Using R for Introductory Statistics, Second Edition

The second edition of a bestselling textbook, Using R for Introductory Statistics guides students through the basics of R, helping them overcome the sometimes steep learning curve. The author does this by breaking the material down into small, task-oriented steps. The second edition maintains the features that made the first edition so popular, while updating data, examples, and changes to R in line with the current version. See What’s New in the Second Edition: Increased emphasis on more idiomatic R provides a grounding in the functionality of base R. Discussions of the use of RStudio helps new R users avoid as many pitfalls as possible. Use of knitr package makes code easier to read and therefore easier to reason about. Additional information on computer-intensive approaches motivates the traditional approach. Updated examples and data make the information current and topical. The book has an accompanying package, UsingR, available from CRAN, R’s repository of user-contributed packages. The package contains the data sets mentioned in the text (data(package="UsingR")), answers to selected problems (answers()), a few demonstrations (demo()), the errata (errata()), and sample code from the text. The topics of this text line up closely with traditional teaching progression; however, the book also highlights computer-intensive approaches to motivate the more traditional approach. The authors emphasize realistic data and examples and rely on visualization techniques to gather insight. They introduce statistics and R seamlessly, giving students the tools they need to use R and the information they need to navigate the sometimes complex world of statistical computing. Front Cover 1 Contents 9 Preface 17 Chapter 1 - Getting started 21 Chapter 2 - Univariate data 40 Chapter 3 - Bivariate data 108 Chapter 4 - Multivariate data 170 Chapter 5 - Multivariate graphics 209 Chapter 6 - Populations 231 Chapter 7 - Statistical inference 262 Chapter 8 - Confidence intervals 282 Chapter 9 - Significance tests 314 Chapter 10 - Goodness of fit 354 Chapter 11 - Linear regression 384 Chapter 12 - Analysis of variance 424 Chapter 13 - Extensions of the linear model 460 Appendix A - Programming 478 Bibliography 509 Back Cover 515