{
 "cells": [
  {
   "attachments": {
    "JuryPanels.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The law in Alameda County, California states that a Jury Panel for a particular trial should be selected by chance (randomly) from the list of eligible residents. In this case, there are 1453 eligible residents from which a Jury Panel of 100 should be randomly selected. Then, the lawyers and judges follow a legal process to non-randomly select a jury of 12.![JuryPanels.png](attachment:JuryPanels.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to look at the distribution of the eligible residents and compare that to the distribution of the selected Jury Panel.  We will attempt to determine how likely it is that the jury panel was selected by random chance. You have already learned enough about writing Python code to make this determination.\n",
    "\n",
    "**Please run all code cells in order starint with the import cell below.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datascience import *\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plots\n",
    "plots.style.use('fivethirtyeight')\n",
    "%matplotlib inline\n",
    "\n",
    "np.set_printoptions(legacy='1.13')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing Distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Ethnicity</th> <th>Eligible</th> <th>Panels</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Asian    </td> <td>0.15    </td> <td>0.26  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Black    </td> <td>0.18    </td> <td>0.08  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Latino   </td> <td>0.12    </td> <td>0.08  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>White    </td> <td>0.54    </td> <td>0.54  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Other    </td> <td>0.01    </td> <td>0.04  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "Ethnicity | Eligible | Panels\n",
       "Asian     | 0.15     | 0.26\n",
       "Black     | 0.18     | 0.08\n",
       "Latino    | 0.12     | 0.08\n",
       "White     | 0.54     | 0.54\n",
       "Other     | 0.01     | 0.04"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jury = Table().with_columns(\n",
    "    'Ethnicity', make_array('Asian', 'Black', 'Latino', 'White', 'Other'),\n",
    "    'Eligible', make_array(0.15, 0.18, 0.12, 0.54, 0.01),\n",
    "    'Panels', make_array(0.26, 0.08, 0.08, 0.54, 0.04)\n",
    ")\n",
