{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%pylab inline\n",
    "\n",
    "pylab.rcParams['figure.figsize'] = (12, 7)\n",
    "\n",
    "from pop_rel_5_statsmodel import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:            log_streams   R-squared:                       0.718\n",
      "Model:                            OLS   Adj. R-squared:                  0.658\n",
      "Method:                 Least Squares   F-statistic:                     11.96\n",
      "Date:                Fri, 21 Sep 2018   Prob (F-statistic):          1.18e-188\n",
      "Time:                        15:50:15   Log-Likelihood:                -2502.4\n",
      "No. Observations:                1344   AIC:                             5479.\n",
      "Df Residuals:                    1107   BIC:                             6712.\n",
      "Df Model:                         236                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==========================================================================================\n",
      "                             coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------------\n",
      "const                      7.7781      0.145     53.778      0.000       7.494       8.062\n",
      "0EVzFau2kULH9e5dwK2OSI    -0.0658      0.703     -0.094      0.925      -1.445       1.313\n",
      "0JyJ4Nt68jBV0sJUErDlmx    -0.2151      0.512     -0.420      0.675      -1.221       0.790\n",
      "0NCspsyf0OS4BsPgGhkQXM     0.3087      0.468      0.659      0.510      -0.610       1.228\n",
      "0RKnf4gm6sb7iosMZyyW1s    -0.4714      0.823     -0.573      0.567      -2.086       1.144\n",
      "0eeKZp7perkDfgJQt4pc6I     0.0989      0.443      0.223      0.823      -0.771       0.969\n",
      "10S981NoNhgn9tdUq9jNFE    -0.4937      0.707     -0.699      0.485      -1.880       0.893\n",
      "1Hu8ON3GQpC1HxeYqyzD1M     0.2620      0.589      0.445      0.657      -0.894       1.418\n",
      "1T2NZYhVC8SqqJfdPxuHNd -4.163e-14   2.17e-14     -1.919      0.055   -8.42e-14    9.31e-16\n",
      "1g4LsUsgYBvR1AiwN16SoI    -2.1955      0.189    -11.610      0.000      -2.567      -1.824\n",
      "20tce2ASDrStpxljpEDSDI    -3.1121      0.709     -4.387      0.000      -4.504      -1.720\n",
      "2QEXsFWUdk2DsJxhThUFwH    -0.6484      0.366     -1.773      0.077      -1.366       0.069\n",
      "2VlzkJvSAd3PnLzo6llbL8    -0.0204      0.412     -0.049      0.961      -0.829       0.788\n",
      "2X5BPknxexv0e7DdDKUj0C  4.435e-14   2.64e-14      1.682      0.093   -7.38e-15    9.61e-14\n",
      "2advhj7iNxHv32Ix2vpVX8    -0.8872      0.497     -1.784      0.075      -1.863       0.089\n",
      "2iMjNpXB1KzwxEryqITNut    -0.3076      0.889     -0.346      0.729      -2.051       1.436\n",
      "2imbTn8YAeXVwvBhOyXUR0    -0.8063      1.910     -0.422      0.673      -4.554       2.942\n",
      "2qTeRwnwFquJUKrAFWnolb     2.8367      1.724      1.646      0.100      -0.546       6.219\n",
      "2tj1iOLK9Uwef7ooLgJMtJ    -1.0859      0.489     -2.219      0.027      -2.046      -0.126\n",
      "307SnTaQdp4wWjQuOU5ivP     5.4137      1.219      4.440      0.000       3.021       7.806\n",
      "33yjWFG5onxZu3HdIzO1Zu     0.9727      0.622      1.564      0.118      -0.248       2.193\n",
      "37i9dQZEVXbINTEnbFeb8d    -0.6784      1.760     -0.385      0.700      -4.132       2.776\n",
      "37i9dQZEVXbIP3c3fqVrJY     0.0743      4.149      0.018      0.986      -8.067       8.216\n",
      "37i9dQZEVXbIPOivNiyjjS     2.4487      1.541      1.589      0.112      -0.575       5.472\n",
      "37i9dQZEVXbIPWwFssbupI     0.4496      1.256      0.358      0.721      -2.016       2.915\n",
      "37i9dQZEVXbIQnj7RRhdSX    -0.5194      1.222     -0.425      0.671      -2.918       1.879\n",
      "37i9dQZEVXbIUY6VUoboP4     1.6629      1.275      1.304      0.193      -0.839       4.165\n",
      "37i9dQZEVXbIVYVBNw9D5K     1.5367      0.899      1.709      0.088      -0.227       3.301\n",
      "37i9dQZEVXbJ7gPAehey5W     2.0745      1.751      1.185      0.236      -1.361       5.510\n",
      "37i9dQZEVXbJNSeeHswcKB    -0.4983      1.821     -0.274      0.784      -4.071       3.074\n",
      "37i9dQZEVXbJPcfkRz0wJ0     0.5099      3.798      0.134      0.893      -6.942       7.961\n",
