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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "from statsmodels.stats.outliers_influence import summary_table\n",
    "### Examples\n",
    "# https://www.statsmodels.org/stable/examples/index.html\n",
    "\n",
    "from scipy import optimize as opt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read data\n",
    "df = pd.read_csv(\"./flow_data.txt\",index_col=0,parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#renaming columns\n",
    "df.columns=[\"h\",\"Q\"]\n",
    "\n",
    "# h -> [m], Q -> [m³/s]\n",
    "df[\"h\"] = df[\"h\"]/1000\n",
    "df[\"Q\"] = df[\"Q\"]/1000\n",
    "\n",
    "# dropping na's\n",
    "df.dropna(how=\"any\",inplace=True)\n",
    "\n",
    "# removing h<=0, Q<=0\n",
    "df = df[(df[\"h\"]>0) & (df[\"Q\"]>0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      Q   R-squared:                       0.972\n",
      "Model:                            OLS   Adj. R-squared:                  0.972\n",
      "Method:                 Least Squares   F-statistic:                 3.602e+06\n",
      "Date:                Wed, 02 Oct 2019   Prob (F-statistic):               0.00\n",
      "Time:                        15:32:56   Log-Likelihood:             4.5342e+05\n",
      "No. Observations:              104727   AIC:                        -9.068e+05\n",
      "Df Residuals:                  104725   BIC:                        -9.068e+05\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -0.0263   2.69e-05   -977.084      0.000      -0.026      -0.026\n",
      "h              0.8122      0.000   1897.876      0.000       0.811       0.813\n",
      "==============================================================================\n",
      "Omnibus:                     9652.361   Durbin-Watson:                   0.110\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            16244.032\n",
      "Skew:                           0.670   Prob(JB):                         0.00\n",
      "Kurtosis:                       4.388   Cond. No.                         43.6\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "linear = smf.ols(formula='Q ~ h', data=df)\n",
    "result_linear = linear.fit()\n",
    "print(result_linear.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "simpletable, data, headers = summary_table(result_linear, alpha=0.05)\n",
    "stat_summary_linear = pd.DataFrame(data,columns=headers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Obs</th>\n",
       "      <th>Dep Var\n",
       "Population</th>\n",
       "      <th>Predicted\n",
       "Value</th>\n",
       "      <th>Std Error\n",
       "Mean Predict</th>\n",
       "      <th>Mean ci\n",
       "95% low</th>\n",
       "      <th>Mean ci\n",
       "95% upp</th>\n",
       "      <th>Predict ci\n",
       "95% low</th>\n",
       "      <th>Predict ci\n",
       "95% upp</th>\n",
       "      <th>Residual</th>\n",
       "      <th>Std Error\n",
       "Residual</th>\n",
       "      <th>Student\n",
       "Residual</th>\n",
       "      <th>Cook's\n",
       "D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.015331</td>\n",
       "      <td>0.016542</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.016522</td>\n",
       "      <td>0.016562</td>\n",
       "      <td>0.010295</td>\n",
       "      <td>0.022790</td>\n",
       "      <td>-0.001212</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.380170</td>\n",
       "      <td>7.339947e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.013111</td>\n",
       "      <td>0.013407</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.013386</td>\n",
       "      <td>0.013428</td>\n",
       "      <td>0.007159</td>\n",
       "      <td>0.019655</td>\n",
       "      <td>-0.000296</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.092730</td>\n",
       "      <td>4.830013e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.013609</td>\n",
       "      <td>0.014007</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.013986</td>\n",
       "      <td>0.014028</td>\n",
       "      <td>0.007759</td>\n",
       "      <td>0.020255</td>\n",
       "      <td>-0.000398</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.124936</td>\n",
       "      <td>8.574328e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.013711</td>\n",
       "      <td>0.013807</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.013786</td>\n",
       "      <td>0.013828</td>\n",
       "      <td>0.007559</td>\n",
       "      <td>0.020055</td>\n",
       "      <td>-0.000096</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.030097</td>\n",
       "      <td>5.012427e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.013748</td>\n",
       "      <td>0.014007</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.013986</td>\n",
       "      <td>0.014028</td>\n",
       "      <td>0.007759</td>\n",
       "      <td>0.020255</td>\n",
       "      <td>-0.000259</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.081142</td>\n",
       "      <td>3.616761e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.014589</td>\n",
       "      <td>0.015208</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.015187</td>\n",
       "      <td>0.015228</td>\n",
       "      <td>0.008960</td>\n",
       "      <td>0.021456</td>\n",
       "      <td>-0.000618</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.194017</td>\n",
       "      <td>1.985622e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.014142</td>\n",
       "      <td>0.014607</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.014587</td>\n",
       "      <td>0.014628</td>\n",
       "      <td>0.008360</td>\n",
       "      <td>0.020855</td>\n",
       "      <td>-0.000466</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.146157</td>\n",
       "      <td>1.149078e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.013588</td>\n",
       "      <td>0.013407</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.013386</td>\n",
       "      <td>0.013428</td>\n",
       "      <td>0.007159</td>\n",
       "      <td>0.019655</td>\n",
       "      <td>0.000182</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>0.056972</td>\n",
       "      <td>1.823194e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.0</td>\n",
       "      <td>0.015052</td>\n",
       "      <td>0.015208</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.015187</td>\n",
       "      <td>0.015228</td>\n",
       "      <td>0.008960</td>\n",
       "      <td>0.021456</td>\n",
       "      <td>-0.000156</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.048864</td>\n",
       "      <td>1.259502e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.013452</td>\n",
       "      <td>0.013407</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.013386</td>\n",
       "      <td>0.013428</td>\n",
       "      <td>0.007159</td>\n",
