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Introductory Statistics with R (Statistics and Computing) Paperback – August 15, 2008

17 customer reviews
ISBN-13: 978-0387790534 ISBN-10: 9780387790534 Edition: 2nd

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Editorial Reviews

Review

From the reviews:

TECHNOMETRICS

"…extensive, well organized, and well documented…The book is an elegant R companion, suitable for the statistically initiated who want to program their own analyses. For experienced statisticians and data analysts, the book provides a good overview of the basic statistical analysis capabilities of R and presumably prepares readers for later migration to S…The format of this compact book is attractive…The book makes excellent use of fonts and intersperses graphics near the codes that produced them. Output from each procedure is dissected line by line to link R code with the computed result…I can recommend [this book] to its target audience. The author provides an excellent overview of R. I found the wealth of clear examples educational and a practical way to preview both R and S."

"The scope of the book, introductory statistics, is a very useful set of methods in parametric and non-parametric statistics up to logistic regression and survival analysis. … Where many constructs in R are very attractive for mathematical oriented users, e.g. matrices, Dalgaard succeeded in convincing me that with little extra effort they can be made very useful to less mathematically oriented people, e.g. by specifying row and column names, and proposing quite attractive ways to specify for example ‘subsets’ of rows and columns." (Dr. H. W. M. Hendriks, Kwantitatieve Methoden, Vol. 72B8, 2003)

"R is an Open Source implementation of the well-known S language. It works on multiple computing platforms and can be freely downloaded. R is thus ideally suited 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. … Brief sections introduce the statistical methods before they are used. A supplementary R package can be downloaded and contains the data sets." (Zentralblatt für Didaktik der Mathematik, August, 2004)

"This is a nice book on statistical methods and statistical computing in R, a language and environment for statistical computing and graphs: this dialect of the S language is available as free software through internet. … Explanation of statistical methods, together with an interpretation of statistical concepts, is the prevailing style of the text. They are illustrated by plenty of practical examples, all computed using R. This book will be useful for novices in applied statistics or in computing in R." (European Mathematical Society Newsletter, September, 2003)

"The book is an elegant R companion, suitable for the statistically initiated who want to program their own analyses. For experienced statisticians and data analysts, the book provides a good overview of the basic statistical analysis capabilities of R … prepares readers for later migration to S. … I can recommend Introductory Statistics With R to its target audience. The author provides an excellent overview of R. I found the wealth of clear examples educational and a practical way to preview both R and S." (Thomas D. Sandry, Technometrics, Vol. 45 (3), 2003)

"R is both a statistical computer environment and a programming language designed to perform statistical analysis and to produce adequate corresponding graphics. … The present book is … a very useful guide for introducing a number of basic concepts and techniques necessary to practical statistics, covering both elementary statistics and actual programming in the R language. The book is organized in 12 chapters and three appendices, each chapter ending with a beneficial section of proposed exercises." (Silvia Curteanu, Zentralblatt MATH, Vol. 1006, 2003)

From the reviews of the second edition:

“This review … roughly cover the introductory topics of a first year statistics course. The Introductory Statistics with R (ISwR) book is targeted for a biometric/medical audience. It covers more topics … like multiple regression and survival analysis and expects the reader to know about basic statistics. … include examples and graphs together with the R code to construct them. … The ISwR book is good for an academic and biometric audience.” (Wolfgang Polasek, Statistical Papers, Vol. 52, 2011)

“This is a welcome addition to the new edition that will be appreciated by its users. … The new edition is well written, and the new materials are well incorporated. Like the first edition, this edition will continue to be useful to the target audience, and I can safely recommend it to them.” (Technometrics, Vol. 51 (2), May, 2009)

From the Back Cover

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.

Peter Dalgaard is associate professor at the Department of Biostatistics at the University of Copenhagen and has extensive experience in teaching within the PhD curriculum at the Faculty of Health Sciences. He has been a member of the R Core Team since 1997.

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Product Details

  • Series: Statistics and Computing
  • Paperback: 364 pages
  • Publisher: Springer; 2nd edition (August 15, 2008)
  • Language: English
  • ISBN-10: 9780387790534
  • ISBN-13: 978-0387790534
  • ASIN: 0387790535
  • Product Dimensions: 6.1 x 0.9 x 9.2 inches
  • Shipping Weight: 1.5 pounds (View shipping rates and policies)
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (17 customer reviews)
  • Amazon Best Sellers Rank: #49,299 in Books (See Top 100 in Books)

Customer Reviews

Most Helpful Customer Reviews

20 of 20 people found the following review helpful By S. Fann on September 28, 2011
Format: Paperback Verified Purchase
R is a useful freeware that can represent a hurdle to students and/or professionals who do not have formal training in computer programming. This book helps to clear those hurdles, and introduces a solid foundation from which statistics users can build new tools for their specific analyses. The rest of this review is broken up for experienced and new users.