      "37i9dQZEVXbJVi45MafAu0  6.492e-14   2.99e-14      2.174      0.030    6.32e-15    1.24e-13\n",
      "37i9dQZEVXbJWZV7aRNQck     4.9726      1.743      2.852      0.004       1.552       8.393\n",
      "37i9dQZEVXbJWuzDrTxbKS    -1.2533      3.537     -0.354      0.723      -8.194       5.687\n",
      "37i9dQZEVXbJajpaXyaKll    -1.0003      0.733     -1.364      0.173      -2.439       0.439\n",
      "37i9dQZEVXbJcpVBLdFV7m    -0.4116      0.601     -0.684      0.494      -1.592       0.768\n",
      "37i9dQZEVXbJfdy5b0KP7W     0.4451      0.840      0.530      0.596      -1.202       2.092\n",
      "37i9dQZEVXbJiZcmkrIHGU    -0.6301      1.723     -0.366      0.715      -4.011       2.751\n",
      "37i9dQZEVXbJiyhoAPEfMK     1.3663      2.413      0.566      0.571      -3.369       6.102\n",
      "37i9dQZEVXbJlM6nvL1nD1     1.5453      0.894      1.729      0.084      -0.208       3.299\n",
      "37i9dQZEVXbJlfUljuZExa   -10.6298      4.039     -2.632      0.009     -18.556      -2.704\n",
      "37i9dQZEVXbJmRv5TqJW16     1.4348      0.865      1.659      0.097      -0.262       3.132\n",
      "37i9dQZEVXbJp9wcIM9Eo5     0.4451      0.840      0.530      0.596      -1.202       2.092\n",
      "37i9dQZEVXbJpRQ294oZ9N    -0.3946      1.105     -0.357      0.721      -2.563       1.774\n",
      "37i9dQZEVXbJqdarpmTJDL    -0.0501      2.427     -0.021      0.984      -4.812       4.711\n",
      "37i9dQZEVXbJqfMFK4d691     3.4947      1.412      2.475      0.013       0.724       6.266\n",
      "37i9dQZEVXbJs8e2vk15a8    -0.4116      0.601     -0.684      0.494      -1.592       0.768\n",
      "37i9dQZEVXbJv2Mvelmc3I     1.6629      1.275      1.304      0.193      -0.839       4.165\n",
      "37i9dQZEVXbJvfa0Yxg7E7    -0.4704      1.620     -0.290      0.772      -3.649       2.708\n",
      "37i9dQZEVXbJx9hUtTN0Sj    -0.4116      0.601     -0.684      0.494      -1.592       0.768\n",
      "37i9dQZEVXbK3Iy2zvpfp4     0.3099      1.826      0.170      0.865      -3.272       3.892\n",
      "37i9dQZEVXbK4KA2JSuft7    -0.4116      0.601     -0.684      0.494      -1.592       0.768\n",
      "37i9dQZEVXbK4gjvS1FjPY    16.0639      6.266      2.564      0.010       3.769      28.359\n",
      "37i9dQZEVXbKAbrMR8uuf7    -0.6369      1.010     -0.630      0.529      -2.619       1.346\n",
      "37i9dQZEVXbKCF6dqVpDkS    -0.0824      0.618     -0.133      0.894      -1.294       1.129\n",
      "37i9dQZEVXbKHoaIcElSSA    -0.4116      0.601     -0.684      0.494      -1.592       0.768\n",
      "37i9dQZEVXbKIVTPX9a2Sb    -0.5836      0.889     -0.657      0.511      -2.327       1.160\n",
      "37i9dQZEVXbKM896FDX8L1    -8.1350      3.300     -2.465      0.014     -14.610      -1.660\n",
      "37i9dQZEVXbKMzVsSGQ49S    -1.1881      0.654     -1.816      0.070      -2.472       0.096\n",
      "37i9dQZEVXbKNHh6NIXu36    -0.1370      2.427     -0.056      0.955      -4.898       4.624\n",
      "37i9dQZEVXbKOefHPXPMyf     0.8345      1.719      0.486      0.627      -2.538       4.207\n",
      "37i9dQZEVXbKXQ4mDTEBXq     0.0089      2.427      0.004      0.997      -4.753       4.770\n",
      "37i9dQZEVXbKXd6qahcpCg    -0.6787      1.216     -0.558      0.577      -3.065       1.708\n",
      "37i9dQZEVXbKbvcwe5owJ1    -0.2869      1.718     -0.167      0.867      -3.658       3.085\n",
      "37i9dQZEVXbKfIuOAZrk7G    -0.4116      0.601     -0.684      0.494      -1.592       0.768\n",
      "37i9dQZEVXbKgCVIE0PTOD    -0.5887      0.629     -0.936      0.350      -1.823       0.646\n",
      "37i9dQZEVXbKj23U1GF4IR     1.5665      1.180      1.328      0.184      -0.748       3.881\n",
      "37i9dQZEVXbKpV6RVDTWcZ -4.942e-14    2.1e-14     -2.351      0.019   -9.07e-14   -8.18e-15\n",
      "37i9dQZEVXbKrooeK9WSFF    -1.5003      1.718     -0.873      0.383      -4.871       1.871\n",
      "37i9dQZEVXbKuaTI1Z1Afx     0.0943      0.892      0.106      0.916      -1.656       1.845\n",
      "37i9dQZEVXbKxYYIUIgn7V    -0.4116      0.601     -0.684      0.494      -1.592       0.768\n",
      "37i9dQZEVXbKyJS56d1pgi     6.8712      3.834      1.792      0.073      -0.652      14.394\n",
      "37i9dQZEVXbKypXHVwk1f0     1.0338      0.620      1.666      0.096      -0.184       2.251\n",
      "37i9dQZEVXbL0GavIqMTeb     1.9012      1.601      1.187      0.235      -1.241       5.043\n",
