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Modern Applied Statistics with S (Statistics and Computing) 4th Edition

4.2 out of 5 stars 21 customer reviews
ISBN-13: 978-0387954578
ISBN-10: 0387954570
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Editorial Reviews

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From the Back Cover

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Product Details

  • Series: Statistics and Computing
  • Hardcover: 498 pages
  • Publisher: Springer; 4th edition (September 2, 2003)
  • Language: English
  • ISBN-10: 0387954570
  • ISBN-13: 978-0387954578
  • Product Dimensions: 6.1 x 1.1 x 9.2 inches
  • Shipping Weight: 1.9 pounds (View shipping rates and policies)
  • Average Customer Review: 4.2 out of 5 stars  See all reviews (21 customer reviews)
  • Amazon Best Sellers Rank: #467,972 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

Format: Hardcover
A worthy update. The authors are matter-of-fact and straightforward. I appreciate their terse style, the broad coverage, and the many examples. It's also good that they're starting to split programming material off into its own monograph (S Programming), making this book all the more appropriate for learning, although not quite beginning, statisticians. Their considerable contribution in software is also very much appreciated.
I do not agree at all with the reviewer who chided them for including R; I say so much the better for it. I very much hope that they will continue to do so.
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Format: Hardcover Verified Purchase
Another reviewer wrote "I suspect most practicioners use S+". He should have been at the UserR! 2004 conference in Vienna this past March, with 500 or so enthusiastic R users including many from big industry (financial, pharmaceutical). And Ripley is the number-one contributor to the R Help mailing list by a long way. So it is completely appropriate that R is so prominant. Many of us appreciate open source not only for its cost ($0) but also its transparency. The reviewer should take another look at R.

As for the book, it is my data anlysis bible. It gets me started in a correct direction, with very well-explained and worked out examples, which I then adapt to my own datasets. The writing couldn't be clearer, and the references to primary sources as well as non-computational statistics texts I have found to be excellent. This is the one book to own if you are more than a beginner.
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Format: Hardcover Verified Purchase
The "search inside this book" feature was not available when this review was posted. Hope it helps.

CONTENTS

Introduction

Data Manipulation

The S Language

Graphics

Univariate Statistics

Linear Statistical Models

Generalized Linear Models

Non-Linear and Smooth Regression

Tree-Based Methods

Random and Mixed Effects

Exploratory Multivariate Analysis

Classification

Survival Analysis

Time Series Analysis

Spatial Statistics

Optimization

Implementation-Specific Details

The S-PLUS GUI

Datasets, Software and Libraries

References

Index
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Format: Kindle Edition Verified Purchase
The printed book (which I own) deserves 5 stars as a comprehensive introduction to the S/R language and statistics.

The one star concerns explicitly the Kindle edition. In short: The rendering of formula and even (plain text!) S/R code is a scandal.

Formula and S/R code are included as bad resolution pictures and NOT rendered professionally in HTML5. They appear in pale grey and cannot be zoomed!

Springer has demonstrated with another Kindle book I purchased (Bapat RB: Linear Algebra and Linear Models) that they are capable of producing a professional Kindle version.

I purchased the Kindle edition in addition to the printed one, just to have it around when I need to look things up.

The same issue is valid for the Springer Kindle versions of:
Dalgaard P: Introductory Statistics with R
Zuur AF et al: Mixed Effects Models and Extensions in Ecology with R

I will repeat this statement in reviews for the above mentioned books.

When I have found out how to contact costumer service at Springer I will formally demand a refund or an update.

Do not buy the Kindle edition.
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Format: Hardcover
This text is very popular and frequently cited in the statistics literature. The authors do an outstanding job of displaying modern statistical methods through the S programming language. It is an intermediate level book and certainly worthy of 5 stars.
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Format: Hardcover Verified Purchase
This is *the* book to have on S+/R. It provides excellent value for its price (indeed, any price): it is concise, broad, informative. All the same, I think it would be useful to identify intended audience for this book (in my view). First, the book is not for novices in Statistics. You'll learn how to fit generalized linear models in the language, not how and why to apply such models properly. To this end, there are plenty of specific monographies, and the majority of them use R for examples. Just to name a few, Friedman, Hastie, Tibshirani, Harrell, Faraway employ R. Also, this book assume some basic knowledge of programming. R is a more elegant language than Matlab and Thinking in R becomes very natural after some practice. But I have not seen so far a tutorial on "R as a first language". Summing up, this is a great book for undergraduates in Statistics/Engineering and up, who want a comprehensive, usable reference. My only criticism is that since 2002 there have been giant changes in the language. First, R is now the main implementation of the language, with S+ being an industry-supported variant. Second, the S4 object model is here to stay and grow, and is crying for a user-friendly introduction. Lastly, the number of packages is probably twenty times what it was in 2002. SVMs, ensemble methods, shrinkage, sparse representations *are* modern applied statistics, and are underrepresented in the book.

Still, this is a must have for any applied statistician.
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