqNCJlr2IgNOljbmuArzgpFTEixbF1gfk5rDtUrWdtFpGrpe395udtCyE709osIkVQESNyJbm5F/4SbaMBvVKONLgJKgXB2TTYu9LqNCKFlKiImTFjBmFhYfj5+REREcHatWsv2XbJkiX07NmTGjVqUKVKFSIjI/nqq68KtVu8eDHNmjXD19eXZs2asXTp0pJEE3G8X9fBiSTwrQJN+1qdRuTq8fSCVoPMbV1SEidkdxGzcOFCxowZw/jx40lISKBbt2706dOHpKSkItuvWbOGnj17smLFCuLj4+nRowd9+/YlISEhv82GDRsYNGgQQ4YMYevWrQwZMoSBAwfyww8/lPyViThKwvkf3s3vBB9/a7OIXG15l5T2fAWnj1qbReQv7C5i3njjDYYPH86IESNo2rQpMTExhISEMHPmzCLbx8TE8NRTT9GhQwcaNWrEyy+/TKNGjVi+fHmBNj179iQ6Oprw8HCio6O56aabiImJKfELE3GIc+mwa5m5nffDXKQ8qdEE6rQHIwe2LbI6jUgBXvY0zszMJD4+nnHjxhXY36tXL9avX1+sY+Tm5nLq1CmqVauWv2/Dhg2MHTu2QLvevXtftojJyMggIyMj/3F6ejoAWVlZZGVlFSuLFJbXd+pDk237Yryy/8QIbEh2UBuwo1/Ul46jvnSMkvajR6t78Dy0GSNhHtntHwabrSziuRS9Jx2nNH1oVxGTmppKTk4OQUFBBfYHBQWRkpJSrGO8/vrrnDlzhoEDB+bvS0lJsfuYkydPZtKkSYX2r1q1Cn9/nfIvrbi4OKsjOIWue6YTCOz2acveL74o0THUl46jvnQMe/vRK7sit9i88Ty2m/WLp3PC/9oySuZ69J4svbNnz5b4uXYVMXlsf6nCDcMotK8o8+fP5/nnn2fZsmXUrFmzVMeMjo4mKioq/3F6ejohISH06NGDwMDA4rwMKUJWVhZxcXH07NkTb29vq+NYK20v3gl7MWweNBrwLI2q1LLr6epLx1FfOkZp+tGWGwc7l9C14q/k9nmsjBK6Dr0nHSctLa3Ez7WriKlevTqenp6FzpAcPXq00JmUv1q4cCHDhw/n448/5uabby7wteDgYLuP6evri6+vb6H93t7eekM5gPoR2L4QAFvDm/EOLPncMOpLx1FfOkaJ+rHdUNi5BM+dS/DsMxm8K5RNOBej92Tplab/7BrY6+PjQ0RERKHTZ3FxcXTufOm1ZObPn88DDzzARx99xG233Vbo65GRkYWOuXLlysseU6RM5WTD1vnmdtsh1mYRcQZh15sTPWacNNcRE3ECdt+dFBUVxX//+1/ee+89du/ezdixY0lKSmLkyJGAeZln6NCh+e3nz5/P0KFDef311+nUqRMpKSmkpKRw8uTJ/DZPPPEEK1euZMqUKfz0009MmTKFr7/+mjFjxpT+FYqUxN6VcPoI+FeHxrdYnUbEeh4e0Ob8HXpb5lqbReQ8u4uYQYMGERMTwwsvvECbNm1Ys2YNK1asIDQ0FIDk5OQCc8a8/fbbZGdnM3r0aGrVqpX/8cQTT+S36dy5MwsWLGDOnDm0atWK2NhYFi5cSMeOHR3wEkVKIG+xx9b3gJePtVlEnEWbewGbuY7Y8V+sTiNSsoG9o0aNYtSoUUV+LTY2tsDj1atXF+uYAwYMYMCAASWJI+JYp47Ani/NbV1KErmgagg0uBH2f2NOAnnTRKsTSTmntZNE/mrrfHNir7odoGa41WlEnEvepI+JH0FujrVZpNxTESNyMcOAhA/MbZ2FESks/DaoUA1OHYb931qdRso5FTEiF/vtB0jbB94VocXfrE4j4ny8fC8sCqkBvmIxFTEiF9ty/ixM8zvBt7K1WUScVbvzZyl//gLOpFqbRco1FTEieTJOwc6l5nY7XUoSuaSg5lC7HeRmwdYFVqeRckxFjEienUsh6wwENoIQ3d4vcll5A3wTPjDHkolYQEWMSJ68S0lt79cqvSJX0nIAeFWAYz/BoXir00g5pSJGBODobvj9R7B5QuvBVqcRcX5+AdCsn7m95X1rs0i5pSJGBCD+/A/hJn2g8uUXMxWR8yKGmZ+3LzbHlIlcZSpiRLLOXVjsMeIBS6OIuJR6kVC9sTmWbPsnVqeRckhFjMjuz+DcCQg4P6W6iBSPzQbtzp+NiY+1NIqUTypiRPIuJbUdAh6e1mYRcTWtB4OnDyQnQvJWq9NIOaMiRsq31H3w6zqweVy4ZVREiq9iIITfbm7Ha4CvXF0qYqR82xJrfm7UCwLqWBpFxGXljSXbtggyz1gaRcoXFTFSfmVnmCvxwoXr+iJiv/rd4JowyLxo1muRq0BFjJRfP30OZ9Ogci3zTIyIlIyHB7Qbam5rgK9cRSpipPzKm6Cr7f3g6WVtFhFX1+Y+8PCC3zfBkV1Wp5FyQkWMlE/HD8AvqwGbeVeSiJRO5SBzskjQDL5y1aiIkfJpy1zzc4Mb4ZpQa7OIuIu8Ab5b50PWn5ZGkfJBRYyUPzlZkPihuR2hAb0iDnPtjRBQD86dhF3LrE4j5YCKGCl/fvocTh+BijWhcR+r04i4j4sH+G5+z9osUi6oiJHyJ++Ha7sh4OVjbRYRd9NuiDnA97cfIGWH1WnEzamIkfIldR8c+A6wabFHkbJQORjCbzO3dTZGypiKGClf8n6oNu4NVetZm0XEXbUfbn7ethAyTlmbRdyaihgpP7L+vDCgN++HrIg4Xlh3CGwImafNpQhEyoiKGCk/di6FcyfMuyca3mR1GhH3ZbNB+4fM7c1zwDCszSNuS0WMlB+b3jU/t38APDwtjSLi9loPBi8/OLLdnMVXpAyoiJHyIXkrHNoMHt7QdqjVaUTcn381aHGXuZ33B4SIg6mIkfIh74doszugUg1rs4iUF3ljz3YuhbPHrc0ibklFjLi/cydh+yfmtgb0ilw9ddpBrdaQk3FhUL2IA6mIEfe3bRFknYEa4RDa2eo0IuVHgQG+70FurrV5xO2oiBH3ZhgX5oZp/5D5Q1VErp6Wd4NvFTj+CxxYbXUacTMqYsS9/fo9HN0F3v7Q+h6r04iUPz4VL/zf+/G/1mYRt6MiRtzbD2+bn1sNAr8Aa7OIlFcd/m5+3vMF/PGrtVnEraiIEfd18ndzxWqA6x62NotIeVajMVzbA4xc2KSzMeI4JSpiZsyYQVhYGH5+fkRERLB27dpLtk1OTubee++lSZMmeHh4MGbMmEJtYmNjsdlshT7OnTtXkngipk3vgpED9btBUDOr04iUbx0fMT9vmQuZZ63NIm7D7iJm4cKFjBkzhvHjx5OQkEC3bt3o06cPSUlJRbbPyMigRo0ajB8/ntatW1/yuFWqVCE5ObnAh5+fn73xRExZ52DL++Z23g9PEbFOo15QNdRc+mP7x1anETdhdxHzxhtvMHz4cEaMGEHTpk2JiYkhJCSEmTNnFtm+fv36vPnmmwwdOpSAgEuPSbDZbAQHBxf4ECmxHYvhbBpUqQuN+1idRkQ8PKHDCHP7x9laT0kcwsuexpmZmcTHxzNu3LgC+3v16sX69etLFeT06dOEhoaSk5NDmzZtePHFF2nbtu0l22dkZJCRkZH/OD09HYCsrCyysrJKlaU8y+s7l+5Dw8Drh1nYgJyIB8nNNSD36r8et+hLJ6G+dAzL+7HlPXitehnbkR1k/7IWo16kNTkcwPK+dCOl6UO7ipjU1FRycnIICgoqsD8oKIiUlJQShwgPDyc2NpaWLVuSnp7Om2++SZcuXdi6dSuNGjUq8jmTJ09m0qRJhfavWrUKf3//EmcRU1xcnNURSuyaM3vpnrKNHJs3K1ODyVyxwtI8rtyXzkZ96RhW9mPrgI7UT1vNkeUvsjnsMctyOIrek6V39mzJx0jZVcTksf1lwjDDMArts0enTp3o1KlT/uMuXbrQrl073nrrLaZNm1bkc6Kjo4mKisp/nJ6eTkhICD169CAwMLDEWcq7rKws4uLi6NmzJ97e3lbHKRHPT807kWwt7+bmvoMsy+EOfeks1JeO4RT9eLQ+vNOd2ifjubVrG6hS25ocpeQUfekm0tLSSvxcu4qY6tWr4+npWeisy9GjRwudnSkNDw8POnTowN69ey/ZxtfXF19f30L7vb299YZyAJftx1MpsPszADw6PYKHE7wGl+1LJ6S+dAxL+7FOawjtiu3XdXgnzoWbJlqTw0H0niy90vSfXQN7fXx8iIiIKHT6LC4ujs6dHbcmjWEYJCYmUqtWLYcdU8qJzXMgNxtCOkLtNlanEZGiXHd+8rv4WPNOQpESsvtyUlRUFEOGDKF9+/ZERkYye/ZskpKSGDlyJGBe5jl06BBz587Nf05iYiJgDt49duwYiYmJ+Pj40KyZOXfHpEmT6NSpE40aNSI9PZ1p06aRmJjI9OnTHfASpdzIzoT4Oea2JrcTcV7ht0OVOpB+CHYuhTaDrU4kLsruImbQoEGkpaXxwgsvkJycTIsWLVixYgWhoaGAObndX+eMufguo/j4eD766CNCQ0M5ePAgACdOnODhhx8mJSWFgIAA2rZty5o1a7juuutK8dKk3Nm5BE4fgcq1oOkdVqcRkUvx9IIOw+GbF2DjDHNtJS3OKiVQooG9o0aNYtSoUUV+LTY2ttA+4wrzAUydOpWpU6eWJIqIyTBgw3/M7ev+Dl4+1uYRkcuLeBC+exVStsHBdRDWzepE4oK0dpK4h4NrIWW7uVp1xINWpxGRK/GvBm3uNbc3aOiAlIyKGHEPeT8E29xr/nAUEefX6fwZ/T1fQOo+a7OIS1IRI64vdR/s+RKwQcdHrU4jIsVVveGFZUF+KHrpGpHLUREjrm/jDPNz41vMH4oi4joiz5+NSfgQzh63Nou4HBUx4trOHofEj8ztyNHWZhER+9XvBsEtIfvPC1MkiBSTihhxbfFzzB9+wa2gfler04iIvWw2iDy/htKP75jzPYkUk4oYcV3ZmfDDbHM78jHNMyHiqpr/DSoFw6lkc/I7kWJSESOua+cSOJ1i/vBrfqfVaUSkpLx8LixFsOE/5rxPIsWgIkZc08WT23V8WJPbibi69g+BV4ULk9+JFIOKGHFNv6w2J7fzqqDJ7UTcwcWT362fZm0WcRkqYsQ1fR9jfo4YpsntRNxF5GiwecDelZCyw+o04gJUxIjrOZxgnomxeeq2ahF3EtgAmvUzt79/09os4hJUxIjrWRdjfm55N1StZ2kUEXGwLmPMzzsWwx+/WhpFnJ+KGHEtafth1zJzu8sT1mYREcer3Qau7QFGjhaGlCtSESOuZf00wDCXGAhqZnUaESkLXceYn7fMhTOplkYR56YiRlzHqZQLSwzknXIWEfcTdj3UamPOxv3jbKvTiBNTESOuY+NMyMmEkI4QGml1GhEpKzYbdB1rbv/wNmSctjaPOC0VMeIazp2Eze+Z23k/3ETEfTXtC9UawLkT5mUlkSKoiBHXsPk9yEiHGuHQqLfVaUSkrHl4QufHze0N07UwpBRJRYw4v6xz5qUkMMfCeOhtK1IutB4MlYIg/XfY/rHVacQJ6beBOL8tc+H0EQgIgZYDrE4jIleLtx90GmVur30dcnOszSNOR0WMOLfsDFg31dzuOhY8va3NIyJXV4cRUKEaHN8PO5ZYnUacjIoYcW4J8+DUYahcG9reb3UaEbnafCtdWF5kzas6GyMFqIgR55WdWfAsjJevtXlExBrXPQx+AZD684UZu0VQESPObOt8OPkbVAqGdkOtTiMiVvGrAp0uPhuTa20ecRoqYsQ55WSZA/nAXCPJ28/aPCJirY6PgG8VOLoLflpudRpxEipixDltWwQnfoWKNSDiAavTiIjVKlSFjiPN7e/+rbMxAqiIEWeUkw1rXzO3O/8DfPytzSMizqHTo+BTGY7sgD1fWJ1GnICKGHE+OxbD8V/APxDaP2R1GhFxFv7VoOPD5vZ3U8AwrM0jllMRI84lN8ccuAcQ+Zh5e6WISJ5Oo8G7IiRvhT1fWZ1GLKYiRpzLtkWQthcqXAPX/d3qNCLibCoGwnUjzO1VL2lsTDmnIkacR3YmrH7Z3O4yBnwrWxpHRJxU5yfMsTEp22C35o0pz1TEiPPY8j6cSDIXfLvuYavTiIizqhgInR8zt799ybwZQMolFTHiHDLPXhgL0/3/dEeSiFxep1Hmmkppe2HbAqvTiEVUxIhz+HG2uVJ11XrQbpjVaUTE2flVMZcjAVj9irlYrJQ7JSpiZsyYQVhYGH5+fkRERLB27dpLtk1OTubee++lSZMmeHh4MGbMmCLbLV68mGbNmuHr60uzZs1YunRpSaKJKzp38sIaSTc8A14+1uYREddw3d+hci1zeZL4WKvTiAXsLmIWLlzImDFjGD9+PAkJCXTr1o0+ffqQlJRUZPuMjAxq1KjB+PHjad26dZFtNmzYwKBBgxgyZAhbt25lyJAhDBw4kB9++MHeeOKKNkyHcyegehNoNdDqNCLiKrwrmJefAda8BplnrM0jV53dRcwbb7zB8OHDGTFiBE2bNiUmJoaQkBBmzpxZZPv69evz5ptvMnToUAICAopsExMTQ8+ePYmOjiY8PJzo6GhuuukmYmJi7I0nruZMqlnEANw4Hjw8rc0jIq6l7RC4pj6cOQo/vG11GrnKvOxpnJmZSXx8POPGjSuwv1evXqxfv77EITZs2MDYsWML7Ovdu/dli5iMjAwyMi5cA01PTwcgKyuLrKysEmcp7/L67mr1ocea1/DMPE1ucGtyGvYBN/q3u9p96c7Ul47hnv1ow9btKbw+G4XxfQzZbYaCX9F/MDuSe/alNUrTh3YVMampqeTk5BAUFFRgf1BQECkpKSUOkZKSYvcxJ0+ezKRJkwrtX7VqFf7+urOltOLi4sr8e1TITOWmXf8FYGPFnhz7wj3XQrkafVleqC8dw+360fCnh18dqpw7xIF5T7C79tW7LO12fWmBs2fPlvi5dhUxeWw2W4HHhmEU2lfWx4yOjiYqKir/cXp6OiEhIfTo0YPAwMBSZSnPsrKyiIuLo2fPnnh7e5fp9/JcNhIPI4vc0C50GPQ0lPI95GyuZl+6O/WlY7hzP9oaesAnQ2mU9jVhd/8LAuqW6fdz57682tLS0kr8XLuKmOrVq+Pp6VnoDMnRo0cLnUmxR3BwsN3H9PX1xdfXt9B+b29vvaEcoMz78VA87PgEAI/eL+Hh4753JOk96TjqS8dwy35sfgds6oLt1+/x/u5luOudq/Jt3bIvr7LS9J9dA3t9fHyIiIgodPosLi6Ozp07lzhEZGRkoWOuXLmyVMcUJ2YYsHKiud3qHqjd1to8IuL6bDbo9S9ze/siOLTF2jxyVdh9OSkqKoohQ4bQvn17IiMjmT17NklJSYwcORIwL/McOnSIuXPn5j8nMTERgNOnT3Ps2DESExPx8fGhWbNmADzxxBN0796dKVOm0K9fP5YtW8bXX3/NunXrHPASxen89Dn8+j14+cFNE61OIyLuok47aDUIti2ElRPggc/d7jK1FGR3ETNo0CDS0tJ44YUXSE5OpkWLFqxYsYLQ0FDAnNzur3PGtG174S/t+Ph4PvroI0JDQzl48CAAnTt3ZsGCBUyYMIGJEyfSoEEDFi5cSMeOHUvx0sQpZWdC3LPmduRjZX7dWkTKmZuehV3LzD+Ufvocmt5udSIpQyUa2Dtq1ChGjRpV5NdiY2ML7TMM44rHHDBgAAMGDChJHHElm9+D4/uhYg3oOsbqNCLibgLqQuRoWPu6+QdTo16aBdyNae0kuXr+PAHfvWJu93gGfCtbGkdE3FTXseYfSsf3Q/wcq9NIGVIRI1fP2tfgzz+gRji0HWp1GhFxV76VzT+UwFwc8s8TlsaRsqMiRq6OtP0XpgTv9S/wLNGVTBGR4mk71PyD6c/j8N2/rU4jZURFjJQ9w4Avx0FOJjS4ERrebHUiEXF3nl7Q+yVz+4dZcHS3tXmkTKiIkbL38xewdyV4eEOfV3XLo4hcHQ1vhvDbwciBFf9n/kElbkVFjJStrD/hy6fN7c6PQfWG1uYRkfKl98vmnFQH18KOxVanEQdTESNla10MnEiCKnWg+/9ZnUZEyptrQqHbP83tlRMg45S1ecShVMRI2Tl+ANZNNbd7vwQ+Fa3NIyLlU+d/wDVhcCpZg3zdjIoYKTtfRkNOBoRdD836W51GRMorbz/oM8Xc3jgDjv1sbR5xGBUxUjb2fAV7vgAPL7hVg3lFxGKNe0PjPpCbrUG+bkRFjDhe1p/wxfnBvJGjoUYTa/OIiAD0ecUc5HvgO9i51Oo04gAqYsTxVr8CfxyAyrWh+1NWpxERMV1T31ySAMw/tM4etzSOlJ6KGHGsw4mw/i1z+7bXwbeSpXFERAroOhaqN4EzR2HlRKvTSCmpiBHHycmGzx43J5ZqfieE32p1IhGRgrx84Y63ABskzoP9q6xOJKWgIkYcZ8NbkLIN/KpCH93GKCJOql5H6DDC3F7+BGSetTaPlJiKGHGMtP3mWBgwZ8isVNPaPCIil3Pzc1ClLpz4FVa9ZHUaKSEVMVJ6ubnw2T8g+xxc2wPa3Gt1IhGRy/OtDLefn4xz4ww4FG9tHikRFTFSelveh1/Xgbc/9I3RnDAi4hoa94KWd4Nx/g+xnCyrE4mdVMRI6Zz8HeKeNbdvnGDewigi4ipueQUqVIMjO2DtG1anETupiJGSy82FpSMhIx3qtIeOI61OJCJin4rVL9yIsObfcGiLtXnELipipOR+mGkub+/tD3e+DR6eVicSEbFfywHmtBC52bDkYd2t5EJUxEjJHNkFX08yt3v9C6o3tDaPiEhJ2Wxw2xtQuRak7b1wiVycnooYsV92hvnXSk4GNOoN7R+yOpGISOn4V4P+M8ztTe/A3q+tzSPFoiJG7LfqZTiyHfwDzZkvdTeSiLiDBjdeGNu3bJTWVnIBKmLEPge/h+/fNLf7ToPKQdbmERFxpJufN9dWOn3EnM3XMKxOJJehIkaK788T5t1IGND2fmh6u9WJREQcy7sC/G02eHjB7s8g8UOrE8llqIiR4jEMWDYaTiZB1VBzbgUREXdUuw30eMbc/vxJ80YGcUoqYqR4Ns6An/4Hnj5wd6w5ZbeIiLvqMtYcI5P9J3w8DDJOW51IiqAiRq7stx8v3HLY+2Wo087aPCIiZc3DA/72DlSuDal74H9jND7GCamIkcs7kwYfP2BOAtX8bxeWrxcRcXcVq8OA98DmCds/hvhYqxPJX6iIkUvLzYWlj0D6IQhsCHdM0+3UIlK+hEbCzc+Z2188Dclbrc0jBaiIkUv7firsiwMvP7j7fY2DEZHyKfJxaNzHnOBz0TA4d9LqRHKeihgpkm3/t/Dtv8wHt74GwS2sDSQiYhUPD3M234B68McBc8by3ByrUwkqYqQIlc4dxnPpCDByzflg2t5vdSQREWv5V4OBseDpC3u+xGP1S1YnElTEyF/9+Qcdf5mKLSMdQjqZi6JpHIyICNSJgH7TAfDcMI26x7+3OJCUqIiZMWMGYWFh+Pn5ERERwdq1ay/b/rvvviMiIgI/Pz+uvfZaZs2aVeDrsbGx2Gy2Qh/nzp0rSTwpqZwsPJcMp1LGEYwqdWHQPPDytTqViIjzaHU3dPsnAG2S3sV2aLPFgco3u4uYhQsXMmbMGMaPH09CQgLdunWjT58+JCUlFdn+wIED3HrrrXTr1o2EhASeeeYZ/vGPf7B48eIC7apUqUJycnKBDz8/v5K9KimZr57B4+Aasj18yR44DyrVsDqRiIjz6TGB3MZ98DSy8fx4KJz83epE5ZbdRcwbb7zB8OHDGTFiBE2bNiUmJoaQkBBmzpxZZPtZs2ZRr149YmJiaNq0KSNGjOChhx7itddeK9DOZrMRHBxc4EOuok3vwo+zAYgPHQlBGsgrIlIkDw9y+s3kpF8ItjNHYf5gyDxjdapyya4iJjMzk/j4eHr16lVgf69evVi/fn2Rz9mwYUOh9r1792bz5s1kZWXl7zt9+jShoaHUrVuX22+/nYSEBHuiSWnsjYMvngIg54bxpFSNsDiQiIiT86nEDw3GYvhXh5Rt8MlwyMm2OlW542VP49TUVHJycggKCiqwPygoiJSUlCKfk5KSUmT77OxsUlNTqVWrFuHh4cTGxtKyZUvS09N588036dKlC1u3bqVRo0ZFHjcjI4OMjIz8x+np6QBkZWUVKI7k8myHNuO5aCi23GxyW9xNRofR8PXX6kMHyOtD9WXpqS8dQ/3oOFlZWfzpU52M/u/iu3Agtj1fkPvZP8i5LUY3Q9ipNO9Hu4qYPLa//AMZhlFo35XaX7y/U6dOdOrUKf/rXbp0oV27drz11ltMmzatyGNOnjyZSZMmFdq/atUq/P39i/dCyrlK5w7Rbc+/8Mo5y5HKLfnBsw/G118DEBcXZ3E696G+dBz1pWOoHx3nq90nCa43kusOTMNj64fsS0lnd+27rY7lUs6ePVvi59pVxFSvXh1PT89CZ12OHj1a6GxLnuDg4CLbe3l5ERgYWORzPDw86NChA3v37r1klujoaKKiovIfp6enExISQo8ePS55XLlI+mG83o/GlnOG3NrtqHbfEvr4VCIrK4u4uDh69uyJt7e31SldmvrScdSXjqF+dJyCfXkrOQn18VoxlsZHltOgVSdyr3vE6oguIy0trcTPtauI8fHxISIigri4OO688878/XFxcfTr16/I50RGRrJ8+fIC+1auXEn79u0v+Z/IMAwSExNp2bLlJbP4+vri61v49l9vb2/957ySs8dhwcDzayI1wuO+T/CoeE2BJupHx1FfOo760jHUj46T35fXPQTnjsO3L+IZNx7PKsHQcoDV8VxCad6Ldt+dFBUVxX//+1/ee+89du/ezdixY0lKSmLkyJGAeYZk6NCh+e1HjhzJr7/+SlRUFLt37+a9997j3Xff5cknn8xvM2nSJL766it++eUXEhMTGT58OImJifnHFAfKOA3z74FjP5lLzA9ZAhV15kpEpNS6/RPyzsAsHQl7v7Y2Tzlg95iYQYMGkZaWxgsvvEBycjItWrRgxYoVhIaGApCcnFxgzpiwsDBWrFjB2LFjmT59OrVr12batGncdddd+W1OnDjBww8/TEpKCgEBAbRt25Y1a9Zw3XXXOeAlSr6M0/Dh3fDbD+AXAPcvhqr1rE4lIuIebDa45RU4mwo7FsOCe+Gej6DRzVYnc1slGtg7atQoRo0aVeTXYmNjC+27/vrr2bJlyyWPN3XqVKZOnVqSKFJcGafhwwGQtAF8A+D+pRDUzOpUIiLuxcMD+s+C7Az46X8qZMqY1k4qDzJOFSxghi6FupoLRkSkTHj5wIA5EH475GSYhYwuLZUJFTHuLuMUzPtLAVNHBYyISJkqVMgMNicWFYdSEePO/jxhFjC/bVQBIyJytXn5wN2x5wuZTPOMzM9fWJ3KraiIcVfph2HOrRcVMJ+qgBERudo8vf9SyNwHWz6wOpXbUBHjjo7tgXd7wdGdUCkIHvgf1GlndSoRkfIpr5BpfS8YOfDZY7DmVTg/e72UnIoYd/Pbj/BeLzj5G1RrAMNXQq1WVqcSESnfPL2h/wzoOtZ8/O2/YMX/QW6OtblcnIoYd/Lzl/D+HfDnH+alo+Er4Zr6VqcSEREw55G5+Xm4ZQpgg03vwCcPQtY5q5O5LBUx7sAwYMN0c/R79p/QsCcMWw4Vq1udTERE/qrTSBjwLnh4w65l8H5fOJVy5edJISpiXF3Wn+b01l89A0YutLkfBs8Hn4pWJxMRkUtpcZc5a7pfAPz+I8y+AX6PtzqVy1ER48pOHoI5fWDbArB5mtNd9/uPee1VRESc27XXw99XQfUmcCrZ/Hme+JHVqVyKihhXlfSDWbkfToAK15gLOXZ61LzmKiIiriGwAYz4Gprcak6K9+mj8GU05GRbncwlqIhxNbm58P00iL0NzhyFms3NSv7aG6xOJiIiJeFXBQZ9CNc/bT7eOAPevx1O/GZtLhegIsaVnDoCH94FcRMhNwua9TfvQKoWZnUyEREpDQ8P6PEMDPwAfCqbS8XM6gI7P7U6mVNTEeMq9sbBzM6w/1vwqgB93zQnT/KtZHUyERFxlGZ3wMi15jQZ507Cx8Pgs39A5lmrkzklFTHOLutP8/rohwPgbCoEtYCHV0PEAxr/IiLijqqFwUNfQdcowAZb3j8/BjLR4mDOR0WMMzuw1jz7snGG+bjjSBjxDdQMtzaXiIiULU9vuPk5GLoMKgVD6s/wzo0Q95z5x60AKmKc058nzNOH798Ox3+ByrXh3kXQZwp4+1mdTkRErpZrr4dH15tjII0c+D4GZnaBg+usTuYUVMQ4E8OA3f+DGZ3M04cA7R+C0RuhcW9rs4mIiDUqBsLA9+Gej6ByLTi+37xDdfkT5h+95ZiKGGdx9CeY9zdYeJ856VG1BvDA53D7VHNGRxERKd/Cb4PRP5hjIgHiY+GtCNj8XrldSFJFjNXOHjdXMs2788jTxxzM9ej3UL+r1elERMSZ+AWYd6cO+x8ENjJv+PjfWHi7OxxYY3W6q05FjFWyM2DjLJjWFn6cbV7rDL/drLJvfg68K1idUEREnFVYNxi1wVwR2y8AjuwwF5JccB+k7rM63VXjZXWAcic7ExI+gLVvQPrv5r6gFtD7ZXMAl4iISHF4epsrYrcaCKsnw6Z34af/wc8roNUg6P5/5rIGbkxFzNWSkwWJH8Ka1+Dk+amkK9eG65+CdkPBw9PafCIi4pr8q8Gtr0L74fD187DnC9g6H7YtgtaDofuTbjuzu4qYspZxChLmmXO9nEgy91UKhm5R0G6YbpkWERHHqBkO9y6AQ/Gw+hXYuxIS58G2BdBiAESOhlqtrE7pUCpiysqJ3+DHtyH+fchIN/dVrAldx0L7BzXmRUREykadCLjvY/h9M6x6GfZ/YxYy2xZAWHeIfAwa9jTXa3JxKmIcKTcXDq41b3vbtcwcrAvmCPLIUdDqHvDxtzSiiIiUE3Xbw5Al5pmZDTNg51LzDqYDa8zfSx1GmONp/KtZnbTEVMQ4Qvphc7xLwjz44+CF/W5W8YqIiAuqEwED3oWbnzfvho1/H9L2wpdPQ9yz0LQvtBsC9bu73O8qFTEldS4dfv4CdiyGfXFg5Jr7fatAywEQ8aDbXXsUEREXVjUEer1o3lCydYE5M3zKdtjxiflRNdS8q6nF36BmU6vTFouKGHtknoE9X8KOJbA3DnIyLnytXmfzLqNm/XTJSEREnJdvZbju7+bH4UTYMhe2fwInfoU1/zY/ajQ1i5nmf4PqDa1OfEkqYq7k5O+w5ytzlPcv30H2RauHBjY0/4FbDYTqjazLKCIiUhK125gfvf5lzjGzYwns+xqO7YZVL5kfNcLN9fsa9YaQjuDpPKWD8yRxFpln4bcfzIFPe1easyBerGroheo0uCXYbNbkFBERcRQff/MP8lYD4c8/4KfPzYLml9Vw7Cfz4/s3wa8qNLwZGtxozhpctZ6lsVXEZJ6BwwnmsuYH1sDvmyAn86IGNgi5Dhr1gsa3QFBzFS4iIuK+KlwDbe83P/78A/Z9Y16R2BdnPs4bQwPmH/Zh3c2Pep0gIOSq/o4sX0VMbi78ccC8d/73H82CJWXHhVuh81SpA/W7mZVmw5vNZdBFRETKmwrXmDertBxgrpT9+2bzKsWBNeat2yd+NZfSSfjAbF8p2Ly1O+Q6qNsBgluBb6Uyi+e+RUzGaUjdY14OStkOydvM7czThdtWrgX1Ii9Uk9Wu1dkWERGRi3l4Qr2O5geYM9InbYQD35lXM1K2w+kUc2zNT/87/ySbuX5TcMvzH63NmYWr1HHI71m3K2I8PxkGZw9eWJ+oUANfqNX6fJXY3qwUA+pe1YwiIiIuz7cyNOppfoA5pjR5q3mV4/cfzbM2p5IhbZ/5sXPphef6VDZviKnRBA/fkBJHKFERM2PGDF599VWSk5Np3rw5MTExdOvW7ZLtv/vuO6Kioti5cye1a9fmqaeeYuTIkQXaLF68mIkTJ7J//34aNGjASy+9xJ133ml3No+Da8D3fHXnX90cwxLc0ixcgluasxQ60chqERERt+DjD6GR5kee08cgZZt5libvc9p+yDwFh7fA4S14Zhgl/pZ2/zZfuHAhY8aMYcaMGXTp0oW3336bPn36sGvXLurVKzxK+cCBA9x66638/e9/Z968eXz//feMGjWKGjVqcNdddwGwYcMGBg0axIsvvsidd97J0qVLGThwIOvWraNjx4525cvu+TI0iIAaTVx6KmURERGXV6kGNLzJ/MiTnQnHf4HUn+HYHnIPbgU+LNHh7S5i3njjDYYPH86IESMAiImJ4auvvmLmzJlMnjy5UPtZs2ZRr149YmJiAGjatCmbN2/mtddeyy9iYmJi6NmzJ9HR0QBER0fz3XffERMTw/z58+3KZ7QeDIEaiCsiIuKUvHzMcTE1wwHIaZFGSYsYuxZJyMzMJD4+nl69ehXY36tXL9avX1/kczZs2FCofe/evdm8eTNZWVmXbXOpY4qIiIjYdSYmNTWVnJwcgoKCCuwPCgoiJSWlyOekpKQU2T47O5vU1FRq1ap1yTaXOiZARkYGGRkXpv0/efIkAMePH7fnJclfZGVlcfbsWdLS0vD29rY6jktTXzqO+tIx1I+Oo750nLzf24Zh/9iYEo1wtf3ltijDMArtu1L7v+6395iTJ09m0qRJhfY3btz40sFFRETEKaWlpREQEGDXc+wqYqpXr46np2ehMyRHjx4tdCYlT3BwcJHtvby8CDw/duVSbS51TDDHzURFReU/PnHiBKGhoSQlJdndCXJBeno6ISEh/Pbbb1SpUsXqOC5Nfek46kvHUD86jvrScU6ePEm9evWoVs3+m3HsKmJ8fHyIiIggLi6uwO3PcXFx9OvXr8jnREZGsnz58gL7Vq5cSfv27fNPwUVGRhIXF8fYsWMLtOncufMls/j6+uLr61tof0BAgN5QDlClShX1o4OoLx1HfekY6kfHUV86joeHXcN0gRJcToqKimLIkCG0b9+eyMhIZs+eTVJSUv68L9HR0Rw6dIi5c+cCMHLkSP7zn/8QFRXF3//+dzZs2MC7775b4K6jJ554gu7duzNlyhT69evHsmXL+Prrr1m3bp3dL0hERETKB7uLmEGDBpGWlsYLL7xAcnIyLVq0YMWKFYSGhgKQnJxMUlJSfvuwsDBWrFjB2LFjmT59OrVr12batGn5t1cDdO7cmQULFjBhwgQmTpxIgwYNWLhwod1zxIiIiEj5UaKBvaNGjWLUqFFFfi02NrbQvuuvv54tW7Zc9pgDBgxgwIABJYkDmJeXnnvuuSIvMUnxqR8dR33pOOpLx1A/Oo760nFK05c2oyT3NImIiIhYzP5RNCIiIiJOQEWMiIiIuCQVMSIiIuKSVMSIiIiIS3LLIuaOO+6gXr16+Pn5UatWLYYMGcLhw4etjuVyDh48yPDhwwkLC6NChQo0aNCA5557jszMTKujuZyXXnqJzp074+/vT9WqVa2O41JmzJhBWFgYfn5+REREsHbtWqsjuaQ1a9bQt29fateujc1m49NPP7U6kkuaPHkyHTp0oHLlytSsWZP+/fvz888/Wx3L5cycOZNWrVrlTxYYGRnJF198Yfdx3LKI6dGjB4sWLeLnn39m8eLF7N+/v1S3b5dXP/30E7m5ubz99tvs3LmTqVOnMmvWLJ555hmro7mczMxM7r77bh599FGro7iUhQsXMmbMGMaPH09CQgLdunWjT58+BeaikuI5c+YMrVu35j//+Y/VUVzad999x+jRo9m4cSNxcXFkZ2fTq1cvzpw5Y3U0l1K3bl1eeeUVNm/ezObNm7nxxhvp168fO3futOs45eIW688++4z+/fuTkZGh1UZL6dVXX2XmzJn88ssvVkdxSbGxsYwZM4YTJ05YHcUldOzYkXbt2jFz5sz8fU2bNqV///5MnjzZwmSuzWazsXTpUvr37291FJd37NgxatasyXfffUf37t2tjuPSqlWrxquvvsrw4cOL/Ry3PBNzsePHj/Phhx/SuXNnFTAOcPLkyRIt0iVir8zMTOLj4+nVq1eB/b169WL9+vUWpRIp6OTJkwD6uVgKOTk5LFiwgDNnzhAZGWnXc922iHn66aepWLEigYGBJCUlsWzZMqsjubz9+/fz1ltv5a+TJVKWUlNTycnJKbSafVBQUKFV70WsYBgGUVFRdO3alRYtWlgdx+Vs376dSpUq4evry8iRI1m6dCnNmjWz6xguU8Q8//zz2Gy2y35s3rw5v/3//d//kZCQwMqVK/H09GTo0KGUgytnxWJvXwIcPnyYW265hbvvvpsRI0ZYlNy5lKQfxX42m63AY8MwCu0TscJjjz3Gtm3bCixoLMXXpEkTEhMT2bhxI48++ijDhg1j165ddh2jRGsnWeGxxx7jnnvuuWyb+vXr529Xr16d6tWr07hxY5o2bUpISAgbN260+1SVO7K3Lw8fPkyPHj3yVy0Xk739KPapXr06np6ehc66HD16tNDZGZGr7fHHH+ezzz5jzZo11K1b1+o4LsnHx4eGDRsC0L59ezZt2sSbb77J22+/XexjuEwRk1eUlETeGZiMjAxHRnJZ9vTloUOH6NGjBxEREcyZMwcPD5c5eVfmSvOelCvz8fEhIiKCuLg47rzzzvz9cXFx9OvXz8JkUp4ZhsHjjz/O0qVLWb16NWFhYVZHchuGYdj9e9plipji+vHHH/nxxx/p2rUr11xzDb/88gvPPvssDRo00FkYOx0+fJgbbriBevXq8dprr3Hs2LH8rwUHB1uYzPUkJSVx/PhxkpKSyMnJITExEYCGDRtSqVIla8M5saioKIYMGUL79u3zzwQmJSVpXFYJnD59mn379uU/PnDgAImJiVSrVo169epZmMy1jB49mo8++ohly5ZRuXLl/DOFAQEBVKhQweJ0ruOZZ56hT58+hISEcOrUKRYsWMDq1av58ssv7TuQ4Wa2bdtm9OjRw6hWrZrh6+tr1K9f3xg5cqTx+++/Wx3N5cyZM8cAivwQ+wwbNqzIfly1apXV0Zze9OnTjdDQUMPHx8do166d8d1331kdySWtWrWqyPfgsGHDrI7mUi71M3HOnDlWR3MpDz30UP7/6xo1ahg33XSTsXLlSruPUy7miRERERH3owEOIiIi4pJUxIiIiIhLUhEjIiIiLklFjIiIiLgkFTEiIiLiklTEiIiIiEtSESMiIiIuSUWMiFxVNpuNTz/91OoYV9XBgwex2Wz5MzWLiGOoiBFxEkePHuWRRx6hXr16+Pr6EhwcTO/evdmwYUOxjxEbG0vVqlXLLqQDJCcn06dPH6tjXFFRxdbzzz9PmzZt7D5WSEgIycnJtGjRwjHhRARww7WTRFzVXXfdRVZWFu+//z7XXnstR44c4ZtvvuH48eNWR3Oo8rjulqenZ7l83SJlzuELIoiI3f744w8DMFavXn3Zdq+//rrRokULw9/f36hbt67x6KOPGqdOnTIMo+i1cZ577jnDMMz1XpYuXVrgWAEBAfnrvRw4cMAAjMWLFxs33HCDUaFCBaNVq1bG+vXr89vPmTPHCAgIML788ksjPDzcqFixotG7d2/j8OHD+W1+/PFH4+abbzYCAwONKlWqGN27dzfi4+MLfN+Ls2RkZBijR482goODDV9fXyM0NNR4+eWXC7SdNWuWcdtttxkVKlQwwsPDjfXr1xt79+41rr/+esPf39/o1KmTsW/fvvznDBs2zOjXr1+B7/nEE08Y119/ff7j0NBQY+rUqQXatG7dOr+/QkNDC/RjaGhokWuJzZkzJ7/vEhIS8o+V9++ZtzbWX9tkZ2cbDz30kFG/fn3Dz8/PaNy4sRETE2NczqRJk4xatWoZqamp+fv69u1rdOvWzcjJybnsc0XclS4niTiBSpUqUalSJT799NPLLkXv4eHBtGnT2LFjB++//z7ffvstTz31FACdO3cmJiaGKlWqkJycTHJyMk8++aRdOcaPH8+TTz5JYmIijRs3ZvDgwWRnZ+d//ezZs7z22mt88MEHrFmzhqSkpALf49SpUwwbNoy1a9eyceNGGjVqxK233sqpU6eK/H7Tpk3js88+Y9GiRfz888/MmzeP+vXrF2jz4osvMnToUBITEwkPD+fee+/lkUceITo6ms2bNwPw2GOP2fU6r2TTpk0AzJkzh+TkZDZt2sSgQYP45z//SfPmzfP7d9CgQSU6fm5uLnXr1mXRokXs2rWLZ599lmeeeYZFixZd8jnjx4+nfv36jBgxAoBZs2axZs0aPvjgAzw89KNcyiddThJxAl5eXsTGxvL3v/+dWbNm0a5dO66//nruueceWrVqld9uzJgx+dthYWG8+OKLPProo8yYMQMfHx8CAgKw2WwlvnTx5JNPcttttwEwadIkmjdvzr59+wgPDwcgKyuLWbNm0aBBA8AsHl544YX85994440Fjvf2229zzTXX8N1333H77bcX+n5JSUk0atSIrl27YrPZCA0NLdTmwQcfZODAgQA8/fTTREZGMnHiRHr37g3AE088wYMPPlii13spNWrUAKBq1aoF+rJSpUp4eXmV+tKQt7c3kyZNyn8cFhbG+vXrWbRoUf5r/StPT0/mzZtHmzZtGDduHG+99RazZ88uss9EyguV7yJO4q677uLw4cN89tln9O7dm9WrV9OuXTtiY2Pz26xatYqePXtSp04dKleuzNChQ0lLS+PMmTMOyXBxwVSrVi3AHHCcx9/fP7+AyWtz8dePHj3KyJEjady4MQEBAQQEBHD69GmSkpKK/H4PPPAAiYmJNGnShH/84x+sXLnyspmCgoIAaNmyZYF9586dIz093d6Xa6lZs2bRvn17atSoQaVKlXjnnXcu2U95rr32Wl577TWmTJlC3759ue+++65SWhHnpCJGxIn4+fnRs2dPnn32WdavX88DDzzAc889B8Cvv/7KrbfeSosWLVi8eDHx8fFMnz4dMM+QXI7NZsMwjAL7inqOt7d3geeAeemjqK8XddwHHniA+Ph4YmJiWL9+PYmJiQQGBpKZmVlkrnbt2nHgwAFefPFF/vzzTwYOHMiAAQOumOlyOT08PK74WovTprjyLuVcfLwrHWvRokWMHTuWhx56iJUrV5KYmMiDDz54yX662Jo1a/D09OTgwYMFLvWJlEcqYkScWLNmzfLPsmzevJns7Gxef/11OnXqROPGjTl8+HCB9j4+PuTk5BQ6To0aNUhOTs5/vHfvXs6ePevwvGvXruUf//gHt956K82bN8fX15fU1NTLPqdKlSoMGjSId955h4ULF7J48eJS3ZH119cKFJqf5a9t0tPTOXDgQIE23t7ehfqyqP7Nu/R08fGuNB/M2rVr6dy5M6NGjaJt27Y0bNiQ/fv3X/Y5AAsXLmTJkiWsXr2a3377jRdffPGKzxFxZypiRJxAWloaN954I/PmzWPbtm0cOHCAjz/+mH//+9/069cPgAYNGpCdnc1bb73FL7/8wgcffMCsWbMKHKd+/fqcPn2ab775htTU1PxC5cYbb+Q///kPW7ZsYfPmzYwcObLQWRVHaNiwIR988AG7d+/mhx9+4L777qNChQqXbD916lQWLFjATz/9xJ49e/j4448JDg4u1Vw3N954I5s3b2bu3Lns3buX5557jh07dhRq88EHH7B27Vp27NjBsGHD8PT0LNCmfv36fPPNN6SkpPDHH3/k7ztw4ACJiYmkpqaSkZFBhQoV6NSpE6+88gq7du1izZo1TJgw4bIZGzZsyObNm/nqq6/Ys2cPEydOzB9MfCm///47jz76KFOmTKFr167ExsYyefJkNm7cWIJeEnETlt4bJSKGYRjGuXPnjHHjxhnt2rUzAgICDH9/f6NJkybGhAkTjLNnz+a3e+ONN4xatWoZFSpUMHr37m3MnTvXAIw//vgjv83IkSONwMDAArdYHzp0yOjVq5dRsWJFo1GjRsaKFSuKvMX6crcJ591ifbGlS5caF/8Y2bJli9G+fXvD19fXaNSokfHxxx8Xup2Zi26xnj17ttGmTRujYsWKRpUqVYybbrrJ2LJlS5FtL5Uz79byi/vg2WefNYKCgoyAgABj7NixxmOPPVbgFuuTJ08aAwcONKpUqWKEhIQYsbGxBW6xNgzD+Oyzz4yGDRsaXl5eRmhoaP6/01133WVUrVo1/xZrwzCMXbt2GZ06dTIqVKhgtGnTxli5cuVlb7E+d+6c8cADDxgBAQFG1apVjUcffdQYN26c0bp1a6Moubm5xk033WT07t3byM3Nzd8/duxYo0GDBvm32YuUNzbD+MuFYRERcaiff/6Z8PBw9u7dS8OGDa2OI+I2dDlJRKQMHT9+nE8++YQqVaoQEhJidRwRt6J5YkREytDw4cOJj49n5syZ+Pr6Wh1HxK3ocpKIiIi4JF1OEhEREZekIkZERERckooYERERcUkqYkRERMQlqYgRERERl6QiRkRERFySihgRERFxSSpiRERExCWpiBERERGX9P/m7RCx38oY3AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_min = -3\n",
    "x_max = 3\n",
    "\n",
    "loc = 0 \n",
    "scale = 1\n",
    "\n",
    "x = np.linspace(x_min, x_max, 100)\n",
    "y = norm.pdf(x, loc, scale)\n",
    "\n",
    "plt.plot(x, y, color='C1')\n",
    "plt.grid()\n",
    "plt.xlim(x_min, x_max)\n",
    "plt.ylim(0, 0.40)\n",
    "plt.title('Normaalijakauma')\n",
    "plt.xlabel('Satunnaismuuttuja x')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On tärkeää huomata, että normaalijakauman yhteydessä ei ole mielekästä puhua yksittäisen satunnaismuuttujan arvon todennäköisyydestä. Normaalijakaumaa noudattava satunnaismuuttujahan on jatkuva eli voi saada mitä tahansa arvoja tietyltä väliltä. Tällöin yksittäiseen arvoon liittyvä todennäköisyys on ainakin teoreettisesti ajatellen 0. Yksittäisten arvojen todennäköisyyksien sijasta on mielekästä puhua kertymätodennäköisyyksistä ja kertymätodennäköisyyksien avulla voidaan edelleen laskea erilaisten välien todennäköisyyksiä.\n",
    "Käytännössä normaalijakaumaa hyödynnetään kertymätodennäköisyyksien avulla. Satunnaismuuttujan arvoon x liittyvä kertymätodennäköisyys on kohdan x vasemmalle puolella oleva käyrän alapuolelle jäävä pinta-ala.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.FillBetweenPolyCollection at 0x21f3d89be00>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_min = -3\n",
    "x_max = 3\n",
    "\n",
    "loc = 0 \n",
    "scale = 1\n",
    "\n",
    "x = np.linspace(x_min, x_max, 100)\n",
    "y = norm.pdf(x, loc, scale)\n",
    "\n",
    "plt.plot(x, y, color='C1')\n",
    "plt.grid()\n",
    "plt.xlim(x_min, x_max)\n",
    "plt.ylim(0, 0.40)\n",
    "plt.title('Kertymätodennäköisyys')\n",
    "plt.xlabel('Satunnaismuuttuja x')\n",
    "\n",
    "x1 = -3\n",
    "x2 = -1\n",
    "r1 = np.linspace(x1, x2, 30)\n",
    "r2 = norm.pdf(r1, loc, scale)\n",
    "plt.fill_between(r1, r2, color='C0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esimerkki. Älykkyysosamäärää voidaan mitata tarkoitusta varten laaditulla testillä. Testin pisteytys on skaalattu sellaiseksi, että amerikkalaisten älykkyysosamäärä noudattaa likimain normaalijakaumaa N(100,16). Merkintä N(100, 16) tarkoittaa normaalijakaumaa, jonka odotusarvo on 100 ja keskihajonta 16."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.10564977366685535)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loc = 100\n",
    "scale = 16\n",
    "norm.cdf(80, loc, scale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.FillBetweenPolyCollection at 0x21f3da247d0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_min = 50\n",
    "x_max = 150\n",
    "\n",
    "loc = 100\n",
    "scale = 16\n",
    "\n",
    "x = np.linspace(x_min, x_max, 100)\n",
    "y = norm.pdf(x, loc, scale)\n",
    "\n",
    "plt.plot(x, y, color='C1')\n",
    "plt.grid()\n",
    "plt.xlim(x_min, x_max)\n",
    "plt.ylim(0, 0.030)\n",
    "plt.xlabel('Älykkyysosamäärä')\n",
    "\n",
    "x1 = 50\n",
    "x2 = 80\n",
    "r1 = np.linspace(x1, x2, 30)\n",
    "r2 = norm.pdf(r1, loc, scale)\n",
    "plt.fill_between(r1, r2, color='C0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yllä on laskettu älykkyysosamäärään 80 liittyvä kertymätodennäköisyys. Tämän mukaan noin 10,6 % amerikkalaisista on älykkyysosamäärältään alle 80. Vastaavasti noin 89,4 % amerikkalaisista on älykkyysosamäärältään yli 80.\n",
    "\n",
    "Todennäköisyys, että henkilön älykkyysosamäärä on täsmälleen 80, on ainakin teoreettisesti ajatellen 0. Käytännössä näin ei ehkä ole, koska älykkyysosamäärätestin pisteytys ei ole mielivaltaisen tarkkaa. Jos esimerkiksi testi pisteytetään pisteen tarkkuudella, niin todennäköisyys älykkyysosamäärälle 80 on varmasti selvästi nollasta poikkeava. Normaalijakaumaa käytettäessä kuitenkin oletetaan yksittäiseen satunnaismuuttujan arvoon liittyväksi todennäköisyydeksi 0.\n",
    "\n",
    "Todennäköisyys, että henkilön älykkyysosamäärä on välillä 80 - 110 saadaan selville vähentämällä arvoon 110 liittyvästä kertymätodennäköisyydestä arvoon 80 liittyvä kertymätodennäköisyys: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.6283646972844441)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm.cdf(110, loc, scale) - norm.cdf(80, loc, scale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.FillBetweenPolyCollection at 0x21f3da89a90>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_min = 50\n",
    "x_max = 150\n",
    "\n",
    "loc = 100\n",
    "scale = 16\n",
    "\n",
    "x = np.linspace(x_min, x_max, 100)\n",
    "y = norm.pdf(x, loc, scale)\n",
    "\n",
    "plt.plot(x, y, color='C1')\n",
    "plt.grid()\n",
    "plt.xlim(x_min, x_max)\n",
    "plt.ylim(0, 0.030)\n",
    "plt.xlabel('Älykkyysosamäärä')\n",
    "\n",
    "x1 = 80\n",
    "x2 = 110\n",
    "r1 = np.linspace(x1, x2, 30)\n",
    "r2 = norm.pdf(r1, loc, scale)\n",
    "plt.fill_between(r1, r2, color='C0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yllä lasketun mukaan noin 62,8 % amerikkalaisista on älykkyysosamäärältään välillä 80 - 110.\n",
    "\n",