      "37i9dQZEVXbL3DLHfQeDmV    -2.7799      2.039     -1.363      0.173      -6.781       1.221\n",
      "37i9dQZEVXbL3J0k32lWnN     7.3116      1.721      4.248      0.000       3.935      10.688\n",
      "37i9dQZEVXbLDLOTfCtAUM     1.6629      1.275      1.304      0.193      -0.839       4.165\n",
      "37i9dQZEVXbLOov4J0GutU     1.6629      1.275      1.304      0.193      -0.839       4.165\n",
      "37i9dQZEVXbLRQDuF5jeBp    -4.9822      2.842     -1.753      0.080     -10.559       0.595\n",
      "37i9dQZEVXbLRmg3qDbY1H    -0.4116      0.601     -0.684      0.494      -1.592       0.768\n",
      "37i9dQZEVXbLesry2Qw2xS     0.0218      1.566      0.014      0.989      -3.052       3.095\n",
      "37i9dQZEVXbLiRSasKsNU9    -0.4116      0.601     -0.684      0.494      -1.592       0.768\n",
      "37i9dQZEVXbLnolsZ8PSNw     5.7843      4.031      1.435      0.152      -2.125      13.694\n",
      "37i9dQZEVXbLo3yC8XJf1e     1.4355      1.516      0.947      0.344      -1.538       4.409\n",
      "37i9dQZEVXbLoATJ81JYXz    -0.6355      1.173     -0.542      0.588      -2.936       1.666\n",
      "37i9dQZEVXbLuUZrygauiA    -2.4809      1.771     -1.401      0.162      -5.956       0.994\n",
      "37i9dQZEVXbLuey1EKVv9I    -0.4116      0.601     -0.684      0.494      -1.592       0.768\n",
      "37i9dQZEVXbLwpL8TjsxOG    -5.7080      3.004     -1.900      0.058     -11.602       0.187\n",
      "37i9dQZEVXbLxoIml4MYkT     0.4451      0.840      0.530      0.596      -1.202       2.092\n",
      "37i9dQZEVXbLy5tBFyQvd4     1.5453      0.894      1.729      0.084      -0.208       3.299\n",
      "37i9dQZEVXbLzhUVGQUCoe    -0.4116      0.601     -0.684      0.494      -1.592       0.768\n",
      "37i9dQZEVXbM1qaaFAyPLz    -0.5887      0.629     -0.936      0.350      -1.823       0.646\n",
      "37i9dQZEVXbM8SIrkERIYl    -8.3462      4.471     -1.867      0.062     -17.119       0.427\n",
      "37i9dQZEVXbMA8BIYDeMkD    -2.2657      1.595     -1.421      0.156      -5.395       0.864\n",
      "37i9dQZEVXbMBNcyQCfU4w     1.3139      1.217      1.080      0.281      -1.074       3.702\n",
      "37i9dQZEVXbMBUm3g7j4Kb    -1.4789      1.718     -0.861      0.390      -4.850       1.892\n",
      "37i9dQZEVXbMDoHDwVN2tF     7.4014      4.278      1.730      0.084      -0.993      15.796\n",
      "37i9dQZEVXbMGnTCc4Vx7v     1.8277      2.104      0.869      0.385      -2.301       5.956\n",
      "37i9dQZEVXbMHnoaLVkVuk     2.0745      1.751      1.185      0.236      -1.361       5.510\n",
      "37i9dQZEVXbMIJZxwqzod6    -2.8527      1.160     -2.459      0.014      -5.129      -0.576\n",
      "37i9dQZEVXbMIO7B1pcKUy    -0.3464      1.255     -0.276      0.783      -2.808       2.116\n",
      "37i9dQZEVXbMJJi3wgRbAy    -1.3559      0.553     -2.453      0.014      -2.441      -0.271\n",
      "37i9dQZEVXbMMy2roB9myp    -1.5042      1.212     -1.241      0.215      -3.882       0.874\n",
      "37i9dQZEVXbMOkSwG072hV    -0.4116      0.601     -0.684      0.494      -1.592       0.768\n",
      "37i9dQZEVXbMQaPQjt027d    -1.1165      1.119     -0.998      0.319      -3.313       1.080\n",
      "37i9dQZEVXbMTKZuy8ORFV    -0.0739      1.656     -0.045      0.964      -3.322       3.175\n",
      "37i9dQZEVXbMXbN3EUUhlg    -0.7043      1.003     -0.702      0.483      -2.672       1.263\n",
      "37i9dQZEVXbMZAjGMynsQX     1.0338      0.620      1.666      0.096      -0.184       2.251\n",
      "37i9dQZEVXbMfVLvbaC3bj    -0.4116      0.601     -0.684      0.494      -1.592       0.768\n",
      "37i9dQZEVXbMjKD6qnoc8p     2.6313      1.886      1.395      0.163      -1.070       6.332\n",
      "37i9dQZEVXbMnZEatlMSiu     1.7937      2.087      0.859      0.390      -2.302       5.890\n",
      "37i9dQZEVXbMnf7ONzeQWM    -0.4887      2.427     -0.201      0.840      -5.250       4.273\n",
      "37i9dQZEVXbMnz8KIWsvf9    -1.4647      1.719     -0.852      0.394      -4.837       1.908\n",
      "37i9dQZEVXbMx56Rdq5lwc     9.6181      6.193      1.553      0.121      -2.533      21.769\n",
      "37i9dQZEVXbMxcczTSoGwZ     0.2999      0.651      0.460      0.645      -0.978       1.578\n",
      "37i9dQZEVXbN6itCcaL3Tt    -0.6573      2.788     -0.236      0.814      -6.128       4.813\n",