       "      <td>0.019655</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>0.014182</td>\n",
       "      <td>1.129779e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.012958</td>\n",
       "      <td>0.012806</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.012785</td>\n",
       "      <td>0.012828</td>\n",
       "      <td>0.006559</td>\n",
       "      <td>0.019054</td>\n",
       "      <td>0.000151</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>0.047418</td>\n",
       "      <td>1.293046e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.013218</td>\n",
       "      <td>0.013007</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.012985</td>\n",
       "      <td>0.013028</td>\n",
       "      <td>0.006759</td>\n",
       "      <td>0.019254</td>\n",
       "      <td>0.000212</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>0.066361</td>\n",
       "      <td>2.512438e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13.0</td>\n",
       "      <td>0.012935</td>\n",
       "      <td>0.012806</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.012785</td>\n",
       "      <td>0.012828</td>\n",
       "      <td>0.006559</td>\n",
       "      <td>0.019054</td>\n",
       "      <td>0.000129</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>0.040454</td>\n",
       "      <td>9.411158e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14.0</td>\n",
       "      <td>0.012691</td>\n",
       "      <td>0.012206</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.012185</td>\n",
       "      <td>0.012228</td>\n",
       "      <td>0.005958</td>\n",
       "      <td>0.018454</td>\n",
       "      <td>0.000485</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>0.152091</td>\n",
       "      <td>1.363474e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15.0</td>\n",
       "      <td>0.012523</td>\n",
       "      <td>0.012008</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.011987</td>\n",
       "      <td>0.012030</td>\n",
       "      <td>0.005760</td>\n",
       "      <td>0.018256</td>\n",
       "      <td>0.000515</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>0.161629</td>\n",
       "      <td>1.552782e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16.0</td>\n",
       "      <td>0.012287</td>\n",
       "      <td>0.012008</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.011987</td>\n",
       "      <td>0.012030</td>\n",
       "      <td>0.005760</td>\n",
       "      <td>0.018256</td>\n",
       "      <td>0.000279</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>0.087499</td>\n",
       "      <td>4.550753e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17.0</td>\n",
       "      <td>0.011805</td>\n",
       "      <td>0.011208</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.011186</td>\n",
       "      <td>0.011230</td>\n",
       "      <td>0.004960</td>\n",
       "      <td>0.017456</td>\n",
       "      <td>0.000597</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>0.187215</td>\n",
       "      <td>2.157301e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18.0</td>\n",
       "      <td>0.012266</td>\n",
       "      <td>0.012008</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.011987</td>\n",
       "      <td>0.012030</td>\n",
       "      <td>0.005760</td>\n",
       "      <td>0.018256</td>\n",
       "      <td>0.000257</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>0.080755</td>\n",
       "      <td>3.876219e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19.0</td>\n",
       "      <td>0.012187</td>\n",
       "      <td>0.011808</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.011786</td>\n",
       "      <td>0.011830</td>\n",
       "      <td>0.005560</td>\n",
       "      <td>0.018056</td>\n",
       "      <td>0.000379</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>0.118812</td>\n",
       "      <td>8.462830e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.011582</td>\n",
       "      <td>0.011008</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.010986</td>\n",
       "      <td>0.011030</td>\n",
       "      <td>0.004760</td>\n",
       "      <td>0.017256</td>\n",
       "      <td>0.000575</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>0.180261</td>\n",
       "      <td>2.018050e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21.0</td>\n",
       "      <td>0.011546</td>\n",
       "      <td>0.010808</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.010785</td>\n",
       "      <td>0.010830</td>\n",
       "      <td>0.004560</td>\n",
       "      <td>0.017055</td>\n",
       "      <td>0.000739</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>0.231808</td>\n",
       "      <td>3.367643e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22.0</td>\n",
       "      <td>0.010843</td>\n",
       "      <td>0.010007</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.009985</td>\n",
       "      <td>0.010030</td>\n",
       "      <td>0.003759</td>\n",
       "      <td>0.016255</td>\n",
       "      <td>0.000836</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>0.262168</td>\n",
       "      <td>4.470675e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23.0</td>\n",
       "      <td>0.011116</td>\n",
       "      <td>0.010207</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.010185</td>\n",
       "      <td>0.010230</td>\n",
       "      <td>0.003959</td>\n",
       "      <td>0.016455</td>\n",
       "      <td>0.000909</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>0.285058</td>\n",
       "      <td>5.235900e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24.0</td>\n",
       "      <td>0.011981</td>\n",
       "      <td>0.011608</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.011586</td>\n",
       "      <td>0.011630</td>\n",
       "      <td>0.005360</td>\n",
       "      <td>0.017856</td>\n",
       "      <td>0.000373</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>0.117060</td>\n",
       "      <td>8.286644e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25.0</td>\n",
       "      <td>0.012814</td>\n",
       "      <td>0.012743</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.012721</td>\n",
       "      <td>0.012764</td>\n",
       "      <td>0.006495</td>\n",
       "      <td>0.018990</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>0.022295</td>\n",
       "      <td>2.865910e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0.016490</td>\n",
       "      <td>0.016943</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.016923</td>\n",
       "      <td>0.016962</td>\n",
       "      <td>0.010695</td>\n",
       "      <td>0.023190</td>\n",
       "      <td>-0.000453</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.142018</td>\n",
       "      <td>1.014326e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27.0</td>\n",
       "      <td>0.018349</td>\n",
