{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"hide": true
},
"source": [
"# Distributions example - elections\n",
"\n",
"##### Keywords: bernoulli distribution, binomial, normal distribution, central limit theorem, uniform distribution, empirical distribution, elections"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Contents\n",
"{:.no_toc}\n",
"* \n",
"{: toc}"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"hide": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"//anaconda/envs/py35/lib/python3.5/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
" warnings.warn(self.msg_depr % (key, alt_key))\n"
]
}
],
"source": [
"# The %... is an iPython thing, and is not part of the Python language.\n",
"# In this case we're just telling the plotting library to draw things on\n",
"# the notebook, instead of on a separate window.\n",
"%matplotlib inline\n",
"# See all the \"as ...\" contructs? They're just aliasing the package names.\n",
"# That way we can call methods like plt.plot() instead of matplotlib.pyplot.plot().\n",
"import numpy as np\n",
"import scipy as sp\n",
"import matplotlib as mpl\n",
"import matplotlib.cm as cm\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import time\n",
"pd.set_option('display.width', 500)\n",
"pd.set_option('display.max_columns', 100)\n",
"pd.set_option('display.notebook_repr_html', True)\n",
"import seaborn as sns\n",
"sns.set_style(\"whitegrid\")\n",
"sns.set_context(\"poster\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the last section, we made a simple simulation of a coin-toss on the computer from a fair-coin model which associated equal probability with heads and tails. Let us consider another model here, a table of probabilities that [PredictWise](http://www.predictwise.com/results/2012/president) made on October 2, 2012 for the US presidential elections. \n",
"PredictWise aggregated polling data and, for each state, estimated the probability that the Obama or Romney would win. Here are those estimated probabilities:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"
\n",
" \n",
" \n",
" | \n",
" Obama | \n",
" Romney | \n",
" Votes | \n",
"
\n",
" \n",
" States | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" Alabama | \n",
" 0.000 | \n",
" 1.000 | \n",
" 9 | \n",
"
\n",
" \n",
" Alaska | \n",
" 0.000 | \n",
" 1.000 | \n",
" 3 | \n",
"
\n",
" \n",
" Arizona | \n",
" 0.062 | \n",
" 0.938 | \n",
" 11 | \n",
"
\n",
" \n",
" Arkansas | \n",
" 0.000 | \n",
" 1.000 | \n",
" 6 | \n",
"
\n",
" \n",
" California | \n",
" 1.000 | \n",
" 0.000 | \n",
" 55 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Obama Romney Votes\n",
"States \n",
"Alabama 0.000 1.000 9\n",
"Alaska 0.000 1.000 3\n",
"Arizona 0.062 0.938 11\n",
"Arkansas 0.000 1.000 6\n",
"California 1.000 0.000 55"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"predictwise = pd.read_csv('data/predictwise.csv').set_index('States')\n",
"predictwise.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Say you toss a coin and have a model which says that the probability of heads is 0.5 (you have figured this out from symmetry, or physics, or something). Still, there will be sequences of flips in which more or less than half the flips are heads. These **fluctuations** induce a distribution on the number of heads (say k) in N coin tosses (this is a binomial distribution).\n",
"\n",
"Similarly, here, if the probability of Romney winning in Arizona is 0.938, it means that if somehow, there were 10000 replications (as if we were running the election in 10000 parallel universes) with an election each, Romney would win in 9380 of those Arizonas **on the average** across the replications. And there would be some replications with Romney winning more, and some with less. We can run these **simulated** universes or replications on a computer though not in real life.\n",
"\n",
"## Simulating a simple election model\n",
"\n",
"To do this, \n",
"we will assume that the outcome in each state is the result of an independent coin flip whose probability of coming up Obama is given by the Predictwise state-wise win probabilities. Lets write a function `simulate_election` that uses this **predictive model** to simulate the outcome of the election given a table of probabilities.\n",
"\n",
"### Bernoulli Random Variables (in scipy.stats)\n",
"\n",
