Details

Statistical Analysis with R For Dummies


Statistical Analysis with R For Dummies


1. Aufl.

von: Joseph Schmuller

21,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 03.03.2017
ISBN/EAN: 9781119337096
Sprache: englisch
Anzahl Seiten: 464

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Beschreibungen

<b>Understanding the world of R programming and analysis has never been easier</b> <p>Most guides to R, whether books or online, focus on R functions and procedures. But now, thanks to <i>Statistical Analysis with R For Dummies</i>, you have access to a trusted, easy-to-follow guide that focuses on the foundational statistical concepts that R addresses—as well as step-by-step guidance that shows you exactly how to implement them using R programming. <p>People are becoming more aware of R every day as major institutions are adopting it as a standard. Part of its appeal is that it's a free tool that's taking the place of costly statistical software packages that sometimes take an inordinate amount of time to learn. Plus, R enables a user to carry out complex statistical analyses by simply entering a few commands, making sophisticated analyses available and understandable to a wide audience. <i>Statistical Analysis with R For Dummies</i> enables you to perform these analyses and to fully understand their implications and results. <ul> <li>Gets you up to speed on the #1 analytics/data science software tool</li> <li>Demonstrates how to easily find, download, and use cutting-edge community-reviewed methods in statistics and predictive modeling</li> <li>Shows you how R offers intel from leading researchers in data science, free of charge</li> <li>Provides information on using R Studio to work with R</li> </ul> <p>Get ready to use R to crunch and analyze your data—the fast and easy way!
<p>Introduction 1</p> <p>About This Book 1</p> <p>Similarity with This Other For Dummies Book 2</p> <p>What You Can Safely Skip 2</p> <p>Foolish Assumptions 2</p> <p>How This Book Is Organized 3</p> <p>Part 1: Getting Started with Statistical Analysis with R 3</p> <p>Part 2: Describing Data 3</p> <p>Part 3: Drawing Conclusions from Data 3</p> <p>Part 4: Working with Probability 3</p> <p>Part 5: The Part of Tens 4</p> <p>Online Appendix A: More on Probability 4</p> <p>Online Appendix B: Non-Parametric Statistics 4</p> <p>Online Appendix C: Ten Topics That Just Didn’t Fit in Any Other Chapter 4</p> <p>Icons Used in This Book 4</p> <p>Where to Go from Here 5</p> <p><b>Part 1: Getting Started with Statistical Analysis with R 7</b></p> <p><b>Chapter 1: Data, Statistics, and Decisions 9</b></p> <p>The Statistical (and Related) Notions You Just Have to Know 10</p> <p>Samples and populations 10</p> <p>Variables: Dependent and independent 11</p> <p>Types of data 12</p> <p>A little probability 13</p> <p>Inferential Statistics: Testing Hypotheses 14</p> <p>Null and alternative hypotheses 14</p> <p>Two types of error 15</p> <p><b>Chapter 2: R: What It Does and How It Does It 17</b></p> <p>Downloading R and RStudio 18</p> <p>A Session with R 21</p> <p>The working directory 21</p> <p>So let’s get started, already 22</p> <p>Missing data 26</p> <p>R Functions 26</p> <p>User-Defined Functions 28</p> <p>Comments 29</p> <p>R Structures 29</p> <p>Vectors 30</p> <p>Numerical vectors 30</p> <p>Matrices 31</p> <p>Factors 33</p> <p>Lists 34</p> <p>Lists and statistics 35</p> <p>Data frames 36</p> <p>Packages 39</p> <p>More Packages 42</p> <p>R Formulas 43</p> <p>Reading and Writing 44</p> <p>Spreadsheets 44</p> <p>CSV files 46</p> <p>Text files 47</p> <p><b>Part 2: Describing Data 49</b></p> <p><b>Chapter 3: Getting Graphic 51</b></p> <p>Finding Patterns 51</p> <p>Graphing a distribution 52</p> <p>Bar-hopping 53</p> <p>Slicing the pie 54</p> <p>The plot of scatter 55</p> <p>Of boxes and whiskers 56</p> <p>Base R Graphics 57</p> <p>Histograms 57</p> <p>Adding graph features 59</p> <p>Bar plots 60</p> <p>Pie graphs 62</p> <p>Dot charts 62</p> <p>Bar plots revisited 64</p> <p>Scatter plots 67</p> <p>Box plots 71</p> <p>Graduating to ggplot2 71</p> <p>Histograms 72</p> <p>Bar plots 74</p> <p>Dot charts 75</p> <p>Bar plots re-revisited 78</p> <p>Scatter plots 82</p> <p>Box plots 86</p> <p>Wrapping Up 89</p> <p><b>Chapter 4: Finding Your Center 91</b></p> <p>Means: The Lure of Averages 91</p> <p>The Average in R: mean() 93</p> <p>What’s your condition? 