       "      <td>0.019408</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.019388</td>\n",
       "      <td>0.019427</td>\n",
       "      <td>0.013160</td>\n",
       "      <td>0.025655</td>\n",
       "      <td>-0.001058</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.331947</td>\n",
       "      <td>5.312514e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28.0</td>\n",
       "      <td>0.017578</td>\n",
       "      <td>0.018343</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.018324</td>\n",
       "      <td>0.018363</td>\n",
       "      <td>0.012095</td>\n",
       "      <td>0.024591</td>\n",
       "      <td>-0.000766</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.240153</td>\n",
       "      <td>2.820608e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29.0</td>\n",
       "      <td>0.016820</td>\n",
       "      <td>0.017807</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.017787</td>\n",
       "      <td>0.017827</td>\n",
       "      <td>0.011559</td>\n",
       "      <td>0.024055</td>\n",
       "      <td>-0.000987</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.309531</td>\n",
       "      <td>4.730425e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30.0</td>\n",
       "      <td>0.016647</td>\n",
       "      <td>0.017207</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.017187</td>\n",
       "      <td>0.017226</td>\n",
       "      <td>0.010959</td>\n",
       "      <td>0.023454</td>\n",
       "      <td>-0.000559</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.175463</td>\n",
       "      <td>1.539029e-07</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104697</th>\n",
       "      <td>104698.0</td>\n",
       "      <td>0.019361</td>\n",
       "      <td>0.020544</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.020525</td>\n",
       "      <td>0.020564</td>\n",
       "      <td>0.014296</td>\n",
       "      <td>0.026792</td>\n",
       "      <td>-0.001183</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.371158</td>\n",
       "      <td>6.586739e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104698</th>\n",
       "      <td>104699.0</td>\n",
       "      <td>0.017428</td>\n",
       "      <td>0.018143</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.018124</td>\n",
       "      <td>0.018163</td>\n",
       "      <td>0.011895</td>\n",
       "      <td>0.024391</td>\n",
       "      <td>-0.000716</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.224463</td>\n",
       "      <td>2.472399e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104699</th>\n",
       "      <td>104700.0</td>\n",
       "      <td>0.020080</td>\n",
       "      <td>0.021743</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.021724</td>\n",
       "      <td>0.021762</td>\n",
       "      <td>0.015495</td>\n",
       "      <td>0.027991</td>\n",
       "      <td>-0.001663</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.521800</td>\n",
       "      <td>1.300803e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104700</th>\n",
       "      <td>104701.0</td>\n",
       "      <td>0.019620</td>\n",
       "      <td>0.020944</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.020925</td>\n",
       "      <td>0.020964</td>\n",
       "      <td>0.014697</td>\n",
       "      <td>0.027192</td>\n",
       "      <td>-0.001325</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.415532</td>\n",
       "      <td>8.246085e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104701</th>\n",
       "      <td>104702.0</td>\n",
       "      <td>0.018962</td>\n",
       "      <td>0.019744</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.019725</td>\n",
       "      <td>0.019763</td>\n",
       "      <td>0.013496</td>\n",
       "      <td>0.025992</td>\n",
       "      <td>-0.000782</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.245305</td>\n",
       "      <td>2.891864e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104702</th>\n",
       "      <td>104703.0</td>\n",
       "      <td>0.019501</td>\n",
       "      <td>0.020944</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.020925</td>\n",
       "      <td>0.020964</td>\n",
       "      <td>0.014697</td>\n",
       "      <td>0.027192</td>\n",
       "      <td>-0.001444</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.452957</td>\n",
       "      <td>9.798367e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104703</th>\n",
       "      <td>104704.0</td>\n",
       "      <td>0.018126</td>\n",
       "      <td>0.018607</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.018588</td>\n",
       "      <td>0.018627</td>\n",
       "      <td>0.012359</td>\n",
       "      <td>0.024855</td>\n",
       "      <td>-0.000482</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.151070</td>\n",
       "      <td>1.111560e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104704</th>\n",
       "      <td>104705.0</td>\n",
       "      <td>0.019185</td>\n",
       "      <td>0.020406</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.020387</td>\n",
       "      <td>0.020425</td>\n",
       "      <td>0.014158</td>\n",
       "      <td>0.026654</td>\n",
       "      <td>-0.001221</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.383007</td>\n",
       "      <td>7.018367e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104705</th>\n",
       "      <td>104706.0</td>\n",
       "      <td>0.021469</td>\n",
       "      <td>0.023608</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.023588</td>\n",
       "      <td>0.023627</td>\n",
       "      <td>0.017360</td>\n",
       "      <td>0.029856</td>\n",
       "      <td>-0.002139</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.671021</td>\n",
       "      <td>2.183630e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104706</th>\n",
       "      <td>104707.0</td>\n",
       "      <td>0.018785</td>\n",
       "      <td>0.020006</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.019987</td>\n",
       "      <td>0.020025</td>\n",
       "      <td>0.013758</td>\n",
       "      <td>0.026254</td>\n",
       "      <td>-0.001221</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.383061</td>\n",
       "      <td>7.037250e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104707</th>\n",
       "      <td>104708.0</td>\n",
       "      <td>0.018561</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.019588</td>\n",
       "      <td>0.019627</td>\n",
       "      <td>0.013360</td>\n",
       "      <td>0.025856</td>\n",
       "      <td>-0.001047</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.328501</td>\n",
       "      <td>5.192438e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104708</th>\n",
       "      <td>104709.0</td>\n",
       "      <td>0.018255</td>\n",
       "      <td>0.018943</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.018924</td>\n",