"The **Bernoulli Distribution** represents the distribution for coin flips. Let the random variable X represent such a coin flip, where X=1 is heads, and X=0 is tails. Let us further say that the probability of heads is p (p=0.5 is a fair coin). \n",
"\n",
"We then say:\n",
"\n",
"$$X \\sim Bernoulli(p),$$\n",
"\n",
"which is to be read as **X has distribution Bernoulli(p)**. The **probability distribution function (pdf)** or **probability mass function** associated with the Bernoulli distribution is\n",
"\n",
"$$\\begin{eqnarray}\n",
"P(X = 1) &=& p \\\\\n",
"P(X = 0) &=& 1 - p \n",
"\\end{eqnarray}$$\n",
"\n",
"for p in the range 0 to 1. \n",
"The **pdf**, or the probability that random variable $X=x$ may thus be written as \n",
"\n",
"$$P(X=x) = p^x(1-p)^{1-x}$$\n",
"\n",
"for x in the set {0,1}.\n",
"\n",
"The Predictwise probability of Obama winning in each state is a Bernoulli Parameter. You can think of it as a different loaded coin being tossed in each state, and thus there is a bernoulli distribution for each state"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note: **some of the code, and ALL of the visual style for the distribution plots below was shamelessly stolen from https://gist.github.com/mattions/6113437/ **."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1 0 0 0 1 0 0 1 1 0 0 0 0 0 1 1 0 0 1 0]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"//anaconda/envs/py35/lib/python3.5/site-packages/matplotlib/__init__.py:892: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
" warnings.warn(self.msg_depr % (key, alt_key))\n"
]
},
{
"data": {
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eZG3btk0bN27Utddeq02bNqmwsFDr169Xc3PzmM/3er36zGc+o2nTpunhhx/W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\nTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from scipy.stats import bernoulli\n",
"#bernoulli random variable\n",
"brv=bernoulli(p=0.3)\n",
"print(brv.rvs(size=20))\n",
"event_space=[0,1]\n",
"plt.figure(figsize=(12,8))\n",
"colors=sns.color_palette()\n",
"for i, p in enumerate([0.1, 0.2, 0.5, 0.7]):\n",
" ax = plt.subplot(1, 4, i+1)\n",
" plt.bar(event_space, bernoulli.pmf(event_space, p), label=p, color=colors[i], alpha=0.5)\n",
" plt.plot(event_space, bernoulli.cdf(event_space, p), color=colors[i], alpha=0.5)\n",
"\n",
" ax.xaxis.set_ticks(event_space)\n",
" \n",
" plt.ylim((0,1))\n",
" plt.legend(loc=0)\n",
" if i == 0:\n",
" plt.ylabel(\"PDF at $k$\")\n",
"plt.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Running the simulation using the Uniform distribution\n",
"\n",
"In the code below, each column simulates a single outcome from the 50 states + DC by choosing a random number between 0 and 1. Obama wins that simulation if the random number is $<$ the win probability. If he wins that simulation, we add in the electoral votes for that state, otherwise we dont. We do this `n_sim` times and return a list of total Obama electoral votes in each simulation."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def simulate_election(model, n_sim):\n",
" simulations = np.random.uniform(size=(51, n_sim))\n",
" obama_votes = (simulations < model.Obama.values.reshape(-1, 1)) * model.Votes.values.reshape(-1, 1)\n",
" #summing over rows gives the total electoral votes for each simulation\n",
" return obama_votes.sum(axis=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first thing to pick up on here is that `np.random.uniform` gives you a random number between 0 and 1, uniformly. In other words, the number is equally likely to be between 0 and 0.1, 0.1 and 0.2, and so on. This is a very intuitive idea, but it is formalized by the notion of the **Uniform Distribution**.\n",
"\n",
"We then say:\n",
"\n",
"$$X \\sim Uniform([0,1),$$\n",
"\n",
"which is to be read as **X has distribution Uniform([0,1])**. The **probability distribution function (pdf)** associated with the Uniform distribution is\n",
"\n",
"\\begin{eqnarray}\n",
"P(X = x) &=& 1 \\, for \\, x \\in [0,1] \\\\\n",
"P(X = x) &=& 0 \\, for \\, x \\notin [0,1]\n",
"\\end{eqnarray}\n",
"\n",