    "Entäpä jos halutaan tietää minkä älykkyysosamäärän ylittää älykkäin kymmenesosa amerikkalaisista? Kysytään siis satunnaismuuttujan arvoa, jonka ylittää 10 % amerikkalaisista eli kysytään satunnaismuuttujan arvoa, jonka alittaa 90 % amerikkalaisista: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(120.5048250487136)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm.ppf(0.90, loc, scale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.FillBetweenPolyCollection at 0x21f3e17ad50>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AyzvNla8DSZM4s1ADs3Db97O1eUQkl4oaESk+hgHzH4Wju8xFKW/6l7m2UqDp+Ag06m7OiPrsfsjJsjqRiKCiRkSK0w/T4JeFYA8yZwyFVLA6Ucmw2+G2KVA+Eg78Al+NtDqRiKCiRkSKS/pP8NXT5nb358xlEAJZ+epwxzuADTZ8AD/NtTqRSJmnokZELl3OCfNjGE+2eaO6q4Zanah0xFwLXf5ubi8YrhvziVhMRY2IXLovn4SMrVA+Cm6bHJjX0ZzLtaOgTgfIzoTPBoHHZXUikTJLRY2IXJptCZD0MWCD3u9CeITViUqXwwm934PQSrBnPayaZHUikTJLRY2IXLzsY+bHLmB+5NTgGkvjWKZyXeg5ztxe9ipkbLM2j0gZpaJGRC7eN89D5m6oXA+uf8bqNNZq1Rca3mBeVzR/GHi9VicSKXNU1IjIxUldA2vfNbdvfgOCw63NYzWbDW6eCEHhkLoK1k+3OpFImaOiRkSKzp1t3mQPA9rcCw2vszqRb6hcF274p7mdMAaO7rE0jkhZo6JGRIruu/HmatXhNSDuRavT+Jb2D0LtKyHnGPwvXoteipQiFTUiUjT7tsDKCeZ2r3EQVtXaPL7G7oBb3jTvqrz1K92UT6QUqagRkcLzes2PnbxuuOxGuPw2qxP5phrN4JrHze0vnzIX+RSREqeiRkQKb+PH5r1YQirCjePL1k32iqpzPFRvBlkZsPRlq9OIlAkqakSkcE4dNadwA3R9CipGW5vH1zmDzY/nwFzoc98Wa/OIlAEqakSkcJaPgxMHoFpjaP9/VqfxDw2ugWa3gOE1P4bSRcMiJUpFjYhcWMY2WDPV3P7LWHMUQgon7gVwhMCOFZC8wOo0IgFNRY2IXNhXo8yLgxv3gMbdrU7jX6rUh6uHmdtLngHXSUvjiAQyFTUicn5bF8NvCeYU5b+MtTqNf+o8AirWgiOpsOotq9OIBCwVNSJybu4cc5QG4KqHoVpDa/P4q+Bw6H76IuuVE3SnYZESoqJGRM5tzVQ4tN28c/A1T1idxr+16A11O4IrC75+1uo0IgFJRY2IFOxEhjnjCaDbGAitaGkcv2ezQc9XARts/hR2rbM6kUjAUVEjIgVb8bq5flHN1tC6n9VpAkPN1nDFveb212M0xVukmKmoEZH8jqTCuvfM7W5jwK5/KorNtaPMKd47V8JvX1udRiSg6F8qEclv6cvgyYEGXaHh9VanCSyVakOH0zcv/Po5cz0tESkWKmpEJK99W2DTbHO72xhLowSszvEQUgn2bYafPrM6jUjAUFEjInl98zxgmCtw12prdZrAFFYVOj9mbn/7ojl1XkQumYoaETlrZyJs/QpsDrh+tNVpAluHIVA+Eo7shPUzrE4jEhBU1IiIyTDO3j+lbX+IaGRtnkAXHG6udg7w3TjIPm5tHpEAoKJGREy/fgm71oCzHHQdaXWasqHtAKja0Fz9PPFtq9OI+D0VNSICXs/pa2mAq4ZAxZrW5ikrHEFw/T/M7VWTzBseishFU1EjIrBlHhxIhtBKcPVwq9OULZffBjXbQM5xWPWm1WlE/JqKGpGyzus5uxxCx0egXGVL45Q5drt5Qz6Ate9qtEbkEqioESnrtsyDjF/NUZoOD1mdpmxq0sMcrXGd0GiNyCVQUSNSlv15lCa0krV5yiqbTaM1IsVARY1IWfbzFxql8RUarRG5ZCpqRMoqjdL4Fo3WiFyyiypqJk+eTIMGDQgNDSU2NpYVK1act/3y5cuJjY0lNDSUmJgYpk6dmuf5d999ly5dulClShWqVKlCt27dWLt27SWfV0TO4+cv4MAvGqXxJRqtEbkkRS5q5syZw/Dhw3nmmWdISkqiS5cu9OzZk9TU1ALbp6Sk0KtXL7p06UJSUhJPP/00w4YNY+7cubltli1bRr9+/Vi6dCmJiYnUrVuXuLg49uzZc9HnFZHz+OMozVV/0yiNr7DZ4NrTNz7UaI1IkRW5qJkwYQIPPPAAgwcPplmzZkycOJE6deowZcqUAttPnTqVunXrMnHiRJo1a8bgwYMZNGgQ48ePz20zc+ZMhg4dSps2bWjatCnvvvsuXq+Xb7755qLPKyLnoVEa39XkL1CztUZrRC6CsyiNc3JyWL9+PSNH5r2FelxcHKtWrSrwNYmJicTFxeXZ16NHD6ZNm4bL5SIoKCjfa7KysnC5XFStWvWizwuQnZ1NdnZ27uPMzEwAXC4XLpfrPO/Ud53J7a/5A4nf9oXhxbnsVWyAp/0QvM5w8Lf3UAC/7Y8C2Do/gfPT+zDWvou7/cMQVs3qSEUSSH3h7wKlLwqbv0hFTUZGBh6Ph8jIyDz7IyMjSU9PL/A16enpBbZ3u91kZGRQs2b+27GPHDmSWrVq0a1bt4s+L8DYsWN57rnn8u1fsmQJYWFh53ydP0hISLA6gpzmb31R88g62mf8issRxpLD9XEvWmR1pGLlb/1RIMOga7n6VD65g98/eYJfovtYneiiBERfBAh/74usrKxCtStSUXOGzWbL89gwjHz7LtS+oP0A48aNY9asWSxbtozQ0NBLOu+oUaOIj4/PfZyZmUmdOnWIi4ujYsWK53ydL3O5XCQkJNC9e/cCR7mk9PhlXxgGjvcnAGC/aghx195pcaDi45f9cR62hl6Yez9Nji4npv8bEFLB6kiFFmh94c8CpS/OfNJyIUUqaiIiInA4HPlGR/bv359vFOWMqKioAts7nU6qVcs7pDp+/Hhefvllvv76a1q1anVJ5wUICQkhJCQk3/6goCC/7lwIjPcQKPyqL7YvhbSN4CyHo9PfcPhL7iLwq/44n+a3wbKXsR3cRtCmj+Dqx6xOVGQB0xcBwN/7orDZi3ShcHBwMLGxsfmGsRISEujUqVOBr+nYsWO+9kuWLKFdu3Z5Qr722mu88MILfPXVV7Rr1+6SzysiBVjxuvm97QAIj7A2i5yf3Q6dh5vbiW+D65SlcUT8QZFnP8XHx/Pee+8xffp0kpOTGTFiBKmpqQwZMgQwP/IZMGBAbvshQ4awc+dO4uPjSU5OZvr06UybNo3HH388t824ceP4xz/+wfTp06lfvz7p6emkp6dz/PjxQp9XRC5g9w+wYwXYndDpUavTSGG0vAsq1obj+2DjTKvTiPi8Il9T07dvXw4ePMjzzz9PWloaLVq0YNGiRdSrVw+AtLS0PPeOadCgAYsWLWLEiBG8/fbbREdHM2nSJHr37p3bZvLkyeTk5NCnT96L4Z599lnGjBlTqPOKyAWsMK+loVVfqFzH2ixSOM5gswD96in4/g1oOxAcF3UppEiZcFG/HUOHDmXo0KEFPjdjxox8+7p27cqGDRvOebwdO3Zc8nlF5Dz2J8Ov/wNscPVwq9NIUbQdAN+NgyM7zRXVWwXOxd0ixU1rP4mUBSv/ZX5vdhNUb2JtFima4DDo8LC5vXICeL3W5hHxYSpqRALd4R2w+TNzu3P8eZuKj2o/GIIrwP6fYdtiq9OI+CwVNSKBbtWbYHgg5jqo1dbqNHIxylWBKweZ2yteh9P3+hKRvFTUiASy4wdgw0fmdheN0vi1q/4GjhDYvQ52fm91GhGfpKJGJJCtfQc82VArFup3sTqNXIoKkdDmHnNbC12KFEhFjUigysmCde+a252GwXmWFBE/0fERwAZbv4IDv1qdRsTnqKgRCVQbZ8LJw1C5HjS72eo0UhwiGkHTG81tjdaI5KOiRiQQeT2Q+Ja53fERsDuszSPF58zdoH+cA8f2WZtFxMeoqBEJRL8sNKdyl6sCV9xrdRopTnWvgtrtwZNjXjMlIrlU1IgEGsOA7yeZ21cOhuBwa/NI8TszWrPuPcg+fv62ImWIihqRQJO6Gvb8YE7/bf9/VqeRktD0RqgaA6eOaKFLkT9QUSMSaM5cQNr6bihfw9osUjLsDuj4N3M78W3wuK3NI+IjVNSIBJKMbfDrInO74yPWZpGS1foeCKtmLnSZPN/qNCI+QUWNSCBJfAsw4LJeWrgy0AWHwZUPmtur3tTSCSKoqBEJHMcPwMZZ5vaZC0klsF05GJyhsHcD7FxldRoRy6moEQkUP0w3l0SIbgt1O1qdRkpD+ermtVMAqydbm0XEB6ioEQkE7mxzei+YF5BqSYSy46qh5vdf/geHfrc2i4jFVNSIBILNn8GJ/VCxFlx+q9VppDRVvwwadQMMWPNvq9OIWEpFjYi/MwxYPcXcbv8gOIKszSOl78xoTdLHcOqotVlELKSiRsTf7VgB+zZDUBjE/tXqNGKFhtdD9aaQcxw2fGR1GhHLqKgR8XeJpy8QbXOPudaTlD02G1z1sLm95t+6GZ+UWSpqRPzZwe2w9Stzu8PD1mYRa7Xqa96M72iquaCpSBmkokbEn62eAhjQ5C8Q0cjqNGKloHLQbpC5rendUkapqBHxVycPn13M8CqN0gjmzfjsQbBrDexeb3UakVKnokbEX234EFxZENkCGnS1Oo34ggpR0LKPua3RGimDVNSI+COPG9a8Y25f9bButidnnRm1+/kLOLrH0igipU1FjYg/+mUBZO6G8OrQoo/VacSX1GwN9TqD1332LtMiZYSKGhF/tHqq+b3dIAgKtTaL+J6rhpjf188A10lLo4iUJhU1Iv5mbxLsWm1eEHpmtovIH13WCyrVhZOHYPOnVqcRKTUqakT8zZn1fZrfbl4YKvJndoe5ZAaYo3qGYW0ekVKiokbEnxzfDz/NNbfPfMQgUpC2/c2lM/ZvMZfSECkDVNSI+JMfpoMnB2q3h1qxVqcRX1auCrTuZ25r9W4pI1TUiPgLdzasm2Zud3jI2iziH878d/LL/+DwDkujiJQGFTUi/mLLF3BiP1SoCZffanUa8QfVLzNX8MaAte9anUakxKmoEfEHhgFrppjbVz4AjiBr84j/6HD62qsNH0H2cWuziJQwFTUi/mDXWnMqtyMEYu+3Oo34k0bdoWpDyD4Km2ZZnUakRKmoEfEHa07fbK/VnRAeYW0W8S92+9lra9a+A16vtXlESpCKGhFfd3QP/Pxfc7uDpnHLRWjdD4IrQMZW+P1bq9OIlBgVNSK+7ofpYHig3tUQ1dLqNOKPQivCFfeZ22cWQhUJQCpqRHyZ6xSsf9/c1jRuuRRn7jC8bQkc3G5tFpESoqJGxJdt+RyyDkLF2nDZjVanEX9WraF50TCGVu+WgKWiRsRXGcbZO8Fe+QA4nNbmEf935pqspI81vVsCkooaEV+1ay2kbTSncbcdaHUaCQQNrz89vTtT07slIKmoEfFVa0+P0rS8E8KrWZtFAsOfp3dr9W4JMCpqRHxRZtofpnH/n7VZJLC07gfB5U9P715qdRqRYqWiRsQX/TAdvG6o2xFqtrY6jQSS0IrQ5l5zW9O7JcCoqBHxNe7ss9O422uURkrAmf+utn4Fh1KszSJSjFTUiPiaLV/AiQNQIRqa3Wx1GglEEY2gUTc0vVsCjYoaEV9z5gLhKwdpNW4pOe1PXzCs1bslgKioEfElu3+APevBEQxt/2p1GglkjbpB1Rhz9e4f51idRqRYqKgR8SVnbrbXog+Ur25tFglsdjtceXrphLXvanq3BISLKmomT55MgwYNCA0NJTY2lhUrVpy3/fLly4mNjSU0NJSYmBimTp2a5/ktW7bQu3dv6tevj81mY+LEifmOMWbMGGw2W56vqKioi4kv4puO7YMt88xtTeOW0nDFvRAUDgeSIeU7q9OIXLIiFzVz5sxh+PDhPPPMMyQlJdGlSxd69uxJampqge1TUlLo1asXXbp0ISkpiaeffpphw4Yxd+7c3DZZWVnExMTwyiuvnLdQad68OWlpablfmzdvLmp8Ed+1fgZ4XVC7PURfYXUaKQtCK0Gbfub2Wk3vFv9X5KJmwoQJPPDAAwwePJhmzZoxceJE6tSpw5QpUwpsP3XqVOrWrcvEiRNp1qwZgwcPZtCgQYwfPz63zZVXXslrr73G3XffTUhIyDnP7XQ6iYqKyv2qXl3D8xIg3DnmvWlAq3FL6TozvfvXRXCk4D9ORfxFkVbIy8nJYf369YwcOTLP/ri4OFatWlXgaxITE4mLi8uzr0ePHkybNg2Xy0VQUOFnd2zbto3o6GhCQkLo0KEDL7/8MjExMedsn52dTXZ2du7jzMxMAFwuFy6Xq9Dn9SVncvtr/kBSnH1h2zIP5/F0jPAauBv3BPVvkel34yJVjsHRoCv2lOV41ryD9/pnL/mQ6gvfESh9Udj8RSpqMjIy8Hg8REZG5tkfGRlJenp6ga9JT08vsL3b7SYjI4OaNWsW6twdOnTgww8/pEmTJuzbt48XX3yRTp06sWXLFqpVK3hdnLFjx/Lcc8/l279kyRLCwsIKdV5flZCQYHUEOa04+qLL1nFUBX6tcDW/Lv760kOVYfrdKLoo2xV0YDmetdNZktUKj/3cI+ZFob7wHf7eF1lZWYVqV6Si5gybzZbnsWEY+fZdqH1B+8+nZ8+eudstW7akY8eONGzYkA8++ID4+PgCXzNq1Kg8z2VmZlKnTh3i4uKoWLFioc/tS1wuFwkJCXTv3r1Io1xS/IqtL9I2EpT0G4Y9iIZ9X6Rh+cgLv0by0e/GJfD2wJj8OcFHU/lLreMYV9x+SYdTX/iOQOmLM5+0XEiRipqIiAgcDke+UZn9+/fnG405IyoqqsD2TqfznCMshREeHk7Lli3Ztm3bOduEhIQUeI1OUFCQX3cuBMZ7CBSX3BfrzWtpbM1vI6hK7WJKVXbpd+NiBEH7ByFhNM710+DK+6EIf3Se86jqC5/h731R2OxFulA4ODiY2NjYfMNYCQkJdOrUqcDXdOzYMV/7JUuW0K5du0v6AWdnZ5OcnFzoj69EfNKJDPjp9EzA9rpAWCx0xX3gLAf7foKdBV8jKeLrijz7KT4+nvfee4/p06eTnJzMiBEjSE1NZciQIYD5kc+AAQNy2w8ZMoSdO3cSHx9PcnIy06dPZ9q0aTz++OO5bXJycti4cSMbN24kJyeHPXv2sHHjRn777bfcNo8//jjLly8nJSWFNWvW0KdPHzIzMxk4cOClvH8Ra62fAZ5siG4LtdtZnUbKsrCq0Oouc/vMUh0ifqbI19T07duXgwcP8vzzz5OWlkaLFi1YtGgR9erVAyAtLS3PPWsaNGjAokWLGDFiBG+//TbR0dFMmjSJ3r1757bZu3cvV1xx9r4c48ePZ/z48XTt2pVly5YBsHv3bvr160dGRgbVq1fnqquuYvXq1bnnFfE7HnfeadzFMNwvckk6PAQbPoDkhXB0N1TSx6HiXy7qQuGhQ4cydOjQAp+bMWNGvn1du3Zlw4YN5zxe/fr1cy8ePpfZs2cXKaOIz/tlIWTugbAIaH5pF2aKFIvI5lCvM+xcaRbcN/zT6kQiRaK1n0Sscmadp3aDwFk8U2hFLtmZmz/+8D64TlqbRaSIVNSIWCHtR0hdBXanWdSI+IrLekHF2nDy0NmL2EX8hIoaESucuRDz8luhombwiQ9xOKH9YHN7zb+1erf4FRU1IqXtxEH48VNzW9O4xRe1HQjOUEj/EVJXW51GpNBU1IiUtg0fmNO4a7aBOu2tTiOSX1hVaHmnua3p3eJHVNSIlCaPG9a9Z253GKJp3OK7zlww/PN8OLrH2iwihaSiRqQ0/XEad4s7rE4jcm5RLc3p3YYHfphmdRqRQlFRI1Ka1r5jfm93v6Zxi+/r8H/m9/UzwHXK0igihaGiRqS0pP0IO7/XNG7xH5fdaE7vzjqo6d3iF1TUiJSWMxdcNrsFKkZbm0WkMPJM756q6d3i81TUiJSGEwdh82fmdgdN4xY/ound4kdU1IiUhg0zwH0KaraGOh2sTiNSeH+c3r1mqrVZRC5ARY1ISfO4YO2ZadwPaxq3+J+rHja/Jy8wV+8W8VEqakRKWvJ8OLYXwmtoGrf4p8jmUL+LOb177btWpxE5JxU1IiVt9RTzu1bjFn92ZrRm/QzIOWFpFJFzUVEjUpJ2r4fd68AepGnc4t+a/AUq14NTR+DHOVanESmQihqRkrTm9ChNi95QIdLaLCKXwu44O3NPq3eLj1JRI1JSMtNgyzxz+6oh1mYRKQ5X3AfB5eHAL/D7UqvTiOSjokakpPwwDbxuqNsRoq+wOo3IpQutBG3uMbdXa3q3+B4VNSIlwXUKfnjf3O6gURoJIO1PfwS1bTEc3G5tFpE/UVEjUhJ++gyyMqBSHWh6k9VpRIpPRCNoHGdur/m3tVlE/kRFjUhxM4yzQ/NXDjbXzxEJJGdGHzfOhFNHrc0i8gcqakSK246VsG8zOMtB2wFWpxEpfg2vh4jLIOc4JH1sdRqRXCpqRIrb6snm9zb9zHVzRAKNzXb2ZnxrpoLXY20ekdNU1IgUp4Pb4dcvze2rhlqbRaQktb4bylWFI6nwy0Kr04gAKmpEiteaqYABjXtARGOr04iUnKByZ++SnTjZ2iwip6moESkuJw+fvb6go0ZppAxo/6C5BMiu1eaSICIWU1EjUlzWfwCuLIhsAQ26Wp1GpORViIKWfczt1W9bm0UEFTUixcPjgrXvmNtXDTUvpBQpC85cO7blCzi629IoIipqRIrDz/+FzD0QXuPsX64iZUHNVlC/Cxge3YxPLKeiRuRSGQYknh56v3IwOEOszSNS2jr+zfy+/gPIPm5tFinTVNSIXCLb7rWwdwM4Qs7OBhEpSxr3gKoNIfsobPzE6jRShqmoEblE9jVTzI1Wd0H56taGEbGC3X72ZnyrJ+tmfGIZFTUilyAsez+2rYvMB2eG4EXKojb3QGhlOJyCbdtiq9NIGaWiRuQSNDywGJvhNdfCqdHM6jgi1gkOh3b3A2Bfo5vxiTVU1IhcrJOHqXtwubndaZi1WUR8QfuHwB6Efddqqpz4zeo0UgapqBG5SPb17+P05mBEtoSYa62OI2K9ijWhVV8AGu3/0uIwUhapqBG5GK5T2H94DwCPbrYnclanRwCoeeQHOJxicRgpa1TUiFyMzf/BdmI/J4OqYjS7zeo0Ir6jRjO8Dbthw8C+ZqrVaaSMUVEjUlReL6x6C4Dt1XuAI8jiQCK+xXt66QT7pk8g65DFaaQsUVEjUlTblkDGrxghFdgZca3VaUR8jlGvC0fK1cfmPgnr3rM6jpQhKmpEimrVmwB4rxiI21HO4jAiPshm47caPc3tNf8G1ylr80iZoaJGpCj2rIedK8HuxHvl/1mdRsRn7a1yJUbF2pCVwb6VM6yOI2WEihqRojh9LQ0t+kDFaGuziPgww+bE2/4hANwr3zSvRRMpYSpqRArr8A74+Qtzu9OjViYR8QveNveRSRi1PLvZkfiZ1XGkDFBRI1JYq96EM0siRLWwOo2I7wupwH+IA8D73b/AMCwOJIFORY1IYRzfD0kfm9ud463NIuJHZtKLbCOImOyfSfvxW6vjSIBTUSNSGGumgvsU1GoH9TtbnUbEbxykMp96rgHgaMI4i9NIoFNRI3IhpzJh7el7bXQeoSURRIroHc9NeAwbTY+v5tBvP1gdRwKYihqRC/lhOmQfhYjL4LJeVqcR8TupRiQLvR0BSP/yFYvTSCC7qKJm8uTJNGjQgNDQUGJjY1mxYsV52y9fvpzY2FhCQ0OJiYlh6tS864Fs2bKF3r17U79+fWw2GxMnTiyW84pcMtcpWD3Z3O48HOz6O0DkYkx13wzAZRlfcyxtq8VpJFAV+V/oOXPmMHz4cJ555hmSkpLo0qULPXv2JDU1tcD2KSkp9OrViy5dupCUlMTTTz/NsGHDmDt3bm6brKwsYmJieOWVV4iKiiqW84oUi02z4Pg+qFjbvDeNiFyUZKMeSz2tcdgMUhe8anUcCVBFLmomTJjAAw88wODBg2nWrBkTJ06kTp06TJkypcD2U6dOpW7dukycOJFmzZoxePBgBg0axPjx43PbXHnllbz22mvcfffdhISEFMt5RS6Z1wPfv2Fud3oEnMHW5hHxc1PctwDQaO9/OXV4r8VpJBAVqajJyclh/fr1xMXF5dkfFxfHqlWrCnxNYmJivvY9evTghx9+wOVyldh5RS7Zz/+FwylQrgq0HWB1GhG/t9ZoynpvY0JwsX3B+Au/QKSInEVpnJGRgcfjITIyMs/+yMhI0tPTC3xNenp6ge3dbjcZGRnUrFmzRM4LkJ2dTXZ2du7jzMxMAFwuV6ELKl9zJre/5vcbhoFzxQRsgKfdg3htwfCnn7n6wreoP3zHH/sixG4Q4jh70733vDcTa59Avd9ncfLIKJzhlS1KWTYEyu9FYfMXqag5w/anKa2GYeTbd6H2Be0v7vOOHTuW5557Lt/+JUuWEBYWVqRz+5qEhASrIwS0Gkc30XHfZtz2YJYcqYdr0aJztlVf+Bb1h+9ISEjgH63/tNNoReYvtah4ag8/zxnJtqhbLMlW1vj770VWVlah2hWpqImIiMDhcOQbHdm/f3++UZQzoqKiCmzvdDqpVq1aiZ0XYNSoUcTHn737a2ZmJnXq1CEuLo6KFSsW6ty+xuVykZCQQPfu3QkKCrI6TmAyDBwfvAmA7crBdO/Wt8Bm6gvfov7wHX/si67jvyMz2/2HZx3cbLuV8c7J1EpfTMN7XsMeWsGyrIEuUH4vznzSciFFKmqCg4OJjY0lISGB22+/PXd/QkICt956a4Gv6dixIwsWLMizb8mSJbRr167QP+CLOS9ASEhIgRceBwUF+XXnQmC8B5/1+zLYsw6coTg6D8dxgZ+z+sK3qD98R1BQENleG9mevCPq8+jI3+xzaWDfR/LiyTTr8w+LEpYd/v57UdjsRZ79FB8fz3vvvcf06dNJTk5mxIgRpKamMmTIEMAcHRkw4OxFlUOGDGHnzp3Ex8eTnJzM9OnTmTZtGo8//nhum5ycHDZu3MjGjRvJyclhz549bNy4kd9++63Q5xUpNstfM7+3HQgVzj0SKCIXx4ODyR7zD9LILe9i5BTuowWRCynyNTV9+/bl4MGDPP/886SlpdGiRQsWLVpEvXr1AEhLS8tz75gGDRqwaNEiRowYwdtvv010dDSTJk2id+/euW327t3LFVdckft4/PjxjB8/nq5du7Js2bJCnVekWOxcBTtXgiMYrn7M6jQiAWuepzOPOT+nNhn8uuhtLrvtCasjSQC4qAuFhw4dytChQwt8bsaMGfn2de3alQ0bNpzzePXr18+9ePhizytSLJafXnCvzb1QqZa1WUQCmBsnU9y38FLQdKptmoJx46PYgkKtjiV+Tvd8Fzlj9w/w+1KwO82FK0WkRH3q6UqaUZUI4yC/Lfm31XEkAKioETnjzChNq7uhij7WFClpOQTxb/dNAFRa/xZ4/PteKmI9FTUiAHs3wrbFYLNDl/gLNheR4jHLcz0HjErU8O7n96/fszqO+DkVNSIA352e8dSiD1RraG0WkTIkm2Decd8IQNjaN8DjvsArRM5NRY1I+k/wy0LABtc8fsHmIlK8Znq6ccgoT5Qnje3fvm91HPFjKmpElo01vze/DapfZmkUkbIoi1DePX1tTfjq13VtjVw0FTVStu1NMkdpbHa4dpTVaUTKrA88cWQYFYnypPGbrq2Ri6SiRsq2padHaVreqVEaEQtlEcpU980AVFwzAcOdbXEi8UcqaqTs2rXu9IwnB3R9yuo0ImXex55u7DcqmzOhdN8auQgqaqTsWvay+b11P814EvEBpwhhsvsWACr98AaG66TFicTfqKiRsmlnImz/1rx7cFetOSPiK2Z5rmevUZUIbwbbv5psdRzxMypqpGxa+pL5/Yr+UKW+pVFE5KxsgnnbfRsAVTe8iZFzwtpA4ldU1EjZ8/ty2LHCXIlb96UR8Tn/8VzLbiOCqsZhti+aZHUc8SMqaqRsMQxYevpamti/QqXalsYRkfxcOJnkvh2AiE1T8J46ZnEi8RcqaqRs2ZYAu1aDMxS6/N3qNCJyDp97urDDG0ll4yhb579mdRzxEypqpOzweuDrMeZ2+/+DClGWxhGRc3PjZIK7DwC1f34X17EDFicSf6CiRsqOzZ/C/i0QWgk6j7A6jYhcwAJvR7Z461GeLH6b+5zVccQPqKiRssGdDd+envHUeQSEVbU2j4hckIGdV913A9BwxyxOHdhhbSDxeSpqpGxYNw2OpkKFaOgwxOo0IlJI33lb8b2nOcG42fHZ01bHER+nokYC36mj8N3pCw2vHQlB5azNIyJFYMsdrWmSvohjOzZaG0d8mooaCXyr3oSThyCiCbS51+o0IlJEPxoNWejpgN1msG+eRmvk3FTUSGA7lg6Jb5vbN/wTHE5r84jIRRnvvgu3YafR0e85uOVbq+OIj1JRI4Ft+avgyoLaV0LTm6xOIyIXaYdRk1me6wE4sfAZ80aaIn+iokYCV8Y2WP+Bud3tObDZrM0jIpdkkvsOsowQ6p78mT2Jc6yOIz5IRY0EriX/AMMDTf4C9a+2Oo2IXKIDVOZdTy8AnN+MwXCdsjiR+BoVNRKYtn8LW78CuxPiXrQ6jYgUk3+7b2afUZlITxrb//e61XHEx6iokcDjccPiZ8ztKx+EiMbW5hGRYpNFKONc5hTvmhvfxHU03eJE4ktU1Ejg2fAB7P8ZQitD1yetTiMixexzb2d+9DYgnJOkfKop3nKWihoJLKeOwtLTyyFc97SWQxAJQAZ2XnD1B6Dh7s85tnOjtYHEZ6iokcDy3WuQddC80V67QVanEZESss5oykJPBxwYZMz9u6Z4C6CiRgLJwe2weqq5HfcSOIKszSMiJeoVdz+yDScNMn/gwPr/Wh1HfICKGgkcCf8Erwsa3gCNu1udRkRK2G6jBtNOT/H2Ln4a3DkWJxKrqaiRwPD7MvhlIdgc0OMl3WhPpIyY7L6FA0YlIl17+F1TvMs8FTXi/9zZ8L/Hze0rH4AazazNIyKl5jhhjHP3BSAqaSI5h3ZZnEispKJG/F/iW3BwG4TXgOuesTqNiJSyzzzXsN7bmDBOkTprhNVxxEIqasS/HUmF5a+Z23EvQrnKlsYRkdJnYGe06348ho1GBxI4uOlLqyOJRVTUiH/7ahS4T0K9ztDqLqvTiIhFfjbq86EnDgD3wsfNj6WlzFFRI/5r6xLz4mC7E24cr4uDRcq4Ce472W9UJtK1mx0LXrU6jlhARY34J9dJ+PIJc/uqh3VxsIhwjDBect0DQNSmN8nJ2GFtICl1KmrEP62cCId3QIVo6DrS6jQi4iP+672aRM/lhJLDntmPWR1HSpmKGvE/B7fDyn+Z2395GULKW5tHRHyIjdHuv+IyHDTIWMaBH76wOpCUIhU14l+8Xpg/DDzZ0PB6uPw2qxOJiI/5zaide6dh+6K/Y5w6anEiKS0qasS/bJgBO1dCUDjcNFEXB4tIgSa67yDFG0k1bwYps56wOo6UEhU14j+O7oEl/zS3b/gnVKlnbR4R8VmnCGGU+0EAYnbO4UjyUosTSWlQUSP+wTBg4QjIOQa1r4T2D1qdSER83Grv5cx03wBAzud/M2dNSkBTUSP+4ae5sG0xOILhlrfA7rA6kYj4gVfc/UgzqlLDtYcdc0dbHUdKmIoa8X0nMuDLJ83ta56AGk2tzSMifuMYYfzDdT8AdX6ZRtaOdRYnkpKkokZ831cjIesg1GgOVw+3Oo2I+JlvvLH819MJB14yZw8Bj8vqSFJCVNSIb0teCJs/BZsdbn0TnMFWJxIRP/ScawCHjPJEnfqN1C+etzqOlBAVNeK7ju2DBcPM7U6PQq1Ya/OIiN86REWedf0VgOjNb3F8+2prA0mJUFEjvskwYP4j5sdOkS3humesTiQifm6BtxP/9XTCiZes2Q9AzgmrI0kxu6iiZvLkyTRo0IDQ0FBiY2NZsWLFedsvX76c2NhYQkNDiYmJYerUqfnazJ07l8svv5yQkBAuv/xy5s2bl+f5MWPGYLPZ8nxFRUVdTHzxB+vfh21LzNlOd7wDzhCrE4lIABjt+it7jarUcO0mddYIq+NIMStyUTNnzhyGDx/OM888Q1JSEl26dKFnz56kpqYW2D4lJYVevXrRpUsXkpKSePrppxk2bBhz587NbZOYmEjfvn3p378/mzZton///tx1112sWbMmz7GaN29OWlpa7tfmzZuLGl/8wcHtsPj0yMwNz0Lk5dbmEZGAkUl5HncNAaBuyhyObFpgcSIpTkUuaiZMmMADDzzA4MGDadasGRMnTqROnTpMmTKlwPZTp06lbt26TJw4kWbNmjF48GAGDRrE+PHjc9tMnDiR7t27M2rUKJo2bcqoUaO44YYbmDhxYp5jOZ1OoqKicr+qV69e1Pji6zxu+Pz/wJUF9bvAVUOtTiQiAWaVtwXvuXuaD/77KMbxA9YGkmLjLErjnJwc1q9fz8iRI/Psj4uLY9WqVQW+JjExkbi4uDz7evTowbRp03C5XAQFBZGYmMiIESPytflzUbNt2zaio6MJCQmhQ4cOvPzyy8TExJwzb3Z2NtnZ2bmPMzMzAXC5XLhc/jml70xuf81/IfYVr+HY8wNGSEXcN70JHo/55YMCvS/8jfrDd/yxL0LsBiEOw+JE+b1h3EVX40cae/ew68MHiRr8n4BcSy5Qfi8Km79IRU1GRgYej4fIyMg8+yMjI0lPTy/wNenp6QW2d7vdZGRkULNmzXO2+eMxO3TowIcffkiTJk3Yt28fL774Ip06dWLLli1Uq1atwHOPHTuW5557Lt/+JUuWEBYWVqj37KsSEhKsjlDsqpzYRuetrwGwIeoedn//I/CjtaEKIRD7wp+pP3xHQkIC/2htdYpzcbA7awgNt46hzv6lbPzoSXZGXGd1qBLj778XWVlZhWpXpKLmDNufqlnDMPLtu1D7P++/0DF79uyZu92yZUs6duxIw4YN+eCDD4iPjy/wvKNGjcrzXGZmJnXq1CEuLo6KFSueM68vc7lcJCQk0L17d4KCgqyOU3yyDuGcNgobXryX306r216glY//1RSwfeGn1B++44990XX8d2Rmu62OdA4xPGC/iycds7h818c0uaE/QbVaWR2qWAXK78WZT1oupEhFTUREBA6HI9+ozP79+/ONtJwRFRVVYHun05k7wnKuNuc6JkB4eDgtW7Zk27Zt52wTEhJCSEj+WTNBQUF+3bkQGO8hl9cLCx+BzD1QtSH2WyZhD/afm+wFVF8EAPWH7wgKCiLbayPb47t/oEzx3Eg7krnesZGjnwygenwihPrnH73n4++/F4XNXqQLhYODg4mNjc03jJWQkECnTp0KfE3Hjh3ztV+yZAnt2rXLDXmuNuc6JpjXyyQnJ1OzZs2ivAXxRaveMKdvO0Phrg8C8h8UEfFNBnbiXQ+zx6hG9Zzd7P7o/8z7ZIlfKvLsp/j4eN577z2mT59OcnIyI0aMIDU1lSFDzClyo0aNYsCAAbnthwwZws6dO4mPjyc5OZnp06czbdo0Hn/88dw2jz32GEuWLOHVV1/ll19+4dVXX+Xrr79m+PDhuW0ef/xxli9fTkpKCmvWrKFPnz5kZmYycODAS3j7YrmdifDNC+Z2z1chqqW1eUSkzDlCBR7NeRSX4aD2ni/JWFbwbF7xfUW+pqZv374cPHiQ559/nrS0NFq0aMGiRYuoV68eAGlpaXnuWdOgQQMWLVrEiBEjePvtt4mOjmbSpEn07t07t02nTp2YPXs2//jHPxg9ejQNGzZkzpw5dOjQIbfN7t276devHxkZGVSvXp2rrrqK1atX555X/NCJDPjsfjA80PIuaKsCVUSsscFowqvuu/lH0EwqLh9NdsOrCKnb1upYUkQXdaHw0KFDGTq04PuHzJgxI9++rl27smHDhvMes0+fPvTp0+ecz8+ePbtIGcXHeb3m/WiOpUFEE7jpXwE5nVJE/Md7nl50sP9Cd8d6Dn58LyHxqyG0ktWxpAi09pNYY+mLsP0bcJaDOz+AkPJWJxKRMs/G310PsduIoFrOXvZOvw+8vnmfLCmYihopfZs/gxWvm9s3v6FlEETEZ2RSniE5wzllBBG9/zvSv9Biuv5ERY2Urr1J8N+/mdtXPwat+1qbR0TkT34yYnjS9RAAUT9O4eiamRYnksJSUSOl59g+mH0vuE9B4zhzsUoRER8039uJye5bAAj9cjjZO9dZnEgKQ0WNlA53Nsy5z7zBXrXG0Ps9sDusTiUick7j3XfxtecKQsjh1Ed3Y2SmWR1JLkBFjZQ8w4CFI2D3WnMmQb/ZmlEgIj7Pi53hrr+xzVuLSu4MDky7E1ynrI4l56GiRkreivGwcSbY7NDnfYhoZHUiEZFCOU4Yg11/54gRTo2jm0n/8K/mLSnEJ6mokZKV9DF8+6K5/ZdXoNEN1uYRESminUYUD7uGk2M4iNr1JQc+i9dSCj5KRY2UnK1LYP4wc/vq4dDhIUvjiIhcrERvc/7uehiA6j+/z6GE1yxOJAVRUSMlY/d6+HSguQRCq7uh2xirE4mIXJIF3k684LoXgKqrXuLYmo8sTiR/pqJGil/Gb/DJneDKgoY3wK1vaQkEEQkI0zw38o77RgDKffkYp35JsDiR/JGKGilemWnw8R2QdRBqtoG7PgRHkNWpRESKzVh3P77wdMKJB+bchztV97DxFSpqpPgcS4cPboIjO6FKfbj3U63pJCIBx8DOE64hrPC0INQ4Rc6M23DvTrI6lqCiRorL8f3wwS1w8DeoVAcGzIfyNaxOJSJSIlw4GeIawTpvE8K8x8mefjOevZusjlXmqaiRS3ciwyxoMn6FirVg4HyoUs/qVCIiJeoE5bg/50k2eBsR7j3GqWk34037yepYZZqKGrk0Jw6aBc2BZKhQEwYugKoxVqcSESkVxwljYM5INnpjCPccJWvajRj7frY6VpmlokYu3omD8NGtsH8LlI80C5pqDa1OJSJSqo4RxoCckWz21qe8+wgn3u2lwsYiKmrk4hzdDdN7QPpmCK9uFjQRja1OJSJiiUzKc1/O02zx1qO8+zAn34nDk7rW6lhljooaKboDW2FaDzi4zbyG5q//g+qXWZ1KRMRSRynPvTlPk+RtRJjnGO73b8b1q+5jU5pU1EjR7FlvjtBk7oZqjWHQYhU0IiKnHaEC9+Y8zXeeloQYp7DNupvsjZ9aHavMUFEjhff7MvOi4JOHIPoKGPQVVK5jdSoREZ+SRSgPuJ5ggecqnLgJ+uJBTq76t9WxygQVNVI4m2bDzDsh5zg0uMa8hiY8wupUIiI+yYWTx1yP8JG7G3YMyi15kuP/Gw1er9XRApqKGjk/rwcSnoV5D4EnB5rdAvd+BiEVrE4mIuLTvNgZ7b6fN9x3AFB+3SSOzugL2cctTha4VNTIuWUfg9n3wvcTzcddHoc7PwBniKWxRET8h41/ufswIudhsg0nlVKXkDn5ejiSanWwgKSiRgp2eCdMi4OtX4IjBO54D24YDXb9JyMiUlTzvF24O2c0B4xKVDz6K1lvX4OxM9HqWAFH/4eS/LZ/C+9eB/t/Nm+qd/+X0OpOq1OJiPi1JKMxt2a/wBZvPcJch3HPuJmcxHfBMKyOFjBU1MhZHjd88zx8dAdkHYSareHBpVA71upkIiIBYS8R9Ml5lkWe9gQZLoIXP86xj/vDqaNWRwsIKmrEdHQPfHATrHgdMKDdIPMeNJVqWZ1MRCSgnCSUoa7HeMF1Ly7DQYXtCzj+5tWwN8nqaH5PRY3A1iUwtTOkJkJwBejzPtz0LwgqZ3UyEZEAZWOa50buzHmW3UYE5U/swv1ud1yrpujjqEugoqYsyz4GC4bDJ3eaN9Sr2RoeWg4t7rA6mYhImbDRaESv7JdZ7GmH03ARtGQkx6ffZq6vJ0Wmoqas2r4UJneE9e+bjzsMgQcStMq2iEgpy6Q8D7lGMMY1gGwjiPK7lpE9qT2eHz7QqE0Rqagpa05lwoLH4KPb4OguqFzXvDtwz1d1/xkREcvYmOH5C71yXmaDtxEhnhM4Fg7jxPRb4cguq8P5DRU1ZYVhQPICmNIJ1s8w9135IDycaC57ICIilttu1KJPzhhedN3LKSOI8F3LyXmzA57EKeYMVTkvFTVlwYGt8NHtMOe+06Mz9WDgQrhxPISUtzqdiIj8gRc773lupFfOWNZ7GxPsOYFj8Uiy3uwIKSusjufTVNQEslOZsOQfMKUj/L4UHMHmUgdDE6FBF6vTiYjIefxuRHNnzrM87XqAw0Z5wo5shQ9u4tSsAeZtOCQfp9UBpAS4cyDpQ1g+Do7vM/c16Ql/eRmqxlibTURECs2LnU88N7DI056/Oz/lHsc3hP76X1zbFmPv9CiOzo9CaCWrY/oMjdQEEq8HNs2Gt9rB//5uFjRVY+CeT+Ge2SpoRET81BEqMNo9iJtzXmKdtwlB3lM4Vr5Gzust8K74F+RkWR3RJ2ikJhB4vfDLQlj6Ehz4xdwXXgOueQJiB2pWk4hIgPjZqM+dOc/Sw76Ox52f0ti1B74ZQ/b3bxN83ZPY2g6AoFCrY1pGRY0/c2fDj/+BVZMgY6u5L7QydB4O7f8PgsOtTCciIiXCxmJvexJy2nGbfSXDg+ZS99QB+PIJspeOI/jqodjaDYJyla0OWupU1PghpycLe+IkWPsOHE83d4ZUNAuZTo+Wyf+QRUTKGi92Pvdew4LsTvR1LGWo879EnzoA3zyHa9lr2K+8H678P6tjlioVNf4kfTP2tdOI+2kWDu8pc1+FmnDVUIj9K4RWtDSeiIiUPhdOPvZ0Z47nOm6yJ/KQcyFN2QWr38a2ZirtKsVi21EeGl0PNpvVcUuUihpfl5MFW+aZyxnsXocDcABGxGXYOg+HFn3AGWxxSBERsZoLJ/O8XZiX05lr7Zt4yLGQjo6fqXVkLcy8g+xKDQhpPwja3Avh1ayOWyJU1Pgirwd2rITNn8LP8yH7qLnf7sR72Y0kuprS/q6/ExSsC4BFROTPbCzztmGZtw2tvDt4o+bXVD+QSPmjKZAwGs83z2M07oGz9V3QuEdAXVisosZXGAbs3QCb58JPc89eKwPmHYBj/wpX3IcnpAoZixaBTbPxRUTk/H6lHj/W+Stj0u6lB4nc4/iGVqTArwvh14W4g8pjv/wW7K3uhPpdwBFkdeRLoqLGSu5s85bXvy6CX7+EY3vPPhdaGS6/FVrdBXU7gf10EeNyWRJVRET8VxahzPZcz2zP9TSz7eQWxypudawi2nUQNn0Cmz7BHVwRe5M47E17QaNufnlTPxU1pckw4HAK/L4Mtn8L25dCzvGzzweFQZMe0PIuaHSD7i8jIiLFLtmoR7K7HuPcfYm1beVWxyp6OdZQLScTfvoMfvoMr82JUe9qHI27Qcy1ENni7B/XPkxFTUk7uhtSV0PKcrOYOZKa9/nyUXBZT7isl7ladgB9tikiIr7LwM4PRlN+cDflWfdfucK2je6ODXR3rKche2HHcvMLcIdWxRHTFVvDa81PDyIa++RMKhU1xcl1CvZvgV1rYdca83vmnxYdswdB7Sshpis07g41r/CL6ldERAKXFzvrjctY776MV9z9iLHt5Tp7Elfbt9DBnkz4qUPw8zzzC3CHVMZetz32Oh2gTgeo2donbiuiouZinTwM+3+B9B8hbROk/QgHksHrztvO5oCoFuYFWDHXQt2OEFLeksgiIiKF8bsRze+eaKZ5biQIN61tv3G1fQudHFtobdtOaPYR2LbE/DrNXTkGZ602ZoFTsxXUuBzKR5bqiI6KmvPxes2Ldw9uh0Pb4cCv5tpK+3/JOzvpj8pVhdrtzMq1Tgeo1VbLFYiIiN9y4TQ/pvI05Q1Pb4Jw08y2k1j7VmLt22hr30a07SDOI7/Dkd9hy+e5r/WEVMJWoxn2Gk2helOo1shcXLly3RKZaVW2ixqvB47vNz8iOpJqXv9ydBcc2WVe0HsoBTzZ5359xVoQ1fJ0VXr6q2Itn/ycUUREpDi4cPKj0ZAfPQ1539MTgKpk0ty+g+a2HbSw76C5fQd12Ycj+yjsWm1+/YFhc+CtVAd7tRhsletCpTrmV+U6UKm2eb3pRdxY9qKKmsmTJ/Paa6+RlpZG8+bNmThxIl26dDln++XLlxMfH8+WLVuIjo7mySefZMiQIXnazJ07l9GjR7N9+3YaNmzISy+9xO23335J5z2n6T3BnQEn9oPhPX9bexBUqWdWlhFNzEqzRjNz2wc+PxQREbHaISqywtuKFbQCj7kvhBxibGk0tu2msX0PTWx7qGdLp55tH+XIwXFkBxzZcc5jespVxVahJvYKUeAo3B2Qi1zUzJkzh+HDhzN58mSuvvpq/v3vf9OzZ09+/vln6tatm699SkoKvXr14sEHH+Tjjz/m+++/Z+jQoVSvXp3evXsDkJiYSN++fXnhhRe4/fbbmTdvHnfddRcrV66kQ4cOF3Xe89q3GUJOj6bY7FAh+mx1WOn096oNzEKmYm1wlO0BLRERkaLKJticPm7UgzzjBwY1OEJ9Wzr17PuoZcugFhnUsh8k2pZBTQ4SYnPjOHkITh4yJ+BkG4U6p80wjMK1PK1Dhw60bduWKVOm5O5r1qwZt912G2PHjs3X/qmnnmL+/PkkJyfn7hsyZAibNm0iMTERgL59+5KZmcmXX36Z2+Yvf/kLVapUYdasWRd13oJkZmZSqVIljv7wGRVrxpiLQYZXB7ujKD8CS7lcLhYtWkSvXr0ICvLvOz/6O/WFb1F/+I4/9kXsS9+Secp94RdJiQhxGIxr7+HJtQ6yPf5xaYQNL5U5Tg3bESJth6lhO0LF7H08+/qHHD16lIoVz/0pSZGGIHJycli/fj0jR47Msz8uLo5Vq1YV+JrExETi4uLy7OvRowfTpk3D5XIRFBREYmIiI0aMyNdm4sSJF31egOzsbLKzz14Tc/SouYbSoWqxuEIqQA6Qc+R8b9nnuFwusrKyOHjwoP7htpj6wreoP3zHH/vCmXMcp9tjdaQyy+k1yMry4nTZ8Xj9o6gBOIaDY1RjO+bHTt7sLOBDLjQOU6SiJiMjA4/HQ2RkZJ79kZGRpKcXPBsoPT29wPZut5uMjAxq1qx5zjZnjnkx5wUYO3Yszz33XL79DRo0OPebFBERCSD3WB2gGB07doxKlc69fMNFXSxi+9PsHsMw8u27UPs/7y/MMYt63lGjRhEfH5/72Ov1cujQIapVq3be1/myzMxM6tSpw65du847BCclT33hW9QfvkN94TsCpS8Mw+DYsWNER0eft12RipqIiAgcDke+0ZH9+/fnG0U5IyoqqsD2TqeTatWqnbfNmWNezHkBQkJCCAnJu35S5cqVz/0G/UjFihX9+j/QQKK+8C3qD9+hvvAdgdAX5xuhOaNI9+cPDg4mNjaWhISEPPsTEhLo1KlTga/p2LFjvvZLliyhXbt2uZ97n6vNmWNezHlFRESkbCnyx0/x8fH079+fdu3a0bFjR9555x1SU1Nz7zszatQo9uzZw4cffgiYM53eeust4uPjefDBB0lMTGTatGm5s5oAHnvsMa655hpeffVVbr31Vv773//y9ddfs3LlykKfV0RERMo44yK8/fbbRr169Yzg4GCjbdu2xvLly3OfGzhwoNG1a9c87ZctW2ZcccUVRnBwsFG/fn1jypQp+Y756aefGpdddpkRFBRkNG3a1Jg7d26RzltWnDp1ynj22WeNU6dOWR2lzFNf+Bb1h+9QX/iOstYXRb5PjYiIiIgvKtI1NSIiIiK+SkWNiIiIBAQVNSIiIhIQVNSIiIhIQFBR46P27NnDfffdR7Vq1QgLC6NNmzasX78+93nDMBgzZgzR0dGUK1eOa6+9li1btliYOHC53W7+8Y9/0KBBA8qVK0dMTAzPP/88Xu/ZZWfVHyXju+++4+abbyY6OhqbzcYXX3yR5/nC/Nyzs7N59NFHiYiIIDw8nFtuuYXdu3eX4rsIDOfrC5fLxVNPPUXLli0JDw8nOjqaAQMGsHfv3jzHUF8Ujwv9XvzRQw89hM1my11L8YxA7QsVNT7o8OHDXH311QQFBfHll1/y888/8/rrr+e5G/K4ceOYMGECb731FuvWrSMqKoru3btz7Ngx64IHqFdffZWpU6fy1ltvkZyczLhx43jttdd48803c9uoP0rGiRMnaN26NW+99VaBzxfm5z58+HDmzZvH7NmzWblyJcePH+emm27C49Eii0Vxvr7Iyspiw4YNjB49mg0bNvD555+zdetWbrnlljzt1BfF40K/F2d88cUXrFmzpsClBQK2LyydUC4Feuqpp4zOnTuf83mv12tERUUZr7zySu6+U6dOGZUqVTKmTp1aGhHLlBtvvNEYNGhQnn133HGHcd999xmGof4oLYAxb9683MeF+bkfOXLECAoKMmbPnp3bZs+ePYbdbje++uqrUsseaP7cFwVZu3atARg7d+40DEN9UVLO1Re7d+82atWqZfz0009GvXr1jH/961+5zwVyX2ikxgfNnz+fdu3aceedd1KjRg2uuOIK3n333dznU1JSSE9PJy4uLndfSEgIXbt2ZdWqVVZEDmidO3fmm2++YevWrQBs2rSJlStX0qtXL0D9YZXC/NzXr1+Py+XK0yY6OpoWLVqob0rY0aNHsdlsuSPM6ovS4/V66d+/P0888QTNmzfP93wg98VFrdItJev3339nypQpxMfH8/TTT7N27VqGDRtGSEgIAwYMyF3Y88+LeUZGRrJz504rIge0p556iqNHj9K0aVMcDgcej4eXXnqJfv36Aag/LFKYn3t6ejrBwcFUqVIlX5s/L5ArxefUqVOMHDmSe+65J3cRRfVF6Xn11VdxOp0MGzaswOcDuS9U1Pggr9dLu3btePnllwG44oor2LJlC1OmTGHAgAG57Ww2W57XGYaRb59cujlz5vDxxx/zySef0Lx5czZu3Mjw4cOJjo5m4MCBue3UH9a4mJ+7+qbkuFwu7r77brxeL5MnT75ge/VF8Vq/fj1vvPEGGzZsKPLPNRD6Qh8/+aCaNWty+eWX59nXrFkzUlNTAYiKigLIV1Hv378/31+tcumeeOIJRo4cyd13303Lli3p378/I0aMYOzYsYD6wyqF+blHRUWRk5PD4cOHz9lGio/L5eKuu+4iJSWFhISE3FEaUF+UlhUrVrB//37q1q2L0+nE6XSyc+dO/v73v1O/fn0gsPtCRY0Puvrqq/n111/z7Nu6dSv16tUDoEGDBkRFRZGQkJD7fE5ODsuXL6dTp06lmrUsyMrKwm7P+6vicDhyp3SrP6xRmJ97bGwsQUFBedqkpaXx008/qW+K2ZmCZtu2bXz99ddUq1Ytz/Pqi9LRv39/fvzxRzZu3Jj7FR0dzRNPPMHixYuBAO8LK69SloKtXbvWcDqdxksvvWRs27bNmDlzphEWFmZ8/PHHuW1eeeUVo1KlSsbnn39ubN682ejXr59Rs2ZNIzMz08LkgWngwIFGrVq1jIULFxopKSnG559/bkRERBhPPvlkbhv1R8k4duyYkZSUZCQlJRmAMWHCBCMpKSl3Rk1hfu5DhgwxateubXz99dfGhg0bjOuvv95o3bq14Xa7rXpbful8feFyuYxbbrnFqF27trFx40YjLS0t9ys7Ozv3GOqL4nGh34s/+/PsJ8MI3L5QUeOjFixYYLRo0cIICQkxmjZtarzzzjt5nvd6vcazzz5rREVFGSEhIcY111xjbN682aK0gS0zM9N47LHHjLp16xqhoaFGTEyM8cwzz+T5x1r9UTKWLl1qAPm+Bg4caBhG4X7uJ0+eNB555BGjatWqRrly5YybbrrJSE1NteDd+Lfz9UVKSkqBzwHG0qVLc4+hvigeF/q9+LOCippA7QubYRhG6Y0LiYiIiJQMXVMjIiIiAUFFjYiIiAQEFTUiIiISEFTUiIiISEBQUSMiIiIBQUWNiIiIBAQVNSIiIhIQVNSIBJgVK1ZQvnx5jh49ylNPPUXPnj2tjiQiUip08z2RAHPy5En27NlDTEwMhw4dIjs7m1q1alkdS0SkxGmkRiTAlCtXjkaNGmG324mIiMhT0CxbtgybzcaRI0cKdawdO3Zgs9nYuHHjOdvYbDa++OKLSwsdYDZu3IjNZmPHjh2MGTOGNm3a5GszfPhwrr32WgDq16/PxIkTi+3YImWVRmpEAtDSpUvp1q0bHTt2ZOXKlbn7ly1bxnXXXcfhw4epXLnyBY+zY8cOGjRoQFJS0jn/52mz2Zg3bx633XZb8YQPAG63m4yMDKpXr87JkyfJzs7Ot2r1sWPHcLlcVK1alQMHDhAeHk5YWFixHFukrNJIjUgAmj59Oo888ghJSUls3brV6jhljtPpJCoqCofDQfny5QssOipUqEDVqlUBqF69+gULmpycnEIfW6SsUlEjEmAyMzP5/PPPefjhh7n55puZMWNGge1OnDhBxYoV+eyzz/LsX7BgAeHh4Rw7dizfa7xeLw8++CBNmjRh586dBR73+eefJzIyku+///6Cx7/++ut55JFH8jx/8OBBQkJC+PbbbwGYPHkyjRs3JjQ0lMjISPr06ZPbNjs7m2HDhlGjRg1CQ0Pp3Lkz69aty33+8OHD3HvvvVSvXp1y5crRuHFj3n///dznn3rqKZo0aUJYWBgxMTGMHj0al8uV+/yZj3emT59O3bp1KV++PA8//DAej4dx48YRFRVFjRo1eOmll/K8h1dffZUWLVoQFhZGnTp1+Nvf/sbx48dzn9+3bx99+/alVq1ahIWF0bJlS2bNmpXnGNdeey2PPPII8fHxRERE0L1790IdW6RMs3KJcBEpfv/+97+Ntm3bGoZhGAsWLDBq1apluN1uwzAMY+nSpQZgHD582DAMw3jwwQeNXr165Xn97bffbgwYMMAwDMNISUkxACMpKcnIzs42evfubbRp08bYt29fbnvAmDdvnuH1eo1hw4YZdevWNbZu3Vqo48+cOdOoUqWKcerUqdzn33jjDaN+/fqG1+s11q1bZzgcDuOTTz4xduzYYWzYsMF44403ctsOGzbMiI6ONhYtWmRs2bLFGDhwoFGlShXj4MGDhmEYxt/+9jejTZs2xrp164yUlBQjISHBmD9/fu7rX3jhBeP77783UlJSjPnz5xuRkZHGq6++mvv8s88+a5QvX97o06ePsWXLFmP+/PlGcHCw0aNHD+PRRx81fvnlF2P69OkGYCQmJua+bvz48cbSpUuNlJQU4+uvvzYuu+wy4+GHH859PiUlxZgwYYKRlJRk/Pbbb8akSZMMh8NhrF69OrdN165djfLlyxtPPPGE8csvvxjJycmFOrZIWaaiRiTAdOjQwZgwYYJhGIbhcrmMiIgI48svvzQMI39Rs2bNGsPhcBh79uwxDMMwDhw4YAQFBRnLli0zDONsUbNixQqjW7duxtVXX20cOXIkz/kA49NPPzXuu+8+o2nTpsauXbtyn7vQ8U+dOmVUrVrVmDNnTu5r2rRpY4wZM8YwDMOYO3euUbFiRSMzMzPf+zx+/LgRFBRkzJw5M3dfTk6OER0dbYwbN84wDMO4+eabjfvvv7/QP7tx48YZsbGxuY+fffZZIywsLM/5e/ToYdSvX9/weDy5+y677DJj7Nix5zzuf/7zH6NatWrnPXevXr2Mv//977mPu3btarRp0+aCmQtzbJGyQh8/iQSQ5ORkfvjhB+6++27AvP6ib9++TJ8+vcD27du3p3nz5nz44YcAfPTRR9StW5drrrkmT7t+/fpx/PhxlixZQqVKlfIdZ8SIESQmJrJixQpq165d6OOHhIRw33335ebbuHEjmzZt4q9//SsA3bt3p169esTExNC/f39mzpxJVlYWANu3b8flcnH11Vfnni8oKIj27duTnJwMwMMPP8zs2bNp06YNTz75JKtWrcqT+7PPPqNz585ERUVRvnx5Ro8eTWpqap429evXp0KFCrmPIyMjufzyy7Hb7Xn27d+/P/fx0qVL6d69O7Vq1aJChQoMGDCAgwcPcuLECQA8Hg8vvfQSrVq1olq1apQvX54lS5bkO3e7du3y/awvdGyRskxFjUgAmT59Oh6Phzp16uB0OnE6nUyZMoX58+dz6NChAl8zePDg3OtM3n//fe6//35sNlueNr169eLHH39k9erVBR6je/fu7Nmzh8WLFxf5+IMHDyYhIYHdu3czffp0brjhBurVqweYF9Nu2LCBWbNmUbNmTf75z3/SunVrjhw5gnF64uafsxqGkbuvZ8+e7Ny5k+HDh7N3715uuOEGHn/8cQBWr17N3XffTc+ePVm4cCFJSUk888wzuRfknhEUFJTnsc1mK3Cf1+sFYOfOnfTq1YsWLVowd+5c1q9fz9tvvw2Qe73O66+/zr/+9S+efPJJvv32WzZu3EiPHj3ynTs8PDzP48IcW6RMs3qoSESKh8vlMqKiooyXX37Z2Lx5c56vhg0bGm+++Wa+j58MwzAOHTpkhIaGGm+88YZht9vzfHz0x2tqJk2aZISHh+d+dHQGp6+p+fzzz43Q0FBj1qxZeZ4/3/HPaN++vfHPf/7TqFatmvHJJ5+c8z0eP37ccDqdxty5c43jx48bwcHB+T5+qlWrlvHaa68V+PqpU6caFSpUMAzDvDYlJiYmz/MPPPCAUalSpdzHzz77rNG6des8bQYOHGjceuutefZ17drVeOyxxwzDMIzPPvvMcDqdeT6eeuGFF/L83G+66SZj0KBBuc97PB6jSZMmeY77x2OeUZhji5RlTksrKhEpNosWLeLAgQM8+OCDRERE5Hmud+/evP/++7z++uv5XlelShXuuOMOnnjiCeLi4vJ8fPRHjz76KB6Ph5tuuokvv/ySzp0753n+9ttv56OPPqJ///44nc7cWUqFOf7gwYN55JFHCAsL4/bbb8/dv3DhQn7//XeuueYaqlSpwqJFi/B6vVx22WWEh4fz8MMP88QTT1C1alXq1q3LuHHjyMrK4oEHHgDgn//8J7GxsTRv3pzs7GwWLlxIs2bNAGjUqBGpqanMnj2bK6+8kv/973/MmzevCD/xgjVs2BC3282bb77JzTffzPfff8/UqVPztGnUqBFz585l1apVVKlShQkTJpCenp6b7VKOLVKW6eMnkQDx/vvv07Vr13wFDZhFzYYNG9i0aVOBr33ggQfIyclh0KBB5z3H8OHDee655+jVq1e+61MA+vTpwwcffED//v35/PPPC338fv364XQ6ueeeewgNDc3dX7lyZT7//HOuv/56mjVrxtSpU5k1axbNmzcH4JVXXqF3797079+ftm3b8ttvv7F48WKqVKkCQHBwMKNGjaJVq1Zcc801OBwOZs+eDcCtt97KiBEjeOSRR2jTpg2rVq1i9OjR533/hdGmTRsmTJiQO/V65syZjB07Nk+b0aNH07ZtW3r06MG1115LVFRUoW5eWJhji5RluqOwiDBz5kwee+wx9u7dS3BwcKkff9euXdSvX59169bRtm3bYj+/iJQN+vhJpAzLysoiJSWFsWPH8tBDDxV7QXOh47tcLtLS0hg5ciRXXXWVChoRuST6+EmkDBs3bhxt2rQhMjKSUaNGlfrxv//+e+rVq8f69et1bYiIXDJ9/CQiIiIBQSM1IiIiEhBU1IiIiEhAUFEjIiIiAUFFjYiIiAQEFTUiIiISEFTUiIiISEBQUSMiIiIBQUWNiIiIBAQVNSIiIhIQ/h86MFzft3nPfwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_min = 50\n",
    "x_max = 150\n",
    "\n",
    "loc = 100\n",
    "scale = 16\n",
    "\n",
    "x = np.linspace(x_min, x_max, 100)\n",
    "y = norm.pdf(x, loc, scale)\n",
    "\n",
    "plt.plot(x, y, color='C1')\n",
    "plt.grid()\n",
    "plt.xlim(x_min, x_max)\n",
    "plt.ylim(0, 0.030)\n",
    "plt.xlabel('Älykkyysosamäärä')\n",
    "\n",
    "x1 = 120.5\n",
    "x2 = 150\n",
    "r1 = np.linspace(x1, x2, 30)\n",
    "r2 = norm.pdf(r1, loc, scale)\n",
    "plt.fill_between(r1, r2, color='C0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yllä lasketun mukaan kyseinen älykkyysosamäärä on noin 120,5.\n",
    "\n",
    "Esimerkki. Oletetaan, että aikaisemmista myyntitilastoista voidaan todeta, että päivittäistavaran kysynnällä lauantaisin on ollut keskiarvo 120 ja keskihajonta 15. Kauppias voi käyttää normaalijakaumaa määrittäessään seuraavan lauantain tilausmäärää kyseisen päivittäistavaran kohdalla. Oletetaan, että kauppias haluaa 95 % varmuuden tavaran riittävyydelle:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(144.67280440427209)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q = 0.95\n",
    "loc = 120\n",
    "scale = 15\n",
    "\n",
    "norm.ppf(q, loc, scale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.FillBetweenPolyCollection at 0x21f3e1cbd90>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_min = 70\n",
    "x_max = 170\n",
    "\n",
    "loc = 120\n",
    "scale = 15\n",
    "\n",
    "x = np.linspace(x_min, x_max, 100)\n",
    "y = norm.pdf(x, loc, scale)\n",
    "\n",
    "plt.plot(x, y, color='C1')\n",
    "plt.grid()\n",
    "plt.xlim(x_min, x_max)\n",
    "plt.ylim(0, 0.030)\n",
    "plt.xlabel('Kysyntä')\n",
    "\n",
    "x1 = 70\n",
    "x2 = 145\n",
    "r1 = np.linspace(x1, x2, 30)\n",
    "r2 = norm.pdf(r1, loc, scale)\n",
    "plt.fill_between(r1, r2, color='C0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pitää siis tilata tavaraa 145 kpl.\n",
    "\n",
    "Esimerkki. Paperikone voidaan säätää valmistamaan eri painoisia paperilaatuja (80 grammaa/neliömetri, 85 grammaa/neliömetri jne.). Teollisesti valmistetun tuotteen ominaisuudet kuitenkin vaihtelevat monien satunnaisten tekijöiden takia. Vaikka kone on säädetty valmistamaan 80 g paperia, niin paperi ei ole kauttaaltaan 80 g. Oletetaan, että paperin tilaaja vaatii, että 90 % paperista täytyy olla yli 80 g. Minkälaista paperia paperikone pitäisi säätää valmistamaan (oletus: paperin neliömetripainon odotusarvo on säädettävissä). Oletetaan, että valmistusprosessiin ja kyseiseen paperikoneeseen liittyvä keskihajonta on 2,5 grammaa/neliömetri."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(80.00612108613849)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q = 0.10 #haetaan rajaa, jonka alapuolelle jää 10 %\n",
    "loc = 83.21 # tähän löydetään sopiva arvo kokeilemalla\n",
    "scale = 2.5\n",
    "\n",
    "norm.ppf(q, loc, scale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.FillBetweenPolyCollection at 0x21f3e270e10>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_min = 70\n",
    "x_max = 96\n",
    "\n",
    "loc = 83.21\n",
    "scale = 2.5\n",
    "\n",
    "x = np.linspace(x_min, x_max, 100)\n",
    "y = norm.pdf(x, loc, scale)\n",
    "\n",
    "plt.plot(x, y, color='C1')\n",
    "plt.grid()\n",
    "plt.xlim(x_min, x_max)\n",
    "plt.ylim(0, 0.20)\n",
    "plt.xlabel('Paperin neliömetripaino')\n",
    "\n",
    "x1 = 80\n",
    "x2 = 96\n",
    "r1 = np.linspace(x1, x2, 30)\n",
    "r2 = norm.pdf(r1, loc, scale)\n",
    "plt.fill_between(r1, r2, color='C0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kokeilemalla odotusarvon loc kohdalle erilaisia arvoja, selviää että säädettäessä odotusarvo 83,21 gramman kohdalle, 90 % paperista on yli 80 grammaista.\n",
    "\n",
    "Edellä olleista esimerkeistä nähdään normaalijakauman soveltuvan malliksi hyvin erilaisiin käytännön tilanteisiin. Yhteistä edellä tarkastelluille satunnaismuuttujille on, että niiden arvoihin vaikuttavat monet satunnaiset tekijät. Tämä pätee yleisemminkin: \n",
    "\n",
    "**jos satunnaismuuttujan arvo määräytyy satunnaisesti lukuisten eri tekijöiden vaikutuksesta, niin muuttuja yleensä noudattaa likimain normaalijakaumaa**.\n",
    "\n",
    "Joitain kaikille normaalijakaumille päteviä todennäköisyyksiä on hyvä pitää mielessä:\n",
    "\n",
    "- Todennäköisyys saada jotain väliltä odotusarvo +/- keskihajonta on noin 68 %\n",
    "- Todennäköisyys saada jotain väliltä odotusarvo +/- 2 keskihajontaa on noin 95 %\n",
    "\n",
    "Huomaa, että yllä oleva pitää paikkansa riippumatta odotusarvosta ja keskiarvosta! Tarkistetaan vielä laskemalla yllä mainitut todennäköisyydet älykkyysosamäärän normaalijakaumalle:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6826894921370859\n",
      "0.9544997361036416\n"
     ]
    }
   ],
   "source": [
    "loc = 100\n",
    "scale = 16\n",
    "\n",
    "print(norm.cdf(loc + scale, loc, scale) - norm.cdf(loc - scale, loc, scale))\n",
    "print(norm.cdf(loc + 2*scale, loc, scale) - norm.cdf(loc - 2*scale, loc, scale))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.FillBetweenPolyCollection at 0x21f3e2e2d50>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_min = 50\n",
    "x_max = 150\n",
    "\n",
    "loc = 100\n",
    "scale = 16\n",
    "\n",
    "x = np.linspace(x_min, x_max, 100)\n",
    "y = norm.pdf(x, loc, scale)\n",
    "\n",
    "plt.plot(x, y, color='C1')\n",
    "plt.grid()\n",
    "plt.xlim(x_min, x_max)\n",
    "plt.ylim(0, 0.030)\n",
    "plt.title('Kahden keskihajonnan päässä odotusarvosta noin 95 %')\n",
    "plt.xlabel('Älykkyysosamäärä')\n",
    "\n",
    "x1 = 68\n",
    "x2 = 132\n",
    "r1 = np.linspace(x1, x2, 30)\n",
    "r2 = norm.pdf(r1, loc, scale)\n",
    "plt.fill_between(r1, r2, color='C0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standardi normaalijauma\n",
    "    \n",
    "Standardoitu satunnaismuuttuja eli standardipiste tarkoittaa satunnaismuuttujaa, jonka arvot on muunnettu standardia normaalijakaumaa vastaaviksi. Standardin normaalijakauman odotusarvo on 0 ja keskihajonta 1 (N(0, 1)). Satunnaismuuttujan standardoinnissa on olennaista todennäköisyyksien säilyttäminen samoina. Edellä jo todettiin, että todennäköisyydet säilyvät kunhan pysytään yhtä monen keskihajonnan päästä keskiarvosta.\n",
    "\n",
    "Esimerkki. Jakauman N(120, 20) satunnaismuuttujan 100 standardoitu arvo on noin -1, koska 100 on yhden keskihajonnan verran odotusarvon vasemmalla puolella.\n",
    "\n",
    "Yleinen kaava satunnaismuuttujan standardoinnille on: vähennä odotusarvo ja jaa kesihajonnalla.\n",
    "\n",
    "Satunnaismuuttujan standardointia käytetään monissa tilastollisissa analyyseissä, mutta myös haluttaessa saada erilaisilla asteikoilla mitatut muuttujat vertailukelpoisiksi keskenään.\n",
    "\n",