       "      <td>0.018963</td>\n",
       "      <td>0.012696</td>\n",
       "      <td>0.025191</td>\n",
       "      <td>-0.000689</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.216049</td>\n",
       "      <td>2.262773e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104709</th>\n",
       "      <td>104710.0</td>\n",
       "      <td>0.018773</td>\n",
       "      <td>0.019944</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.019925</td>\n",
       "      <td>0.019963</td>\n",
       "      <td>0.013696</td>\n",
       "      <td>0.026192</td>\n",
       "      <td>-0.001170</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.367191</td>\n",
       "      <td>6.469186e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104710</th>\n",
       "      <td>104711.0</td>\n",
       "      <td>0.018157</td>\n",
       "      <td>0.019144</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.019124</td>\n",
       "      <td>0.019163</td>\n",
       "      <td>0.012896</td>\n",
       "      <td>0.025391</td>\n",
       "      <td>-0.000987</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.309664</td>\n",
       "      <td>4.636943e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104711</th>\n",
       "      <td>104712.0</td>\n",
       "      <td>0.017701</td>\n",
       "      <td>0.018543</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.018524</td>\n",
       "      <td>0.018563</td>\n",
       "      <td>0.012295</td>\n",
       "      <td>0.024791</td>\n",
       "      <td>-0.000842</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.264282</td>\n",
       "      <td>3.405111e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104712</th>\n",
       "      <td>104713.0</td>\n",
       "      <td>0.017023</td>\n",
       "      <td>0.017743</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.017723</td>\n",
       "      <td>0.017763</td>\n",
       "      <td>0.011495</td>\n",
       "      <td>0.023991</td>\n",
       "      <td>-0.000720</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.225922</td>\n",
       "      <td>2.523164e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104713</th>\n",
       "      <td>104714.0</td>\n",
       "      <td>0.017009</td>\n",
       "      <td>0.017943</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.017923</td>\n",
       "      <td>0.017963</td>\n",
       "      <td>0.011695</td>\n",
       "      <td>0.024191</td>\n",
       "      <td>-0.000934</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.292941</td>\n",
       "      <td>4.226137e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104714</th>\n",
       "      <td>104715.0</td>\n",
       "      <td>0.015766</td>\n",
       "      <td>0.016542</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.016522</td>\n",
       "      <td>0.016562</td>\n",
       "      <td>0.010295</td>\n",
       "      <td>0.022790</td>\n",
       "      <td>-0.000777</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.243676</td>\n",
       "      <td>3.015510e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104715</th>\n",
       "      <td>104716.0</td>\n",
       "      <td>0.016982</td>\n",
       "      <td>0.017543</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.017523</td>\n",
       "      <td>0.017563</td>\n",
       "      <td>0.011295</td>\n",
       "      <td>0.023791</td>\n",
       "      <td>-0.000561</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.176069</td>\n",
       "      <td>1.538612e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104716</th>\n",
       "      <td>104717.0</td>\n",
       "      <td>0.017523</td>\n",
       "      <td>0.018343</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.018324</td>\n",
       "      <td>0.018363</td>\n",
       "      <td>0.012095</td>\n",
       "      <td>0.024591</td>\n",
       "      <td>-0.000820</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.257281</td>\n",
       "      <td>3.237306e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104717</th>\n",
       "      <td>104718.0</td>\n",
       "      <td>0.015814</td>\n",
       "      <td>0.016806</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.016787</td>\n",
       "      <td>0.016826</td>\n",
       "      <td>0.010559</td>\n",
       "      <td>0.023054</td>\n",
       "      <td>-0.000992</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.311227</td>\n",
       "      <td>4.887112e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104718</th>\n",
       "      <td>104719.0</td>\n",
       "      <td>0.016676</td>\n",
       "      <td>0.017343</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.017323</td>\n",
       "      <td>0.017362</td>\n",
       "      <td>0.011095</td>\n",
       "      <td>0.023591</td>\n",
       "      <td>-0.000666</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.209067</td>\n",
       "      <td>2.178486e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104719</th>\n",
       "      <td>104720.0</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>0.016542</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.016522</td>\n",
       "      <td>0.016562</td>\n",
       "      <td>0.010295</td>\n",
       "      <td>0.022790</td>\n",
       "      <td>-0.000495</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.155367</td>\n",
       "      <td>1.225892e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104720</th>\n",
       "      <td>104721.0</td>\n",
       "      <td>0.016902</td>\n",
       "      <td>0.017543</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.017523</td>\n",
       "      <td>0.017563</td>\n",
       "      <td>0.011295</td>\n",
       "      <td>0.023791</td>\n",
       "      <td>-0.000641</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.201040</td>\n",
       "      <td>2.005991e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104721</th>\n",
       "      <td>104722.0</td>\n",
       "      <td>0.016201</td>\n",
       "      <td>0.016542</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.016522</td>\n",
       "      <td>0.016562</td>\n",
       "      <td>0.010295</td>\n",
       "      <td>0.022790</td>\n",
       "      <td>-0.000342</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.107181</td>\n",
       "      <td>5.834078e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104722</th>\n",
       "      <td>104723.0</td>\n",
       "      <td>0.015813</td>\n",
       "      <td>0.016142</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.016122</td>\n",
       "      <td>0.016162</td>\n",
       "      <td>0.009894</td>\n",
       "      <td>0.022390</td>\n",
       "      <td>-0.000329</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.103125</td>\n",
       "      <td>5.458064e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104723</th>\n",
       "      <td>104724.0</td>\n",
       "      <td>0.015622</td>\n",
       "      <td>0.015944</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.015924</td>\n",
