Using R for introductory statistics
著者
書誌事項
Using R for introductory statistics
（The R series）
CRC Press, c2014
2nd ed
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注記
"A Chapman & Hall Book" -- on t.p.
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
Data. Univariate Data. Bivariate Data. Multivariate Data. Describing Populations. Simulation. Confidence Intervals. Significance Tests. Goodness of Fit. Linear Regression. Analysis of Variance. Two Extensions of the Linear Model. Appendices. Index.
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