"What assigning the vote to Obama when the random variable **drawn** from the Uniform distribution is less than the Predictwise probability of Obama winning (which is a Bernoulli Parameter) does for us is this: if we have a large number of simulations and $p_{Obama}=0.7$ , then 70\\% of the time, the random numbes drawn will be below 0.7. And then, assigning those as Obama wins will hew to the frequentist notion of probability of the Obama win. But remember, of course, that in 30% of the simulations, Obama wont win, and this will induce fluctuations and a distribution on the total number of electoral college votes that Obama gets. And this is what we see in the histogram below. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following code takes the necessary probabilities for the Predictwise data, and runs 10000 simulations. If you think of this in terms of our coins, think of it as having 51 biased coins, one for each state, and tossing them 10,000 times each.\n",
"\n",
"We use the results to compute the number of simulations, according to this predictive model, that Obama wins the election (i.e., the probability that he receives 269 or more electoral college votes)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"9955\n"
]
}
],
"source": [
"result = simulate_election(predictwise, 10000)\n",
"print((result >= 269).sum())"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([303, 326, 329, ..., 332, 281, 324])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are roughly only 50 simulations in which Romney wins the election!\n",
"\n",
"## Displaying the prediction\n",
"\n",
"Now, lets visualize the simulation. We will build a histogram from the result of `simulate_election`. We will **normalize** the histogram by dividing the frequency of a vote tally by the number of simulations. We'll overplot the \"victory threshold\" of 269 votes as a vertical black line and the result (Obama winning 332 votes) as a vertical red line.\n",
"\n",
"We also compute the number of votes at the 5th and 95th quantiles, which we call the spread, and display it (this is an estimate of the outcome's uncertainty). By 5th quantile we mean that if we ordered the number of votes Obama gets in each simulation in increasing order, the 5th quantile is the number below which 5\\% of the simulations lie. \n",
"\n",
"We also display the probability of an Obama victory \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def plot_simulation(simulation): \n",
" plt.hist(simulation, bins=np.arange(200, 538, 1), \n",
" label='simulations', align='left', normed=True)\n",
" plt.axvline(332, 0, .5, color='r', label='Actual Outcome')\n",
" plt.axvline(269, 0, .5, color='k', label='Victory Threshold')\n",
" p05 = np.percentile(simulation, 5.)\n",
" p95 = np.percentile(simulation, 95.)\n",
" iq = int(p95 - p05)\n",
" pwin = ((simulation >= 269).mean() * 100)\n",
" plt.title(\"Chance of Obama Victory: %0.2f%%, Spread: %d votes\" % (pwin, iq))\n",
" plt.legend(frameon=False, loc='upper left')\n",
" plt.xlabel(\"Obama Electoral College Votes\")\n",
" plt.ylabel(\"Probability\")\n",
" sns.despine()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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+vr6YPn06fH19ER4ejr59+0JFRQWJiYkICQlBVlYWZs+eLdYNojTF/0BbWFhg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\nAAAAUyCcAAAAADAFwgkAAAAAUyCcAAAAADAFwgk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0CuEEADqAgoICPfbMc74u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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_simulation(result)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The model created by combining the probabilities we obtained from Predictwise with the simulation of a biased coin flip corresponding to the win probability in each states leads us to obtain a histogram of election outcomes. We are plotting the probabilities of a prediction, so we call this distribution over outcomes the **predictive distribution**. Simulating from our model and plotting a histogram allows us to visualize this predictive distribution. In general, such a set of probabilities is called a **probability mass function**. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Empirical Distribution"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is an **empirical Probability Mass Function**. \n",
"\n",
"Lets summarize: the way the mass function arose here that we did ran 10,000 tosses (for each state), and depending on the value, assigned the state to Obama or Romney, and then summed up the electoral votes over the states.\n",
"\n",