93</p> <p>Eliminate $-signs forth with() 94</p> <p>Exploring the data 95</p> <p>Outliers: The flaw of averages 96</p> <p>Other means to an end 97</p> <p>Medians: Caught in the Middle 99</p> <p>The Median in R: median() 100</p> <p>Statistics à la Mode 101</p> <p>The Mode in R 101</p> <p><b>Chapter 5: Deviating from the Average 103</b></p> <p>Measuring Variation 104</p> <p>Averaging squared deviations: Variance and how to calculate it 104</p> <p>Sample variance 107</p> <p>Variance in R 107</p> <p>Back to the Roots: Standard Deviation 108</p> <p>Population standard deviation 108</p> <p>Sample standard deviation 109</p> <p>Standard Deviation in R 109</p> <p>Conditions, Conditions, Conditions 110</p> <p><b>Chapter 6: Meeting Standards and Standings 111</b></p> <p>Catching Some Z’s 112</p> <p>Characteristics of z-scores 112</p> <p>Bonds versus the Bambino 113</p> <p>Exam scores 114</p> <p>Standard Scores in R 114</p> <p>Where Do You Stand? 117</p> <p>Ranking in R 117</p> <p>Tied scores 117</p> <p>Nth smallest, Nth largest 118</p> <p>Percentiles 118</p> <p>Percent ranks 120</p> <p>Summarizing 121</p> <p><b>Chapter 7: Summarizing It All 123</b></p> <p>How Many? 123</p> <p>The High and the Low 125</p> <p>Living in the Moments 125</p> <p>A teachable moment 126</p> <p>Back to descriptives 126</p> <p>Skewness 127</p> <p>Kurtosis 130</p> <p>Tuning in the Frequency 131</p> <p>Nominal variables: table() et al 131</p> <p>Numerical variables: hist() 132</p> <p>Numerical variables: stem() 138</p> <p>Summarizing a Data Frame 139</p> <p><b>Chapter 8: What’s Normal? 143</b></p> <p>Hitting the Curve 143</p> <p>Digging deeper 144</p> <p>Parameters of a normal distribution 145</p> <p>Working with Normal Distributions 147</p> <p>Distributions in R 147</p> <p>Normal density function 147</p> <p>Cumulative density function 152</p> <p>Quantiles of normal distributions 155</p> <p>Random sampling 156</p> <p>A Distinguished Member of the Family 158</p> <p><b>Part 3: Drawing Conclusions From Data 161</b></p> <p><b>Chapter 9: The Confidence Game: Estimation 163</b></p> <p>Understanding Sampling Distributions 164</p> <p>An EXTREMELY Important Idea: The Central Limit Theorem 165</p> <p>(Approximately) Simulating the central limit theorem 167</p> <p>Predictions of the central limit theorem 171</p> <p>Confidence: It Has Its Limits! 173</p> <p>Finding confidence limits for a mean 173</p> <p>Fit to a t 175</p> <p><b>Chapter 10: One-Sample Hypothesis Testing 179</b></p> <p>Hypotheses, Tests, and Errors 179</p> <p>Hypothesis Tests and Sampling Distributions 181</p> <p>Catching Some Z’s Again 183</p> <p>Z Testing in R 185</p> <p>t for One 187</p> <p>t Testing in R 188</p> <p>Working with t-Distributions 189</p> <p>Visualizing t-Distributions 190</p> <p>Plotting t in base R graphics 191</p> <p>Plotting t in ggplot2 192</p> <p>One more thing about ggplot2 197</p> <p>Testing a Variance 198</p> <p>Testing in R 199</p> <p>Working with Chi-Square Distributions 201</p> <p>Visualizing Chi-Square Distributions 201</p> <p>Plotting chi-square in base R graphics 202</p> <p>Plotting chi-square in ggplot2 203</p> <p><b>Chapter 11: Two-Sample Hypothesis Testing 205</b></p> <p>Hypotheses Built for Two 205</p> <p>Sampling Distributions Revisited 206</p> <p>Applying the central limit theorem 