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TutorialX_StatisticsRefresher.html
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TutorialX_StatisticsRefresher.html
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<?xml version="1.0" encoding="utf-8" ?>
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta http-equiv="Content-Style-Type" content="text/css" />
<meta name="generator" content="pandoc" />
<meta name="author" content="R Bootcamp HTML Slides" />
<title>Basic Statistical Concepts</title>
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</head>
<body>
<div class="slide titlepage">
<h1 class="title">Basic Statistical Concepts</h1>
<p class="author">
R Bootcamp HTML Slides
</p>
<p class="date">Jared Knowles</p>
</div>
<<<<<<< HEAD
<div class="section slide level1" id="introduction">
<h1>Introduction</h1>
<p>We will review the following statistical concepts here through the lens of R:</p>
<ul class="incremental">
<li>Probability</li>
<li>Statistical distributions</li>
<li>Attributes of distributions</li>
<li>Samples</li>
<li>Statistical models</li>
<li>Univariate Regression</li>
<li>Hypothesis testing</li>
<li>Multivariate Regression</li>
<li>Causality</li>
</ul>
</div>
<div class="section slide level1" id="what-is-statistics">
<h1>What is statistics?</h1>
<ul class="incremental">
<li>What do you think?</li>
<li>From <a href="http://en.wikipedia.org/wiki/Statistics">Wikipedia</a>:</li>
</ul>
<blockquote>
<p>“Statistics is the study of the collection, organization, analysis, interpretation, and presentation of data. It deals with all aspects of this, including the planning of data collection”</p>
</blockquote>
<ul class="incremental">
<li>Statistics gives us a way to quantify and express our uncertainty about the future or about the relationship of a sample to the population</li>
</ul>
<h3 id="key-concepts-to-statistical-thinking">Key Concepts to Statistical Thinking</h3>
<ol class="incremental" style="list-style-type: decimal">
<li>Probability</li>
<li>How to describe data</li>
<li>Drawing inferences from data</li>
<li>Causation</li>
</ol>
</div>
<div class="section slide level1" id="probability">
<h1>Probability</h1>
<ul class="incremental">
<li>Probability rests on the concepts of randomness</li>
<li>Randomness is an abstract principle that describes the behavior of events in the world</li>
<li>Random phenomena have outcomes we cannot perfectly predict, but that have a regular distribution over many repetitions</li>
<li>Probability describes the the proportion of times an event will occur in many repeated trials of observation</li>
<li>The probability of the Packers winning the coin toss is 0.5</li>
<li>Let’s look at an example of coin tosses!</li>
</ul>
</div>
<div class="section slide level1" id="probabilitys-uses">
<h1>Probability’s uses</h1>
<ul class="incremental">
<li>For truly random independent events like coin flips, this is easy and uncertainty can become certainty</li>
<li>But, what about events that depend on one another?</li>
</ul>
</div>
<div class="section slide level1" id="but">
<h1>But…</h1>
<ul class="incremental">
<li>What does it mean then when someone says “Kobe Bryant’s 3 has a 40% chance of going in.”</li>
<li>Or “Mason Crosby has a 65% chance of making that field goal?”</li>
<li>These aren’t random events, are they?</li>
<li>But we can model them as such with large enough sample size</li>
</ul>
</div>
<div class="section slide level1" id="interdependence-and-independence">
<h1>Interdependence and Independence</h1>
<ul class="incremental">
<li>Coins are obviously independent in their flipping (though we can think of conditions where this is not true)</li>
<li>Other events have probability that varies based on other factors - joint probabilities</li>
<li>Let’s look at a simple example of this</li>
<li><a href="http://glimmer.rstudio.com/wisconsindpi/coin/">Coin Flip Demo</a></li>
</ul>
</div>
<div class="section slide level1" id="describing-data">
<h1>Describing Data</h1>
<ul class="incremental">
<li>At it’s most basic level, statistics is about summarizing and understanding data</li>
<li>Data are themselves abstractions of real world concepts we care about</li>
<li>There are a number of useful ways to describe a set of data, some of which we have become familiar with so far</li>
</ul>
</div>
<div class="section slide level1" id="spread-of-the-data">
<h1>Spread of the Data</h1>
<ul class="incremental">
<li>Data spread describes how scattered a data set is</li>
<li>One type of data, categorical data, describes groups</li>
</ul>
<img src="data:image/png;base64,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" title="plot of chunk barplot" alt="plot of chunk barplot" width="650px" height="370px" />
<ul class="incremental">
<li>What can we learn here?</li>
</ul>
</div>