"There is a second, very useful question, we can ask of any such probability mass or probability density: what is the probability that a random variable is less than some value. In other words: $P(X < x)$. This is *also* a probability distribution and is called the **Cumulative Distribution Function**, or CDF (sometimes just called the **distribution**, as opposed to the **density**, or **mass function**). Its obtained by \"summing\" the probability density function for all $X$ less than $x$."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Obama Win CDF at votes= 200 is 0.0\n",
"Obama Win CDF at votes= 300 is 0.1447\n",
"Obama Win CDF at votes= 320 is 0.4439\n",
"Obama Win CDF at votes= 340 is 0.839\n",
"Obama Win CDF at votes= 360 is 0.9979\n",
"Obama Win CDF at votes= 400 is 1.0\n",
"Obama Win CDF at votes= 500 is 1.0\n"
]
}
],
"source": [
"CDF = lambda x: np.float(np.sum(result < x))/result.shape[0]\n",
"for votes in [200, 300, 320, 340, 360, 400, 500]:\n",
" print(\"Obama Win CDF at votes=\", votes, \" is \", CDF(votes))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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A4GU27Tpe6thmtWj80LYMswLgEpeLR3Jysp544gmNGzfOk3kAAIAXstsdWrP5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\nAAAAADb3/wCNX36S3pqjtQAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"votelist=np.arange(0, 540, 5)\n",
"plt.plot(votelist, [CDF(v) for v in votelist], '.-');\n",
"plt.xlim([200,400])\n",
"plt.ylim([-0.1,1.1])\n",
"plt.xlabel(\"votes for Obama\")\n",
"plt.ylabel(\"probability of Obama win\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Binomial Distribution \n",
"\n",
"Let us consider a population of coinflips, n of them to be precise, $x_1,x_2,...,x_n$. The distribution of coin flips is the binomial distribution. By this we mean that each coin flip represents a bernoulli random variable (or comes from a bernoulli distribution) with $p=0.5$.\n",
"\n",
"At this point, you might want to ask the question, what is the probability of obtaining $k$ heads in $n$ flips of the coin. We have seen this before, when we flipped 2 coins. What happens when when we flip 3?\n",
"\n",
"(This diagram is taken from the Feynman Lectures on Physics, volume 1. The chapter on probability is http://www.feynmanlectures.caltech.edu/I_06.html)\n",
"\n",
"\n",
"We draw a possibilities diagram like we did with the 2 coin flips, and see that there are different probabilities associated with the events of 0, 1,2, and 3 heads with 1 and 2 heads being the most likely. \n",
"The probability of each of these events is given by the **Binomial Distribution**, the distribution of the number of successes in a sequence of $n$ independent yes/no experiments, or Bernoulli trials, each of which yields success with probability $p$. The Binomial distribution is an extension of the Bernoulli when $n>1$ or the Bernoulli is the a special case of the Binomial when $n=1$. \n",
"\n",
"$$P(X = k; n, p) = {n\\choose k}p^k(1-p)^{n-k} $$\n",
"\n",
"where\n",
"\n",
"$${n\\choose k}=\\frac{n!}{k!(n-k)!}$$\n",
"\n",
"How did we obtain this? The $p^k(1-p)^{n-k}$ comes simply from multiplying the probabilities for each bernoulli trial; there are $k$ 1's or yes's, and $n-k$ 0's or no's. The ${n\\choose k}$ comes from counting the number of ways in which each event happens: this corresponds to counting all the paths that give the same number of heads in the diagram above.\n",
"\n",
"We show the distribution below for 200 trials."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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kWFnrjYiIiKhNUlX/yj+dSQgFQuOu4qHG8EYBqbU6A94x0p+C3kREREQUfpri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\nIqI4wOSNiIiIiIgoDvx/xW0rfOkxoboAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from scipy.stats import binom\n",
"plt.figure(figsize=(12,6))\n",
"k = np.arange(0, 200)\n",
"for p, color in zip([0.1, 0.3, 0.7, 0.7, 0.9], colors):\n",
" rv = binom(200, p)\n",
" plt.plot(k, rv.pmf(k), '.', lw=2, color=color, label=p)\n",
" plt.fill_between(k, rv.pmf(k), color=color, alpha=0.5)\n",
"q=plt.legend()\n",
"plt.title(\"Binomial distribution\")\n",
"plt.tight_layout()\n",
"q=plt.ylabel(\"PDF at $k$\")\n",
"q=plt.xlabel(\"$k$\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Applying the CLT to elections: Binomial distribution in the large n, large k limit\n",
"\n",
"Consider the binomial distribution Binomial(n,k, p) in the limit of large n. The number of successes k in n trials can be regarded as the sum of n IID Bernoulli variables with values 1 or 0. Call these $x_i$.\n",
"\n",
"Then:\n",
"\n",
"$$S_n = \\frac{1}{n} \\sum_i x_i .$$\n",
"\n",
"\n",
