solutions/chap10soln.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Examples and Exercises from Think Stats, 2nd Edition\n",
    "\n",
    "http://thinkstats2.com\n",
    "\n",
    "Copyright 2016 Allen B. Downey\n",
    "\n",
    "MIT License: https://opensource.org/licenses/MIT\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function, division\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import random\n",
    "\n",
    "import thinkstats2\n",
    "import thinkplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Least squares\n",
    "\n",
    "One more time, let's load up the NSFG data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import first\n",
    "live, firsts, others = first.MakeFrames()\n",
    "live = live.dropna(subset=['agepreg', 'totalwgt_lb'])\n",
    "ages = live.agepreg\n",
    "weights = live.totalwgt_lb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The following function computes the intercept and slope of the least squares fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from thinkstats2 import Mean, MeanVar, Var, Std, Cov\n",
    "\n",
    "def LeastSquares(xs, ys):\n",
    "    meanx, varx = MeanVar(xs)\n",
    "    meany = Mean(ys)\n",
    "\n",
    "    slope = Cov(xs, ys, meanx, meany) / varx\n",
    "    inter = meany - slope * meanx\n",
    "\n",
    "    return inter, slope"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Here's the least squares fit to birth weight as a function of mother's age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6.8303969733110526, 0.017453851471802753)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inter, slope = LeastSquares(ages, weights)\n",
    "inter, slope"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The intercept is often easier to interpret if we evaluate it at the mean of the independent variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.2667432601061215"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inter + slope * 25"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "And the slope is easier to interpret if we express it in pounds per decade (or ounces per year)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.17453851471802753"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slope * 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The following function evaluates the fitted line at the given `xs`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def FitLine(xs, inter, slope):\n",
    "    fit_xs = np.sort(xs)\n",
    "    fit_ys = inter + slope * fit_xs\n",
    "    return fit_xs, fit_ys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "And here's an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "fit_xs, fit_ys = FitLine(ages, inter, slope)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Here's a scatterplot of the data with the fitted line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.Scatter(ages, weights, color='blue', alpha=0.1, s=10)\n",
    "thinkplot.Plot(fit_xs, fit_ys, color='white', linewidth=3)\n",
    "thinkplot.Plot(fit_xs, fit_ys, color='red', linewidth=2)\n",
    "thinkplot.Config(xlabel=\"Mother's age (years)\",\n",
    "                 ylabel='Birth weight (lbs)',\n",
    "                 axis=[10, 45, 0, 15],\n",
    "                 legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Residuals\n",
    "\n",
    "The following functon computes the residuals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Residuals(xs, ys, inter, slope):\n",
    "    xs = np.asarray(xs)\n",
    "    ys = np.asarray(ys)\n",
    "    res = ys - (inter + slope * xs)\n",
    "    return res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Now we can add the residuals as a column in the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "live['residual'] = Residuals(ages, weights, inter, slope)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "To visualize the residuals, I'll split the respondents into groups by age, then plot the percentiles of the residuals versus the average age in each group.\n",
    "\n",
    "First I'll make the groups and compute the average age in each group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[15.212333333333312,\n",
       " 17.74035928143719,\n",
       " 20.506304824561838,\n",
       " 23.455752212389893,\n",
       " 26.435156146179903,\n",
       " 29.411177432543294,\n",
       " 32.30232530120497,\n",
       " 35.240273631840736,\n",
       " 38.10876470588231,\n",
       " 40.91205882352941]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = np.arange(10, 48, 3)\n",
    "indices = np.digitize(live.agepreg, bins)\n",
    "groups = live.groupby(indices)\n",
    "\n",
    "age_means = [group.agepreg.mean() for _, group in groups][1:-1]\n",
