0014 Hypothesis Testing with Bird Strike Incidents.ipynb

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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "import scipy\n",
      "import matplotlib.pyplot as plt\n",
      "import matplotlib.mlab as mlab\n",
      "import seaborn as sns\n",
      "from scipy import stats\n",
      "import math\n",
      "from __future__ import division\n",
      "\n",
      "# turn of data table rendering\n",
      "pd.set_option('display.notebook_repr_html', False)\n",
      "\n",
      "sns.set_palette(['#00A99D', '#F5CA0C', '#B6129F', '#76620C', '#095C57'])\n",
      "np.version.full_version"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "'1.9.1'"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Data\n",
      "The data used in this example contains reporting from the [Federal Aviation Administration Wildlife Strike Database](http://wildlife.faa.gov/database.aspx) in 2012 and 2013 in the US state of California. We only use daily wild life strike incident frequencies."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Load the data from a csv file. \n",
      "data = pd.read_csv('data/wildlife.csv')\n",
      "\n",
      "# We only need the incident dates this time\n",
      "data = data[['INCIDENT_DATE']]\n",
      "data.INCIDENT_DATE = pd.to_datetime(data.INCIDENT_DATE)\n",
      "data = data.groupby('INCIDENT_DATE')['INCIDENT_DATE'].size()\n",
      "data.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "INCIDENT_DATE\n",
        "2012-01-02       1\n",
        "2012-01-04       2\n",
        "2012-01-05       5\n",
        "2012-01-06       3\n",
        "2012-01-07       2\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Set the index to contain all days in 2012 and 2013\n",
      "days_2012_2013 = pd.date_range('1/1/2012', '12/31/2013')\n",
      "data = pd.DataFrame(data, index=days_2012_2013, columns=['incidents'])\n",
      "data.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "            incidents\n",
        "2012-01-01        NaN\n",
        "2012-01-02          1\n",
        "2012-01-03        NaN\n",
        "2012-01-04          2\n",
        "2012-01-05          5"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Fill the data with no strike incidents (NaN) with a zero\n",
      "data = data.fillna(0)\n",
      "data.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "            incidents\n",
        "2012-01-01          0\n",
        "2012-01-02          1\n",
        "2012-01-03          0\n",
        "2012-01-04          2\n",
        "2012-01-05          5"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plot of the daily strike incident frequencies of 2012 and 2013\n",
      "f, (ax1, ax2) = plt.subplots(2, 1, sharex=True, sharey=True)\n",
      "sns.barplot(data['2012'].incidents, ax=ax1)\n",
      "sns.barplot(data['2013'].incidents, ax=ax2)\n",
      "ax1.set_title('Wildlife Strike Incidents 2012')\n",
      "ax2.set_title('Wildlife Strike Incidents 2013')\n",
      "ax1.set_xlabel('')\n",
      "ax2.set_xlabel('')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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HK4GjsIirYQYG1nDI1ZfD/Hm9CXD/Om477gQWLdq/N9uX1BPtFPGrgOsj4u+pWs6vB67e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\nfzUMzQb6crxNAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x17a21f98>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Hypothesis testing\n",
      "Let's asume that the Federal Aviation Administration launched a new Wildlife Strike Prevention Program in 2013. We want to know if there is a significant decline in the number of daily strike incidents in 2013 as opposed to 2012. This forms our operational definition. We choose an alpha level of .05 as our level of significance. A probability smaller than the alpha level leads to rejecting the null hypothesis.\n",
      "\n",
      "|Hypothesis|$\\alpha = .05$||\n",
      "|---|---|---|:---:|\n",
      "|Null|$H_0:$|$\\bar{x} = \\mu$|\n",
      "|Alternative|$H_a:$|$\\bar{x} \\lt \\mu$|\n",
      "\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We assume stats from the wildlife strike incidents in 2012\n",
      "# to be our population parameters\n",
      "mu = data['2012'].incidents.mean()\n",
      "sigma = data['2012'].incidents.std()\n",
      "mu, sigma"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "(2.5546448087431695, 1.6818182461905784)"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We also take the average of strike incidents in 2013\n",
      "xbar = data['2013'].incidents.mean()\n",
      "xbar"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "2.4328767123287673"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# What is our sample size\n",
      "n = data['2013'].incidents.count()\n",
      "n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "365"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Calculate the standard error\n",
      "SE = sigma / math.sqrt(n)\n",
      "SE"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "0.08803039035048664"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We notice the mean average wildlife strike incidents is somewhat lower in 2013. We need to find out if this is due to the normal variation in the data. In other words, could we draw this mean by pure random chance? To find out, we calculate the z score for our sample mean and check this against the significance level alpha. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Calculate the z score for the mean of 2013 (xbar)\n",
      "zscore = (xbar - mu) / SE\n",
      "zscore"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "-1.3832506697924574"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Calculate the critical value of the alpha level on one side of the\n",
      "# distribution, because we're only intrested in a mean decline\n",
      "alpha = .05\n",
      "critical_value = stats.norm.ppf(alpha)\n",
      "critical_value"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "-1.6448536269514729"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plot the normal distribution\n",
      "samples = 100\n",
      "x_plot = np.linspace(-3.5, 3.5, samples)\n",
      "y_plot = stats.norm.pdf(x_plot, 0, 1)\n",
      "plt.plot(x_plot, y_plot)\n",
      "\n",
      "# Plot the critical region\n",
      "x_crit = np.linspace(-3.5, critical_value, samples)\n",
      "y_crit = stats.norm.pdf(x_crit, 0, 1)\n",
      "plt.fill_between(x_crit,  y_crit, alpha=.5)\n",
      "\n",
      "# Plot the z score\n",
      "plt.plot([zscore, zscore], [0, stats.norm.pdf(zscore)])\n",
      "\n",
      "# Show legend\n",
      "plt.legend(['critical region', 'z score'])\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Oyi/B7XCwpKWO2+wuRmQfjRjSkbPja3d6ev2wv+/uHofL96UokVjxRN02AE5T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\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x182e3518>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# So given these values let's test the alternative hypothesis\n",
      "zscore < critical_value"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "False"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This means we could have drawn a mean sample xbar from the real population pure by random chance, therefore we fail to reject the null hypothesis. In other words, there is no significant difference of average daily strike incidents between 2013 and 2012."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# The probability of drawing this sample mean is about 8%\n",
      "p = stats.norm.cdf(zscore)\n",
      "p"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "0.083294009028340998"
       ]
      }
     ],
     "prompt_number": 14
    }
   ],
   "metadata": {}
  }
 ]
}