<div class="section slide level1" id="lets-try-another">
<h1>Let’s try another</h1>
<ul class="incremental">
<li>What can we learn from this chart type about the data?</li>
</ul>
<img src="data:image/png;base64,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" title="plot of chunk diamondplot" alt="plot of chunk diamondplot" width="650px" height="370px" />
<ul class="incremental">
<li>What else might we want to learn?</li>
</ul>
</div>
<div class="section slide level1" id="how-about">
<h1>How about?</h1>
<img src="data:image/png;base64,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" title="plot of chunk diamondplot2" alt="plot of chunk diamondplot2" width="650px" height="370px" />
</div>
<div class="section slide level1" id="still-more">
<h1>Still more</h1>
<ul class="incremental">
<li>With diamonds we immediately want to look at price</li>
</ul>
<img 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" title="plot of chunk diamondplot3" alt="plot of chunk diamondplot3" width="650px" height="370px" />
<ul class="incremental">
<li>What do you see?</li>
<li>Outliers?</li>
<li>Data modes?</li>
<li>Clusters?</li>
</ul>
</div>
<div class="section slide level1" id="graphical-depictions-of-data">
<h1>Graphical Depictions of Data</h1>
<ul class="incremental">
<li>These are ways to show data with graphics</li>
<li>Graphical displays are driven by the concept of dimensions</li>
<li>One dimension–a single category</li>
<li>Two dimensions–two categories</li>
</ul>
</div>
<div class="section slide level1" id="levels-of-measurement">
<h1>Levels of Measurement</h1>
<ul class="incremental">
<li>Any given dimension may be measured at different <a href="http://en.wikipedia.org/wiki/Level_of_measurement">levels of measure</a></li>
<li><strong>Nominal:</strong> unordered categories of data</li>
<li><strong>Ordinal:</strong> ordered categories of data, relative size and degree of difference between categories is unknown</li>
<li><strong>Interval:</strong> ordered categories of data, fixed width, like discrete temperature scales</li>
<li><strong>Continuous (ratio):</strong> a measurement scale in a continuous space with a meaningful zero–physical measurements</li>
<li>This classification was derived by Stanley Smith Stevens in the 1940s and 50s</li>
</ul>
</div>
<div class="section slide level1" id="quiz-1">
<h1>Quiz 1</h1>
<ul class="incremental">
<li>Color (of diamonds) is what level of measurement?</li>
<li><strong>Nominal</strong></li>
<li>Carats are what level of measurement?</li>
<li><strong>Continuous</strong></li>
</ul>
</div>
<div class="section slide level1" id="levels-of-measurement-matter">
<h1>Levels of measurement matter</h1>
<ul class="incremental">
<li>How you depict the data</li>
<li>What you can calculate using the data</li>
</ul>
</div>
<div class="section slide level1" id="describing-data-with-numbers">
<h1>Describing Data with Numbers</h1>
<ul class="incremental">
<li>What types of measures can we use to describe different levels of measurement?</li>
</ul>
<table>
<thead>
<tr class="header">
<th align="left">Level of Meas.</th>
<th align="center">Stats</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Nominal</td>
<td align="center">mode, Chi-squared</td>
</tr>
<tr class="even">
<td align="left">Ordinal</td>
<td align="center">median, percentile</td>
</tr>
<tr class="odd">
<td align="left">Interval</td>
<td align="center">mean, std. deviation, correlation, ANOVA</td>
</tr>
<tr class="even">
<td align="left">Continuous</td>
<td align="center">geometric mean, harmonic mean, logarithms</td>
</tr>
</tbody>
</table>
</div>
<div class="section slide level1" id="lets-talk-about-these-statistics">
<h1>Let’s talk about these statistics</h1>
<ul class="incremental">
<li><strong>STATISTIC:</strong> a single measure of some attribute of a sample (e.g. its arithmetic mean value). It is calculated by applying a function (statistical algorithm) to the values of the items comprising the sample which are known together as a set of data. (Wikipedia)[http://en.wikipedia.org/wiki/Statistic]</li>
<li>These statistics can measure a number of features of a dataset, but we tend to think of them as measuring either <strong>central tendency</strong>, <strong>spread</strong>, or <strong>association</strong></li>
<li>We’ll focus on these today.</li>
</ul>
</div>
<div class="section slide level1" id="measures-of-central-tendency">
<h1>Measures of Central Tendency</h1>
<ul class="incremental">
<li>These are the three canonical measures of central tendency:</li>
<li>Mean</li>
<li>Median</li>
<li>Mode</li>
<li>How are these different? What properties do they have? Why does this matter?</li>
</ul>