    "age_means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Next I'll compute the CDF of the residuals in each group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "cdfs = [thinkstats2.Cdf(group.residual) for _, group in groups][1:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The following function plots percentiles of the residuals against the average age in each group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def PlotPercentiles(age_means, cdfs):\n",
    "    thinkplot.PrePlot(3)\n",
    "    for percent in [75, 50, 25]:\n",
    "        weight_percentiles = [cdf.Percentile(percent) for cdf in cdfs]\n",
    "        label = '%dth' % percent\n",
    "        thinkplot.Plot(age_means, weight_percentiles, label=label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following figure shows the 25th, 50th, and 75th percentiles.\n",
    "\n",
    "Curvature in the residuals suggests a non-linear relationship."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "PlotPercentiles(age_means, cdfs)\n",
    "\n",
    "thinkplot.Config(xlabel=\"Mother's age (years)\",\n",
    "                 ylabel='Residual (lbs)',\n",
    "                 xlim=[10, 45])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Sampling distribution\n",
    "\n",
    "To estimate the sampling distribution of `inter` and `slope`, I'll use resampling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def SampleRows(df, nrows, replace=False):\n",
    "    \"\"\"Choose a sample of rows from a DataFrame.\n",
    "\n",
    "    df: DataFrame\n",
    "    nrows: number of rows\n",
    "    replace: whether to sample with replacement\n",
    "\n",
    "    returns: DataDf\n",
    "    \"\"\"\n",
    "    indices = np.random.choice(df.index, nrows, replace=replace)\n",
    "    sample = df.loc[indices]\n",
    "    return sample\n",
    "\n",
    "def ResampleRows(df):\n",
    "    \"\"\"Resamples rows from a DataFrame.\n",
    "\n",
    "    df: DataFrame\n",
    "\n",
    "    returns: DataFrame\n",
    "    \"\"\"\n",
    "    return SampleRows(df, len(df), replace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The following function resamples the given dataframe and returns lists of estimates for `inter` and `slope`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def SamplingDistributions(live, iters=101):\n",
    "    t = []\n",
    "    for _ in range(iters):\n",
    "        sample = ResampleRows(live)\n",
    "        ages = sample.agepreg\n",
    "        weights = sample.totalwgt_lb\n",
    "        estimates = LeastSquares(ages, weights)\n",
    "        t.append(estimates)\n",
    "\n",
    "    inters, slopes = zip(*t)\n",
    "    return inters, slopes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Here's an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "inters, slopes = SamplingDistributions(live, iters=1001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The following function takes a list of estimates and prints the mean, standard error, and 90% confidence interval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Summarize(estimates, actual=None):\n",
    "    mean = Mean(estimates)\n",
    "    stderr = Std(estimates, mu=actual)\n",
    "    cdf = thinkstats2.Cdf(estimates)\n",
    "    ci = cdf.ConfidenceInterval(90)\n",
    "    print('mean, SE, CI', mean, stderr, ci)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Here's  the summary for `inter`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean, SE, CI 6.829185459088737 0.06767011520211424 (6.716748354462339, 6.941896381986979)\n"
     ]
    }
   ],
   "source": [
    "Summarize(inters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "And for `slope`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean, SE, CI 0.017480239500445995 0.002670358753454292 (0.013028573267487135, 0.022022292160010386)\n"
     ]
    }
   ],
   "source": [
    "Summarize(slopes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Exercise:** Use `ResampleRows` and generate a list of estimates for the mean birth weight.  Use `Summarize` to compute the SE and CI for these estimates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean, SE, CI 7.265321586634211 0.014517304608097607 (7.240851128568267, 7.2885870767869)\n"
     ]
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "iters = 1000\n",
    "estimates = [ResampleRows(live).totalwgt_lb.mean()\n",
    "             for _ in range(iters)]\n",
    "Summarize(estimates)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Visualizing uncertainty\n",
    "\n",
    "To show the uncertainty of the estimated slope and intercept, we can generate a fitted line for each resampled estimate and plot them on top of each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for slope, inter in zip(slopes, inters):\n",
