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

Michael J. Crawley (Author)

Book Description

Publication Date: May 15, 2002 | ISBN-10: 0471560405 | ISBN-13: 978-0471560401 | Edition: 1

Many statistical modelling and data analysis techniques can be difficult to grasp and apply, and it is often necessary to use computer software to aid the implementation of large data sets and to obtain useful results. S-Plus is recognised as one of the most powerful and flexible statistical software packages, and it enables the user to apply a number of statistical methods, ranging from simple regression to time series or multivariate analysis. This text offers extensive coverage of many basic and more advanced statistical methods, concentrating on graphical inspection, and features step-by-step instructions to help the non-statistician to understand fully the methodology.
* Extensive coverage of basic, intermediate and advanced statistical methods
* Uses S-Plus, which is recognised globally as one of the most powerful and flexible statistical software packages
* Emphasis is on graphical data inspection, parameter estimation and model criticism
* Features hundreds of worked examples to illustrate the techniques described
* Accessible to scientists from a large number of disciplines with minimal statistical knowledge
* Written by a leading figure in the field, who runs a number of successful international short courses
* Accompanied by a Web site featuring worked examples, data sets, exercises and solutions
A valuable reference resource for researchers, professionals, lecturers and students from statistics, the life sciences, medicine, engineering, economics and the social sciences.

Editorial Reviews

Review

"...suitable as a reference book for experienced statisticians, a vehicle for learning the S statistical computing language, or a resource for statistics instructors..." (The American Statistician, Vol. 58, No. 1, February 2004)

"...especially useful as an introduction to a wide variety of data analysis techniques." (R News)

"...The book is well written - there is an air of common sense throughout - and is at a level which ensures its usefulness for a wide range of readers..." (Zentralblatt Math, Vol. 1001, No.01, 2003)

"...the book is a useful and practical introduction to many areas of statistical data analysis." (Computational STatistics & Data Analysis)

"...surely not the last statistics book you’ll ever need, but it might well be the first you will ever really use." (Basic Applied Ecology, Vol. 4, No. 3)

"...recommended...contains a wealth of sage advice..." (Technometrics, Vol. 45, No. 4, November 2003)

“...a practical introduction to statistics...does not cover all...sophisticated statistical and graphical features of the S-Plus system, but provides a first class starting point—and, probably, for most readers, a sufficient end point.” (Quarterly of Applied Mathematics, LXI, No. 4, December 2003)

“…a valiant and useful first attempt to present both statistics and S-PLUS together…” (Journal of The Royal Statistical Society Vol.167 No.4)   

From the Back Cover

S-Plus is a first-rate graphical environment, used by thousands worldwide to perform basic, intermediate and advanced statistical analysis. It is remarkably powerful, yet relatively simple to use, once you have the basics at your fingertips. Statistical Computing: An Introduction to Data Analysis using S-Plus provides a pragmatic introduction to analysing data using S-Plus, whilst covering a huge breadth of topics, and assuming minimal statistical knowledge.
* Provides an accessible yet comprehensive introduction to statistical computing, and can be used as a reference volume for S-Plus.

* Covers a breadth of topics, including the basics, such as sampling and measures of central tendency and variation; the intermediate, such as analysis of variance and regression; and the most advanced modern methods, such as nonlinear mixed effects modelling and tree models.

* Develops each concept from first principles in small steps, with worked examples and implementation advice throughout.

* Assumes minimal experience of statistics and computing.

* Emphasises graphical data inspection, parameter estimation and model criticism.

* Supported by a Web site featuring all the data-frames, along with problems and worked examples.
This is very much an introductory statistics book for all scientists. It is based on the premise that effective data analysis requires the mastery of a core of central ideas and methods, and that these cut across the boundaries of academic disciplines. It is suitable for advanced undergraduate, graduate students, researchers, and industry professionals from science, medicine, engineering, economics, the social sciences, and many other disciplines that have a need for statistical data analysis.

Product Details

• Hardcover: 772 pages
• Publisher: Wiley; 1 edition (May 15, 2002)
• Language: English
• ISBN-10: 0471560405
• ISBN-13: 978-0471560401
• Product Dimensions: 7.7 x 2 x 9.9 inches
• Shipping Weight: 4 pounds (View shipping rates and policies)
• Average Customer Review: 4.3 out of 5 stars  See all reviews (3 customer reviews)
• Amazon Best Sellers Rank: #1,237,848 in Books (See Top 100 in Books)

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Most Helpful Customer Reviews

11 of 12 people found the following review helpful:

5.0 out of 5 stars Teachers, students, and researchers - stop here!, June 13, 2004

By 

"pansophy" (New York, NY USA) - See all my reviews

This review is from: Statistical Computing: An Introduction to Data Analysis using S-Plus (Hardcover)

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.

7 of 8 people found the following review helpful:

3.0 out of 5 stars Ambitious, but doesn't fully meet its ambitions, June 1, 2005

By 

Peter Flom (New York City) - See all my reviews
(REAL NAME)   

This review is from: Statistical Computing: An Introduction to Data Analysis using S-Plus (Hardcover)

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.

5.0 out of 5 stars The best, October 24, 2010

By 

GenghisKhan - See all my reviews

This review is from: Statistical Computing: An Introduction to Data Analysis using S-Plus (Hardcover)

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.