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA2AAAAKICAMAAAAhLGnNAAAAGFBMVEUAAAAAAP9/f3/l5eX19fX/pQD/1wD///+azX3LAAAACXBIWXMAAAsSAAALEgHS3X78AAAeOUlEQVR4nO3di0LbSrZFUZ2bpPn/P74uP7AFsrV3aVWpljRnn04MAb+WR0QMCdMXETVr2vsKEB05gBE1DGBEDQMYUcMARtQwgBE1DGBEDQMYUcMARtQwgBE1DGBEDQMYUcMARtSwemD/R0S/kwGrfs9cfztdzqM/wvP63//+93zhv//+W3yjxRv4798/4fWYpbyBoZwXDDW7gQBbCWDqnBcMBbBMAFPnvGAogGUCmDrnBUMBLBPA1DkvGApgmQCmznnBUADLBDB1zguGAlgmgKlzXjAUwDIBTJ3zgqEAlglg6pwXDAWwTABT57xgKIBlApg65wVDASwTwNQ5LxgKYJkAps55wVAAywQwdc4LhgJYJoCpc14wFMAyAUyd84KhAJYJYOqcFwwFsEwAU+e8YCiAZQKYOucFQwEsE8DUOS8YCmCZAKbOecFQlcCmS/OTL68BWKRPwL7v278/Xnv9BYDVZwJsev7y/eQ0eweArfcR2P2+/QXsGsDqA1gq53kAVnJeMJQeWPlYEWDrff4Qcbr9+Pd6f15fNT0+RLy84t/1Fxv80+YAU6cC9vwz2BXYH1rrAuz5wgXY669Nl//df7yduP53/+nPv3/Pl8gpPkRcqeOTHLcD1t/7oez1p8uHiM9XiOMIpo4/g2XaA9jtQ4NXZ6+vEAcwdQDLtNMR7OsV2MQRbEMAS+U8z9rnwa7/X/gQ8QKMI1h1JsC+P7v8cpJPNOcKAns+i/jNauIIVp0LsJUAth5fKlVyXjAUwDIBTJ3zgqEAlglg6pwXDAWwTABT57xgKIBlApg65wVDASwTwNQ5LxgKYJkAps55wVAAywQwdc4LhgJYJoCpc14wFMAyAUyd84KhAJYJYOqcFwwFsEwAU+e8YCiAZQKYOucFQwEsE8DUOS8YCmCZAKbOecFQAMsEMHXOC4YCWCaAqXNeMBTAMgFMnfOCoQCWCWDqnBcMBbBMAFPnvGAogGUCmDrnBUMBLBPA1DkvGApgmQCmznnBUADLBDB1zguGAlgmgKlzXjAUwDIBTJ3zgqEAlglg6pwXDAWwTABT57xgKIBlApg65wVDASwTwNQ5LxgKYJkAps55wVAAywQwdc4LhgJYJoCpc14wFMAyAUyd84KhAJYJYOqcFwwFsEwAU+e8YCiAZQKYOucFQwEsE8DUOS8YCmCZAKbOecFQAMsEMHXOC4YCWCaAqXNeMBTAMgFMnfOCoQCWCWDqnBcMBbBMAFPnvGAogGUCmDrnBUMBLBPA1DkvGApgmQCmznnBUADLNAywqSS8Mo8Apg5gmUYANk2PI1gDYgBTB7BMAwCbXj9ElAsDmDqAZdofWNnk5c9g6oMYwNQBLNPewG6eZk9yaIUBTB3AMu0M7D7I/FlEqTCAqQNYpn2BPfb48TS9UhjA1AEs05DAlMIApg5gmXYF9j0HwOoDWCrnebLAnmv8+koOnTCAqQNYph2BvYzx+0ulZMIApg5gmUYFJhMGMHUAywQwdc4LhgJYpv2AvW6x9NX0ImEAU9cI2N9j9kd4XhdgzxcuwD6/9fRy+gLs469vSHkDh2zfG8gRbKXdjmCzKRb/PpjmEMYRTB0fImbaC9h8ieW/cCkRBjB1AMsEMHXOC4YCWKadgP0Y4s0/GaAQBjB1AMsEMHXOC4YCWKZ9gP3c4d0/eiMQBjB1AMs0NjCBMICpA1imXYD9mgFg9QEslfM8DYBtFwYwdQDLtAew3ysArD6ApXKepwWwzcIApg5gmQCmznnBUADLtAOwhREAVh/AUjnP0wTYVmEAUwewTP2BLW0AsPoAlsp5njbANgoDmDqAZeoObHECgNUHsFTO8zQCtk0YwNQBLBPA1DkvGApgmXoDm959h8tPZ7xFGMDUASwTwNQ5LxgKYJk6A5uWb+DaN0HfIAxg6gCWCWDqnBcMBbBMfYFNb27gGrANwwFMHcAyAUyd84KhAJYJYOqcFwwFsExdgZW7vw5Y/XIAUwewTABT57xgKIBlApg65wVDASxTT2DXe78SWPV0AFMHsEwAU+e8YCiAZeoI7HbnA0wcwFI5z9MUWO12AFMHsEwAU+e8YCiAZfIBVjkewNQBLFM/YPf7HmDiAJbKeZ7GwOrWA5g6gGUCmDrnBUMBLBPA1DkvGApgmboBe9z1G4BVzQcwdQDLBDB1zguGAlgmK2A1+wFMHcAy9QL2fc8DTBzAUjnPA7CS84KhAJapE7DnHb8JWMWAAFMHsEwAU+e8YCiAZQKYOucFQwEskxmw/IIAUwewTH2AvdzvABMHsFTO8/QAlp4QYOoAlglg6pwXDAWwTABT57xgKIBl6gLs9W7fCiy7IcDUASwTwNQ5LxgKYJl6AJvd6wATB7BUzvMArOS8YCiAZfIDlhwRYOoAlglg6pwXDAWwTB2Aze90gIkDWCrneToBy60IMHUAywQwdc4LhgJYJkdgqRkBpq4S2HRpfvLlNScFNv34eaVlYD/eGWDiTIBNz1++n5xm73BOYNPrT6sBrDTUgi2SAnt3Rs0aap5RgWWEAUydHlj5WPGcwK53wv3HG7P7R8+L6ABWGmrBFumATd+/g1+A/Tlh0+V/9x9vJ75/up+cdwH2fOEC7H4egS7AkleMxmgLsOn1HU56BLsdvm7/Pe6M+UsvLR7Bfr6Z5AiWOIRxBFPHkxyZwsBmT63Ggf16K4CJA1iqoeb5cQT7+pa1O7C4MICpOyGw2Wfscq1+HmxOaro9pwiw9wFsuceHQC8nPT7RPHt6L10U2PNiOIJ9DmBVDQtsWjwZrvGXSv2+SiJg4RsLMHXnAja7RRUHMYCpA1hVgwL7eYPSNxBg6gBW1ZjAft+e7C0EmDqAVTUksKWbk7yJbYEtXBkVsOgNBZi68wBbvjW52wgwdQCrakBg725M6kYCTB3AqhoP2MonzYM1BbZ0RWTAgjcTYOoAttff5gBYCWBVDQfs0005B7DYzQSYOoDt9cXmACsBrKrRgK3ckl2+VO8nsMUrATBxAEsVnGfthgDsEcDUASz0BvecgYVuJMDUnQHY+u0A2D2AqQNY9E1KAFMHsKqGAha5GQMAW3wjJbDIjQSYOoAl3ghg8gBW1UjAgrei+5Ns/YEFbiPA1AEs82YAUwewqgYCFr4RvZ8DAFgJYFUBbL0dgK3fRoCpOzqwxG3o/BwAwEoAqwpg6wGsBLCqhgGWugl9/4gCsBLAqgLYegArAayqUYAlb0HXx98rsKkTsNVbCDB1AMu8OcDUAawqU2BdH38AKwGsKoCttwuwtVsIMHWHBpa+ATsBm3o9yQEwgKVSA+v5+NsH2MotBJi6IwOruP67AJu6PU3/BbDOlwew1PsATB3AqhoCWNXVB1jfAFaVL7DP73UAYP1uYCiAVQWw9b6BlUsEWKcAlurTPJXXHmBdA1hVxsA+vt8RgHW7gaEAVhXA1gNYCWBVDQCs/sp/eM9DAOt1A0MBrCqArfcAdr04gHUKYKnez1N/3T+9L8DUAawqgK23H7BONzAUwKoC2HoAKwGsKm9g798ZYOoAVtXuwDb56grsdlkA6xTAUgGsKoD1C2CZd9cDu19SX2B9fgcJBbCqALYewEoAq2pvYBt9AaxbAKvKHdi7MzgKsC6/g4QCWFUAW+8K7HE5AOsUwFI1A/bmHA4DrMfvIKEAVtXOwLb7AlinAFYVwNYDWAlgVe0LTODrzZmogX1fCMA6BbBUAHs0/fh56aXALwFMHcDetXQuYmDPi9gObHr96fu17y8eYH0C2LsA1iOAVbUrMI2vxfMZGNj16t5/vDGbZi8tvMdCAFPXCNjfHZvanc8f0VmXCrDvFy7A4u95AfbrddP16l5/vJ24/vd86XeLr1TewCHb9wZyBFs5n3G/Cfp0P3zd/vv900KtPwYOxRGsqkMAWzgjR2BT5mNEgKk7HjCZr4MAe3sFANYjgL3PDNjyx4Z8iDgLYKkW5tH5cgX2fBZx+vQsYvM/ZIYCWFXHAPb7vMYFVhPAOgSwDwGseQCrCmDrAawEsKr2A6b09fvcjgWs8R8yQwGsKoCtB7ASwKo6CrCfZyecZxoAWNuPgUMBrCqArZ8zwEoAq+owwH6cH8DUAayq3YCpfR0cWNM/ZIYCWFUAWz9fgJUAVtVxgM3PEWDqAFYVwNbPFmAlgFW1FzC9r/l5AkwdwKoC2PqZDgGs4echYgGsqp2AtfAFsKYBrKojAXs9V9U85SwBVgJYVQBbP8sxgLX7RF8sgFUFsPWzBFgJYFXtA6yNr9fzPSCwVp+HCAawqgC2foYAKwGsqmMBe54xwNQBrCqArZ8fwEoAq+pgwL7PWTPP7dxGAdboS1WCAayqXYC18wWwZgGsKoCtnxnASgCrag9gDX0dG1iLT/SFA1hVRwP2OHOAqQNYVQBbPauBgDX4THo4gFUFsNWzAtg1gFW1A7Cmvh5nDzB1AKsKYGvnBLBbAKvqeMBu539QYPIvVYkHsKoA9vl8SgArAayqAwK7XgDA1AGsqv7AmvsCWJMAVhXAPp3LLYCVAFZVd2DtfV0v46jAxF/NnAhgVQHs/Xk8AlgJYFUdEtjlQgCmDmCP06mHMMDensV3QwGTfjVzJoA9TpfC59obWBdfl4vZOs/r9QRYCWDPlxLGAPbmDF5OjwVM+LVgqQA2e0WUGMDenMHLaYCVAPZ8aYofwwC2/P6vLwCsBLDH6YetMwPbejkA+xXAHqenhVe+DWDr7z0YMN1fF0gFsNnp6AOsM7Bevr7+bLokgP0OYLdTmT+A/T6jZgFMF8CaFDyC5XwdF9iWi/rxrqMBU/11gVwAWzi9HsAWAthCAKvquMA2XNbowDR/XSAZwK4npuTHiH2BdfO1CdjPdwRYCWDXEwC79WfDpQFsKYBV1RVYP18Akwewqo4MrPLyfr3XeMAUfx8nG8BeXhj0aXqAqQKYvsSXSo36Z7DewKou8Pf7AKwEsOfpYuz7xSe258nn2/cE1tEXwOQB7Pt0YTT7it/px8kJYG9beBeAlQD2OD1N048vqT8tsIqL9AC2/W+UpgPY9wsvB7BFYI8PIAtFgM1begeAlQC22CqwP/2aOl5W7WUuvf0F2POFC7D4uV2AJS8/2h735Zn6fAR7fWLjN7DXA1zPDxF7HsC+f/9LXujim494BOt7b5Y4gj1OA+zrZZ7cpQLsbQBbOL0EbP6ligB7bfmNhwS27W+UVgSw9y/8fBZxlyNY3wfEc57M5QLsfQB7nJ4fou6np+fJL4C9C2DvA9jj9KB/XWUvYIkLfvOWACsBrKoTAItfshWw3k9zAOz7hdcvRVyrG7DOD4caYO/eDmAlgD1Oz75Uaq0zAIs+FAH2KYA9T0/x37UBtv5WgwLrLAxgz9MA+zFP5MLfvw3ASgB7nB7zWUSAadvyj45UBbDvF4b8l313BRZ4KH54C4CVAFZVL2C9P2vzc561y//06wArAawqgK3/MsBKALueGPW7q+wNbOUKeALreq8C7HoCYLd+z/PpLvl87QBWAtjrC6M9yTENMM/7+2Tl3gJYaYAF22b9HS5HAPb2Plm7rwBWGmHBpll/HmwIYO8ej77AegobYsGWOX8ebBpknqW7Zf2+AlhpjAUb5vw0/SjAFu6owF0HsNIgC7YLYJnezfPznorccwArjbJgswCW6e08048/rwbOa2BgHYUNs2CrjIFNQ83z49+0Ww9gpYEWbBPAMn2eJ/dM68jA+gkba8EGASyTch6AlZwXDAWwTABT57xgKIBlOg2wbsKcFwzlC6xcO+d5AFZyXjAUwDIBTJ3zgqEAlglg6pwXDGUL7HrlnOcZG1gvYc4LhgJYJoCpc14wFMAynQhYJ2HOC4YCWCaAqXNeMJQrsNt1c54HYCXnBUMBLNOZgPUR5rxgKIBlApg65wVDASwTwNQ5LxgKYJkAps55wVCmwO5XzXme4YF1Eea8YCiAZQKYOucFQwEs07mA9RDmvGAogGUCmDrnBUMBLBPA1DkvGMoT2OOaOc9jAKyDMOcFQwEsE8DUOS8YCmCZAKbOecFQlsC+r5jzPAArOS8YCmCZzgasvTDnBUMBLBPA1DkvGApgmQCmznnBUI7AntfLeR6AlZwXDAWwTKcD1lyY84KhAJYJYOqcFwwFsEznA9ZamPOCoQyBvVwt53kAVnJeMBTAMgFMnfOCoQCW6YTAGgtzXjCUH7DXa+U8D8BKzguGAlgmgKlzXjAUwDKdEVhbYc4LhgJYJoCpc14wlB2w2ZVynscGWFNhzguGAlgmgKlzXjBUI2B/mzW1O+v1/gjP6wLs+cIFWPw9L8CE12PW4g3c9S4Xp1wwH0ewlTiCqXNeMBQfImY6J7CWwpwXDOUGbH6dnOcBWMl5wVAAywQwdc4LhgJYJoCpc14wFMAynRRYQ2HOC4YyA/bjKjnPA7CS84KhAJYJYOqcFwwFsExnBdZOmPOCobyA/bxGzvMArOS8YCiAZTotsGbCnBcMBbBMAFPnvGAogGUCmDrnBUNZAft1hZzn8QLWSpjzgqEAlglg6pwXDAWwTABT57xgKIBlOjGwRsKcFwzlBOz39XGeB2Al5wVDASzTmYG1Eea8YCgjYAtXx3kegJWcFwwFsEwAU+e8YCiAZTo1sCbCnBcMBbBMAFPnvGAoH2BL18Z5HoCVnBcMBbBMAFPnvGAogGU6N7AWwpwXDAWwTABT57xgKBtgi1fGeR6AlZwXDAWwTCcH1kCY84KhXIAtXxfneQBWcl4wFMAynR2YXpjzgqEAlglg6gt0XjAUwDKdHphcmPOCoUyAvbkqzvMArOS8YCiAZQIYwJIBLBPA1MKcFwwFsEwAA1gygGUCmFqY84KhPIC9uybO8wCs5LxgKIBlAhjAkgEsE8C+xMKcFwxlAeztFXGeB2Al5wVDASwTwL4AlssB2Pvr4TyPLTCpMOcFQwEsE8BKAEsEsEwAKwEsEcAyAeyaUJjzgqEMgH24Gs7zAKzkvGAogGUC2C2dMOcFQwEsE8BuASzc+MA+XQvneZyB6YQ5LxgKYJkAdg9g0YYH9vFKOM8DsJLzgqEAlglgj1TCnBcMBbBMAHsEsGAAywSw70TCnBcMNTqwz9fBeR6AlZwXDAWwTAB7phHmvGAogGUC2DOAhQJYJoA9A1iowYGtXAXnedyBaYQ5LxgKYJkA9hLAIgEsE8BeUwhzXjBUJbDp0vzky2t0wNYmdJ4HYCXnBUPVAZuev3w/Oc3eAWDr+QNTCHNeMJQK2MvpSyJgqwM6zwOwkvOCoRoB+yNp0pzNmF2APV+4AIu/5wWY/urUdOh9WiQBtiC1No5gYx/BBIcw5wVDKY9g6ic51udzngdgJecFQwmBvbw5wNY7ArDtwpwXDKUD9vrWAFsPYCXnBUPJgM3eWAIsMJ7zPIcAtlmY84Khtn2i+fkZ5ml6/UwzwNYDWMl5wVDjfqlU5NKd5zkGsK3CnBcMBbBMAPsVwD4HsEwA+902Yc4LhhoWWOjCnecBWMl5wVAAywSwhTYJc14wFMAyAWwhgH0KYJkAttQWYc4LhhoVWOyynecBWMl5wVAAywSwpQD2oUGBBS/aeZ7jANsizHnBUADLBLDFAPa+MYFFL9l5ngMB2/BIcV4wFMAyAWw5gL0NYJkA9qbqh4rzgqGGBBa+YOd5AFZyXjAUwDIB7F21jxXnBUONCCx+uc7zHAtY7YPFecFQAMsEsLcBbLkBgSUu1nmegwGrfLQ4LxgKYJkA9j6ALQawTAD7UNXDxXnBUOMBy1yq8zwAKzkvGApgmQD2qZrHi/OCoQCWCWCfAthCwwFLXajzPMcDVvOAcV4wFMAyAexz+UeM84KhRgOWu0zneQBWcl4wFMAyAWyl9EPGecFQgwFLXqTzPAArOS8YCmCZALZW9jHjvGCosYCdaZ5jAjvTb5GhAJYJYKsBbN5QwE71EfxBgZ3oaapQAMsEsPUANmskYOf6LMpRgZ3nSwVCASwTwCKd5atJQw0E7GRfyQawkvOCoQCWCWChTvJXZkONA+xsf9kBYCXnBUMBLBPAYp3j3wULNQyw0/2F8yMDO8c/HRtqFGDn+zeJDg3sFN9dIBTAMgEs3Am+w1sogGUCWLzjfxPgUIMAO+E/vHx0YLFNnRcMBbBMAEsEsNIYwM74zW8ODyy0qvOCoYYAdspv3wawkvOCoQCWCWCpArs6LxhqBGDn/BbaJwAWWNZ5wVAAywSwZKvTOi8YagBg9RdjPc8pgK2O67xgqP2BbfBlPc85gK3N67xgqN2BbfFlPc9JgK0M7LxgKIBlAlhFHxd2XjDU3sA2+bKeB2Al5wVDASwTwGr6tLHzgqF2BrbNl/U85wH2Nb2f2XnBUPsC2+jLep4TAfuws/OCoXYFttWX9TxnAvZ+aecFQzUC9jfUFHuzcfojPK8LsOcLF2Dx97wAE16PWcobOGsaZOtmNzBU5yPY5gOY9e9/pzqCfb1b23nBUDt+iLjdl/U8ACs5LxhqP2ACX9bznA3Y8uDOC4YCWCaAbWnp2XrnBUMBLBPAtvV7c+cFQwEsE8A29usg5rxgKIBlAtjmfszuvGAogGUC2PbmuzsvGApgmQAmaPZhovOCoQCWCWCSXqZ3XjAUwDIBTNPzIOa8YCiAZQKYqsf6zguGAlgmgMm6H8ScFwwFsEwAE3Z9ADgvGApgmQCmrDwCnBcMBbBMAJM2TZPzgqEAlglg6j78cx1NAlgqgInb4fHXVxjAUgFM3B6Pv6nnUQxgqQAmbqfHXz9hAEsFMHF7Pf66HcQAlgpg4vZ7/HUiBrBUABO35+OvCzGApQKYuH0ffx2e7gBYKoCJ2/fx99X+6Q6ApQKYuN2BtRYGsFQAE7c/sMYfJwIsFcDEDQDsq+nTHQBLBTBxYwBreBgDWCqAiRsF2FerP4wBLBXAxA0ErM1RDGCpACZuJGBfLY5iAEsFMHGDAdMfxQCWCmDiRgP2pT6KASwVwMQNCEz7pD3AUgFM3IjApB8oAiwVwMSNCexL94EiwFIBTNywwFRHMYClApi4cYF9aR4xAEsFMHFDA1McxQCWCmDixgb2tf1RA7BUABM3PLCtRzGApQKYuPGBfW37xBjAUgFMnAWwLYcxgKUCmDgTYF/VDx+ApQKYOB9glUcxgKUCmDgjYF9VxgCWCmDivIB95Z/wAFgqgImzA5YlBrBUABNnCCz3kSLAUgFMnCWwr8RhDGCpACbOFdj1MBZRBrBUABPnC+zaOrENN3Baafm9AJYJYOrUC64dyJI3cB3Q802XXw2wTABT12TBDxxiNzCKavY+y68GWCaAqWu24Bsh729g4mC1/P7LrwZYJoCpa73gTzR/Fn+tFtXskpZfDbBMAFPXb8HosxLV57/8aoBlApg65wXnAUwQwNQ5LzgPYIIAps55wXkAEwQwdc4LzgOYIICpc15wnhbYy5Mw95Ozp2UAth7ASs4LzpMCm56/fD85zd4BYOsBrOS84LxuwMqhDGDrAazkvOC8vsD+rLf2xclH7wLs+cIFWPw9L8D0V4e2FnjMd/0QUZHz738cwUrOC4ba789gipznAVjJecFQAMsEMHXOC4YCWCaAqXNeMBTAMgFMnfOCobZ9ovnlM8z3nxbPqFnO8wCs5LxgqP2+VEqR8zwAKzkvGApgmQCmznnBUADLBDB1zguGAlgmgKlzXjAUwDIBTJ3zgqEAlglg6pwXDAWwTABT57xgKIBlApg65wVDASwTwNQ5LxgKYJkAps55wVAAywQwdc4LhgJYJoCpc14wFMAyAUyd84KhAJYJYOqcFwwFsEwAU+e8YCiAZQKYOucFQwEsE8DUOS8YCmCZAKbOecFQAMsEMHXOC4YCWCaAqXNeMFQjYMdsmva+Bo3jBjZOBeygqb9v9nBxA7s21JUZobHmaRA3sGtDXZkRGmueBnEDuzbUlSE6WgAjahjAiBoGMKKGAYyoYQB7tvQ9ZA7V4W/g1+ybAQ3RMFdk/xa/C9qROvwN/Lo9Rz/UDRzleozSwR9/h7+BABu8gz/+jn4Dr7duqBs4yvUYpYM//ob7DV4cwAZvtHn0Hfp3kGm8BUe5HmP0+J7UB75bANa3Ua7HEE3zHw7ZkYFN18a6gaNcjxGanj8e8m45w9P0HMHG7f7731CfptTGJ5r7N8wVITpiACNqGMCIGgYwooYBjKhhAHNtnCfK6EOs5BrALGIl1wBmESu5Vr4maLo7e/nsKu7Gijlcm27C7l97ArBBYw7XHoevK7LpeXLv60Wz2MO1V2BfABs19nDtCex2DAPYkLGHa89nOO4fJN5P0lAxiGsvwB4/AGy8GORIAWy4GORI4Wu4WOQ4jfQXeekeixA1DGBEDQMYUcMARtQwgBE1DGBEDQMYUcP+H4yNdWEJ1uN8AAAAAElFTkSuQmCC" title="plot of chunk centraltend" alt="plot of chunk centraltend" width="650px" height="370px" />
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(xtable)
<span class="kw">print</span>(<span class="kw">xtable</span>(<span class="kw">table</span>(mpg$hwy)), <span class="dt">type =</span> <span class="st">"html"</span>)</code></pre>
<!-- html table generated in R 2.15.2 by xtable 1.7-1 package -->
<!-- Fri Mar 29 10:29:41 2013 -->
<TABLE border="1">
<TR> <TH> </TH> <TH>
V1
</TH> </TR>
<TR> <TD align="right">
12
</TD> <TD align="right">
5
</TD> </TR>
<TR> <TD align="right">
14
</TD> <TD align="right">
2
</TD> </TR>
<TR> <TD align="right">
15
</TD> <TD align="right">
10
</TD> </TR>
<TR> <TD align="right">
16
</TD> <TD align="right">
7
</TD> </TR>
<TR> <TD align="right">
17
</TD> <TD align="right">
31
</TD> </TR>
<TR> <TD align="right">
18
</TD> <TD align="right">
10
</TD> </TR>
<TR> <TD align="right">
19
</TD> <TD align="right">
13
</TD> </TR>
<TR> <TD align="right">
20
</TD> <TD align="right">
11
</TD> </TR>
<TR> <TD align="right">
21
</TD> <TD align="right">
2
</TD> </TR>
<TR> <TD align="right">
22
</TD> <TD align="right">
7
</TD> </TR>
<TR> <TD align="right">
23
</TD> <TD align="right">
7
</TD> </TR>
<TR> <TD align="right">
24
</TD> <TD align="right">
13
</TD> </TR>
<TR> <TD align="right">
25
</TD> <TD align="right">
15
</TD> </TR>
<TR> <TD align="right">
26
</TD> <TD align="right">
32
</TD> </TR>
<TR> <TD align="right">
27
</TD> <TD align="right">
14
</TD> </TR>
<TR> <TD align="right">
28
</TD> <TD align="right">
7
</TD> </TR>
<TR> <TD align="right">
29
</TD> <TD align="right">
22
</TD> </TR>
<TR> <TD align="right">
30
</TD> <TD align="right">
4
</TD> </TR>
<TR> <TD align="right">
31
</TD> <TD align="right">
7
</TD> </TR>
<TR> <TD align="right">
32
</TD> <TD align="right">
4
</TD> </TR>
<TR> <TD align="right">
33
</TD> <TD align="right">
2
</TD> </TR>
<TR> <TD align="right">
34
</TD> <TD align="right">
1
</TD> </TR>
<TR> <TD align="right">
35
</TD> <TD align="right">
2
</TD> </TR>
<TR> <TD align="right">
36
</TD> <TD align="right">
2
</TD> </TR>
<TR> <TD align="right">
37
</TD> <TD align="right">
1
</TD> </TR>
<TR> <TD align="right">
41
</TD> <TD align="right">
1
</TD> </TR>
<TR> <TD align="right">
44
</TD> <TD align="right">
2
</TD> </TR>
</TABLE>
</div>
<div class="section slide level1" id="the-mean">
<h1>The Mean</h1>
<ul class="incremental">
<li>Commonly thought of as the average</li>
<li>Add up all the values, divide by the number of observations</li>
<li>2 + 6 + 3 + 9 + 1 = 21</li>
<li>Divide by 5</li>
<li>4.2</li>
<li>The mean is sensitive to the number of observations and the spread of the data</li>
</ul>
</div>
<div class="section slide level1" id="the-median">
<h1>The Median</h1>
<ul class="incremental">
<li>This is a measure of the value the middle observation takes</li>
<li>Order the data: 1 2 3 6 9 ; get the length (5)</li>
<li>Count (length / 2) + 1 in this case, the 3rd observation = 3</li>
<li>What if we add another number?</li>
<li>1 2 3 6 9 13</li>
<li>Count (length /2) from each side and average the two observations that remain</li>
<li>3 + 6 = 9</li>
<li>9/2 = 4.5</li>
<li>The median is sensitive to the spread of the data</li>
</ul>
</div>
<div class="section slide level1" id="the-mode">
<h1>The Mode</h1>
<ul class="incremental">
<li>The most common observation</li>
<li>This can be problematic since skewed distributions can have a mode that diverges far from the mean or median</li>
<li>The mode is useful in cases where you are looking for data errors (the most common value) and is also used a lot in fitting statistical models</li>
</ul>
</div>
<div class="section slide level1" id="measures-of-spread">
<h1>Measures of spread</h1>
<ul class="incremental">
<li>It is handy to describe the central tendency of the data, but we also need a sense of how much the data resembles the central tendency</li>
<li>Measures of spread help us achieve this</li>
</ul>
</div>
<div class="section slide level1" id="quantiles">
<h1>Quantiles</h1>
<ul class="incremental">