    "    fxs, fys = FitLine(age_means, inter, slope)\n",
    "    thinkplot.Plot(fxs, fys, color='gray', alpha=0.01)\n",
    "    \n",
    "thinkplot.Config(xlabel=\"Mother's age (years)\",\n",
    "                 ylabel='Residual (lbs)',\n",
    "                 xlim=[10, 45])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Or we can make a neater (and more efficient plot) by computing fitted lines and finding percentiles of the fits for each value of the dependent variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def PlotConfidenceIntervals(xs, inters, slopes, percent=90, **options):\n",
    "    fys_seq = []\n",
    "    for inter, slope in zip(inters, slopes):\n",
    "        fxs, fys = FitLine(xs, inter, slope)\n",
    "        fys_seq.append(fys)\n",
    "\n",
    "    p = (100 - percent) / 2\n",
    "    percents = p, 100 - p\n",
    "    low, high = thinkstats2.PercentileRows(fys_seq, percents)\n",
    "    thinkplot.FillBetween(fxs, low, high, **options)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "This example shows the confidence interval for the fitted values at each mother's age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "PlotConfidenceIntervals(age_means, inters, slopes, percent=90, \n",
    "                        color='gray', alpha=0.3, label='90% CI')\n",
    "PlotConfidenceIntervals(age_means, inters, slopes, percent=50,\n",
    "                        color='gray', alpha=0.5, label='50% CI')\n",
    "\n",
    "thinkplot.Config(xlabel=\"Mother's age (years)\",\n",
    "                 ylabel='Residual (lbs)',\n",
    "                 xlim=[10, 45])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Coefficient of determination\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The coefficient compares the variance of the residuals to the variance of the dependent variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def CoefDetermination(ys, res):\n",
    "    return 1 - Var(res) / Var(ys)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For birth weight and mother's age $R^2$ is very small, indicating that the mother's age predicts a small part of the variance in birth weight."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.004738115474710258"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inter, slope = LeastSquares(ages, weights)\n",
    "res = Residuals(ages, weights, inter, slope)\n",
    "r2 = CoefDetermination(weights, res)\n",
    "r2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We can confirm that $R^2 = \\rho^2$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rho 0.06883397035410908\n",
      "R 0.06883397035410828\n"
     ]
    }
   ],
   "source": [
    "print('rho', thinkstats2.Corr(ages, weights))\n",
    "print('R', np.sqrt(r2))    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "To express predictive power, I think it's useful to compare the standard deviation of the residuals to the standard deviation of the dependent variable, as a measure RMSE if you try to guess birth weight with and without taking into account mother's age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Std(ys) 1.4082155338406197\n",
      "Std(res) 1.4048754287857832\n"
     ]
    }
   ],
   "source": [
    "print('Std(ys)', Std(weights))\n",
    "print('Std(res)', Std(res))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "As another example of the same idea, here's how much we can improve guesses about IQ if we know someone's SAT scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.409610943738484"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_ys = 15**2\n",
    "rho = 0.72\n",
    "r2 = rho**2\n",
    "var_res = (1 - r2) * var_ys\n",
    "std_res = np.sqrt(var_res)\n",
    "std_res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Hypothesis testing with slopes\n",
    "\n",
    "Here's a `HypothesisTest` that uses permutation to test whether the observed slope is statistically significant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SlopeTest(thinkstats2.HypothesisTest):\n",
    "\n",
    "    def TestStatistic(self, data):\n",
    "        ages, weights = data\n",
    "        _, slope = thinkstats2.LeastSquares(ages, weights)\n",
    "        return slope\n",
    "\n",
    "    def MakeModel(self):\n",
    "        _, weights = self.data\n",
    "        self.ybar = weights.mean()\n",
    "        self.res = weights - self.ybar\n",
    "\n",
    "    def RunModel(self):\n",
    "        ages, _ = self.data\n",
    "        weights = self.ybar + np.random.permutation(self.res)\n",
    "        return ages, weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "And it is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ht = SlopeTest((ages, weights))\n",