      "37i9dQZEVXbN7gfhgaomhA    -1.6735      1.374     -1.218      0.224      -4.370       1.023\n",
      "37i9dQZEVXbNBxnXSWuAcX     1.6629      1.275      1.304      0.193      -0.839       4.165\n",
      "37i9dQZEVXbNBz9cRCSFkY    -9.5983      6.033     -1.591      0.112     -21.435       2.238\n",
      "37i9dQZEVXbNF1heNYHDnE     0.7935      1.095      0.725      0.469      -1.355       2.942\n",
      "37i9dQZEVXbNFJfN1Vw8d9    -0.4597      1.204     -0.382      0.703      -2.822       1.902\n",
      "37i9dQZEVXbNGGDnE9UFTF    -0.4116      0.601     -0.684      0.494      -1.592       0.768\n",
      "37i9dQZEVXbNHwMxAkvmF8    -8.7818      3.739     -2.349      0.019     -16.117      -1.446\n",
      "37i9dQZEVXbNOUPGj7tW6T     1.0338      0.620      1.666      0.096      -0.184       2.251\n",
      "37i9dQZEVXbNjqq6Tw4Fb0     2.8539      0.979      2.914      0.004       0.932       4.776\n",
      "37i9dQZEVXbNpKdqfZ9Upp    -0.5651      1.512     -0.374      0.709      -3.533       2.403\n",
      "37i9dQZEVXbNv6cjoMVCyg     1.4236      1.277      1.114      0.265      -1.083       3.930\n",
      "37i9dQZEVXbNxY4E5g33Gy     0.6184      1.182      0.523      0.601      -1.701       2.938\n",
      "37i9dQZEVXbO3qyFxbkOE1    -1.5042      1.212     -1.241      0.215      -3.882       0.874\n",
      "37i9dQZEVXbO5MSE9RdfN2    -0.4116      0.601     -0.684      0.494      -1.592       0.768\n",
      "37i9dQZEVXbOa2lmxNORXQ    -0.9881      1.248     -0.792      0.429      -3.436       1.460\n",
      "37i9dQZEVXbObFQZ3JLcXt    -0.2807      2.131     -0.132      0.895      -4.462       3.900\n",
      "37i9dQZEVXbOcsE2WCaJa2    -2.0945      2.607     -0.803      0.422      -7.210       3.021\n",
      "37i9dQZEVXbooCkvhE2523     0.7450      1.217      0.612      0.541      -1.643       3.133\n",
      "37i9dQZF1DWSB4xFQzDS0k     0.6171      0.725      0.851      0.395      -0.806       2.040\n",
      "37i9dQZF1DWSBZhfF4ZHr8     0.1008      0.817      0.123      0.902      -1.502       1.704\n",
      "37i9dQZF1DWSJHnPb1f0X3     0.0756      0.782      0.097      0.923      -1.458       1.609\n",
      "37i9dQZF1DWSK8os4XIQBk     1.6537      0.812      2.036      0.042       0.060       3.247\n",
      "37i9dQZF1DWSOnWAjPOVxs     2.4062      1.719      1.400      0.162      -0.966       5.778\n",
      "37i9dQZF1DWSVtp02hITpN    -0.1822      2.307     -0.079      0.937      -4.708       4.344\n",
      "37i9dQZF1DWSfMe9z89s9B    -3.0253      2.501     -1.210      0.227      -7.932       1.881\n",
      "37i9dQZF1DWT1y71ZcMPe5    -0.1185      0.970     -0.122      0.903      -2.021       1.784\n",
      "37i9dQZF1DWT2SPAYawYcO    -0.8894      0.340     -2.614      0.009      -1.557      -0.222\n",
      "37i9dQZF1DWT2jS7NwYPVI     0.6132      0.733      0.836      0.403      -0.825       2.052\n",
      "37i9dQZF1DWT6MhXz0jw61     1.1210      1.760      0.637      0.524      -2.333       4.575\n",
      "37i9dQZF1DWTIfBdh7WtFL     0.2602      0.499      0.522      0.602      -0.719       1.239\n",
      "37i9dQZF1DWTIykNHRogOx    -1.4874      1.748     -0.851      0.395      -4.918       1.943\n",
      "37i9dQZF1DWTMYgB8TqtmR     0.0329      1.683      0.020      0.984      -3.270       3.335\n",
      "37i9dQZF1DWTRqg6ucMOrz    -0.2033      0.810     -0.251      0.802      -1.792       1.386\n",
      "37i9dQZF1DWU4xkXueiKGW    -0.3660      0.419     -0.873      0.383      -1.188       0.456\n",
      "37i9dQZF1DWUJF24WXSSyO     2.2953      2.062      1.113      0.266      -1.750       6.341\n",
      "37i9dQZF1DWUSKuTscFFGY     0.1228      1.352      0.091      0.928      -2.530       2.776\n",
      "37i9dQZF1DWUa8ZRTfalHk     0.0722      0.520      0.139      0.890      -0.948       1.093\n",
      "37i9dQZF1DWUuQAo5SmZL4     0.4903      2.431      0.202      0.840      -4.280       5.261\n",
      "37i9dQZF1DWUzFXarNiofw     0.5514      0.598      0.922      0.357      -0.622       1.725\n",
      "37i9dQZF1DWVPRVPGc4ZVv     1.8796      0.889      2.113      0.035       0.135       3.625\n",
      "37i9dQZF1DWVdgXTbYm2r0     0.6171      0.725      0.851      0.395      -0.806       2.040\n",
      "37i9dQZF1DWVk7x1ClrO0Y    -1.2450      1.119     -1.113      0.266      -3.441       0.951\n",
      "37i9dQZF1DWVlLVXKTOAYa     2.6243      1.922      1.366      0.172      -1.147       6.395\n",