<li>Quantiles are used to divide the data into an even number of observations per “bin”</li>
<li>An example is percentiles where data is divided evently into 100 groups, or quartiles where the data is divided into 4 groups</li>
<li>This is handy to look at the way the distribution of the data behaves</li>
</ul>
</div>
<div class="section slide level1" id="standard-deviation-and-variance">
<h1>Standard Deviation and Variance</h1>
<ul class="incremental">
<li>When we have multiple observations of data we are interested in how spread apart these values can be</li>
<li>The standard way to measure this is to use the variance and standard deviation (which is the square root of the variance)</li>
<li>Variance measures how far away from the mean the data can be and can be easily calculated by subtracting each element of data from the mean, squaring that difference, adding them together, and dividing by the number of elements</li>
</ul>
</div>
<div class="section slide level1" id="skew">
<h1>Skew</h1>
<ul class="incremental">
<li>Skew describes the symmetry of the distribution</li>
<li>Is it balanced? Are there clusters?</li>
<li>Skew is in reference to a norm - most often the normal distribution, but not always</li>
</ul>
</div>
<div class="section slide level1" id="distributions-of-data">
<h1>Distributions of Data</h1>
<ul class="incremental">
<li>All of the above are the concepts we use to describe a univariate group of data</li>
<li>Statisticians use the idea of a distribution to summarize how data generated through some process looks</li>
<li>Think about the difference between a coin flip and a test score</li>
<li>Both are generated by a stochastic process</li>
<li>Both may generate the same amount of numbers</li>
<li>But, we have very different expectations about what values those numbers take in the real world</li>
<li>A <strong>distribution</strong> describes our expectation not just about the mean of the data (the average value), but also of how spread out it is likely to be</li>
</ul>
</div>
<div class="section slide level1" id="pictures-of-distributions">
<h1>Pictures of distributions</h1>
<ul class="incremental">
<li>Let’s look at some distributions of data and talk about what they might represent</li>
<li>Different distributions describe different data generating processes, and these processes can be used to represent a large array of data types</li>
</ul>
</div>
<div class="section slide level1" id="normal">
<h1>Normal</h1>
<ul class="incremental">
<li>The bell curve</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">qplot</span>(<span class="kw">rnorm</span>(<span class="dv">3000</span>), <span class="dt">geom =</span> <span class="st">"density"</span>, <span class="dt">adjust =</span> <span class="dv">2</span>) + <span class="kw">theme_dpi</span>()</code></pre>
<img src="data:image/png;base64,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" title="plot of chunk bellcurve" alt="plot of chunk bellcurve" width="650px" height="370px" />
</div>
<div class="section slide level1" id="uniform">
<h1>Uniform</h1>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">qplot</span>(<span class="kw">runif</span>(<span class="fl">1e+05</span>, <span class="dt">min =</span> -<span class="dv">5</span>, <span class="dt">max =</span> <span class="dv">5</span>), <span class="dt">geom =</span> <span class="st">"bar"</span>) + <span class="kw">theme_dpi</span>()</code></pre>
<img src="data:image/png;base64,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" title="plot of chunk uniform" alt="plot of chunk uniform" width="650px" height="370px" />
</div>
<div class="section slide level1" id="poisson">
<h1>Poisson</h1>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">qplot</span>(<span class="kw">rpois</span>(<span class="dv">3000</span>, <span class="dt">lambda =</span> <span class="dv">3</span>), <span class="dt">geom =</span> <span class="st">"density"</span>, <span class="dt">adjust =</span> <span class="dv">2</span>) + <span class="kw">theme_dpi</span>()</code></pre>
<img src="data:image/png;base64,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" title="plot of chunk poisson" alt="plot of chunk poisson" width="650px" height="370px" />
</div>
<div class="section slide level1" id="binomial">
<h1>Binomial</h1>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">qplot</span>(<span class="kw">rbinom</span>(<span class="dv">3000</span>, <span class="dv">1</span>, <span class="fl">0.5</span>), <span class="dt">geom =</span> <span class="st">"bar"</span>) + <span class="kw">theme_dpi</span>()</code></pre>
<img src="data:image/png;base64,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" title="plot of chunk binom" alt="plot of chunk binom" width="650px" height="370px" />
</div>
<div class="section slide level1" id="weibull">
<h1>Weibull</h1>
<ul class="incremental">