    "pvalue = ht.PValue()\n",
    "pvalue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Under the null hypothesis, the largest slope we observe after 1000 tries is substantially less than the observed value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.017453851471802753, 0.009339357664930548)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ht.actual, ht.MaxTestStat()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We can also use resampling to estimate the sampling distribution of the slope."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "sampling_cdf = thinkstats2.Cdf(slopes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The distribution of slopes under the null hypothesis, and the sampling distribution of the slope under resampling, have the same shape, but one has mean at 0 and the other has mean at the observed slope.\n",
    "\n",
    "To compute a p-value, we can count how often the estimated slope under the null hypothesis exceeds the observed slope, or how often the estimated slope under resampling falls below 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bb6332JSU+j/svF/n+Lb3FPLGGCoqKhqN19+1HT9ffVPKhzNjDDsPHPWUzxrYycFootdv399YZ1+0TEYYiGZXPeVEd7idO3fStm1brr32Wlq2bMmsWbM8CSIjI4MjR44wd+5crrjiihM677Zt2/jiiy8YOXIks2fPZsyYMSccW5s2bUhNTeXLL79kxIgRvPHGG36PGzt2LC+99BLXX389e/fuZdGiRXXuSvbt20dCQgKXX3453bt358YbbwQgNTWVkpKSgGOaM2cO06ZNY86cOYwc6ZoO4/gU8hMnTuSdd96hsrKy0XOPHTuW119/nYcffpjFixeTkZHR4N1PuFu02toD7cKhXRyKJHpV+k4sBTw4zp6JDyNVs0saTlizZg33338/MTExxMfH88ILL5CWlsaUKVPIyckhKyuLoUOHnvB5+/Tpw6uvvsqPf/xjevbsyU9/+tOTiu+VV15hypQppKSkkJeX56k+83bppZeycOFCcnJy6NWrF2eccUadY3bs2MFNN93kuSN49NFHAVcj9k9+8hOSk5MDqm4qLy9n+PDh1NTUMHv2bACmTJnChAkTGDZsGGeffbbnTmXAgAHExcUxcOBAbrzxRgYPHuw5zyOPPMJNN93EgAEDaNGiBa+++uqJ/3DCyCc+VVNdT9G11EPtrW+sA/nuObMbLRP1Y9KbTo0epgoKCrjwwgs9jc5NceTIEU9vpMcee4xdu3bx9NNPN/m8kS7cpkb3nvp83OBMfnx+3ybHFW2a+nvwnfa8OQ3kC9bU6JpCo8D8+fN59NFHqaqqomvXrsyaNcvpkJSPsgprm8vovqc6FEn0MsZYEsa4Pu2cCyaMadIIU1lZWUG5ywBXT6aGejMp5731RYGl3LdzG0fiiGav51u7O4/ISnMokvDWbHpPRVo1m3JWuL1f5n5mXR9FR3+HljGG9buPWPYlxun4GH+aRdJISkpi//79YfdBoMKTMYb9+/eTlBQeK9+Vllurpm4+t/nUo0eKzfussy1codOF1KtZVE9lZmZSWFhIUVGR06GoCJGUlERmZqbTYQCwaI21q+147Wobcq9+td1SHty5bg9D5dIskkZ8fDzZ2dlOh6HUSXnlQ/umvlGNM8ZQ41VJkdEywblgIkCzqJ5SKlJV+wwmG9m7vUORRK8Dx6wrI04ZpXd6DdGkoZSD7n3ZOhjyrotzHIoken259aClrIP5GqZJQykHFe4/ainHx+mfZKh97pM0VMP0HaqUQ46UWqtFbh2nvaZCzbfH5Rk90h2KJHJo0lDKIWt+OGApn5+rdemh9tqyQkv5v3q0dSiSyKFJQymHvPhe7brrXdrp5IRO+H6vtXowWRe8apStSUNEzhORDSKySUTqLBknIl1EZJGIrBSR1SJygZ3xKBVOvKunWqfoqnChVuXTc23iEB3QFwjbkoaIxALPAecDfYFJIuI7beevgH8YYwYDVwPP2xWPUuHkaJlPN88fNa9ZmiOBb1fbgZ10QF8g7LzTGAZsMsZsMcZUAG8AE3yOMcDxVXNaAztRKgpc/z+LLGVd1jX0Vu887HQIEcnODsmdAO+x+YXAcJ9jHgE+FJE7gBTgHBvjUSos+FaLKGcs3rjfs52u1YMBs/NOw980nb4zCk4CZhljMoELgP8TkToxichUEckXkXydX0pFunXbrOMCnr/tvxyKJLp597bN6Ri5ywSHmp1JoxDo7FXOpG710y3APwCMMV8ASUCG74mMMTONMbnGmNx27XRhFBXZfjdnpaXcPi3ZoUiiV43P+IxR3XT9kkDZmTSWAz1FJFtEEnA1dM/zOWYbcDaAiPTBlTT0VkI1a9U1tdVTp7Zp4WAk0ev7Pda1M1ISdOqQQNmWNIwxVcDtwAfAely9pNaKyAwRudh92M+BKSLyDTAbuNHoohiqGTt8rMJSfuiqwQ5FEt0+3Xyg8YOUX7amV2PMAmCBz77pXtvrgNF2xqBUOPl07W5LuWNb7TXlBO/qqW4Zerd3InREuFIh9NePdO2McLDtYJlne3Cmjs84EZo0lAqhTK/xGKf30E4dTvCtAe/YOtGhSCKTJg2lQsh7KvSJY7o5GEn0KquyjpM5JVWTxonQpKGUQ1qn6LKiTvhut7XnVIz4G1Km6qNJQ6kQOVBSbim3banfcJ2wseho4wepemnSUCpEvtm631KOjdU/Pyes31Pi2e7RTntOnSh91yoVIuu367Ki4aCiqrYhvO+puo7JidKkoVSIrC6oHVCmPaecsdenirDPqS0diiRyadJQKkSKDpV6tkf3ae9gJNFrjc906KmJOn3IidKkoVQI7C0utZT7dNYJ8pyw/Idiz3ZqYiyiPadOmCYNpULgN7NXWMqn6My2IVdjDCXl1Z7yQB0JflI0aSgVArsPHvNst9Guto744YD1bu/0zpo0ToYmDaVsdqy8ylL+5USd2dYJxaXWNcF1JPjJ0aShlM0Wr7GuPZbdXrt5OuGLrbVdnvtqr6mTpklDKZtt2W3tsaONr87YUVw7s+2RiuoGjlQN0aShlM3Wb6/tsTOs1ykORqKOG9OtrdMhRCxNGkrZzLsRvGdHbXwNBx10OvSTpklDqRDq20XHZzih0GecTOukeIciiXyaNJSyUU2NdcGfDm10gjwn/O8X2y3l2BhtVzpZmjSUstHqAuvMtq1a6DdcJ3gvvJQQpwmjKTRpKGWjWR9/bylrz6nQ813e9bqhmQ5F0jxo0lDKRkWHa7t5ZrRKcjCS6LXHZ2bbzDY6hUtTaNJQykZlFbWjwS8ZkeVcIFFsy/5jlnKCLn7VJPrTU8omvo3gPbS7rSO855xKTYx1MJLmQZOGUjb5evM+S1mnD3GG98JL3dulOBhJ86BJQymbbNx5yFKO02oRR+wtqfBs99Ck0WT6LlbKJh+uLPRsZ+ldhiNKfeaY6p6h42SaSpOGUjY5fKz2G25OV53ryAkFB6yN4K10JHiTadJQygbevaYAxg3RsQFOWLSxdnClDgIPDk0aStmgcN9RS7ljW61Ld0JKQm1vqVNb6SSFwaBJQykbPPXOGqdDUMD3e2uTd26XNAcjaT5sTRoicp6IbBCRTSIyrZ5jJorIOhFZKyJ/tzMepULFezr0lslajx4O2uvyrkERZ9eJRSQWeA44FygElovIPGPMOq9jegIPAqONMQdFRFeoURFv6brdlvLDVw1xKBLlrW2KJu9gsPNOYxiwyRizxRhTAbwBTPA5ZgrwnDHmIIAxZq+N8SgVEn9fvNFS1pHgzth+0LqGRstE274jRxU7k0YnwHsS+0L3Pm+9gF4i8pmIfCki5/k7kYhMFZF8EckvKiqyKVylgmOP14I/uuiSc77YetBSjtEZhoPCzqTh7zdkfMpxQE8gD5gEvCwidVqrjDEzjTG5xpjcdu3aBT1QpYKlwmvdBoDJeT0dikR5z27bKU1nGA4WO5NGIdDZq5wJ7PRzzDvGmEpjzFZgA64kolREWra52FLunak9dpyy+3Bt0ujdvqWDkTQvdiaN5UBPEckWkQTgamCezzFvA2cCiEgGruqqLTbGpJStvt91xOkQFFDjs/BST51zKmhsSxrGmCrgduADYD3wD2PMWhGZISIXuw/7ANgvIuuARcD9xpj9/s+