      "37i9dQZF1DWVlm7xgnWdvJ     0.6170      1.150      0.536      0.592      -1.640       2.874\n",
      "37i9dQZF1DWWEcRhUVtL8n     1.4281      0.857      1.667      0.096      -0.253       3.109\n",
      "37i9dQZF1DWWqNV5cS50j6    -2.1150      0.767     -2.757      0.006      -3.620      -0.610\n",
      "37i9dQZF1DWWv6MSZULLBi     1.0278      0.783      1.313      0.190      -0.508       2.564\n",
      "37i9dQZF1DWXHUDqB4m8ab    -1.8292      1.303     -1.404      0.161      -4.386       0.728\n",
      "37i9dQZF1DWXT8uSSn6PRy    -3.2783      2.032     -1.613      0.107      -7.266       0.709\n",
      "37i9dQZF1DWY4lFlS4Pnso    -6.6956      5.326     -1.257      0.209     -17.146       3.755\n",
      "37i9dQZF1DWY4xHQp97fN6     2.1970      0.710      3.096      0.002       0.805       3.589\n",
      "37i9dQZF1DWY6tYEFs22tT     2.7453      0.607      4.520      0.000       1.554       3.937\n",
      "37i9dQZF1DWYMfG0Phlxx8     1.0044      0.686      1.464      0.144      -0.342       2.351\n",
      "37i9dQZF1DWYSNbqvqvhBQ     1.5275      0.856      1.784      0.075      -0.153       3.208\n",
      "37i9dQZF1DWYVURwQHUqnN     0.9531      0.713      1.336      0.182      -0.447       2.353\n",
      "37i9dQZF1DWYs83FtTMQFw    -0.9159      0.916     -1.000      0.318      -2.713       0.882\n",
      "37i9dQZF1DWZZbpkxU5t9L    -0.7769      0.398     -1.954      0.051      -1.557       0.003\n",
      "37i9dQZF1DWZk2SPZ3bugX    -0.0912      1.851     -0.049      0.961      -3.723       3.541\n",
      "37i9dQZF1DWZryfp6NSvtz     0.0264      0.655      0.040      0.968      -1.258       1.311\n",
      "37i9dQZF1DX0AgrgHFR9aa    -0.2756      0.862     -0.320      0.749      -1.967       1.416\n",
      "37i9dQZF1DX0FGW2dUyDef    -0.1754      2.005     -0.088      0.930      -4.109       3.758\n",
      "37i9dQZF1DX0FJ8JYkqiJu     0.4164      1.354      0.307      0.759      -2.241       3.074\n",
      "37i9dQZF1DX0MLFaUdXnjA    -2.3378      0.906     -2.579      0.010      -4.116      -0.560\n",
      "37i9dQZF1DX0UrRvztWcAU    -0.6048      0.725     -0.834      0.405      -2.028       0.818\n",
      "37i9dQZF1DX0XUsuxWHRQd     0.5591      0.783      0.714      0.476      -0.978       2.096\n",
      "37i9dQZF1DX0sDai2F5jCQ    -0.1153      1.607     -0.072      0.943      -3.269       3.038\n",
      "37i9dQZF1DX1BNoP9erXlf    -1.2258      0.997     -1.230      0.219      -3.181       0.730\n",
      "37i9dQZF1DX1EYoiDq3BXX     1.7090      0.903      1.892      0.059      -0.063       3.481\n",
      "37i9dQZF1DX1N5uK98ms5p    -0.9081      0.981     -0.926      0.355      -2.832       1.016\n",
      "37i9dQZF1DX1X23oiQRTB5     0.2809      1.816      0.155      0.877      -3.282       3.844\n",
      "37i9dQZF1DX1lVhptIYRda     3.7979      3.080      1.233      0.218      -2.246       9.842\n",
      "37i9dQZF1DX1lp03JVa0o6    -1.6356      1.374     -1.190      0.234      -4.332       1.061\n",
      "37i9dQZF1DX2A29LI7xHn1     1.4945      0.641      2.331      0.020       0.237       2.752\n",
      "37i9dQZF1DX2Nc3B70tvx0     5.4886      1.263      4.347      0.000       3.011       7.966\n",
      "37i9dQZF1DX2RxBh64BHjQ     0.1052      0.482      0.218      0.827      -0.840       1.050\n",
      "37i9dQZF1DX2WkIBRaChxW    -0.6154      1.073     -0.573      0.566      -2.721       1.490\n",
      "37i9dQZF1DX2Wvd8VINtcF    -0.2773      1.235     -0.224      0.822      -2.701       2.147\n",
      "37i9dQZF1DX2YSAZIuAiB1     1.8688      1.184      1.578      0.115      -0.454       4.192\n",
      "37i9dQZF1DX2lUf1uE6Mre     0.6079      1.219      0.499      0.618      -1.783       2.999\n",
      "37i9dQZF1DX2sUQwD7tbmL     1.6509      1.725      0.957      0.339      -1.734       5.036\n",
      "37i9dQZF1DX2vTOtsQ5Isl    -1.5128      1.217     -1.243      0.214      -3.901       0.875\n",
      "37i9dQZF1DX3rxVfibe1L0     0.9981      0.561      1.778      0.076      -0.103       2.100\n",
      "37i9dQZF1DX4JAvHpjipBk     0.8034      0.175      4.589      0.000       0.460       1.147\n",
      "37i9dQZF1DX4SBhb3fqCJd     0.9367      1.208      0.775      0.438      -1.434       3.308\n",
      "37i9dQZF1DX4SO57lOJWRB     0.2419      1.311      0.184      0.854      -2.331       2.815\n",