    "jury"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhkAAAD1CAYAAAD9A+dbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAHNZJREFUeJzt3Xt4VfWd7/HPNxASCBDuVxFoJxREOVQuB5Q6ivQ441gcldFa48jRtocZb8WeMqjttF6wPHgUrT0z2gFH5el4qZZqrfVy0KigKLUqilAjindAQIMJ5v49f+wds9nksjfs317Jzvv1PHlce63fWuu7vybkk3XZy9xdAAAAmZYXdQEAACA3ETIAAEAQhAwAABAEIQMAAARByAAAAEF0j7qAbKioqOAWGgDIccXFxRZ1DdgfRzIAAEAQhAwAABAEIaMLKi8vj7qEDoE+xNCHZvQihj4gUwgZAAAgCEIGAAAIokvcXQIAwMF66aWX8goLCxfl5+dPEH+cJ2usq6vbXF1dvWzKlCmNyQsJGQAAtKGwsHDR4MGDzywoKDjglyikmpqaoz755BNJWpq8jEQGAEAb8vPzJxAwWldQUNAYP8pzAEIGAABt43dl+1rsEY0DAKCDGzBgwJRp06Yd0fR11VVXDZOk2bNnf23dunW9JOnkk0/+q927d3drazuLFy8e8Yc//KGPJI0fP/6o7du3H3DZxBVXXDFiyZIlQzNRd5e7JmP9y5ujLiFyFRUV2l1ZH2kNI4cO0qgRgyOtAQAOxjvvbx/6wfZdPTK1vcOGDaodO2rYjrbGFBQUNG7YsOGNtsY88sgjb7W3r6VLl36Ubn2HosuFjOUrV0ddQuSqqqpUVFQUaQ0LLziNkAGgU/pg+64ey269ryBT21u04EyNHTXskLczfvz4o8rKyjYPGzas/ic/+cnwBx98cMCwYcNq+/fvXz9p0qR9V1555Y5zzz13zEknnVRRWlr6qSQtW7Zs6Pr16/tK0sqVK9+eMGFCTeI2t2zZUrBw4cLD9+zZ072wsLDxlltueXfSpEnVqdbE6RIAADq4mpqavMTTJXfeeWf/1sauW7eu16OPPtr/+eeff+O+++7bumnTplb/quzTp0/Dc889t3n+/Pk7f/SjH41KXn7RRReNvuGGG9574YUXNl9zzTUfXHbZZYenU3eXO5IBAEBnk8rpkiZr167tPWfOnM+Kiopckp9wwgmftTb2O9/5zh5Jmj9//p5rr712v5Cxd+/evI0bN/Y+77zzvto0r7a2Nq0n3RIyAADIIe6e8lgzS5zeb8WGhgb16dOnPtVw0xJOlwAAkENmzZpVuWbNmuJ9+/bZ3r17855++ul+rY29++67B0jSXXfd1X/SpElVicv69+/fOGLEiNpVq1b1l6TGxkZt2LChZzq1cCQDAIAOrumajKbXxx13XMUNN9zwYUtjZ82atW/OnDkVM2bMmDh8+PCaiRMnVhUXFze0NLa2ttaOOeaY8e5ut99++9vJy1euXPn2pZdeOvrmm28eXl9fb6eccsqeadOmfZFq3YQMAADScNiwQbWLFpyZ0e21N2bPnj0vtTT/ySef/EvT9JYtW15rml68ePH266677qPKysq8OXPmfO2SSy7ZIUmrVq3a1sL4jxO3ed111315m+u4ceNq//jHP5an+l6SETIAAEjD2FHDdmTiltOQvv/974/eunVrz5qaGjv99NN3z5w5c18UdRAyAADIMffcc887UdcgceEnAAAIhJABAACCIGQAAIAgIgsZZnaambmZjW9n3CNm1uo9vgAAoGOK8kjG2ZLWSvp2W4Pc/WR3b/UjUQEAyHVNj3o/+uijJ86bN+8rlZWVGf39feuttw5csGBBWs8lSUUkd5eYWW9Jx0o6QdJDkn5mZsMl3Supb7yuf3L3Z81sm6Sp7r7LzH4naZSkQkk3u/uv4turlHSzpFMkfSHpVHdv87G5AAAcDKv9cGhe3Y6MPeq9MX9orfcYmfKj3s8+++yxt9xyy+DLL7+8w/+ei+oW1r+X9Ki7v2lme8zsaMUCx2PuvsTMuknq1cJ657v7HjPrKWmDmT3g7rslFUla7+5XmtkySd+TdG1LO77x0mlh3hHS0rNgpz7/aFekNQwrkj7/aHukNbSlVv20p6owK/sqLz/oz9rJOfQiprP0oaSkJOv7zKvb0aNw1x0Ze9R79aD5augxMuXxM2bMqNy0aVNPSTr11FO/un379h61tbV5559//o6LL754lyQNHz7866WlpTufeuqp4oKCgsb77rvvrZEjR9Z//PHH3S+88MLRH3/8cQ9JWrJkyXuzZ8/e7+PEV61a1X/58uUj8vLyvHfv3g1lZWV/ObCK1EQVMs6WdFN8+p74699Lut3M8iX9zt1faWG9S8zstPj0KEklknZLqpX0cHz+S5K+2dqOSwqfOPTqO7mqqioVFbX65N/safFDbrOnw/ShFTUDSzVwRPh/QMvLyyP5h7ojohcx9KHjqqur05o1a/rOnj17ryStWLFi2+DBgxuqqqrsG9/4xhFnnXXWp0OGDGn44osv8qZPn155/fXXf7hw4cLDbrvttsFXX331xwsXLhx14YUX7jjxxBMrt27d2uP0008vefXVVzcl7mP58uXDV69e/ebo0aPrdu/e3e1Q6s16yDCzgZJmSzoy/sS3bpJc0iJJx0n6O0mrzOx6d78rYb3jJc2RNNPd95lZmWKnTSSpzpsfO9cgPmQMAJBDEp9dMnXq1M8XLFiwS5KWL18+9PHHH+8nSTt27MjfvHlz4ZAhQ6ry8/P9jDPOqJCkyZMnV5WVlfWVpPXr1/fdunXrlw85q6qq6vbZZ5/td33H0UcfXfm9731vzNy5cz8988wzPz2UuqP4ZTxP0l3u/r+aZpjZ04oFjHXu/h9mViTpaEl3JaxXLOnTeMAYL2lGNosGACAqiddkNHnsscf6rFu3rk9ZWdmW3r17N86ePftr1dXVeZLUvXt3z8uLZYdu3bqpvr7epNhj4MvKyjYXFRW1+jz4FStWvPfMM88UPfLII8WzZs2auHbt2k1Dhgw5qGPPUdxdcrak1UnzHpB0h6RXzOxlSWcodiFnokcldTezjZKukbQ+cJ0AAHRYn332Wbe+ffs29O7du3Hjxo2Fr7/+ervnf2fOnLl3+fLlQ5pev/jiiwc8un3Lli0Fxx13XNXSpUs/6tevX/22bdsO+iLXrB/JcPfjW5j3C0m/aGX8mISXf9vKmN4J0/dLuv+QigQAoIObO3duxR133DF4ypQpR4wdO7b6yCOPrGpvnZtuuun9iy+++PApU6Yc0dDQYFOnTv18+vTp7yWOufzyyw979913CyTZjBkz9k6dOjXlR7sns+ZLGXJXRUXFl2+y1/uLoiylQ+joFzxmS0fvQ83AUjX0mhR8P1zk14xexHTWPhQXF1uI7b755purhg4dOqHpdRS3sHZ0O3bs2Dxu3Lhzk+dzgSQAAGnwHiN3pHPLaVfGs0sAAEAQhAwAABAEIQMAgLY1Rl1AJ9BijwgZAAC0oa6ubnNNTQ2/L1tRU1OTV1dXt7mlZVz4CQBAG6qrq5d98sknys/PnyD+OE/WWFdXt7m6unpZSwsJGQAAtGHKlCmNkpZGXUdnRCIDAABBEDIAAEAQhAwAABBEl7smo2ZgadQlRK6yW4W69yuOuozIdfQ+NHYf0v4gAOjAulzIyMazIDq67R+Wq8+IzvdcgkyjDwAQFqdLAABAEIQMAAAQBCEDAAAEQcgAAABBEDIAAEAQhAwAABBEl7uFdf3LLT4orkupqKjQ7sr6qMvIqJFDB2nUiMFRlwEASNDlQsbylaujLiFyVVVVKioqirqMjFp4wWmEDADoYDhdAgAAgiBkAACAIAgZAAAgCEIGAAAIgpABAACCIGQAAIAgCBkAACAIQgYAAAiCkAEAAIIgZAAAgCAIGQAAIIhIQoaZNZjZK2b2qpn92cyOic8fY2avH+Q2y8xsamYrBQAAByuqB6R94e6TJcnMTpL0c0l/HVEtAAAggI5wuqSvpE+TZ8aPajwbP9Lx5dGO+LJFZvZa/EjI0qT18szsTjO7Ngu1AwCAVpi7Z3+nZg2SXpNUKGm4pNnu/pKZjZH0sLsfaWa9JDW6e7WZlUi6292nmtnfSvqJpDnuvs/MBrj7HjMrk7RY0qWSXnf3JU37q6io+PJNflj+RJbeZXhV9X31zo7s///riIYM6KvePfOjLgNAlpWUlHw5XVxcbBGWghZ0hNMlMyXdZWZHJo3Jl/RLM5ssqUHSuPj8OZL+0933SZK770lY5zZJ9yUGjGQlhbkTMmoGlmrchElpr1deXr7fD2ZXRR9i6EMzehFDH5ApkZ8ucffnJQ2SNDhp0UJJOyT9N0lTJfWIzzdJrf35/pykE8ysMECpAAAgDZGHDDMbL6mbpN1Ji4olfezujZLOjY+RpMclnR8/nSIzG5CwzkpJj0j6jZlFdZQGAAAoutMlPc3slfi0STrP3RvM9jud9m+SHjCzf5D0lKQqSXL3R+OnUP5kZrWKhYormlZy9xvNrFjSKjM7Jx5SAABAlkUSMty9Wyvzt0k6Mj5dLinxgoPLE8YtlbQ0ad3jE6Z/mrlqAQDAwYj8dAkAAMhNhAwAABAEIQMAAARByAAAAEEQMgAAQBCEDAAAEAQhAwAABEHIAAAAQRAyAABAEIQMAAAQBCEDAAAE0eWeVFozsDTqEjKmsfuQqEsAAKBVKYcMMxvo7smPY+90GnpNan8QAAA4ZOmcLnnfzB40s3lm1iNYRQAAICekEzJGS1oj6V8kbTezX5nZrDBlAQCAzi7lkOHun7j7L9x9mqSZknZKWmVmb5vZ1WY2OliVAACg0znYu0uGxb/6StoqaaSkl81scaYKAwAAnVs6F35OlFQq6RxJlZLulDTJ3T+ML79G0kZJSwPUCQAAOpl0bmF9RtLdkua5+4vJC919m5ndlLHKAABAp5ZOyDjN3Z9Jnmlm05tCh7v/a8YqC2T9y5ujLiFyFRUV2l1Zn9Y6I4cO0qgRgwNVBADIRemEjIcVuwYj2aOSBmSmnPCWr1wddQmRq6qqUlFRUVrrLLzgNEIGACAt7YYMM8uTZLFJs/h0k69KSu9PYgAA0CWkciSjXpInTCdqlLQkoxUBAICckErIGKvY0YunJR2XMN8lfeLuX4QoDAAAdG7thgx3fzc+yYdtAQCAlLUZMszsV+7+/fj0Xa2Nc/d/zHRhAACgc2vvSMY7CdNbQxYCAAByS5shw91/njB9VfhyAABArkj52SVmttjMpiXNm25mizJfFgAA6OzSeUDapZLeSJr3hqQfZK4cAACQK9IJGT0k1SXNq5VUmLlyAABArkgnZLwk6Z+T5i2Q9OfMlQMAAHJFOs8uWSjpCTM7V7E7Tf5K0lBJ32xvRTOrdPfeqezEzI6XVOvuz8VfL5C0z91bvYUWAAB0PCmHDHffZGbjJJ0iaZSk30p62N0rM1zT8ZIqJT0X3++tGd4+AADIgnSOZCgeKO7JxI7N7FuSfqzYtR67JZ0jqadip2AazKxU0sWSTpRU6e7/x8zKJL0g6QRJ/SRd4O7PmlmhpH+XNFWx56tc5u5PZaJOAABwcNr7xM9H3f1v4tPPqvlBaftx9+Namt+OtZJmuLub2XclLXL3H5rZrYqHivh+T0yu2d2nm9nJkn4qaY6kC+N1HGVm4yU9bmbj3L36IOoCAAAZ0N6RjMTrIFZkeN+HSbrXzIYrdjTjnXbGN/lt/L8vSRoTn54l6RZJcvctZvaupHGSNiavfOOl05JndVpV9X31zo4Wc1/GFXRzlZeXZ2Vf2ZSL7+lg0Idm9CKms/ShpKQk6hLQhvY+8fO/EqbvzPC+b5F0o7s/FL/Y82cprlcT/2+Dmuu3VHdaUvhEqkM7vJqBpRo3YVLa65WXl/ODKfrQhD40oxcx9AGZktY1GWb2PyRNlrTfnSLu/q8Hse9iSR/Gp89LmP+5pL5pbusZxa7peDJ+cerhkv5yEDUBAIAMSTlkmNkvJZ0p6SlJ+9LcTy8z+yDh9Y2KHbn4jZl9KGm9pLHxZb+XdL+ZnarYhZ+p+DdJt5rZa4pd+Dnf3WvaWQcAAASUzpGMsyVNdvf3092Ju7f2oV8PtjD2TUmJ5wCeTVh2fML0LsWvyYhf4Dk/3boAAEA46Xzi525Jn4UqBAAA5JZ0jmTcIOnXZvZzSTsSF7j72xmtCgAAdHrphIx/j//3lKT5LqlbZsoBAAC5Ip2PFU/n1AoAAOjiCA4AACCIdG5hHStpiVr+nIzDM1wXAADo5NK5JuO/FHvE+w+V/udkAACALiadkDFR0rHu3hiqGAAAkDvSuSbjGUlfD1UIAADILe096v3qhJfbJD1mZr+VtD1x3EE+uwQAAOSw9k6XjEp6/XtJ+S3MBwAA2E97j3r/n9kqJFtqBpZGXULGNHYfEnUJAAC0Kp1bWPe4+4AW5u90907z266h16T2BwEAgEOWzoWf+ckzzCxffKQ4AABoQbtHMszsWcWeT1JoZs8kLT5M0vMhCgMAAJ1bKqdLVkgySdMkrUyY74o9jXVNgLoAAEAnl0rImOLul5jZenffYmYXuPuXYcPMHpB0RrgSAQBAZ5TKNRnzJcndt8RfX5+0/JuZLAgAAOSGVEKGtfMaAADgAKmcLvF2Xncq61/eHHUJkauoqNDuyvqoy4gcfYihD83oRUy2+jBy6CCNGjE4+H4QnVRCRnczO0HNRzCSX3eqW1iXr1wddQmRq6qqUlFRUdRlRI4+xNCHZvQiJlt9WHjBaYSMHJdKyNgp6faE17uTXu/MaEUAACAntBsy3H1MFuoAAAA5Jp1P/AQAAEgZIQMAAARByAAAAEEQMgAAQBCEDAAAEAQhAwAABEHIAAAAQRAyAABAEIQMAAAQRNZChpktN7MfJLx+zMxWJLy+wcwuM7OHW1l/hZkdEZ++InzFAADgUGTzSMZzko6RJDPLkzRI0sSE5cdIym9tZXf/rru/EX9JyAAAoIPLZshYp3jIUCxcvC7pczPrb2YFkiZIellSbzO738y2mNmvzcwkyczKzGyqmS2V1NPMXjGzX8eXlZrZi/F5t5lZp3oyLAAAuShrIcPdP5JUb2aHKxY2npf0gqSZkqZK2iipVtLXJf1A0hGSviLp2KTtLJb0hbtPdvdzzGyCpLMkHevukyU1SDonO+8KAAC0JpVHvWdS09GMYyTdKGlkfLpCsdMpkvSiu38gSWb2iqQxkta2sc0TJU2RtCF+0KOn2nj8/I2XTjukNwAAyAzrUavy8vJD2kZJSUmGqkEI2Q4ZTddlHKXY6ZL3Jf1Q0l5Jt8fH1CSMb1D7NZqkO9398lQKKCl8Ip16c1JVVZWKioqiLiNy9CGGPjSjFzHZ6kPNwFI19CIk5LJs38K6TtIpkva4e4O775HUT7FTJs+nsZ06M2u6SHSNpHlmNkSSzGyAmY3OZNEAACB92Q4Zryl2V8n6pHkV7r4rje38StJGM/t1/I6TH0t63Mw2SnpC0vBMFQwAAA6OuXvUNQRXUVHx5Zvs9f6iKEvpEDgkHEMfYuhDM3oRk93TJZMytr3i4mLL2MaQEXziJwAACIKQAQAAgiBkAACAIAgZAAAgCEIGAAAIgpABAACCIGQAAIAgCBkAACAIQgYAAAiCkAEAAIIgZAAAgCCy/aj3yNUMLI26hMhVdqtQ937FUZcROfoQQx+a0YuYbPWhsfuQ4PtAtLpcyMjkw3g6q+0flqvPiJKoy4gcfYihD83oRQx9QKZwugQAAARByAAAAEEQMgAAQBCEDAAAEAQhAwAABEHIAAAAQXS5W1jXv7z5gHkjhw7SqBGDI6gGAIDc1eVCxvKVqw+Yt/CC0wgZAABkGKdLAABAEIQMAAAQBCEDAAAEQcgAAABBEDIAAEAQhAwAABAEIQMAAARByAAAAEEQMgAAQBCEDAAAEAQhAwAABJH1kGFmh5nZg2ZWbmZbzexmM+thZpPN7OSEcT8zs/+d7foAAEBmZDVkmJlJ+q2k37l7iaRxknpLWiJpsqST21g93X11y9S2AABA+rJ9JGO2pGp3/09JcvcGSQslfVfSMklnmdkrZnZWfPwRZlZmZm+b2SVNGzGzUjN7MT72tqZAYWaVZna1mb0gaWZW3xkAANhPtkPGREkvJc5w972Stkm6VtK97j7Z3e+NLx4v6SRJ0yX91MzyzWyCpLMkHevukyU1SDonPr5I0uvu/t/dfW3wdwMAAFrVPcv7M0mexvw/uHuNpBoz2ylpqKQTJU2RtCF29kU9Je2Mj2+Q9EBbBdx46bQD5vUs2KnPP9qV4ltoX636aU9VYca2F0J5eXnUJXQI9CGGPjSjFzGdpQ8lJSVRl4A2ZDtkbJJ0RuIMM+sraZRiASFZTcJ0g2L1mqQ73f3yFsZXx0/BtKqk8ImWF7S5VnpqBpZq4IiO+41fXl7OD6boQxP60IxexNAHZEq2T5eskdTLzP5R+vLizBsk3SFph6Q+KW5jnpkNiW9jgJmNDlMuAAA4WFkNGe7ukk6T9A9mVi7pTUnVkq6Q9JRiF3omXvjZ0jbekPRjSY+b2UZJT0gaHrx4AACQlmyfLpG7vy/pWy0sqpF04AUTzesdmTB9r6R7WxjTOxM1AgCAQ8cnfgIAgCAIGQAAIAhCBgAACIKQAQAAgiBkAACAIAgZAAAgCEIGAAAIgpABAACCIGQAAIAgCBkAACAIQgYAAAgi688uiVrNwNLg+2jsPiT4PgAA6Oi6XMho6DUp6hIAAOgSOF0CAACCIGQAAIAgCBkAACAIQgYAAAiCkAEAAIIgZAAAgCAIGQAAIAhz96hrCK6ioiL33yQAdHHFxcUWdQ3YH0cyAABAEIQMAAAQRJc4XQIAALKPIxkAACCInAsZZvY3ZvYXM3vLzBa3sLzAzO6NL3/BzMZkv8rwUujDcWb2ZzOrN7N5UdSYLSn04jIze8PMNprZGjMbHUWdoaXQhwVm9pqZvWJma83siCjqDK29PiSMm2dmbmZTs1lfNqXwPTHfzD6Jf0+8YmbfjaJOdGLunjNfkrpJ2irpK5J6SHpV0hFJY/5Z0q3x6W9LujfquiPqwxhJkyTdJWle1DVH3IsTJPWKT/9TF/6e6JswPVfSo1HXHUUf4uP6SHpG0npJU6OuO8LvifmSfhl1rXx13q9cO5IxXdJb7v62u9dKukfSqUljTpV0Z3z6fkknmlmu3fbUbh/cfZu7b5TUGEWBWZRKL55y933xl+slHZblGrMhlT7sTXhZJCkXL9hK5d8ISbpG0jJJ1dksLstS7QVw0HItZIyU9H7C6w/i81oc4+71kiokDcxKddmTSh+6inR7cYGkPwatKBop9cHMLjSzrYr9gr0kS7VlU7t9MLOvSxrl7g9ns7AIpPqzcUb8VOL9ZjYqO6UhV+RayGjpiETyX2OpjOnsusJ7TFXKvTCzUklTJV0ftKJopNQHd/+/7v5VSf8i6cfBq8q+NvtgZnmSlkv6YdYqik4q3xO/lzTG3SdJ+n9qPgoMpCTXQsYHkhKT9mGSPmptjJl1l1QsaU9WqsueVPrQVaTUCzObI+lKSXPdvSZLtWVTut8T90j6+6AVRaO9PvSRdKSkMjPbJmmGpIdy9OLPdr8n3H13ws/Df0iakqXakCNyLWRskFRiZmPNrIdiF3Y+lDTmIUnnxafnSXrS3XPtr/xU+tBVtNuL+OHx2xQLGDsjqDEbUulDScLLv5NUnsX6sqXNPrh7hbsPcvcx7j5GsWt05rr7n6IpN6hUvieGJ7ycK2lzFutDDugedQGZ5O71ZnaRpMcUu3L6dnffZGZXS/qTuz8kaaWkVWb2lmJHML4dXcVhpNIHM5smabWk/pK+ZWZXufvECMsOIsXviesl9Zb0m/g1wO+5+9zIig4gxT5cFD+iUyfpUzWH8ZyRYh+6hBR7cYmZzZVUr9i/l/MjKxidEp/4CQAAgsi10yUAAKCDIGQAAIAgCBkAACAIQgYAAAiCkAEAAIIgZAAAgCAIGQAAIAhCBgAACOL/A1rQaKEWHUjAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "jury.barh('Ethnicity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Ethnicity</th> <th>Eligible</th> <th>Panels</th> <th>Difference</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Asian    </td> <td>0.15    </td> <td>0.26  </td> <td>0.11      </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Black    </td> <td>0.18    </td> <td>0.08  </td> <td>-0.1      </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Latino   </td> <td>0.12    </td> <td>0.08  </td> <td>-0.04     </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>White    </td> <td>0.54    </td> <td>0.54  </td> <td>0         </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Other    </td> <td>0.01    </td> <td>0.04  </td> <td>0.03      </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "Ethnicity | Eligible | Panels | Difference\n",
       "Asian     | 0.15     | 0.26   | 0.11\n",
       "Black     | 0.18     | 0.08   | -0.1\n",
       "Latino    | 0.12     | 0.08   | -0.04\n",
       "White     | 0.54     | 0.54   | 0\n",
       "Other     | 0.01     | 0.04   | 0.03"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jury_with_diffs = jury.with_column('Difference', jury.column('Panels') - jury.column('Eligible'))\n",
    "jury_with_diffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Ethnicity</th> <th>Eligible</th> <th>Panels</th> <th>Difference</th> <th>Absolute Difference</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Asian    </td> <td>0.15    </td> <td>0.26  </td> <td>0.11      </td> <td>0.11               </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Black    </td> <td>0.18    </td> <td>0.08  </td> <td>-0.1      </td> <td>0.1                </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Latino   </td> <td>0.12    </td> <td>0.08  </td> <td>-0.04     </td> <td>0.04               </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>White    </td> <td>0.54    </td> <td>0.54  </td> <td>0         </td> <td>0                  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Other    </td> <td>0.01    </td> <td>0.04  </td> <td>0.03      </td> <td>0.03               </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "Ethnicity | Eligible | Panels | Difference | Absolute Difference\n",
       "Asian     | 0.15     | 0.26   | 0.11       | 0.11\n",
       "Black     | 0.18     | 0.08   | -0.1       | 0.1\n",
       "Latino    | 0.12     | 0.08   | -0.04      | 0.04\n",