oVPjbtq920aUzB3R0MJLoVlJhTRpaPRU8tnYnMMYsABb47JvutW2Ae93/lIp4+0rKSUlx/VnpfFPO2VlShfd3Ym0EDx4dEa6UTfp01p5TTlmxu7Y9Iz5WE0YwadJQKki27rXOqNqutVaJOKW0qrZ6qoP+HoJKk4ZSQfLf8zdbyqJVIo4p90oaZ/RIdzCS5keThlI26N6hldMhRK0qn7XZO+jstkGlSUMpG/zi8kFOhxC1fjhkXcukVZJOHxJMmjSUCoLySutI8HSdUdUxO49Yk4ZWEwaXJg2lgmDjbuugPv2gcs63RbXL7OrCS8HXYNIQkVle2zfYHo1SEWrdDh0JHi6qvW76BnTStqVga+xOY6DX9l12BqJUJPtswwHPts435Rzf6UNyOqQ6FEnz1VjS8J2VVinlR7VXj52hvXQtMaeUVlo/stqmJDgUSfPVWLeCTBF5Btc058e3PYwxd9oWmVIRorra2gg+qnd7hyJR+0qrnQ6h2WssadzvtZ1vZyBKRaqdB60jwU9JS3YoErX5YKVnW6cPsUeDScMY82qoAlEqUm0oLG78IBUS6/bVJo2ubTV526HRLrcicoOIfC0iR93/8kXk+lAEp1Qk+PK72qXtkxNiHYxEeTtNF16yRYN3Gu7kcDeuqcu/xtW2MQR4QkQwxrxmf4hKhbeVW/Z5tnt31A8qpxSXVlrKgzq1diiS5q2xO43bgEuNMYuMMYeMMcXGmIXA5e7HlIpqR8usH1Rj++jkeE7ZdqDUUm6hd322aCxptDLGFPjudO/TUTMq6m3YcchS7tG+hUORqM37jnq2E+O0EdwujSWN0pN8TKmosL1Ipw8JF9/uKvFsd0jRuwy7NNblto+IrPazX4BuNsSjVESZ82ntGhqDumodupPKvCaNbK9JwzaNJY2BQHtgu8/+rsBOWyJSKkLU1BjKK2sHkyUl6Pyf4SI7Ld7pEJqtxt7lTwGHjTE/eP8DjrkfUypq7Sm21tBeOFhHgjvlkE/PqbQkTeB2aewnm2WMqVM9ZYzJB7JsiUipCLHNpz0jLUW/3TplU9FRSzkuRtuW7NJY0mhoRXYdbqmi2g9FtQ2vmTr62FG+YzSUfRpLGstFZIrvThG5BVhhT0hKRYZVW/Z7tmP1m62jFn5f+7vo0FIbwe3UWEP43cBbIjKZ2iSRCyQAl9oZmFLhznvOqT6ddCR4uOjSStcEt1NjExbuAUaJyJlAf/fu+e5R4Uoptx6npjgdQtTybQTvk6FraNgpoJRsjFkELLI5FqUixl6fnlPddSS4Y9buLrGUW2rXZ1vpT1epk/DxqkJLOT5W/5Scsm5XbS+2lARtW7KbvtOVOglrfjjo2U5J0q62Ttq6v3YRrK7anmE7TRpKnYTvd9Q2gufldHAwkujm256Rc0qiQ5FED1uThoicJyIbRGSTiExr4LgrRMSISK6d8ShlhxGn6Uhwp2w7aG1batdCu9vazbakISKxwHPA+UBfYJKI9PVzXCpwJ/CVXbEoFUxV1TWWcvcOukqAU77dWdsInhSnFSehYOdPeRiwyRizxRhTAbwBTPBz3G+BPwJlNsaiVNAs31hkKSfG67dbp3hPh57ZpqEJLFSw2Jk0OmGdHbfQvc9DRAYDnY0x79oYh1JB9dJ7650OQQEVVdY7vtM7pzkUSXSxM2n46/tmPA+KxOCaKffnjZ5IZKqI5ItIflFRUWOHK2WrktIKz3a/Lm0djCS6zVuz21Luc6qOyg8FO5NGIdDZq5yJdQ2OVFyjzBeLSAEwApjnrzHcGDPTGJNrjMlt166djSErdWKuGJ3tdAhRa2XhYUtZx8qEhp0/5eVATxHJFpEE4Gpg3vEHjTGHjDEZxpgsY0wW8CVwsXvadaXC0tEyaxfPvl3aOBSJ8nZu7wynQ4gatiUNY0wVcDvwAbAe+IcxZq2IzBCRi+16XaXslO/TCB6n324d4duekdtF2zNCxdbhk8aYBcACn33T6zk2z85YlAqGZRu1TS0cLN1ywFJumagjwUNFvyYpdQL2eA0m652p326d8p8N+5wOIWpp0lDqBOzYX7us6LBepzgYSfQyxljKQzq3diiS6KRJQ6kAVVXXUFFV7SlntU91MJrotWL7IUv5nNO0ETyUNGkoFaCvN1urRLR6yhlb9h2zlFsn6yzDoaRJQ6kAzf1si6Ws04c4Y++Rcs92j3a6+FWoadJQKkA/7K1d7KdHB61Hd8quQ7VJo2c7HQUeapo0lAqQ9+y25+d2buBIZZfqGmsjeNe2yQ5FEr00aSh1Evp01pHgTvBdPyMzTWe2DTVNGkoFYE+x9cMqPVVXiHPCDp/fg4iuCR5qmjSUCsCqLdaeUzp9iDOW/VC7zG56ivaacoK+85UKwKLVtRM0t2qR4GAk0W3/0doJI7u00fYMJ2jSUCoAG3fWDijLydI1NJxQcMA6PuN0naTQEZo0lGpEWUWVpTxheJYzgUS5L7cetJSz03WMhhM0aSjViA9XFlrK3Tu0ciiS6LbvaO2KiZ07CG+xAAASOklEQVR1PXDHaNJQqhGv/ud7p0NQWAf15XTUxO0UTRpKNcB3RtWhPXW54XCgVVPO0aShVAMK9pRYylPO6+NQJNHNdyR4u5bag80pmjSUaoD3+hkA6alal+6EIq9JCgHidZyMY/Qnr1QD/rZ4k2c7u73Woztlb0lF4wepkNCkoVQDig7VTlvROkWrRJyyeGPtiPwuOkmhozRpKBWgcwd3cjqEqLXH607Dt3OCCi1NGkrVw/fDqX8XHQkeDkZ309+DkzRpKFWPw8cqLeWUpDiHIolu5V7rsgP0aJfiUCQKNGkoVa+CvdbutjoNtzO2HyyzlJN1mV1HadJQqh4bdhQ3fpCy3QavZXaV8zRpKFWP1VsPeLZPy9QZVZ3y+ZbaiQp1zinnadJQqh7rt9d+WHVK13r0cDCkc2unQ4h6mjSU8uNYuXU6dJ1zyhm+y7ue3lnv+JymSUMpP+7/65eWcm4PTRpO+NxnDY3YGO2M4DRNGkr5MMaw+6B1lbgY/bByxKrCw57tti10TfBwoElDKR8rt+y3lB+8crBDkUQ335ltT++i7RnhwNakISLnicgGEdkkItP8PH6viKwTkdUi8h8R6WpnPEoF4vdzvraUc7U9wxGvLdtuKQ/v2sahSJQ325KGiMQCzwHnA32BSSLS1+ewlUCuMWYAMBf4o13xKBWIikrr6OMu7VIdikRtKrJWESYn6KC+cGDnncYwYJMxZosxpgJ4A5jgfYAxZpEx5vg740sg08Z4lGrU218VWMpP3jzcmUCUxeShOllkuLAzaXQCvO8vC9376nML8J6/B0Rkqojki0h+UVFREENUymrXAeu321hd7McR+49a18/oqfNNhQ07/yL8dTfxO6exiFwL5AJP+HvcGDPTGJNrjMlt107rl5V9lny7y7P9oyGdHYwkum0qsq6YqCv1hQ87p+0sBLz/6jKBnb4Hicg5wEPAGcaYct/HlQqV4qPWt9+wXvoFxSnLt9XO+6W9ncOLnel7OdBTRLJFJAG4GpjnfYCIDAZeAi42xuy1MRalGnXnS59bygOz0x2KJLoZY9h1qDaBn95FR4GHE9uShjGmCrgd+ABYD/zDGLNWRGaIyMXuw54AWgJvisgqEZlXz+mUst3RMuv6GToVujP2+bRnDNXxGWHF1lVljDELgAU++6Z7bZ9j5+srFahDPh9Uz/5kjEORqL/n77CUO6XpmuDhRFuXlAI+WGkdSHZqG/2gcsrekorGD1KO0aShFPCvzwssZa2acsZhnyrCKwd3cCgSVR9NGkoBlV7rUI8brGNMnfK35daqqUGZ2p4RbjRpqKhnjHX40AVDuzgUidpRXNb4QcpRmjRU1NtWZF2DulNbHX3shKrqGkv5mlydOiQcadJQUe+9FdZGcF07wxmrdhy2lHu3b+lQJKohmjRU1PtkTe3UIbEx+ifhlLe+2W0p6yp94Un/QlTUq/BqBL9sVLaDkUSvMp8p6Xu0a+FQJKoxmjRUVPNtBB+u8005YuZnP1jKVw7u6FAkqjGaNFRUW7Fpn6Wc1V4XXQo1Ywx7fAb0tUy0dbIK1QSaNFRUm/n+ektZB/WF3oJ11rlKbxiu42TCmSYNFdX2l9SOC0hLSXQwkuj1+ZaDlnKvU7TXVDjTpKGi1tJ11t46t4zr7VAk0cu3TemsXjodfbjTpKGi1lNvr7aUR/Y+xaFIotev3t1gKZ/RQ5NGuNOkoaKS71ToI3q31/aMEFu/u6TOvjhd1jXs6W9IRaX//dj6DffnlwxwKJLo5Ts54S/O6e5QJOpEaNJQUenTtbssZZ06JLSOlFdZykO7ptE6Od6haNSJ0KShos5z89dayvdMyHEokuj16IebLOUJOe0dikSdKB1Bo6LKkdJKFn5jrRYZ008X+gmViqoa/m95YZ392p4UOTRpqKhyw1OLLOV7L9W2jFD6zXvf19n32wtPcyASdbK0ekpFjaM+S4kCjO5zqgORRKcdxaV19p19WgYxepcRUfROQ0WN6//Hepfxt5+f5VAk0en5T62TEt48sjPdM3TBq0ijdxoqKrzzZUGdfck6KV7IVPisytc6OU4TRoTSvxrV7NXUGF5baK1Lf/YnYxyKJvrsKSnnmcVbLfsmDtGpzyOVJg3VrB0tq6xTLTWyd3s6tNVFfkIhf1txnRX5ALL05x+xtHpKNVvGmDoJA+C+ywY6EE30Wb3jsN+E8cC5OvI7kumdhmq2rnj0ozr7tPE7NH44cIw5X++ss/93F56mYzIinCYN1