<li>Skewness</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">qplot</span>(<span class="kw">rweibull</span>(<span class="dv">3000</span>, <span class="dt">shape =</span> <span class="dv">18</span>, <span class="dt">scale =</span> <span class="dv">1</span>), <span class="dt">geom =</span> <span class="st">"density"</span>) + <span class="kw">theme_dpi</span>()</code></pre>
<img src="data:image/png;base64,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" title="plot of chunk weibull" alt="plot of chunk weibull" width="650px" height="370px" />
</div>
<div class="section slide level1" id="distribution-demo">
<h1>Distribution Demo</h1>
<ul class="incremental">
<li><a href="http://glimmer.rstudio.com/wisconsindpi/distribution/">Drawing Distributions</a></li>
<li>These parameters define distributions (there are others as well), but you can see what a distribution does–it summarizes data</li>
</ul>
</div>
<div class="section slide level1" id="sampling">
<h1>Sampling</h1>
<ul class="incremental">
<li>Much of traditional statistics is based on the idea of samples</li>
<li>Sampling is the process of picking observations out of a population in a way that makes the smaller set of observations reflective of the population</li>
<li>How we sample determines what conclusions we can draw in the population from our data</li>
<li>The size of the sample relative to the population we are making inference about determines our confidence / precision</li>
<li>Let’s look at a demo at some different types of sampling-</li>
<li><a href="http://glimmer.rstudio.com/wisconsindpi/sampling/">Sampling Demo</a></li>
</ul>
</div>
<div class="section slide level1" id="measures-of-association">
<h1>Measures of association</h1>
<ul class="incremental">
<li>Correlation is not causation</li>
<li>Correlation is a measure of the dependence between two variables and there are a number of different versions of measuring correlation</li>
<li>The most common is <a href="http://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient">Pearson’s <em>r</em></a> which measures linear dependence on a scale of -1 to 1</li>
<li>Rank correlation is an option as well using <a href="http://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient">Spearman’s rank correlation coefficient</a> or <a href="http://en.wikipedia.org/wiki/Kendall_tau_rank_correlation_coefficient">Kendall’s tau rank correlation coefficient</a></li>
</ul>
</div>
<div class="section slide level1" id="lets-look-at-pearsons-coefficient">
<h1>Let’s look at Pearson’s Coefficient</h1>
<ul class="incremental">
<li><a href="http://glimmer.rstudio.com/wisconsindpi/correlation/">Visualizing Correlations</a></li>
</ul>
</div>
<div class="section slide level1" id="we-can-identify-other-types-of-correlation-though">
<h1>We can identify other types of correlation though:</h1>
<img src="data:image/png;base64,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" title="plot of chunk sophcorr" alt="plot of chunk sophcorr" width="650px" height="370px" />
</div>
<div class="section slide level1" id="regression-and-statistical-models">
<h1>Regression and Statistical Models</h1>
<ul class="incremental">
<li>Statistical models are mathematical representations of real world phenomena</li>
<li>We use statistical models to summarize and describe the relationships between pieces of data</li>
<li>Let’s look at the simplest version of regression model and the basis of many statistical techniques: Ordinary Least Squares regression (OLS)</li>
<li><strong>DEMO</strong></li>
</ul>
</div>
<div class="section slide level1" id="hypothesis-testing">
<h1>Hypothesis testing</h1>
<ul class="incremental">
<li>What does it mean when someone says something is statistically significant?</li>
<li>“A significance is a formal procedure for comparing observed data with a hypothesis whose truth we want to assess.”~ Moore and McCabe p. 436</li>
<li>Hypothesis testing is based on the idea of sampling and centers around the question of “Do we have enough data to believe the relationship we are seeing in our sample is true in the population?”</li>
<li>In a hypothesis test we specify two hypotheses–the null hypothesis and the alternative hypothesis</li>
<li>A test of significance then assesses the evidence against the null hypothesis in terms of probability</li>
</ul>
</div>
<div class="section slide level1" id="most-common-applications">
<h1>Most Common Applications</h1>
<ul class="incremental">
<li>The most common application of these is when you compare a sample to a population or a true fixed value</li>
<li>A classic example is fluctuation in weight, for an athlete for example</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r">wt <- <span class="kw">c</span>(<span class="fl">190.5</span>, <span class="dv">189</span>, <span class="fl">195.5</span>, <span class="dv">187</span>, <span class="dv">191</span>, <span class="fl">190.4</span>, <span class="dv">186</span>, <span class="dv">183</span>, <span class="dv">193</span>, <span class="dv">188</span>)