      "37i9dQZF1DX4a0nQYnltiQ    -0.1441      1.371     -0.105      0.916      -2.835       2.546\n",
      "37i9dQZF1DX4dyzvuaRJ0n     1.5794      0.628      2.516      0.012       0.348       2.811\n",
      "37i9dQZF1DX4y8h9WqDPAE     0.5197      1.028      0.505      0.613      -1.498       2.537\n",
      "37i9dQZF1DX50QitC6Oqtn     2.1501      0.949      2.265      0.024       0.288       4.013\n",
      "37i9dQZF1DX59ogDi1Z2XL    -0.8008      0.871     -0.920      0.358      -2.509       0.907\n",
      "37i9dQZF1DX5BAPG29mHS8 -1.837e-15   1.04e-15     -1.767      0.078   -3.88e-15    2.03e-16\n",
      "37i9dQZF1DX5CdVP4rz81C     0.5623      0.639      0.880      0.379      -0.692       1.816\n",
      "37i9dQZF1DX5DfG8gQdC3F     0.0460      0.177      0.259      0.796      -0.302       0.394\n",
      "37i9dQZF1DX5WTH49Vcnqp     1.3299      1.102      1.207      0.228      -0.832       3.492\n",
      "37i9dQZF1DX5qwHeIGQ14o     0.1528      2.163      0.071      0.944      -4.091       4.397\n",
      "37i9dQZF1DX5wB72P2sVsT    -2.0432      2.338     -0.874      0.382      -6.630       2.544\n",
      "37i9dQZF1DX5y8xoSWyhcz    -0.1134      0.742     -0.153      0.879      -1.570       1.343\n",
      "37i9dQZF1DX60OAKjsWlA2    -6.1833      1.989     -3.108      0.002     -10.087      -2.280\n",
      "37i9dQZF1DX6PKX5dyBKeq     0.0893      0.675      0.132      0.895      -1.236       1.414\n",
      "37i9dQZF1DX6aTaZa0K6VA    -1.7254      1.934     -0.892      0.373      -5.521       2.070\n",
      "37i9dQZF1DX742okrrpwah     0.4597      0.200      2.293      0.022       0.066       0.853\n",
      "37i9dQZF1DX76Wlfdnj7AP     2.3366      1.720      1.358      0.175      -1.039       5.712\n",
      "37i9dQZF1DX76t638V6CA8    -1.9775      1.718     -1.151      0.250      -5.348       1.393\n",
      "37i9dQZF1DX7XNgsy4UFju     0.1444      0.646      0.224      0.823      -1.123       1.412\n",
      "37i9dQZF1DX7i7SKKuAK4o     1.8066      1.857      0.973      0.331      -1.836       5.449\n",
      "37i9dQZF1DX7oMO417tEZs     0.5950      0.998      0.596      0.551      -1.363       2.553\n",
      "37i9dQZF1DX82GYcclJ3Ug    -1.1170      1.057     -1.057      0.291      -3.191       0.957\n",
      "37i9dQZF1DX843Qf4lrFtZ    -1.0675      0.867     -1.231      0.219      -2.769       0.634\n",
      "37i9dQZF1DX889U0CL85jj     1.8972      2.203      0.861      0.389      -2.426       6.220\n",
      "37i9dQZF1DX8S0uQvJ4gaa     0.0146      0.863      0.017      0.987      -1.678       1.708\n",
      "37i9dQZF1DX8tZsk68tuDw    -0.6671      0.689     -0.968      0.333      -2.019       0.685\n",
      "37i9dQZF1DX8vwRmUsEIMT    -1.3058      1.359     -0.961      0.337      -3.972       1.360\n",
      "37i9dQZF1DX924zU1IARaD     1.9880      1.719      1.157      0.248      -1.385       5.361\n",
      "37i9dQZF1DX9ND1QF5hZNF     1.7948      1.728      1.038      0.299      -1.597       5.186\n",
      "37i9dQZF1DX9SvXmR7wQty    -0.1428      0.339     -0.421      0.674      -0.808       0.522\n",
      "37i9dQZF1DXa41CMuUARjl     1.7831      0.584      3.056      0.002       0.638       2.928\n",
      "37i9dQZF1DXa49JU4zzjb9    -0.3625      0.829     -0.437      0.662      -1.989       1.264\n",
      "37i9dQZF1DXaXB8fQg7xif     1.8075      1.355      1.334      0.182      -0.850       4.465\n",
      "37i9dQZF1DXarRysLJmuju     0.3514      0.982      0.358      0.721      -1.576       2.279\n",
      "37i9dQZF1DXayDMsJG9ZBv     0.0030      1.981      0.002      0.999      -3.885       3.891\n",
      "37i9dQZF1DXbEm2sKzgoJ8     0.5059      0.931      0.543      0.587      -1.321       2.333\n",
      "37i9dQZF1DXbhVuSJBP0MW -1.555e-16   1.37e-16     -1.136      0.256   -4.24e-16    1.13e-16\n",
      "37i9dQZF1DXboDblu798Pk     0.2721      1.065      0.255      0.798      -1.818       2.362\n",
      "37i9dQZF1DXbpmT3HUTsZm     0.4638      0.556      0.833      0.405      -0.628       1.556\n",
      "37i9dQZF1DXcBWIGoYBM5M     4.1088      1.329      3.092      0.002       1.501       6.716\n",
      "37i9dQZF1DXcDoDDetPsEg    -1.4729      1.488     -0.990      0.322      -4.392       1.447\n",
      "37i9dQZF1DXcF6B6QPhFDv    -0.8711      1.875     -0.465      0.642      -4.549       2.807\n",
      "37i9dQZF1DXcZDD7cfEKhW     0.4823      1.059      0.455      0.649      -1.596       2.561\n",