eyUV1ZzzRP/qbP/jV+cQ3yc3lzb7XBZJTM/21ZnvyaM5kGThmpW9h0u48fPLqmz/4mbR2jCCIHqGsPjH22us//3F+laJc2FJg3VLBwrr+Lmpz+hsqq6zmMPXTWEbqe2ciCq6LHzUBnPLSnw+5gmjObF1qQhIucBTwOxwMvGmMd8Hk8EXgNOB/YDVxljCuyMSTUfRYdKmb98G/9e9kO9x/zljjNom6rLuNpl3e4SXveZ4tzb73SKkGbHtqQhIrHAc8C5QCGwXETmGWPWeR12C3DQGNNDRK4GHgeusismFTlqagyF+49algPdeeAYpeVVdWaprc+cB87RRX1scOBYBct+KObTTQcaPO43F/TSNoxmyM47jWHAJmPMFgAReQOYAHgnjQnAI+7tucCzIiLGd+Fg1WwdKCnn9cUb2bH/KAAbdx5q0vlSkxN4aspI2rTUu4tgWLPzMF8VFLN1/7GAn3Pf2d1p00LXxmiu7EwanYDtXuVCYHh9xxhjqkTkEJAO7LMxLhVG3svfxuI1dbtmnqi8nI5MzuupVVFBVFldw1vf7Ka8qqbRY087JYXrhmXqnUUUsDNp+Hv3+N5BBHIMIjIVmArQpUuXpkemwsaBI+WNHtO5XUvP9vaiI+T2aEe71slcOaYbrVMS7AwvqpVX1TSYMOJjheFZbTivTztNFlHEzqRRCHT2KmcCvl8pjx9TKCJxQGugTkWpMWYmMBMgNzdXq66akTP6d6Dbqa3YtOsQOV3bkpmRQnJiHB3atCA2RvTDyEGJcTFc1L89h8uqOFpRxdCuabRPTSRGhFhdHjdq2Zk0lgM9RSQb2AFcDVzjc8w84AbgC+AKYKG2Z0SXAdnpDMhOdzqMJuvRo4fTIQRdfGwMI7LbOB3GCWmOv4dwY1vScLdR3A58gKvL7V+NMWtFZAaQb4yZB7wC/J+IbMJ1h3G1XfEopZRqOlvHaRhjFgALfPZN99ouA660MwallFLBo53YlVJKBUyThlJKqYBp0lBKKRUwTRpKKaUCpklDKaVUwCTShkWISAmwwek4bJRB855GpTlfX3O+NtDri3SnGWNSm3qSSFxPY4MxJtfpIOwiIvl6fZGpOV8b6PVFOhHJD8Z5tHpKKaVUwDRpKKWUClgkJo2ZTgdgM72+yNWcrw30+iJdUK4v4hrClVJKOScS7zSUUko5JCyThoi0FZGPRGSj+3+/8zOLyA3uYzaKyA1e+98XkW9EZK2IvOherzxsNOX6RKSFiMwXke/c1/dYaKNvXBB+f78Xke0iciR0UTdMRM4TkQ0isklEpvl5PFFE5rgf/0pEsrwee9C9f4OI/CiUcQfqZK9PRNJFZJGIHBGRZ0Mdd6CacH3nisgKEVnj/v+sUMceiCZc3zARWeX+942IXNroixljwu4f8Edgmnt7GvC4n2PaAlvc/7dxb7dxP9bK/b8A/wSudvqagnV9QAvgTPcxCcCnwPlOX1OQf38jgA7AEaevxR1PLLAZ6Ob+mX8D9PU55jbgRff21cAc93Zf9/GJQLb7PLFOX1MQry8FGAP8BHjW6Wux4foGAx3d2/2BHU5fT5CvrwUQ597uAOw9Xq7vX1jeaQATgFfd268Cl/g55kfAR8aYA8aYg8BHwHkAxpjD7mPicP0Qw63h5qSvzxhzzBizCMAYUwF8jWtVxHDS1N/fl8aYXSGJNDDDgE3GmC3un/kbuK7Rm/c1zwXOFteygxOAN4wx5caYrcAm9/nCyUlfnzHmqDFmKVAWunBPWFOub6Ux5viKo2uBJBEJt4Xom3J9x4wxVe79SQTwWRmuSaP98Q8N9/+n+DmmE7Ddq1zo3geAiHyAK2uW4PohhZMmXx+AiKQBFwH/sSnOkxWU6wsjgcTqOcb9R3gISA/wuU5ryvVFgmBd3+XASmNM4wvbh1aTrk9EhovIWmAN8BOvJOKXYyPCReRj4FQ/Dz0U6Cn87PNkSWPMj0QkCXgdOAvXN9mQsfv63GuqzwaeMcZsOfEIm8bu6wszgcRa3zGRcJ1Nub5I0OTrE5F+wOPAuCDGFSxNuj5jzFdAPxHpA7wqIu8Z1wJ5fjmWNIwx59T3mIjsEZEOxphdInK8ns1XIZDnVc4EFvu8RpmIzMN1axbSpBGC65sJbDTG/CkI4Z6wUPz+wkgh0NmrnAnsrOeYQndCb41rCeNAnuu0plxfJGjS9YlIJvAWcL0xZrP94Z6woPz+jDHrReQorrabeqccCdfqqXnA8d40NwDv+DnmA2CciLRx984ZB3wgIi3dH1THv41fAHwXgphPxElfH4CI/A7XL/3uEMR6Mpp0fWFoOdBTRLJFJAFXQ+I8n2O8r/kKYKFxtS7OA652917JBnoCy0IUd6Cacn2R4KSvz10FPB940BjzWcgiPjFNub5s9+ckItIVOA0oaPDVnG75r6c3QDquevqN7v/buvfnAi97HXczrobFTcBN7n3t3T/E1bgarv5MI70BIuz6MnHdVq4HVrn/3er0NQXr+tz7/4jrm1GN+/9HwuCaLgC+x9VL5SH3vhnAxe7tJOBN97UsA7p5Pfch9/M2EGY93YJ0fQW4vrUecf+++oY6fruuD/gVcNTrb20VcIrT1xPE67vO/Tm5Clenmksaey0dEa6UUipg4Vo9pZRSKgxp0lBKKRUwTRpKKaUCpklDKaVUwDRpKKWUCpgmDRXRRGSxiNi6rrOIXCIi093bj4jIfcF4bRF5SURGByvORl4rR0RmheK1VPOmSUOpxv0CeN6G8w4HvrThvBYiEmeMWQNkikgXu19PNW+aNFREEJEUca0j8o2IfCsiV/k5ZpJ73YNvReRxr/1HROS/ReRrEfmPiLRz7+8urrVXVojIpyLS2885ewHlxph99YR2rYh87n7NYe7nnOG1RsFKEUn1c94+wPfGmGqvfakislVE4t3lViJSICLx9cUqIheJa32ElSLysYi0d+9/RERmisiHwGvul/g3rtHCSp00TRoqUpwH7DTGDDTG9Afe935QRDrimlDuLGAQMFREjk/JngJ8bYwZAnwC/Nq9fyZwhzHmdOA+/N9NjMY1UrY+KcaYUbjWK/ire999wM+MMYOA/wJK/TzvfN9rMMaU4Jp/a7x719XAP40xlQ3EuhQYYYwZjGtK7F94nfJ0YIIx5hp3Od8dj1InzbEJC5U6QWuAJ913EO8aYz71eXwosNgYUwQgIq8DY4G3cU1HMsd93N+Af4lIS2AU8KaIZwJQf+skdACKGohrNoAxZon7ziAN+Az4H3cM/zLGFPp53o+Am/zsfxnXB//b7senNBJrJjDHPd9aArDV61zzjDHeCWsv0LGBa1GqUXqnoSKCMeZ7XN+c1wCPHm+Y9uJv6ud6T4frvV9sjBnk9a+Pn2NLcc3b09C5fEI1jwG3AsnAl77VXiLSAkgztYv7eD/5MyBLRM7AtcLft43E+mdcK+blAD/2ifWoz+mT8H/Xo1TANGmoiOCufjpmjPkb8CQwxOeQr4AzRCRDXGvCT8JVFQWu9/kV7u1rgKXGtbrjVhG50n1+EZGBfl56PdCjgdCucj9/DHDIGHNIRLobY9YYYx7HVSXk21ZyJrCogXO+husO5n/BsxJlfbG2Bna4t2/wPZGPXsC3jRyjVIM0aahIkQMsE5FVuGaN/Z33g8a1QuCDuD6Mv8HVhnF8SvajuBaZWYGrzWOGe/9k4BYR+QbXTJ++S2QCLAEGi1e9kI+DIvI58CJwi3vf3e6G8W9wfbN/z+c5ddozfLyOa9302V776ov1EVzVVp8C9TXWH3cmrmm+lTppOsutavZE5IgxpmUTnv808G9jzMdBiudrYLi7gdvf41fgasC+Lhiv5z5nIq47rzGmkeU8lWqIJg3V7AUhabTH9SHvu7BN0InIn3HdiVzgbscJ1nl7Ap2MMYuDdU4VnTRpKKWUCpi2aSillAqYJg2llFIB06ShlFIqYJo0lFJKBUyThlJKqYBp0lBKKRWw/wesz8l8aAmwhgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.PrePlot(2)\n",
    "thinkplot.Plot([0, 0], [0, 1], color='0.8')\n",
    "ht.PlotCdf(label='null hypothesis')\n",
    "\n",
    "thinkplot.Cdf(sampling_cdf, label='sampling distribution')\n",
    "\n",
    "thinkplot.Config(xlabel='slope (lbs / year)',\n",
    "                   ylabel='CDF',\n",
    "                   xlim=[-0.03, 0.03],\n",
    "                   legend=True, loc='upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Here's how to get a p-value from the sampling distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pvalue = sampling_cdf[0]\n",
    "pvalue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Resampling with weights\n",
    "\n",