       "      <td>0.015964</td>\n",
       "      <td>0.009696</td>\n",
       "      <td>0.022192</td>\n",
       "      <td>-0.000322</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.101085</td>\n",
       "      <td>5.273089e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104724</th>\n",
       "      <td>104725.0</td>\n",
       "      <td>0.015399</td>\n",
       "      <td>0.015344</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.015324</td>\n",
       "      <td>0.015364</td>\n",
       "      <td>0.009096</td>\n",
       "      <td>0.021592</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>0.017166</td>\n",
       "      <td>1.547818e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104725</th>\n",
       "      <td>104726.0</td>\n",
       "      <td>0.016066</td>\n",
       "      <td>0.016142</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.016122</td>\n",
       "      <td>0.016162</td>\n",
       "      <td>0.009894</td>\n",
       "      <td>0.022390</td>\n",
       "      <td>-0.000077</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.024008</td>\n",
       "      <td>2.958058e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104726</th>\n",
       "      <td>104727.0</td>\n",
       "      <td>0.015568</td>\n",
       "      <td>0.015808</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.015788</td>\n",
       "      <td>0.015828</td>\n",
       "      <td>0.009560</td>\n",
       "      <td>0.022056</td>\n",
       "      <td>-0.000240</td>\n",
       "      <td>0.003188</td>\n",
       "      <td>-0.075236</td>\n",
       "      <td>2.932410e-08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>104727 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Obs  Dep Var\\nPopulation  Predicted\\nValue  \\\n",
       "0            1.0             0.015331          0.016542   \n",
       "1            2.0             0.013111          0.013407   \n",
       "2            3.0             0.013609          0.014007   \n",
       "3            4.0             0.013711          0.013807   \n",
       "4            5.0             0.013748          0.014007   \n",
       "5            6.0             0.014589          0.015208   \n",
       "6            7.0             0.014142          0.014607   \n",
       "7            8.0             0.013588          0.013407   \n",
       "8            9.0             0.015052          0.015208   \n",
       "9           10.0             0.013452          0.013407   \n",
       "10          11.0             0.012958          0.012806   \n",
       "11          12.0             0.013218          0.013007   \n",
       "12          13.0             0.012935          0.012806   \n",
       "13          14.0             0.012691          0.012206   \n",
       "14          15.0             0.012523          0.012008   \n",
       "15          16.0             0.012287          0.012008   \n",
       "16          17.0             0.011805          0.011208   \n",
       "17          18.0             0.012266          0.012008   \n",
       "18          19.0             0.012187          0.011808   \n",
       "19          20.0             0.011582          0.011008   \n",
       "20          21.0             0.011546          0.010808   \n",
       "21          22.0             0.010843          0.010007   \n",
       "22          23.0             0.011116          0.010207   \n",
       "23          24.0             0.011981          0.011608   \n",
       "24          25.0             0.012814          0.012743   \n",
       "25          26.0             0.016490          0.016943   \n",
       "26          27.0             0.018349          0.019408   \n",
       "27          28.0             0.017578          0.018343   \n",
       "28          29.0             0.016820          0.017807   \n",
       "29          30.0             0.016647          0.017207   \n",
       "...          ...                  ...               ...   \n",
       "104697  104698.0             0.019361          0.020544   \n",
       "104698  104699.0             0.017428          0.018143   \n",
       "104699  104700.0             0.020080          0.021743   \n",
       "104700  104701.0             0.019620          0.020944   \n",
       "104701  104702.0             0.018962          0.019744   \n",
       "104702  104703.0             0.019501          0.020944   \n",
       "104703  104704.0             0.018126          0.018607   \n",
       "104704  104705.0             0.019185          0.020406   \n",
       "104705  104706.0             0.021469          0.023608   \n",
       "104706  104707.0             0.018785          0.020006   \n",
       "104707  104708.0             0.018561          0.019608   \n",
       "104708  104709.0             0.018255          0.018943   \n",
       "104709  104710.0             0.018773          0.019944   \n",
       "104710  104711.0             0.018157          0.019144   \n",
       "104711  104712.0             0.017701          0.018543   \n",
       "104712  104713.0             0.017023          0.017743   \n",
       "104713  104714.0             0.017009          0.017943   \n",
       "104714  104715.0             0.015766          0.016542   \n",
       "104715  104716.0             0.016982          0.017543   \n",
       "104716  104717.0             0.017523          0.018343   \n",
       "104717  104718.0             0.015814          0.016806   \n",
       "104718  104719.0             0.016676          0.017343   \n",
       "104719  104720.0             0.016047          0.016542   \n",
       "104720  104721.0             0.016902          0.017543   \n",
       "104721  104722.0             0.016201          0.016542   \n",
       "104722  104723.0             0.015813          0.016142   \n",
       "104723  104724.0             0.015622          0.015944   \n",
       "104724  104725.0             0.015399          0.015344   \n",
       "104725  104726.0             0.016066          0.016142   \n",
       "104726  104727.0             0.015568          0.015808   \n",
       "\n",
       "        Std Error\\nMean Predict  Mean ci\\n95% low  Mean ci\\n95% upp  \\\n",
       "0                      0.000010          0.016522          0.016562   \n",
       "1                      0.000011          0.013386          0.013428   \n",
       "2                      0.000011          0.013986          0.014028   \n",
       "3                      0.000011          0.013786          0.013828   \n",
       "4                      0.000011          0.013986          0.014028   \n",
       "5                      0.000010          0.015187          0.015228   \n",
       "6                      0.000010          0.014587          0.014628   \n",
       "7                      0.000011          0.013386          0.013428   \n",
       "8                      0.000010          0.015187          0.015228   \n",
