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.

Product Details

• Amazon Sales Rank: #42793 in Books
• Published on: 2008-09-01
• Original language: English
• Number of items: 1
• Binding: Paperback
• 364 pages

About the Author
Peter Dalgaard is associate professor at the Biostatistical Department at the University of Copenhagen and has extensive experience in teaching within the PhD curriculum at the Faculty of Health Sciences. He was chairman of the Danish Society for Theoretical Statistics from 1996 to 2000. Peter Dalgaard has been a key member of the R Core Team since August 1997 and is well known among R users for his activity on

Customer Reviews

Learning to use R to help you with statistics
Having spent quite a bit of time using the commercial SPSS package over the last two years as a student, I was interested in other computing packages that would allow me to go a little further especially with using multivariate statistics.
I found several more specialist packages including LISREL for dealing with Structural Equation Modelling before running across R on the net. The R Statistical project homepage has access to lots of information including how to obtain this GPL product including references. I initially found the excellent "Modern Applied Statistics with S" 4th ed by Venables & Ripley.
However I found that this assumed that you were already comfortable with using R and sought out a book that provided assistance to a relative newcomer and this book certainly fits into that slot. It presents information about how to use R for your situation steadily and in a very accessible manner. The exercises at the end of each chapter provide a useful refresher mechanism for each topic. The overall layout of the book allows a user to quickly dip into a variety of topics to get a feel for the areas they are interested in and start experimenting with their own data and presentations.
Well worth having this book beside you while introducing yourself to this statistical programming language.

Well worth the money
R is the future of statistical computation, and can be intimidating to students who encounter it for the first time. Dalgaard writes very clearly, beginning with basic statistics at the same time as basic R. There is no reason why R should not be introduced in a first course, even though it is a tool the statistician will never outgrow. This book is affordable, small enough to carry around, and quite well written. All the examples can be run by the student, using the add-on library written by Dalgaard.

Not too great for R noobs, but great for pros
One of my modules at uni is R programming. This book was the recommended text, so i brought it. To say i was a little disapointed was an understatment. This book is definatly aimed at people with advanced statistical knowledge. The book uses an overly complex language that me and many other students had trouble understanding. It presumes that the reader knows too much, and because of this it shows you how to do something statistical through R, but then doesn't actually explain the purpose of the statistical method. This can be quite annoying. That said, this book would be great if you actually have a very firm grasp of statistics and want to know how to use R to apply this statistical knowledge.