    "Resampling provides a convenient way to take into account the sampling weights associated with respondents in a stratified survey design.\n",
    "\n",
    "The following function resamples rows with probabilities proportional to weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ResampleRowsWeighted(df, column='finalwgt'):\n",
    "    weights = df[column]\n",
    "    cdf = thinkstats2.Cdf(dict(weights))\n",
    "    indices = cdf.Sample(len(weights))\n",
    "    sample = df.loc[indices]\n",
    "    return sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use it to estimate the mean birthweight and compute SE and CI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean, SE, CI 7.347540385040938 0.013571205961903726 (7.321607379951317, 7.368651803496348)\n"
     ]
    }
   ],
   "source": [
    "iters = 100\n",
    "estimates = [ResampleRowsWeighted(live).totalwgt_lb.mean()\n",
    "             for _ in range(iters)]\n",
    "Summarize(estimates)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "And here's what the same calculation looks like if we ignore the weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean, SE, CI 7.268162342332374 0.014782284407590501 (7.2425038725381725, 7.29244578446559)\n"
     ]
    }
   ],
   "source": [
    "estimates = [thinkstats2.ResampleRows(live).totalwgt_lb.mean()\n",
    "             for _ in range(iters)]\n",
    "Summarize(estimates)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The difference is non-negligible, which suggests that there are differences in birth weight between the strata in the survey."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercises\n",
    "\n",
    "**Exercise:** Using the data from the BRFSS, compute the linear least squares fit for log(weight) versus height. How would you best present the estimated parameters for a model like this where one of the variables is log-transformed? If you were trying to guess someone’s weight, how much would it help to know their height?\n",
    "\n",
    "Like the NSFG, the BRFSS oversamples some groups and provides a sampling weight for each respondent. In the BRFSS data, the variable name for these weights is totalwt. Use resampling, with and without weights, to estimate the mean height of respondents in the BRFSS, the standard error of the mean, and a 90% confidence interval. How much does correct weighting affect the estimates?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the BRFSS data and extract heights and log weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import brfss\n",
    "\n",
    "df = brfss.ReadBrfss(nrows=None)\n",
    "df = df.dropna(subset=['htm3', 'wtkg2'])\n",
    "heights, weights = df.htm3, df.wtkg2\n",
    "log_weights = np.log10(weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimate intercept and slope."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9930804163917621, 0.005281454169418104)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "inter, slope = thinkstats2.LeastSquares(heights, log_weights)\n",
    "inter, slope"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a scatter plot of the data and show the fitted line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "thinkplot.Scatter(heights, log_weights, alpha=0.01, s=5)\n",
    "fxs, fys = thinkstats2.FitLine(heights, inter, slope)\n",
    "thinkplot.Plot(fxs, fys, color='red')\n",
    "thinkplot.Config(xlabel='Height (cm)', ylabel='log10 weight (kg)', legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make the same plot but apply the inverse transform to show weights on a linear (not log) scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "thinkplot.Scatter(heights, weights, alpha=0.01, s=5)\n",
    "fxs, fys = thinkstats2.FitLine(heights, inter, slope)\n",
    "thinkplot.Plot(fxs, 10**fys, color='red')\n",
    "thinkplot.Config(xlabel='Height (cm)', ylabel='Weight (kg)', legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot percentiles of the residuals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "# The lines are flat over most of the range, \n",
    "# indicating that the relationship is linear.\n",
    "\n",
    "# The lines are mostly parallel, indicating \n",
    "# that the variance of the residuals is the \n",
    "# same over the range.\n",
    "\n",
    "res = thinkstats2.Residuals(heights, log_weights, inter, slope)\n",
    "df['residual'] = res\n",
    "\n",
    "bins = np.arange(130, 210, 5)\n",
    "indices = np.digitize(df.htm3, bins)\n",
    "groups = df.groupby(indices)\n",
    "\n",
    "means = [group.htm3.mean() for i, group in groups][1:-1]\n",
    "cdfs = [thinkstats2.Cdf(group.residual) for i, group in groups][1:-1]\n",
    "\n",
    "thinkplot.PrePlot(3)\n",
    "for percent in [75, 50, 25]:\n",
    "    ys = [cdf.Percentile(percent) for cdf in cdfs]\n",
