Chapter17_Parametric_non_tests.ipynb

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    "from IPython.core.display import HTML\n",
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    "Setting up the required python &#8482; environment"
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   "source": [
    "import numpy as np\n",
    "#import pandas as pd\n",
    "from scipy import stats\n",
    "#from scipy.stats import bayes_mvs\n",
    "#from math import factorial\n",
    "#import scikits.bootstrap as bs\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from warnings import filterwarnings\n",
    "\n",
    "%matplotlib inline\n",
    "sns.set_style('dark')\n",
    "sns.set_context('paper', font_scale = 2.0, rc = {'lines.linewidth': 1.5, 'figure.figsize' : (10, 8)})\n",
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   "source": [
    "# Parametric vs non-parametric statistical tests"
   ]
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   "source": [
    "## Introduction"
   ]
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   "source": [
    "<p>Before, I mentioned that a histogram of the mean of all possible means will be normally distributed.  Actually things are a bit more complicated than that.  No surprise!  In the grander scheme of things, there are two types of statistical tests, called parametric and non-parametric tests.  These two groups exist, because using the appropriate type of test for an appropriate setting allows for more accurate anlaysis and a more proper representation of what we are trying to achieve (infering statistics on population parameters).</p>"
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   "source": [
    "## The QQ plot"
   ]
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    "<p>A parametric test is used when the sample data is taken from a normal distribution and a non-parametric test if it is not.  Wow!  We only have our sample data.  How can we know this?  Well, there's the probability plot from the *scipy.stats* library.  It is more commonly known as the QQ-plot.  It plots every data point against its quartile on a Cartesian plane.  It then gives you a visual representation of how correlated these are.  If they are not, the data is not from a normal distribution and you have to use a non-parametric test.</p>"
   ]
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   "source": [
    "<p>First, I'll generate 40 random values from the normal distribution, and constrain it to a mean of 100 and a standand deviation of 20.</p>"
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   "source": [
    "rv1_values = stats.norm.rvs(loc = 100, scale = 20, size = 40)"
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    "<p>Now let's do the QQ plot</p>\n",
    "<p>The *stats.probplot()* function takes three arguments.  The first is the acutal data values, the second teels the software to compare our values to normal distribution.  If I wanted to see if my valuyes fit some other distribution I could have used something other than 'norm' and lastly we plot.</p>\n",
    "<p>The plot will state a R<sup>2</sup> value.  A value of 1.0 means an absolute fit and 0.0 means no fit (i.e. realy, really not from a normal distribution.</p>"
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Rg2OhIyIiInJwLHREREREDo6FjoiIiMjBsdAREREROTgWOiIiIiIHx0JHRERE5OBY6IiI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\neLn/DzzY/K+8ZyPhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x103f60ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,8))\n",
    "stats.probplot(rv1_values, dist=\"norm\", plot=plt) # \n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Now I'll generate 40 values from anotehr type of continuous random distribution called the exponential distribution and see how it compares.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rv2_values = stats.expon.rvs(size = 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jrAEAAOQzFhgAAAAUEoQ1AAAAAyOsAQAAGBhhDQAAwMAIawAAAAZGWAMAADCwAhHWUlJS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\njLRkOJeTxrcAgIx4DQogvYQE+c36UH6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+HgNBXY/qhe+getG7EK2spU5GREQkWyxr1CZM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108ec9358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stats.probplot(rv2_values, dist=\"norm\", plot=plt);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Now we only get an *0.9482* fit.  It's really difficult to say what the exact cut-off is.  Clearly an *R<sup>2</sup>* value of *0.9482* indicates that the sample was not taken from a normal distribution.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tests for numerical data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>The quintessential parametric test for ratio, numerical data is Student's t-test.</p>\n",
    "<p>An example of a non-parametric t-test is the Mann-Whitney-Wilcoxon rank sums test</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "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",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}