Introductory Statistics with R (Statistics and Computing) (Paperback)

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Editorial Reviews

Review

From the reviews:

"This book is a thorough introduction on how to do basic data analysis using R. The new material consists mainly of new chapters on advanced data handling, Poisson regression and nonlinear regression…. It is often claimed that it is difficult to start using R but the author does in my opinion a great job in smoothing the way to make it as easy as possible. I can therefore recommend this book to anyone who wants to learn how to use R for practical data analysis. Furthermore the book can also be easily used as a basis for a course on this topic." (Klauss Nordhausen, International Statistical Review, 2009, 77, 1)

The second edition of a successful book targeting the acclaimed free statistical environment R.... The new edition of this book comes with additional chapters covering advanced data manipulation, Poisson regression and non-linear curve fitting....The book present the information in a very straight forward manner: Each chapter starts introducing a minimum of theoretical background on the topic discussed, and then proceeds to explain the commands in R that are related to the topic. An interesting additional aspect is that common mistakes often carried out by non-expert users when starting to use R are carefully explained, as well as ways of avoiding them.

The audience…is made up of students that already have a good grasp of introductory statistics and need to learn how to apply them in practice using R. In summary, graduate students of any discipline looking for learning the currently most praised free system for statistics will find this book very appealing, but undergraduate students attending to their first course in introductory statistics will not.

Product Description

R is an Open Source implementation of the S language. It works on multiple computing platforms and can be freely downloaded. R is now in widespread use for teaching at many levels as well as for practical data analysis and methodological development.

This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. A supplementary R package can be downloaded and contains the data sets.

The statistical methodology includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one- and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last six chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, survival analysis, Poisson regression, and nonlinear regression.

In the second edition, the text and code have been updated to R version 2.6.2. The last two methodological chapters are new, as is a chapter on advanced data handling. The introductory chapter has been extended and reorganized as two chapters. Exercises have been revised and answers are now provided in an Appendix.

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Product Details

• Paperback: 364 pages
• Publisher: Springer; 2nd edition (August 15, 2008)
• Language: English
• ISBN-10: 0387790535
• ISBN-13: 978-0387790534
• Product Dimensions: 9.1 x 6.1 x 0.8 inches
• Shipping Weight: 1.2 pounds (View shipping rates and policies)
• Average Customer Review: 4.3 out of 5 stars  See all reviews (24 customer reviews)
• Amazon.com Sales Rank: #5,980 in Books (See Bestsellers in Books)

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Customer Reviews

Most Helpful Customer Reviews

60 of 60 people found the following review helpful:

5.0 out of 5 stars A good book where there are few, September 4, 2002

By	Roger Peng (Baltimore, MD USA) - See all my reviews (REAL NAME)

This review is from: Introductory Statistics with R (Paperback)

Introductory Statistics with R is an important book for a rapidly developing field. R is an extremely powerful statistical computing environment which suffers from the same problem as almost every other free software project -- a lack of quality documentation. Dalgaard fills a major gap with this book, that is, a guide to using R for many standard statistical problems.

For some time now, users have had to make do with S-PLUS books which contained some overlap with R. Now R users have a book they can call their own. After briefly discussing the R system and the language basics, Dalgaard goes through what might be covered in an advanced undergraduate data analysis course. Throughout the book, code examples and output are carefully interspersed so that the reader doesn't go too long without having a concrete example.

Dalgaard leaves out some advanced topics such as time series, spatial statistics, etc. (some of which are nicely covered in Modern Applied Statistics with S by Venables and Ripley) but that is probably for the best. The book is not bloated, nicely priced and I would recommend it to any advanced undergrad or first year grad student wanting to learn how to do statistical analysis in R.

29 of 30 people found the following review helpful:

5.0 out of 5 stars Twofold win: great introduction to useful statistics and R programming, August 28, 2005

By	Isaac S. Kohane "zakthehack" (Newton, MA USA) - See all my reviews (REAL NAME)

This review is from: Introductory Statistics with R (Paperback)

Despite the web, there are learning curves sufficiently steep that a well-organized book is the most effective introduction. However, too many of these introductions, particularly in programming and/or statistics are written with low content and high redundancy or with impenetrably high-density content. So, it is a rare sign of pedagogical mastery combined with the genuine confidence of the experienced practioner when an introductory book manages to achieve a balance that is just right.

As I become more familiar with R, I still carry around this book in my briefcase for the occasional reread during which I uncover a nugget I had missed. When I have told this to my colleagues in computer science or bioinformatics, they immediately reveal that they share my enthusiasm for Dalgaard's work.

Let's be clear: this is a book that walks you through introductory and highly useful statistics while introducing you to the most effective ways to use R to perform these biostatistical analyses. It is not a programming book, nor is that its intent.

43 of 47 people found the following review helpful:

5.0 out of 5 stars Very readable introduction, April 24, 2003

By	Alan Mead (Lockport, IL United States) - See all my reviews (REAL NAME)

This review is from: Introductory Statistics with R (Paperback)

This book provides a very readable introduction to basic statistical analysis using R (with occational references to S-Plus). The table of contents displays the topics and I thought they were generally well covered in enough detail to compute the statistics (but this is not a statistics text). Especially helpful are the additional analysis steps, such as graphing results, and the peripheral R issues. Small things I would change: expanded coverage of manipulating data (e.g., SPSS's RECODE, TEMPORARY, MERGE FILE,...), more explicit instructions on installing the example data (it's at the end of the installation Appendix), discussion of interactions in ANOVA and regression, discussion of ANCOVA, and finally I would have liked a quick overview of the available packages and the stats they provide. But these are small issues; it's a great book.