207</p> <p>Z’s once more 208</p> <p>Z-testing for two samples in R 210</p> <p>t for Two 212</p> <p>Like Peas in a Pod: Equal Variances 212</p> <p>t-Testing in R 214</p> <p>Working with two vectors 214</p> <p>Working with a data frame and a formula 215</p> <p>Visualizing the results 216</p> <p>Like p’s and q’s: Unequal variances 219</p> <p>A Matched Set: Hypothesis Testing for Paired Samples 220</p> <p>Paired Sample t-testing in R 222</p> <p>Testing Two Variances 222</p> <p>F-testing in R 224</p> <p>F in conjunction with t 225</p> <p>Working with F-Distributions 226</p> <p>Visualizing F-Distributions 226</p> <p><b>Chapter 12: Testing More than Two Samples 231</b></p> <p>Testing More Than Two 231</p> <p>A thorny problem 232</p> <p>A solution 233</p> <p>Meaningful relationships 237</p> <p>ANOVA in R 237</p> <p>Visualizing the results 239</p> <p>After the ANOVA 239</p> <p>Contrasts in R 242</p> <p>Unplanned comparisons 243</p> <p>Another Kind of Hypothesis, Another Kind of Test 244</p> <p>Working with repeated measures ANOVA 245</p> <p>Repeated measures ANOVA in R 247</p> <p>Visualizing the results 249</p> <p>Getting Trendy 250</p> <p>Trend Analysis in R 254</p> <p><b>Chapter 13: More Complicated Testing 255</b></p> <p>Cracking the Combinations 255</p> <p>Interactions 257</p> <p>The analysis 257</p> <p>Two-Way ANOVA in R 259</p> <p>Visualizing the two-way results 261</p> <p>Two Kinds of Variables at Once 263</p> <p>Mixed ANOVA in R 266</p> <p>Visualizing the Mixed ANOVA results 268</p> <p>After the Analysis 269</p> <p>Multivariate Analysis of Variance 270</p> <p>MANOVA in R 271</p> <p>Visualizing the MANOVA results 273</p> <p>After the analysis 275</p> <p><b>Chapter 14: Regression: Linear, Multiple, and the General Linear Model 277</b></p> <p>The Plot of Scatter 277</p> <p>Graphing Lines 279</p> <p>Regression: What a Line! 281</p> <p>Using regression for forecasting 283</p> <p>Variation around the regression line 283</p> <p>Testing hypotheses about regression 285</p> <p>Linear Regression in R 290</p> <p>Features of the linear model 292</p> <p>Making predictions 292</p> <p>Visualizing the scatter plot and regression line 293</p> <p>Plotting the residuals 294</p> <p>Juggling Many Relationships at Once: Multiple Regression 295</p> <p>Multiple regression in R 297</p> <p>Making predictions 298</p> <p>Visualizing the 3D scatter plot and regression plane 298</p> <p>ANOVA: Another Look 301</p> <p>Analysis of Covariance: The Final Component of the GLM 305</p> <p>But wait — there’s more 311</p> <p><b>Chapter 15: Correlation: The Rise and Fall of Relationships 313</b></p> <p>Scatter plots Again 313</p> <p>Understanding Correlation 314</p> <p>Correlation and Regression 316</p> <p>Testing Hypotheses About Correlation 319</p> <p>Is a correlation coefficient greater than zero? 319</p> <p>Do two correlation coefficients differ? 320</p> <p>Correlation in R 322</p> <p>Calculating a correlation coefficient 322</p> <p>Testing a correlation coefficient 322</p> <p>Testing the difference between two correlation coefficients 323</p> <p>Calculating a correlation matrix 324</p> <p>Visualizing correlation matrices 324</p> <p>Multiple Correlation 326</p> <p>Multiple correlation in R 327</p> <p>Adjusting R-squared 328</p> <p>Partial Correlation 329</p> <p>Partial Correlation in R 330</p> <p>Semipartial Correlation 331</p> <p>Semipartial Correlation in R 332</p> <p><b>Chapter 16: Curvilinear Regression: When Relationships Get Complicated 335</b></p> <p>What Is a Logarithm? 336</p> <p>What Is e? 338</p> <p>Power Regression 341</p> <p>Exponential Regression 346</p> <p>Logarithmic Regression 350</p> <p>Polynomial Regression: A Higher Power 354</p> <p>Which Model Should You Use? 358</p> <p><b>Part 4: Working with Probability 359</b></p> <p><b>Chapter 17: Introducing Probability 361</b></p> <p>What Is Probability? 