      "37i9dQZF1DXca8AyWK6Y7g     1.7377      3.338      0.521      0.603      -4.812       8.288\n",
      "37i9dQZF1DXcgNXUHsZlwX    -2.4418      1.747     -1.398      0.163      -5.870       0.986\n",
      "37i9dQZF1DXcxvFzl58uP7    -0.7387      1.718     -0.430      0.667      -4.110       2.632\n",
      "37i9dQZF1DXd5DCuoVuFY3     2.9235      2.665      1.097      0.273      -2.306       8.153\n",
      "37i9dQZF1DXdEF3AqJpXE3     1.9494      1.966      0.991      0.322      -1.909       5.808\n",
      "37i9dQZF1DXdIpacQDPDV5     0.4293      0.769      0.558      0.577      -1.080       1.939\n",
      "37i9dQZF1DXdJFpsr4Sn91    -0.3224      0.602     -0.536      0.592      -1.503       0.858\n",
      "37i9dQZF1DXdbXrPNafg9d    -0.0364      0.502     -0.073      0.942      -1.021       0.948\n",
      "37i9dQZF1DXdeMORbC1XNa     1.0397      0.865      1.202      0.230      -0.657       2.737\n",
      "37i9dQZF1DXdwmD5Q7Gxah     0.0827      2.029      0.041      0.967      -3.898       4.063\n",
      "3OyvSycblmE3oPeY5rcqNe    -1.3241      3.408     -0.389      0.698      -8.010       5.362\n",
      "3aG2GZfk5wE6MT9yy5wtRs    -2.0354      1.054     -1.930      0.054      -4.104       0.034\n",
      "3hojaDtnWmBFMGvnMu5Lqj    -0.4116      0.601     -0.684      0.494      -1.592       0.768\n",
      "3mAGaD6LFDC3i9UvKLnini    -0.3781      0.886     -0.427      0.670      -2.116       1.360\n",
      "3us5km5KclgqJhJk4xQ6Ek    -1.4239      2.167     -0.657      0.511      -5.675       2.827\n",
      "3zn59U9FkTNzQwE0T5mW4I     0.6620      1.104      0.600      0.549      -1.505       2.829\n",
      "4EV5Q5voEcmeogepQwnQqX    -1.4460      0.423     -3.417      0.001      -2.276      -0.616\n",
      "4OPCerBHEq6Idz5w5XULVi    -0.1491      0.435     -0.343      0.732      -1.003       0.705\n",
      "4VdLs4MBgLTfw9tGfhBEz0    -2.7049      2.029     -1.333      0.183      -6.686       1.276\n",
      "4c6G93bHqsUbwqlqRDND9k    -0.2739      0.430     -0.638      0.524      -1.117       0.569\n",
      "4eDSwVknkNyDJGFrUHcDrS     0.5567      0.359      1.552      0.121      -0.147       1.260\n",
      "4qIZ8kswrih9GR4EN2eZaC     0.6859      1.321      0.519      0.604      -1.905       3.277\n",
      "4qj1iQi2spcVQqba9ColRk     0.6635      0.902      0.736      0.462      -1.106       2.433\n",
      "5359l8Co8qztllR0Mxk4Zv     1.1088      0.693      1.601      0.110      -0.250       2.468\n",
      "59ZrWq6NDDZXQJcUHnkruk    -1.3885      0.612     -2.268      0.024      -2.590      -0.187\n",
      "5HEiuySFNy9YKjZTvNn6ox    -1.6078      2.423     -0.664      0.507      -6.361       3.145\n",
      "5s7cNVeGfehrRfCatNN43P     0.6131      0.497      1.235      0.217      -0.361       1.587\n",
      "62nYoBdWTQYtLJzmWqMUTI    -1.9076      1.719     -1.110      0.267      -5.280       1.464\n",
      "65n4lLrDBqxE172HZqKpK2    -0.6944      2.720     -0.255      0.799      -6.032       4.643\n",
      "65xSncKQzG6Suseh5gfYP1    -0.6324      0.573     -1.104      0.270      -1.756       0.491\n",
      "66CQFlDNJJu0Mrln8WmSnj    -3.6608      1.220     -3.000      0.003      -6.055      -1.267\n",
      "6Ek1eTWRVXuSFM9lSc7LlA     0.5010      0.534      0.938      0.349      -0.547       1.549\n",
      "6Hw1sUOCpE59V7AlIIHhCz     0.1010      0.566      0.178      0.858      -1.010       1.212\n",
      "6LHwwT5kakVUc7UqUuMLYT    -0.0845      0.620     -0.136      0.892      -1.301       1.132\n",
      "6LY8RIt0Wg6IkpJBtxP2xu     0.3156      0.186      1.700      0.089      -0.049       0.680\n",
      "6M9bQZk3grXylwzKiTWhe9     0.8752      1.216      0.719      0.472      -1.512       3.262\n",
      "6WWgGke7FrcjW8ZOvPGQaI     0.6728      0.712      0.944      0.345      -0.725       2.071\n",
      "6X35NoAOCzPvpYaq6r2dO3    -1.9516      0.532     -3.671      0.000      -2.995      -0.909\n",
      "6rtFoJyXT5OTP3pR01vjr4    -0.5783      0.342     -1.692      0.091      -1.249       0.092\n",
      "73iSQPz35bOpvQouxkAT6K     2.9230      1.421      2.057      0.040       0.134       5.712\n",
      "75dwLdmL07hDEDWqX17QeE     0.8054      0.329      2.447      0.015       0.160       1.451\n",
      "79bqVRd4ZsTFw0SfrSLi7x     0.1817      0.375      0.484      0.628      -0.554       0.918\n",
      "7nnyvG2CHYgiCOrUlh67v7     4.2034      1.074      3.915      0.000       2.097       6.310\n",