       "9                      0.000011          0.013386          0.013428   \n",
       "10                     0.000011          0.012785          0.012828   \n",
       "11                     0.000011          0.012985          0.013028   \n",
       "12                     0.000011          0.012785          0.012828   \n",
       "13                     0.000011          0.012185          0.012228   \n",
       "14                     0.000011          0.011987          0.012030   \n",
       "15                     0.000011          0.011987          0.012030   \n",
       "16                     0.000011          0.011186          0.011230   \n",
       "17                     0.000011          0.011987          0.012030   \n",
       "18                     0.000011          0.011786          0.011830   \n",
       "19                     0.000011          0.010986          0.011030   \n",
       "20                     0.000011          0.010785          0.010830   \n",
       "21                     0.000011          0.009985          0.010030   \n",
       "22                     0.000011          0.010185          0.010230   \n",
       "23                     0.000011          0.011586          0.011630   \n",
       "24                     0.000011          0.012721          0.012764   \n",
       "25                     0.000010          0.016923          0.016962   \n",
       "26                     0.000010          0.019388          0.019427   \n",
       "27                     0.000010          0.018324          0.018363   \n",
       "28                     0.000010          0.017787          0.017827   \n",
       "29                     0.000010          0.017187          0.017226   \n",
       "...                         ...               ...               ...   \n",
       "104697                 0.000010          0.020525          0.020564   \n",
       "104698                 0.000010          0.018124          0.018163   \n",
       "104699                 0.000010          0.021724          0.021762   \n",
       "104700                 0.000010          0.020925          0.020964   \n",
       "104701                 0.000010          0.019725          0.019763   \n",
       "104702                 0.000010          0.020925          0.020964   \n",
       "104703                 0.000010          0.018588          0.018627   \n",
       "104704                 0.000010          0.020387          0.020425   \n",
       "104705                 0.000010          0.023588          0.023627   \n",
       "104706                 0.000010          0.019987          0.020025   \n",
       "104707                 0.000010          0.019588          0.019627   \n",
       "104708                 0.000010          0.018924          0.018963   \n",
       "104709                 0.000010          0.019925          0.019963   \n",
       "104710                 0.000010          0.019124          0.019163   \n",
       "104711                 0.000010          0.018524          0.018563   \n",
       "104712                 0.000010          0.017723          0.017763   \n",
       "104713                 0.000010          0.017923          0.017963   \n",
       "104714                 0.000010          0.016522          0.016562   \n",
       "104715                 0.000010          0.017523          0.017563   \n",
       "104716                 0.000010          0.018324          0.018363   \n",
       "104717                 0.000010          0.016787          0.016826   \n",
       "104718                 0.000010          0.017323          0.017362   \n",
       "104719                 0.000010          0.016522          0.016562   \n",
       "104720                 0.000010          0.017523          0.017563   \n",
       "104721                 0.000010          0.016522          0.016562   \n",
       "104722                 0.000010          0.016122          0.016162   \n",
       "104723                 0.000010          0.015924          0.015964   \n",
       "104724                 0.000010          0.015324          0.015364   \n",
       "104725                 0.000010          0.016122          0.016162   \n",
       "104726                 0.000010          0.015788          0.015828   \n",
       "\n",
       "        Predict ci\\n95% low  Predict ci\\n95% upp  Residual  \\\n",
       "0                  0.010295             0.022790 -0.001212   \n",
       "1                  0.007159             0.019655 -0.000296   \n",
       "2                  0.007759             0.020255 -0.000398   \n",
       "3                  0.007559             0.020055 -0.000096   \n",
       "4                  0.007759             0.020255 -0.000259   \n",
       "5                  0.008960             0.021456 -0.000618   \n",
       "6                  0.008360             0.020855 -0.000466   \n",
       "7                  0.007159             0.019655  0.000182   \n",
       "8                  0.008960             0.021456 -0.000156   \n",
       "9                  0.007159             0.019655  0.000045   \n",
       "10                 0.006559             0.019054  0.000151   \n",
       "11                 0.006759             0.019254  0.000212   \n",
       "12                 0.006559             0.019054  0.000129   \n",
       "13                 0.005958             0.018454  0.000485   \n",
       "14                 0.005760             0.018256  0.000515   \n",
       "15                 0.005760             0.018256  0.000279   \n",
       "16                 0.004960             0.017456  0.000597   \n",
       "17                 0.005760             0.018256  0.000257   \n",
       "18                 0.005560             0.018056  0.000379   \n",
       "19                 0.004760             0.017256  0.000575   \n",
       "20                 0.004560             0.017055  0.000739   \n",
       "21                 0.003759             0.016255  0.000836   \n",
       "22                 0.003959             0.016455  0.000909   \n",
       "23                 0.005360             0.017856  0.000373   \n",
       "24                 0.006495             0.018990  0.000071   \n",
       "25                 0.010695             0.023190 -0.000453   \n",
       "26                 0.013160             0.025655 -0.001058   \n",
       "27                 0.012095             0.024591 -0.000766   \n",
       "28                 0.011559             0.024055 -0.000987   \n",
       "29                 0.010959             0.023454 -0.000559   \n",
       "...                     ...                  ...       ...   \n",
       "104697             0.014296             0.026792 -0.001183   \n",
       "104698             0.011895             0.024391 -0.000716   \n",
       "104699             0.015495             0.027991 -0.001663   \n",
       "104700             0.014697             0.027192 -0.001325   \n",
       "104701             0.013496             0.025992 -0.000782   \n",