****If you do not have a solid foundation in statistics, this book is not going to help you bridge that gap. Although the title is "Introductory Statistics with R" the author is clear that this is a book to learn how to program intro stats with R, and is not designed to teach any statistics tools. The author assumes you understand statistics and does not clarify statistics terms like p-value, test statistic, degrees of freedom, ANOVA, and the like. ****

New to R:
Although it may sound like a conundrum, the only way to learn a program is to program. Thankfully learning R can be easy, since the program is free, installs well on nearly all machines, and has detailed help files in various languages around the world. This is an excellent book for the R beginner, but I must stress the importance of ACTUALLY PROGRAMMING while you read this book. You CAN NOT read this book cover to cover and expect to learn R, programming doesn't work that way. This book can be a great resource for people who are brand new to R, but it requires hands on utilization of the source codes provided. Thankfully, this step is made that much easier for new users with a detailed explanation of how to obtain the ISWR package used with this text. Like everything in R, packages are free, and contain suites of functions and sometimes data.
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24 of 25 people found the following review helpful By Love J on December 18, 2008
Format: Paperback
This is a great intro-intermediate R book for intro stats.
I recommend you to read "Using R for Introductory Statistics" by John Verzani first, if you know general things about stats.
Using R is written in more step-by-step way and there are a lot of repetitions that helped you learn R language by merely reading through the book.
After you finish Using R, proceed to Dalgaard's book.
As one of the reviewer said, Dalgaard's book can be a concise reference book since it covers more stuff than Using R does.
It is a nice, compact book on many techniques, but it sometimes lacks suffice explanations.
This is why Using R should come first and Dalgaard's book comes next.
If you finish these books, you are ready to explore other R and S-Plus books as you need.
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6 of 6 people found the following review helpful By J. Felipe Ortega Soto on March 22, 2012
Format: Paperback Verified Purchase
I have prepared and delivered introductory courses and workshops on statistics and R for the past 3 years. As part of this work, I have reviewed more than a dozen different introductory R books. This is one of my favourite choices (if not my top one). Pete Dalgaard has been a member of the R Core Team since 1997, being a very active and knowledgeable expert on statistics with R. This quickly becomes apparent in the book, since you will find many tricks and smart procedures to accomplish many R tasks, most notably in the data preparation stage (where you spend 70-80% of all working time).

A previous requirement is to acquire basic knowledge on the statistical tools and techniques presented throughout the book. This volume is focused on performing statistical analyses with R, not offering a complete introductory statistics course. However, each chapter starts with a very useful recap of foundations and theory details for the statistical methods and tools presented in it. You can also find good references for further reading.

Summarizing the main positive points:

* Very clear explanations. The writing style is direct, informative, easy-to-follow.

* Content organization is very clear. Every chapter has been conceived as an independent unit that you can read separately (except for the first introductory chapters to R syntax and routinary operations). Thus, you can either read it cover to cover or just jump directly into the chapter or section of your interest (as a reference).

* There is an accompaning R package 'ISwR', that can be found in CRAN (as usual). It includes all datasets and utility functions presented in the text. This is a must to speed up practical sessions using this text as a reference, as well as for self-study.
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30 of 42 people found the following review helpful By kmir on May 11, 2009
Format: Paperback Verified Purchase
While most reviews on the book are positive, I would like to warn the potential buyer that this book *cannot* provide more than a very first quick look for someone who is totally new to R and wants to play around a little.

Here is the good stuff:

All the example code I tried (I am almost done with the entire book) works. Browsing this book and trying out the code exemplified is a no-brainer and completely frustration free.

Here is the not so good stuff:

If you seriously want to a) learn something about R or b) use a book to help you through serious analyses of your data, you should look somewhere else.
Regarding a), the R-intro file that comes with the R base installation contains more information on R as a programming language, how data are read by R (categorical variables as factors, e.g.) and how models can be specified. I hope that other books out there will provide even more information, but this R-intro is not a bad place to start. If you want to do b), this book will help you load your data into R (and even that is a little limited, see other reviews) and maybe create the most standard first analysis (but who is ever interested in that?) Not to mention the missing (and sometimes incorrect! - see page 119 on standard error of the mean confidence intervals) information on statistical procedures, you will need to look somewhere else to calculate basic statistics such as, e.g., partial or semi-partial correlations.

All in all I decided to give the book 3 stars based on its superficial user friendliness. You will not be able to really learn from this book, however.
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