       "White     | 0.54     | 0.54   | 0          | 0\n",
       "Other     | 0.01     | 0.04   | 0.03       | 0.03"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jury_with_diffs = jury_with_diffs.with_column('Absolute Difference', np.abs(jury_with_diffs.column('Difference')))\n",
    "jury_with_diffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.28000000000000003"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(jury_with_diffs.column('Absolute Difference'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14000000000000001"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(jury_with_diffs.column('Absolute Difference')) / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def total_variation_distance(distribution_1, distribution_2):\n",
    "    return sum(np.abs(distribution_1 - distribution_2)) / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14000000000000001"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_variation_distance(jury.column('Panels'), jury.column('Eligible'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far, we have a useful table and a way to calculate how far one distribution is from another.  Let's use what we have done to explore whether the Jury Panel that was selected was likely to have been selected by random chance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "eligible = jury.column('Eligible')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Ethnicity</th> <th>Eligible</th> <th>Panels</th> <th>Random Sample</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Asian    </td> <td>0.15    </td> <td>0.26  </td> <td>0.131452     </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Black    </td> <td>0.18    </td> <td>0.08  </td> <td>0.175499     </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Latino   </td> <td>0.12    </td> <td>0.08  </td> <td>0.105988     </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>White    </td> <td>0.54    </td> <td>0.54  </td> <td>0.576738     </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Other    </td> <td>0.01    </td> <td>0.04  </td> <td>0.0103235    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "Ethnicity | Eligible | Panels | Random Sample\n",
       "Asian     | 0.15     | 0.26   | 0.131452\n",
       "Black     | 0.18     | 0.08   | 0.175499\n",
       "Latino    | 0.12     | 0.08   | 0.105988\n",
       "White     | 0.54     | 0.54   | 0.576738\n",