361</p> <p>Experiments, trials, events, and sample spaces 362</p> <p>Sample spaces and probability 362</p> <p>Compound Events 363</p> <p>Union and intersection 363</p> <p>Intersection again 364</p> <p>Conditional Probability 365</p> <p>Working with the probabilities 366</p> <p>The foundation of hypothesis testing 366</p> <p>Large Sample Spaces 366</p> <p>Permutations 367</p> <p>Combinations 368</p> <p>R Functions for Counting Rules 369</p> <p>Random Variables: Discrete and Continuous 371</p> <p>Probability Distributions and Density Functions 371</p> <p>The Binomial Distribution 374</p> <p>The Binomial and Negative Binomial in R 375</p> <p>Binomial distribution 375</p> <p>Negative binomial distribution 377</p> <p>Hypothesis Testing with the Binomial Distribution 378</p> <p>More on Hypothesis Testing: R versus Tradition 380</p> <p><b>Chapter 18: Introducing Modeling 383</b></p> <p>Modeling a Distribution 383</p> <p>Plunging into the Poisson distribution 384</p> <p>Modeling with the Poisson distribution 385</p> <p>Testing the model’s fit 388</p> <p>A word about chisqtest() 391</p> <p>Playing ball with a model 392</p> <p>A Simulating Discussion 396</p> <p>Taking a chance: The Monte Carlo method 396</p> <p>Loading the dice 396</p> <p>Simulating the central limit theorem 401</p> <p><b>Part 5: The Part of Tens 405</b></p> <p><b>Chapter 19: Ten Tips for Excel Emigrés 407</b></p> <p>Defining a Vector in R Is Like Naming a Range in Excel 407</p> <p>Operating on Vectors Is Like Operating on Named Ranges 408</p> <p>Sometimes Statistical Functions Work the Same Way 412</p> <p>And Sometimes They Don’t 412</p> <p>Contrast: Excel and R Work with Different Data Formats 413</p> <p>Distribution Functions Are (Somewhat) Similar 414</p> <p>A Data Frame Is (Something) Like a Multicolumn Named Range 416</p> <p>The sapply() Function Is Like Dragging 417</p> <p>Using edit() Is (Almost) Like Editing a Spreadsheet 418</p> <p>Use the Clipboard to Import a Table from Excel into R 419</p> <p><b>Chapter 20: Ten Valuable Online R Resources 421</b></p> <p>Websites for R Users 421</p> <p>R-bloggers 421</p> <p>Microsoft R Application Network 422</p> <p>Quick-R 422</p> <p>RStudio Online Learning 422</p> <p>Stack Overflow 422</p> <p>Online Books and Documentation 423</p> <p>R manuals 423</p> <p>R documentation 423</p> <p>RDocumentation 423</p> <p>YOU CANanalytics 423</p> <p>The R Journal 424</p> <p>Index 425</p>
<p><b>Joseph Schmuller, PhD,</b> has taught undergraduate and graduate statistics, and has 25 years of IT experience. The author of four editions of <i>Statistical Analysis with Excel For Dummies</i> and three editions of <i>Teach Yourself UML in 24 Hours</i> (SAMS), he has created online coursework for Lynda.com and is a former Editor in Chief of <i>PC AI</i> magazine. He is a Research Scholar at the University of North Florida.
<ul> <li>Leverage R as a powerful statistical tool</li> <li>Test your hypotheses and draw conclusions</li> <li>Use R to give meaning to your data</li> </ul> <p><b>The easy, practical guide to R</b> <p>R is powerful, free software for statistical analysis—full of many tools and functions. R makes it possible to carry out complex statistical analyses by simply entering a few commands. This practical, step-by-step guide explains foundational statistical concepts and shows you how to implement them. You'll be able to perform analyses, understand their implications and results, and make them available to a wide audience. <p><b>Inside…</b> <ul> <li>How data is evaluated</li> <li>Installing and setting up R</li> <li>Using R for graphing</li> <li>All about variations and deviations</li> <li>How to draw conclusions</li> <li>Working with regression</li> <li>Using non-parametric statistics</li> <li>Probability and distributions</li> </ul>

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