      "7rn2o6gP7zXaRR9lq93Klk     0.5659      0.550      1.029      0.304      -0.513       1.645\n",
      "7vFQNWXoblEJXpbnTuyz76    -0.2209      0.916     -0.241      0.810      -2.018       1.577\n",
      "7xom7CO8dOHxx9fnGwwrnO     0.6766      1.327      0.510      0.610      -1.928       3.281\n",
      "pop_5                      0.0976      0.004     25.110      0.000       0.090       0.105\n",
      "==============================================================================\n",
      "Omnibus:                       40.154   Durbin-Watson:                   2.048\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               63.929\n",
      "Skew:                           0.263   Prob(JB):                     1.31e-14\n",
      "Kurtosis:                       3.930   Cond. No.                     4.75e+17\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The smallest eigenvalue is 9.99e-30. This might indicate that there are\n",
      "strong multicollinearity problems or that the design matrix is singular.\n",
      "Best model: <statsmodels.regression.linear_model.RegressionResultsWrapper object at 0x7fd93c8427b8>\n",
      "Training error:\n",
      "RMSE: 0.0\n",
      "Rsq: 0.718330580133109\n",
      "Test error:\n",
      "RMSE: 0.0\n",
      "Rsq: 0.5146017952023583\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/paperspace/anaconda3/envs/whitelist/lib/python3.6/site-packages/pandas/core/indexes/base.py:3772: RuntimeWarning: '<' not supported between instances of 'str' and 'int', sort order is undefined for incomparable objects\n",
      "  return this.join(other, how=how, return_indexers=return_indexers)\n",
      "/home/paperspace/anaconda3/envs/whitelist/lib/python3.6/site-packages/matplotlib/cbook/deprecation.py:107: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_model_with(base_playlist_dummy, '../data/pop5_play_dummy.feather')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:            log_streams   R-squared:                       0.530\n",
      "Model:                            OLS   Adj. R-squared:                  0.529\n",
      "Method:                 Least Squares   F-statistic:                     1511.\n",
      "Date:                Fri, 21 Sep 2018   Prob (F-statistic):          4.73e-222\n",
      "Time:                        15:50:16   Log-Likelihood:                -2847.0\n",
      "No. Observations:                1344   AIC:                             5698.\n",
      "Df Residuals:                    1342   BIC:                             5708.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          7.1808      0.123     58.230      0.000       6.939       7.423\n",
      "pop_5          0.1170      0.003     38.874      0.000       0.111       0.123\n",
      "==============================================================================\n",
      "Omnibus:                       64.672   Durbin-Watson:                   2.045\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               76.012\n",
      "Skew:                           0.512   Prob(JB):                     3.12e-17\n",
      "Kurtosis:                       3.555   Cond. No.                         92.0\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "Best model: <statsmodels.regression.linear_model.RegressionResultsWrapper object at 0x7fd93c7f2080>\n",
      "Training error:\n",
      "RMSE: 0.0\n",
      "Rsq: 0.5296428445603323\n",
      "Test error:\n",
      "RMSE: 0.0\n",
      "Rsq: 0.5441836626160941\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/paperspace/anaconda3/envs/whitelist/lib/python3.6/site-packages/pandas/core/indexes/base.py:3772: RuntimeWarning: '<' not supported between instances of 'str' and 'int', sort order is undefined for incomparable objects\n",
      "  return this.join(other, how=how, return_indexers=return_indexers)\n",
      "/home/paperspace/anaconda3/envs/whitelist/lib/python3.6/site-packages/matplotlib/cbook/deprecation.py:107: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_model_with(base_model, '../data/pop5_play_dummy.feather')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:whitelist]",
   "language": "python",
   "name": "conda-env-whitelist-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}