       "Other     | 0.01     | 0.04   | 0.0103235"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "panels_and_sample = jury.with_column('Random Sample', sample_proportions(1453, eligible))\n",
    "panels_and_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "panels_and_sample.barh('Ethnicity')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Qualitatively, we see that there is a noticeable difference in what was actually selected for the Jury Panel and what we would expect if the Jury Panel were selected by chance. Let's put some number to this.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.037061252580867163"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We have already defined a function named total_variation distance that takes two distirubtions as input\n",
    "total_variation_distance(panels_and_sample.column('Random Sample'), eligible)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14000000000000001"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_variation_distance(jury.column('Panels'), eligible)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like the random sample produces results close the the ethnicity of the eligible population.  But, the Jury Panel that was actually selected is not that close.  Maybe we just had bad luck with the one random sample that we took.  Let's take 10,000 random samples and see if any of them come close to the actual Jury Panel that was selected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "tvds = make_array()\n",
    "\n",
    "repetitions = 10000\n",
    "for i in np.arange(repetitions):\n",
    "    sample_distribution = sample_proportions(1453, eligible)\n",
    "    new_tvd = total_variation_distance(sample_distribution, eligible)\n",
    "    tvds = np.append(tvds, new_tvd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Table().with_column('Total Variation Distance', tvds).hist(bins = np.arange(0, 0.2, 0.005), ec='w')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of the random samples show a very small total_variation_distance from the eligible population.  The Jury Panel that was actually selected showed a total_variation_distance of .14 which would be way tothe right on the chart above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_variation_distance(jury.column('Panels'), eligible)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the charts and numbers you have seen, do you think our Jury Panel selected by random chance or was something else involved?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