<span class="kw">t.test</span>(wt, <span class="dt">mu =</span> <span class="dv">187</span>, <span class="dt">alternative =</span> <span class="st">"two.sided"</span>)</code></pre>
<pre><code>##
## One Sample t-test
##
## data: wt
## t = 2.067, df = 9, p-value = 0.06866
## alternative hypothesis: true mean is not equal to 187
## 95 percent confidence interval:
## 186.8 191.9
## sample estimates:
## mean of x
## 189.3</code></pre>
</div>
<div class="section slide level1" id="questions">
<h1>Questions</h1>
<pre><code>##
## One Sample t-test
##
## data: wt
## t = 2.067, df = 9, p-value = 0.06866
## alternative hypothesis: true mean is not equal to 187
## 95 percent confidence interval:
## 186.8 191.9
## sample estimates:
## mean of x
## 189.3</code></pre>
<ul class="incremental">
<li>the P value reflects our belief about the probability of the test statistic taking a value as extreme or more extreme than what we observe</li>
<li>This translates into our belief about “extremeness” of the value of interest, the average weight, in relation to the comparison group, in this case <strong>187</strong></li>
<li>Traditionally, researchers set a fixed value of <em>p</em> for comparison before conducting the test, usually <strong>.05</strong>, sometimes <strong>.10</strong> and sometimes <strong>.01</strong></li>
<li>If our <em>p</em> is <strong>less than</strong> the pre-specified <em>p value</em> we set, then we say the observed value is <strong>statistically significant</strong></li>
</ul>
</div>
<div class="section slide level1" id="lets-look-at-an-example">
<h1>Let’s look at an example</h1>
<p><strong>DEMO</strong></p>
</div>
<div class="section slide level1" id="some-practical-advice-about-statistical-signficance">
<h1>Some practical advice about statistical signficance</h1>
<ul class="incremental">
<li>Statistical significance is very different from substantive significance. Variables can be statistically significant but have no observable difference. For example, with enough precise measurements we could demonstrate a statistically significant difference in weight of 0.2 pounds, but in most cases this is substantively meaningless.</li>
<li>Statistical significance tells us nothing about the true value in the population except that it is <strong>not</strong> equal to the assumption of the null hypothesis. We are not testing what range of values our measurement could take in the real world, only if it is plausibly different from another set of values.</li>
<li>Statistical significance is only valid under assumptions about the data. What might be a few ways that the data in our weight example could invalidate the usefulness of a significance test?</li>
</ul>
</div>
<div class="section slide level1" id="session-info">
<h1>Session Info</h1>
<p>It is good to include the session info, e.g. this document is produced with <strong>knitr</strong> version 1.1. Here is my session info:</p>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">print</span>(<span class="kw">sessionInfo</span>(), <span class="dt">locale =</span> <span class="ot">FALSE</span>)</code></pre>
<pre><code>## R version 2.15.2 (2012-10-26)
## Platform: i386-w64-mingw32/i386 (32-bit)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] xtable_1.7-1 mgcv_1.7-22 eeptools_0.2 ggplot2_0.9.3.1
## [5] knitr_1.1 Cairo_1.5-2 shiny_0.4.0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-5 caTools_1.14 colorspace_1.2-1
## [4] dichromat_2.0-0 digest_0.6.3 evaluate_0.4.3
## [7] formatR_0.7 grid_2.15.2 gtable_0.1.2
## [10] labeling_0.1 lattice_0.20-15 MASS_7.3-23
## [13] Matrix_1.0-11 munsell_0.4 nlme_3.1-108
## [16] plyr_1.8 proto_0.3-10 RColorBrewer_1.0-5
## [19] reshape2_1.2.2 RJSONIO_1.0-2 scales_0.2.3
## [22] stringr_0.6.2 tools_2.15.1 websockets_1.1.7</code></pre>
</div>
<div class="section slide level1" id="attribution-and-license">
<h1>Attribution and License</h1>
<p xmlns:dct="http://purl.org/dc/terms/">
<a rel="license" href="http://creativecommons.org/publicdomain/mark/1.0/"> <img src="data:image/png;base64,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" style="border-style: none;" alt="Public Domain Mark" /> </a> <br /> This work (<span property="dct:title">R Tutorial for Education</span>, by <a href="www.jaredknowles.com" rel="dct:creator"><span property="dct:title">Jared E. Knowles</span></a>), in service of the <a href="http://www.dpi.wi.gov" rel="dct:publisher"><span property="dct:title">Wisconsin Department of Public Instruction</span></a>, is free of known copyright restrictions.
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