Customer Reviews

Statistical Computing: An Introduction to Data Analysis using S-Plus

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This is far and away the best statistics book I have found for the non-statistician. As a researcher and introductory behavioral statistics teacher this book has proven invaluable to me.

Crawley explains the connecting ideas that foster understanding that so many others seem to omit. Teaching statistics graphically rather than formulaically using the free student version of s-plus, my students come away understanding the foundations of statistics including measures of central tendency, probability distributions, regression, and ANOVA.

The interesting thing is that when I need an introduction to survival analysis or non-linear regression, I'm able to pick up the same book. Crawley explains many more advanced analyses with the same care and thoroughness that he does the basics including log-linear analysis, mixed-effects models, Generalised linear models, and time series analysis to name but a few.

I originally picked up this book to learn how to conduct analyses using s-plus but what I found was so much more.

To those of you that are really only interested in learning s-plus this book does that elegantly as well and I prefer it to Venables and Ripley (Modern Applied Statistics with S). I suspect that statisticians will prefer the later title but if you are like me and want some help understanding the analysis and not just how to do it in s-plus then stick with this title.

Mathematical level: Moderate

For the right audience, this is a very good book. The problem is finding the right audience. The book attempts to cover a lot of ground; in a typical graduate program in the social sciences, it would be at least 3 semesters worth, maybe more. But, if you've had the statistics, and need to learn S-Plus, this book offers a lot of good hints. The problem is that it doesn't teach S-Plus as give a lot of interesting examples, so you would also need an S-Plus book. Therefore, the right audience is someone who knows some statistics and some S-Plus, but wants to get some hints for both.

I own something like 30 statistics and modeling books, accumulated over the years in classes and working on projects, and this is by far the best of them. I am a biologist with a decent background in statistics/modeling and a good knowledge of S-Plus (earned the hard way), but I am NOT a true statistician or mathematician. Crawley's descriptions are wonderfully lucid, written in ENGLISH rather than mathematical jargon, and his analyses are thoughtful and interesting. I learn as much from watching the way that he approaches a problem as I do from his explanations, and he gives you code so that you can try out and modify the examples. S-Plus and R are nearly identical, so although the book was written for S-Plus, it is equally useful in R. The book covers nearly every topic that a scientist could conceivably need for data analysis, to a degree of sophistication that will be adequate for 99% of its readers. For anything more, you are probably going to need to see a statistician anyway. The book's website includes 3 additional chapters (on gamma errors, additive models, and multivariate statistics).

I use this book in two ways: 1) as a valuable reference/cookbook for things I haven't tried, and 2) to remember, or to teach myself for the first time, how statistical tests work. In case after case, Crawley goes out of his way to show how and why statistical tests are calculated the way they are. S is ideally suited for this, since it makes the math painless and hides it behind nice graphical output, and lets you concentrate on understanding the concepts. If you are a student or professional who uses statistics and R or S-Plus, I can't recommend it highly enough, especially if you are someone who doesn't naturally think in mathematical symbols, or if you are more interested in learning how to do something and in understanding why it works, than in reading proofs or doing the underlying algebra.