       "104702             0.014697             0.027192 -0.001444   \n",
       "104703             0.012359             0.024855 -0.000482   \n",
       "104704             0.014158             0.026654 -0.001221   \n",
       "104705             0.017360             0.029856 -0.002139   \n",
       "104706             0.013758             0.026254 -0.001221   \n",
       "104707             0.013360             0.025856 -0.001047   \n",
       "104708             0.012696             0.025191 -0.000689   \n",
       "104709             0.013696             0.026192 -0.001170   \n",
       "104710             0.012896             0.025391 -0.000987   \n",
       "104711             0.012295             0.024791 -0.000842   \n",
       "104712             0.011495             0.023991 -0.000720   \n",
       "104713             0.011695             0.024191 -0.000934   \n",
       "104714             0.010295             0.022790 -0.000777   \n",
       "104715             0.011295             0.023791 -0.000561   \n",
       "104716             0.012095             0.024591 -0.000820   \n",
       "104717             0.010559             0.023054 -0.000992   \n",
       "104718             0.011095             0.023591 -0.000666   \n",
       "104719             0.010295             0.022790 -0.000495   \n",
       "104720             0.011295             0.023791 -0.000641   \n",
       "104721             0.010295             0.022790 -0.000342   \n",
       "104722             0.009894             0.022390 -0.000329   \n",
       "104723             0.009696             0.022192 -0.000322   \n",
       "104724             0.009096             0.021592  0.000055   \n",
       "104725             0.009894             0.022390 -0.000077   \n",
       "104726             0.009560             0.022056 -0.000240   \n",
       "\n",
       "        Std Error\\nResidual  Student\\nResidual     Cook's\\nD  \n",
       "0                  0.003188          -0.380170  7.339947e-07  \n",
       "1                  0.003188          -0.092730  4.830013e-08  \n",
       "2                  0.003188          -0.124936  8.574328e-08  \n",
       "3                  0.003188          -0.030097  5.012427e-09  \n",
       "4                  0.003188          -0.081142  3.616761e-08  \n",
       "5                  0.003188          -0.194017  1.985622e-07  \n",
       "6                  0.003188          -0.146157  1.149078e-07  \n",
       "7                  0.003188           0.056972  1.823194e-08  \n",
       "8                  0.003188          -0.048864  1.259502e-08  \n",
       "9                  0.003188           0.014182  1.129779e-09  \n",
       "10                 0.003188           0.047418  1.293046e-08  \n",
       "11                 0.003188           0.066361  2.512438e-08  \n",
       "12                 0.003188           0.040454  9.411158e-09  \n",
       "13                 0.003188           0.152091  1.363474e-07  \n",
       "14                 0.003188           0.161629  1.552782e-07  \n",
       "15                 0.003188           0.087499  4.550753e-08  \n",
       "16                 0.003188           0.187215  2.157301e-07  \n",
       "17                 0.003188           0.080755  3.876219e-08  \n",
       "18                 0.003188           0.118812  8.462830e-08  \n",
       "19                 0.003188           0.180261  2.018050e-07  \n",
       "20                 0.003188           0.231808  3.367643e-07  \n",
       "21                 0.003188           0.262168  4.470675e-07  \n",
       "22                 0.003188           0.285058  5.235900e-07  \n",
       "23                 0.003188           0.117060  8.286644e-08  \n",
       "24                 0.003188           0.022295  2.865910e-09  \n",
       "25                 0.003188          -0.142018  1.014326e-07  \n",
       "26                 0.003188          -0.331947  5.312514e-07  \n",
       "27                 0.003188          -0.240153  2.820608e-07  \n",
       "28                 0.003188          -0.309531  4.730425e-07  \n",
       "29                 0.003188          -0.175463  1.539029e-07  \n",
       "...                     ...                ...           ...  \n",
       "104697             0.003188          -0.371158  6.586739e-07  \n",
       "104698             0.003188          -0.224463  2.472399e-07  \n",
       "104699             0.003188          -0.521800  1.300803e-06  \n",
       "104700             0.003188          -0.415532  8.246085e-07  \n",
       "104701             0.003188          -0.245305  2.891864e-07  \n",
       "104702             0.003188          -0.452957  9.798367e-07  \n",
       "104703             0.003188          -0.151070  1.111560e-07  \n",
       "104704             0.003188          -0.383007  7.018367e-07  \n",
       "104705             0.003188          -0.671021  2.183630e-06  \n",
       "104706             0.003188          -0.383061  7.037250e-07  \n",
       "104707             0.003188          -0.328501  5.192438e-07  \n",
       "104708             0.003188          -0.216049  2.262773e-07  \n",
       "104709             0.003188          -0.367191  6.469186e-07  \n",
       "104710             0.003188          -0.309664  4.636943e-07  \n",
       "104711             0.003188          -0.264282  3.405111e-07  \n",
       "104712             0.003188          -0.225922  2.523164e-07  \n",
       "104713             0.003188          -0.292941  4.226137e-07  \n",
       "104714             0.003188          -0.243676  3.015510e-07  \n",
       "104715             0.003188          -0.176069  1.538612e-07  \n",
       "104716             0.003188          -0.257281  3.237306e-07  \n",
       "104717             0.003188          -0.311227  4.887112e-07  \n",
       "104718             0.003188          -0.209067  2.178486e-07  \n",
       "104719             0.003188          -0.155367  1.225892e-07  \n",
       "104720             0.003188          -0.201040  2.005991e-07  \n",
       "104721             0.003188          -0.107181  5.834078e-08  \n",
       "104722             0.003188          -0.103125  5.458064e-08  \n",
       "104723             0.003188          -0.101085  5.273089e-08  \n",
       "104724             0.003188           0.017166  1.547818e-09  \n",
       "104725             0.003188          -0.024008  2.958058e-09  \n",
       "104726             0.003188          -0.075236  2.932410e-08  \n",
       "\n",
       "[104727 rows x 12 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stat_summary_linear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f3de23651d0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df[\"h\"],df[\"Q\"],label = \"data\")\n",
    "plt.plot(df[\"h\"],stat_summary_linear[\"Predicted\\nValue\"],label=\"linear model\",c=\"r\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:              np.log(Q)   R-squared:                       0.990\n",
