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Using R for Introductory Statistics

✍ Scribed by John Verzani


Publisher
CRC Press
Year
2014
Tongue
English
Leaves
515
Category
Library

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✦ Synopsis


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.

✦ Table of Contents


Front Cover
Contents
Preface
Chapter 1 - Getting started
Chapter 2 - Univariate data
Chapter 3 - Bivariate data
Chapter 4 - Multivariate data
Chapter 5 - Multivariate graphics
Chapter 6 - Populations
Chapter 7 - Statistical inference
Chapter 8 - Confidence intervals
Chapter 9 - Significance tests
Chapter 10 - Goodness of fit
Chapter 11 - Linear regression
Chapter 12 - Analysis of variance
Chapter 13 - Extensions of the linear model
Appendix A - Programming
Bibliography
Back Cover


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