    "    label = '%dth' % percent\n",
    "    thinkplot.Plot(means, ys, label=label)\n",
    "    \n",
    "thinkplot.Config(xlabel='height (cm)', ylabel='residual weight (kg)', legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5317282605983589"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "rho = thinkstats2.Corr(heights, log_weights)\n",
    "rho"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute coefficient of determination."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2827349431189434"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "r2 = thinkstats2.CoefDetermination(log_weights, res)\n",
    "r2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confirm that $R^2 = \\rho^2$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2823075934420558e-14"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "rho**2 - r2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute Std(ys), which is the RMSE of predictions that don't use height."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10320725030004975"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "std_ys = thinkstats2.Std(log_weights)\n",
    "std_ys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute Std(res), the RMSE of predictions that do use height."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08740777080416137"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "std_res = thinkstats2.Std(res)\n",
    "std_res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How much does height information reduce RMSE?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15308497658793607"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "1 - std_res / std_ys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use resampling to compute sampling distributions for inter and slope."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "\n",
    "t = []\n",
    "for _ in range(100):\n",
    "    sample = thinkstats2.ResampleRows(df)\n",
    "    estimates = thinkstats2.LeastSquares(sample.htm3, np.log10(sample.wtkg2))\n",
    "    t.append(estimates)\n",
    "\n",
    "inters, slopes = zip(*t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the sampling distribution of slope."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'xscale': 'linear', 'yscale': 'linear'}"
      ]
     },
     "execution_count": 52,
     "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": [
    "# Solution\n",
    "\n",
    "cdf = thinkstats2.Cdf(slopes)\n",
    "thinkplot.Cdf(cdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the p-value of the slope."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "pvalue = cdf[0]\n",
    "pvalue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the 90% confidence interval of slope."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.005256179585510808, 0.005305925811393488)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "ci = cdf.Percentile(5), cdf.Percentile(95)\n",
    "ci"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the mean of the sampling distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.005280974367722204"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "mean = thinkstats2.Mean(slopes)\n",
    "mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the standard deviation of the sampling distribution, which is the standard error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4328644946448544e-05"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "stderr = thinkstats2.Std(slopes)\n",
    "stderr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resample rows without weights, compute mean height, and summarize results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean, SE, CI 168.9567580690798 0.01719931368075356 (168.9266633319186, 168.9880782756321)\n"
     ]
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "estimates_unweighted = [thinkstats2.ResampleRows(df).htm3.mean() for _ in range(100)]\n",
    "Summarize(estimates_unweighted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resample rows with weights.  Note that the weight column in this dataset is called `finalwt`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean, SE, CI 170.497091619677 0.01746768128310096 (170.46624325471413, 170.52783756745285)\n"
     ]
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "# The estimated mean height is almost 2 cm taller \n",
    "# if we take into account the sampling weights,\n",
    "# and this difference is much bigger than the sampling error.\n",
    "\n",
    "estimates_weighted = [ResampleRowsWeighted(df, 'finalwt').htm3.mean() for _ in range(100)]\n",
    "Summarize(estimates_weighted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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