      "Model:                            OLS   Adj. R-squared:                  0.990\n",
      "Method:                 Least Squares   F-statistic:                 1.001e+07\n",
      "Date:                Wed, 02 Oct 2019   Prob (F-statistic):               0.00\n",
      "Time:                        15:33:04   Log-Likelihood:             1.2339e+05\n",
      "No. Observations:              104727   AIC:                        -2.468e+05\n",
      "Df Residuals:                  104725   BIC:                        -2.468e+05\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      1.9205      0.002    994.149      0.000       1.917       1.924\n",
      "np.log(h)      2.0909      0.001   3164.035      0.000       2.090       2.092\n",
      "==============================================================================\n",
      "Omnibus:                    28185.843   Durbin-Watson:                   0.167\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          2908303.157\n",
      "Skew:                          -0.083   Prob(JB):                         0.00\n",
      "Kurtosis:                      28.816   Cond. No.                         27.4\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "power = smf.ols(formula='np.log(Q) ~ np.log(h)', data=df)\n",
    "result_power = power.fit()\n",
    "print(result_power.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "simpletable, data, headers = summary_table(result_power, alpha=0.05)\n",
    "stat_summary_power = pd.DataFrame(data,columns=headers)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting predicted values\n",
    "powerlaw_data = np.exp(stat_summary_power[[\"Predicted\\nValue\"]])\n",
    "powerlaw_data[\"h\"] = df[\"h\"].values\n",
    "\n",
    "# sorting for plot\n",
    "powerlaw_data = powerlaw_data.sort_values(by=\"h\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f3dad3769b0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df[\"h\"],df[\"Q\"],label = \"data\")\n",
    "plt.plot(df[\"h\"], stat_summary_linear[\"Predicted\\nValue\"], label = \"linear model\", c = \"r\")\n",
    "plt.plot(powerlaw_data[\"h\"], powerlaw_data[\"Predicted\\nValue\"], label = \"power law\", c = \"k\")\n",
    "plt.legend(loc = 'upper left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Circle profile geometrics\n",
    "def ACIR(w,r):\n",
    "    h=np.asarray(w)\n",
    "    alpha=np.arccos((r-h)/r)\n",
    "    A= r**2*alpha - (r-h)*np.sin(alpha)*r\n",
    "    return A\n",
    "def LuCIR(w,r):\n",
    "    w=np.asarray(w)\n",
    "    alpha=np.arccos((r-w)/r)    \n",
    "    L=alpha*r*2\n",
    "    return L\n",
    "def ManningCIR(h,r,kst,I):\n",
    "    h=np.asarray(h)\n",
    "    Rhy=ACIR(h,r)/LuCIR(h,r)\n",
    "    Q=(ACIR(h,r)*kst*(Rhy)**(2/3.)*(I**(1/2.)))\n",
    "    return Q\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      fun: 0.27177122424682365\n",
       " hess_inv: array([[0.49582364]])\n",
       "      jac: array([0.])\n",
       "  message: 'Optimization terminated successfully.'\n",
       "     nfev: 18\n",
       "      nit: 4\n",
       "     njev: 6\n",
       "   status: 0\n",
       "  success: True\n",
       "        x: array([9.1664631])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def cost_func(param,h,Q):\n",
    "    kst = param[0]\n",
    "    r=0.2\n",
    "    I=2.57924387429577\n",
    "    return np.sum(np.square(Q-ManningCIR(h,r,kst,I)))\n",
    "\n",
    "\n",
    "result_manning = opt.minimize(cost_func,[50],(df.h.values,df.Q.values,))\n",
    "result_manning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# parameters\n",
    "r=0.2\n",
    "I=2.57924387429577\n",
    "# optimization result!\n",
    "kst = result_manning.x[0]\n",
    "\n",
    "# converting predicted values\n",
    "manning_data = df[[\"h\"]]\n",
    "manning_data[\"Q\"] = ManningCIR(df.h.values,r,kst,I)\n",
    "\n",
    "# sorting for plot\n",
    "manning_data = manning_data.sort_values(by=\"h\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f3e74cf1ac8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df[\"h\"],df[\"Q\"],label = \"data\")\n",
    "plt.plot(df[\"h\"], stat_summary_linear[\"Predicted\\nValue\"], label = \"linear model\", c = \"r\")\n",
    "plt.plot(powerlaw_data[\"h\"], powerlaw_data[\"Predicted\\nValue\"], label = \"power law\", c = \"k\")\n",
    "plt.plot(manning_data[\"h\"], manning_data[\"Q\"], label = \"Manning\", c = \"orange\")\n",
    "plt.legend(loc = 'upper left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# choosing powerlaw nice plot!\n",
    "\n",
    "data = np.exp(stat_summary_power[['Predicted\\nValue','Mean ci\\n95% low','Mean ci\\n95% upp','Predict ci\\n95% low','Predict ci\\n95% upp']]).copy()\n",
    "resid = stat_summary_power[[\"Residual\"]].copy()\n",
    "\n",
    "data.insert(0, \"Q\", df.Q.values)\n",
    "\n",
    "data.index=df.h.values\n",
    "resid.index=df.h.values\n",
    "\n",
    "data = data.sort_index()\n",
    "resid = resid.sort_index()\n",
    "\n",
    "lower=data['Mean ci\\n95% low']\n",
    "upper=data['Mean ci\\n95% upp']\n",
    "\n",
    "lowerpred=data['Predict ci\\n95% low']\n",
    "upperpred=data['Predict ci\\n95% upp']\n",
    "\n",
    "plt.style.use('seaborn-white')\n",
    "plt.rc(\"font\",size=18)\n",
    "\n",
    "fig, (ax1,ax2) = plt.subplots(2,1,figsize=(16,8), sharex=True, gridspec_kw={'height_ratios':[1,4]})\n",
    "fig.subplots_adjust(hspace=0)\n",
    "\n",
    "resid.plot(style=[\"ro\"],ax=ax1,legend=False)\n",
    "data.plot(style=[\"kp\",\"k-\",\"r-\",\"r-\",\"c--\",\"c--\"],ax=ax2,legend=False)\n",
    "\n",
    "ymin1,ymax1=ax1.get_ylim()\n",
    "ticks1=ax1.get_yticks()\n",
    "ax1.set_ylim(ymin1*1.3,ymax1*1.3)\n",
    "ax1.set_yticks(ticks1[1:-1])\n",
    "\n",
    "ymin2,ymax2=ax2.get_ylim()\n",
    "ticks2=ax2.get_yticks()\n",
    "ax2.set_ylim(ymin2*1.07,ymax2*1.07)\n",
    "ax2.set_yticks(ticks2[1:-1])\n",
    "\n",
    "ax1.tick_params(left=True,bottom=False,length=9)\n",
    "ax2.tick_params(left=True,bottom=True,length=9)\n",
    "\n",
    "ax2.fill_between(data.index.tolist(), lower.tolist(), upper.tolist(),facecolor='r',alpha=0.5)\n",
    "ax2.fill_between(data.index.tolist(), upper.tolist(), upperpred.tolist(),facecolor='c',alpha=0.5)\n",
    "ax2.fill_between(data.index.tolist(), lower.tolist(), lowerpred.tolist(),facecolor='c',alpha=0.5)\n",
    "\n",
    "ax2.set_xlabel(\"\\nh [m]\")\n",
    "ax2.set_ylabel(\"Q [m³/s]\")\n",
    "\n",
    "mpl_lines=ax1.lines+ax2.lines\n",
    "fig.legend(mpl_lines,[l.get_label() for l in mpl_lines],bbox_to_anchor=(1.04, 0.89),bbox_transform=plt.gcf().transFigure)\n",
    "\n",
    "fig.text(0.5, 0.91, \"Residual - Fit - Plot\", fontsize=22, horizontalalignment=\"center\")\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.style.use('default')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model diagnostics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorische Variablen"
   ]
  }
 ],
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