"The CLT tells us then that for large n, we have:\n",
"\n",
"$$S_n \\sim N(p, \\frac{p(1-p)}{n}),$$\n",
"\n",
"since the mean of a Bernoulli is $p$, and its variance $p*(1-p)$.\n",
"\n",
"This means that we can replace the binomial distribution at large n by a gaussian where k is now a continuous variable, and whose mean is the mean of the binomial $np$ ($nS_n$, since the binomial distribution is on the sum, not on the average) and whose variance is $np(1-p)$.\n",
"\n",
"The accuracy of this approximation depends on the variance. A large variance makes for a broad distribution spanning many discrete k, thus justifying the transition from a discrete to a continuous distribution.\n",
"\n",
"This approximation is used a lot in studying elections. For example, suppose I told you that I'd polled 1000 people in Ohio and found that 600 would vote Democratic, and 400 republican. Imagine that this 1000 is a \"sample\" drawn from the voting \"population\" of Ohio. Assume then that these are 1000 independent bernoulli trials with p=600/1000 = 0.6. Then we can say that, from the CLT, the mean of the sampling distribution of the mean of the bernoulli or is 0.6 (equivalently the binomial's mean is 600), with a variance of $0.6*0.4/1000 = 0.00024$ (equivalently the binomials variance is 240). Thus the standard deviation is 0.015 for a mean of 0.6, or 1.5% on a mean of 60% voting Democratic. This 1.5% if part of what pollsters quote as the margin of error of a candidates winning; they often include other factors such as errors in polling methodology."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Gallup Party Affiliation Poll\n",
"\n",
"[Earlier](probability.html) we had used the Predictwise probabilities from Octover 12th to create a predictive model for the elections. This time we will try to **estimate** our own win probabilities to plug into our predictive model.\n",
"\n",
"We will start with a simple forecast model. We will try to predict the outcome of the election based the estimated proportion of people in each state who identify with one one political party or the other.\n",
"\n",
"Gallup measures the political leaning of each state, based on asking random people which party they identify or affiliate with. [Here's the data](http://www.gallup.com/poll/156437/heavily-democratic-states-concentrated-east.aspx#2) they collected from January-June of 2012:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Democrat | \n",
" Republican | \n",
" Dem_Adv | \n",
" N | \n",
" Unknown | \n",
"
\n",
" \n",
" State | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" Alabama | \n",
" 36.0 | \n",
" 49.6 | \n",
" -13.6 | \n",
" 3197 | \n",
" 14.4 | \n",
"
\n",
" \n",
" Alaska | \n",
" 35.9 | \n",
" 44.3 | \n",
" -8.4 | \n",
" 402 | \n",
" 19.8 | \n",
"
\n",
" \n",
" Arizona | \n",
" 39.8 | \n",
" 47.3 | \n",
" -7.5 | \n",
" 4325 | \n",
" 12.9 | \n",
"
\n",
" \n",
" Arkansas | \n",
" 41.5 | \n",
" 40.8 | \n",
" 0.7 | \n",
" 2071 | \n",
" 17.7 | \n",
"
\n",
" \n",
" California | \n",
" 48.3 | \n",
" 34.6 | \n",
" 13.7 | \n",
" 16197 | \n",
" 17.1 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Democrat Republican Dem_Adv N Unknown\n",
"State \n",
"Alabama 36.0 49.6 -13.6 3197 14.4\n",
"Alaska 35.9 44.3 -8.4 402 19.8\n",
"Arizona 39.8 47.3 -7.5 4325 12.9\n",
"Arkansas 41.5 40.8 0.7 2071 17.7\n",
"California 48.3 34.6 13.7 16197 17.1"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gallup_2012=pd.read_csv(\"data/g12.csv\").set_index('State')\n",
"gallup_2012[\"Unknown\"] = 100 - gallup_2012.Democrat - gallup_2012.Republican\n",
"gallup_2012.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Each row lists a state, the percent of surveyed individuals who identify as Democrat/Republican, the percent whose identification is unknown or who haven't made an affiliation yet, the margin between Democrats and Republicans (`Dem_Adv`: the percentage identifying as Democrats minus the percentage identifying as Republicans), and the number `N` of people surveyed.\n",
"\n",
"The most obvious source of error in the Gallup data is the finite sample size -- Gallup did not poll *everybody* in America, and thus the party affilitions are subject to sampling errors. How much uncertainty does this introduce? Lets estimate the sampling error using the definition of the standard error (we use N-1 rather than N; see the sample error section in the page on the [CLT](SamplingCLT.html))."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Democrat | \n",