    "Standardipisteistä nähdään nopeasti esimerkiksi poikkeavat arvot. Esimerkiksi kaikki standardipistettä -2 pienemmät ja standardipistettä 2 suuremmat ovat melko harvinaisia, koska todennäköisyys näin äärimmäisille arvoille on alle 5 %."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.FillBetweenPolyCollection at 0x21f3e46fd90>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_min = -4\n",
    "x_max = 4\n",
    "\n",
    "loc = 0\n",
    "scale = 1\n",
    "\n",
    "x = np.linspace(x_min, x_max, 100)\n",
    "y = norm.pdf(x, loc, scale)\n",
    "\n",
    "plt.plot(x, y, color='C1')\n",
    "plt.grid()\n",
    "plt.xlim(x_min, x_max)\n",
    "plt.ylim(0, 0.40)\n",
    "plt.title('Kahden keskihajonnan päässä odotusarvosta noin 95 %')\n",
    "plt.xlabel('Standardipisteet')\n",
    "\n",
    "x1 = -2\n",
    "x2 = 2\n",
    "r1 = np.linspace(x1, x2, 30)\n",
    "r2 = norm.pdf(r1, loc, scale)\n",
    "plt.fill_between(r1, r2, color='C0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seuraavassa esimerkki standardipisteiden laskennasta:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>nro</th>\n",
       "      <th>sukup</th>\n",
       "      <th>ikä</th>\n",
       "      <th>perhe</th>\n",
       "      <th>koulutus</th>\n",
       "      <th>palveluv</th>\n",
       "      <th>palkka</th>\n",
       "      <th>johto</th>\n",
       "      <th>työtov</th>\n",
       "      <th>työymp</th>\n",
       "      <th>palkkat</th>\n",
       "      <th>työteht</th>\n",
       "      <th>työterv</th>\n",
       "      <th>lomaosa</th>\n",
       "      <th>kuntosa</th>\n",
       "      <th>hieroja</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3587</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2963</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1989</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2144</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2183</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1598</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1638</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2612</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2808</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2183</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    nro  sukup  ikä  perhe  koulutus  palveluv  palkka  johto  työtov  työymp  \\\n",
       "0     1      1   38      1       1.0      22.0    3587      3     3.0       3   \n",
       "1     2      1   29      2       2.0      10.0    2963      1     5.0       2   \n",
       "2     3      1   30      1       1.0       7.0    1989      3     4.0       1   \n",
       "3     4      1   36      2       1.0      14.0    2144      3     3.0       3   \n",
       "4     5      1   24      1       2.0       4.0    2183      2     3.0       2   \n",
       "..  ...    ...  ...    ...       ...       ...     ...    ...     ...     ...   \n",
       "77   78      1   22      1       3.0       0.0    1598      4     4.0       4   \n",
       "78   79      1   33      1       1.0       2.0    1638      1     3.0       2   \n",
       "79   80      1   27      1       2.0       7.0    2612      3     4.0       3   \n",
       "80   81      1   35      2       2.0      16.0    2808      3     4.0       3   \n",
       "81   82      2   35      2       3.0      15.0    2183      3     4.0       4   \n",
       "\n",
       "    palkkat  työteht  työterv  lomaosa  kuntosa  hieroja  \n",
       "0         3        3      NaN      NaN      NaN      NaN  \n",
       "1         1        3      NaN      NaN      NaN      NaN  \n",
       "2         1        3      1.0      NaN      NaN      NaN  \n",
       "3         3        3      1.0      NaN      NaN      NaN  \n",
       "4         1        2      1.0      NaN      NaN      NaN  \n",
       "..      ...      ...      ...      ...      ...      ...  \n",
       "77        3        4      NaN      1.0      1.0      NaN  \n",
       "78        1        2      1.0      NaN      NaN      NaN  \n",
       "79        3        3      1.0      NaN      1.0      NaN  \n",
       "80        3        3      NaN      NaN      NaN      NaN  \n",
       "81        3        4      1.0      NaN      NaN      NaN  \n",
       "\n",
       "[82 rows x 16 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Datan avaus\n",
    "df = pd.read_excel('https://taanila.fi/data1.xlsx')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>nro</th>\n",
       "      <th>sukup</th>\n",
       "      <th>ikä</th>\n",
       "      <th>perhe</th>\n",
       "      <th>koulutus</th>\n",
       "      <th>palveluv</th>\n",
       "      <th>palkka</th>\n",
       "      <th>johto</th>\n",
       "      <th>työtov</th>\n",
       "      <th>työymp</th>\n",
       "      <th>palkkat</th>\n",
       "      <th>työteht</th>\n",
       "      <th>työterv</th>\n",
       "      <th>lomaosa</th>\n",
       "      <th>kuntosa</th>\n",
       "      <th>hieroja</th>\n",
       "      <th>palkka_s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.204594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2963</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.469914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1989</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.676844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2144</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.494352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2183</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.448435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1598</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.137196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1638</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.090102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2612</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.056657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2808</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.287422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2183</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.448435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    nro  sukup  ikä  perhe  koulutus  palveluv  palkka  johto  työtov  työymp  \\\n",
       "0     1      1   38      1       1.0      22.0    3587      3     3.0       3   \n",
       "1     2      1   29      2       2.0      10.0    2963      1     5.0       2   \n",
       "2     3      1   30      1       1.0       7.0    1989      3     4.0       1   \n",
       "3     4      1   36      2       1.0      14.0    2144      3     3.0       3   \n",
       "4     5      1   24      1       2.0       4.0    2183      2     3.0       2   \n",
       "..  ...    ...  ...    ...       ...       ...     ...    ...     ...     ...   \n",
       "77   78      1   22      1       3.0       0.0    1598      4     4.0       4   \n",
       "78   79      1   33      1       1.0       2.0    1638      1     3.0       2   \n",
       "79   80      1   27      1       2.0       7.0    2612      3     4.0       3   \n",
       "80   81      1   35      2       2.0      16.0    2808      3     4.0       3   \n",
       "81   82      2   35      2       3.0      15.0    2183      3     4.0       4   \n",
       "\n",
       "    palkkat  työteht  työterv  lomaosa  kuntosa  hieroja  palkka_s  \n",
       "0         3        3      NaN      NaN      NaN      NaN  1.204594  \n",
       "1         1        3      NaN      NaN      NaN      NaN  0.469914  \n",
       "2         1        3      1.0      NaN      NaN      NaN -0.676844  \n",
       "3         3        3      1.0      NaN      NaN      NaN -0.494352  \n",
       "4         1        2      1.0      NaN      NaN      NaN -0.448435  \n",
       "..      ...      ...      ...      ...      ...      ...       ...  \n",
       "77        3        4      NaN      1.0      1.0      NaN -1.137196  \n",
       "78        1        2      1.0      NaN      NaN      NaN -1.090102  \n",
       "79        3        3      1.0      NaN      1.0      NaN  0.056657  \n",
       "80        3        3      NaN      NaN      NaN      NaN  0.287422  \n",
       "81        3        4      1.0      NaN      NaN      NaN -0.448435  \n",
       "\n",
       "[82 rows x 17 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lasketaan palkan standardipisteet uuteen muuttujaan\n",
    "df['palkka_s'] = (df['palkka'] - df['palkka'].mean()) / df['palkka'].std()\n",
    "\n",
    "# Nyt datassa on palkan standardipisteet sarakkeessa 'palkka_s'\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lisätietoa: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.norm.html"
   ]
  }
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