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Statistical Models in S, by J. M. Chambers, T.J. Hastie
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Statistical Models in S extends the S language to fit and analyze a variety of statistical models, including analysis of variance, generalized linear models, additive models, local regression, and tree-based models. The contributions of the ten authors-most of whom work in the statistics research department at AT&T Bell Laboratories-represent results of research in both the computational and statistical aspects of modeling data.
- Sales Rank: #2615238 in Books
- Published on: 1991-10-01
- Original language: English
- Number of items: 1
- Dimensions: 9.50" h x 6.25" w x 1.25" l,
- Binding: Paperback
- 608 pages
Most helpful customer reviews
1 of 1 people found the following review helpful.
Modelling data with the S (or R) language
By R. J. V. Jackson
This describes the panoply of programs available in the
R language for the exploration of data. One needs some
familiarity with statistics, but it is amazing to see what the
crew at AT&T has done with their "language."
0 of 0 people found the following review helpful.
Good and useful.
By MR PIERRE JEAN
Very good, very useful, with nice examples. I recommand it.
18 of 18 people found the following review helpful.
Simply the Best (for those who want to know what they're doing)
By wjd
If you really want to know what you're doing when you use S, buy this book. Don't waste your money on a book like Venables and Ripley -- you will be sorely dissappointed, unless you just want a large collections of example calls to canned S routines. The authors of the present book, on the other hand, are Chambers and Hastie of AT&T (where S was invented), and they clearly understand the importance of detailed explanations of the theory underlying the S functions they describe. Just as important, in my opinion, they also describe the algorithms used by these functions. These two components are missing from other books (like the popular Venables and Ripley) but they are critical in order to know -- and be able to explain and justify to others -- how and why your statistical analyses were performed and what the results really mean. The other way of doing statistics (i.e. throwing canned procedures at your data and seeing what pretty graphs and figures you can produce) is meaningless.
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