" Republican | \n",
" Dem_Adv | \n",
" N | \n",
" Unknown | \n",
" SE_percentage | \n",
"
\n",
" \n",
" State | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" Alabama | \n",
" 36.0 | \n",
" 49.6 | \n",
" -13.6 | \n",
" 3197 | \n",
" 14.4 | \n",
" 0.849059 | \n",
"
\n",
" \n",
" Alaska | \n",
" 35.9 | \n",
" 44.3 | \n",
" -8.4 | \n",
" 402 | \n",
" 19.8 | \n",
" 2.395543 | \n",
"
\n",
" \n",
" Arizona | \n",
" 39.8 | \n",
" 47.3 | \n",
" -7.5 | \n",
" 4325 | \n",
" 12.9 | \n",
" 0.744384 | \n",
"
\n",
" \n",
" Arkansas | \n",
" 41.5 | \n",
" 40.8 | \n",
" 0.7 | \n",
" 2071 | \n",
" 17.7 | \n",
" 1.082971 | \n",
"
\n",
" \n",
" California | \n",
" 48.3 | \n",
" 34.6 | \n",
" 13.7 | \n",
" 16197 | \n",
" 17.1 | \n",
" 0.392658 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Democrat Republican Dem_Adv N Unknown SE_percentage\n",
"State \n",
"Alabama 36.0 49.6 -13.6 3197 14.4 0.849059\n",
"Alaska 35.9 44.3 -8.4 402 19.8 2.395543\n",
"Arizona 39.8 47.3 -7.5 4325 12.9 0.744384\n",
"Arkansas 41.5 40.8 0.7 2071 17.7 1.082971\n",
"California 48.3 34.6 13.7 16197 17.1 0.392658"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gallup_2012[\"SE_percentage\"]=100.0*np.sqrt((gallup_2012.Democrat/100.)*((100. - gallup_2012.Democrat)/100.)/(gallup_2012.N -1))\n",
"gallup_2012.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"On their [webpage](http://www.gallup.com/poll/156437/heavily-democratic-states-concentrated-east.aspx#2) discussing these data, Gallup notes that the sampling error for the states is between 3 and 6%, with it being 3% for most states. This is more than what we find, so lets go with what Gallup says.\n",
"\n",
"We now use Gallup's estimate of 3% to build a Gallup model with some uncertainty. We will, using the CLT, assume that the sampling distribution of the Obama win percentage is a gaussian with mean the democrat percentage and standard error the sampling error of 3\\%. \n",
"\n",
"We'll build the model in the function `uncertain_gallup_model`, and return a forecast where the probability of an Obama victory is given by the probability that a sample from the `Dem_Adv` Gaussian is positive.\n",
"\n",
"To do this we simply need to find the area under the curve of a Gaussian that is on the positive side of the x-axis.\n",
"The probability that a sample from a Gaussian with mean $\\mu$ and standard deviation $\\sigma$ exceeds a threhold $z$ can be found using the the Cumulative Distribution Function of a Gaussian:\n",
"\n",
"$$\n",
"CDF(z) = \\frac1{2}\\left(1 + {\\mathrm erf}\\left(\\frac{z - \\mu}{\\sqrt{2 \\sigma^2}}\\right)\\right) \n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from scipy.special import erf\n",
"def uncertain_gallup_model(gallup):\n",
" sigma = 3\n",
" prob = .5 * (1 + erf(gallup.Dem_Adv / np.sqrt(2 * sigma**2)))\n",
" return pd.DataFrame(dict(Obama=prob), index=gallup.index)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model = uncertain_gallup_model(gallup_2012)\n",
"model = model.join(predictwise.Votes)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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2559/sqKiImZoaMjs7OxEtscYY15eXkwgELC7d+9We4z5+flMX1+fDRw4kL17904kzsPD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\nIzCxsbFyOBwqKipy+4fnyZMnZbVaGz2mqKjIbZv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WrVunv/zlL3rvvfdUXFws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\n2DEEBQXx2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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"prediction = simulate_election(model, 10000)\n",
"plot_simulation(prediction)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Multiple Pollsters\n",
"\n",
"If one has results from multiple pollsters, one can now treat them as independent samples from the voting population. Now we use the CLT again. Then the average from these samples will approach the average in the population, with the sample means distributed normally around it. So we can average the averages of the samples to get the population mean, and estimate the variance around this population mean as well."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"celltoolbar": "Edit Metadata",
"kernelspec": {
"display_name": "Python [conda env:py35]",
"language": "python",
"name": "conda-env-py35-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}