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Linear Models with R (Chapman & Hall/CRC Texts in Statistical Science) Hardcover – July 26, 2004

ISBN-13: 978-1584884255 ISBN-10: 1584884258 Edition: 1st

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Frequently Bought Together

Linear Models with R (Chapman & Hall/CRC Texts in Statistical Science) + Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models (Chapman & Hall/CRC Texts in Statistical Science)
Price for both: $175.65

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Product Details

  • Series: Chapman & Hall/CRC Texts in Statistical Science (Book 63)
  • Hardcover: 240 pages
  • Publisher: Chapman and Hall/CRC; 1 edition (July 26, 2004)
  • Language: English
  • ISBN-10: 1584884258
  • ISBN-13: 978-1584884255
  • Product Dimensions: 9.4 x 6.4 x 0.7 inches
  • Shipping Weight: 1 pounds (View shipping rates and policies)
  • Average Customer Review: 4.1 out of 5 stars  See all reviews (14 customer reviews)
  • Amazon Best Sellers Rank: #193,594 in Books (See Top 100 in Books)

Editorial Reviews

Review

One danger with applied books such as this is that they become recipe lists of the kind 'press this key to get that result.' This is not so with Faraway's book. Throughout, it gives plenty of insight on what is going on, with comments that even the seasoned practitioner will appreciate. Interspersed with R code and the output that it produces one can find many little gems of what I think is sound statistical advice, well epitomized with the examples chosen…I read it with delight and think that the same will be true with anyone who is engaged in the use or teaching of linear models…I find this book a valuable buy for anyone who is involved with R and linear models, and it is essential in any university library where those topics are taught.
-Journal of the Royal Statistical Society

Overall, Linear Models with R is well written and, given the increasing popularity of R, it is an important contribution.
-Technometrics, Vol. 47, No. 3, August 2005

There are many books on regression and analysis of variance on the market, but this one is unique and has a novel approach to these statistical methods. The author uses R throughout the text to teach data analysis…The text also contains a wealth of references for the reader to pursue on related issues. This book is recommended for all who wish to use R for statistical investigations.
-Short Book Reviews of the International Statistical
Institute

The book is very comprehensibly written and can therefore be recommended for beginners in linear models. It is clearly and simply explained how to use R and which packages are necessary to analyze linear models. …All in all, this book is recommendable as a textbook for computational linear regression courses and therefore for students and lecturers, but also for applied statisticians who want to get started on regression analysis using the software R.
-Biometrics

Dr Faraway uses many examples and graphical procedures to illustrate the methods. This is a great strength of the book. …Linear Models with R is one of several books appearing to make R more accessible by bringing together functions from a number of packages and illustrating their use. From this perspective alone it is an important contribution. …I feel this book does a nice job of describing the methods available in linear modeling and illustrating the realistic implementation of these methods in a careful data analysis.
-Statistics in Medicine, 2006

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Customer Reviews

4.1 out of 5 stars
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Very clear, very concise.
John Student
If you are smart, like to think and explore data using R/Splus, or have a good mentor, this book fits you well.
Kenny
I recommend this book as a brief guide to learning stats.
Madison Hansen

Most Helpful Customer Reviews

24 of 27 people found the following review helpful By Kenny on June 22, 2005
Format: Hardcover Verified Purchase
I took Prof. Faraway's course 10 years ago in which we used a very early version of this book. I am glad to see that this book is finally in print. For years I have been using Prof. Faraway's notes teaching a graduate course on regression. What I like this book most is its emphasis on practical usage and pitfalls of linear models. This is how linear regression and statistical modeling should be taught but I am not aware of any other textbooks doing the same thing.

This book, just as its author, is thin, clean and concise. It does not always explain and reveal things in full detail. If you are smart, like to think and explore data using R/Splus, or have a good mentor, this book fits you well. Its discussion on principle component analysis, in my opinion, is a little bit weak.
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10 of 11 people found the following review helpful By LostInTokyo on January 29, 2009
Format: Hardcover
This volume is the best hands-on R book I could find which opens the door to lm() in R. The book is thin and the contents somewhat dense - there is no room for hand-holding: you need to learn the basics of R and statistical modeling elsewhere. But if you meet the prerequisites, buy the book, read it, and most importantly, TRY THE EXERCISES!

GOOD POINTS
- exercises (deserves 5+ stars for learning concepts with real data)
- short chapters, so you can quickly test your understanding via exercise
- chock full of R examples that you can try with library( faraway )

BAD POINTS
- proofs are not rigorous enough for mathematicians, but too dense for practitioners (who would prefer more intuition)
- helps if you have played with R a bit and understand basic statistics
- no answers to exercises
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8 of 10 people found the following review helpful By David on March 23, 2007
Format: Hardcover Verified Purchase
The two things you typically want in a book, this one has -- it is clear and concise. I'm a stickler for how things are explained and this book surpassed all expectations, explaining topics elegantly. Not only are methods explained well, but so is how to interpret data as well as general advice and guidelines on fitting models and checking that assumptions are met. When reading this book I feel like I am getting a lot more than just how to fit linear models but how to analyze and judge if a model is appropriate, which is a crucial step to fitting.

I've read a good portion of the book, reading the first several chapters and skipping around more on a need-to-know level for the other topics. Below is a list of the chapters:

1. Introduction.

2. Estimation

3. Inference

4. Diagnostics

5. Problems with Predictors

6. Problems with the Error

7. Transformation

8. Variable Selection

9. Shrinkage Methods

10. Statistical Strategy and Model Uncertainty

11. Insurance Redlining -- A Complete Example.

12. Missing Data

13. Analysis of Covariance

14. One-Way Analysis of Variance

15. Factorial Designs

16. Block Designs

I will inevitably be buying what is like the second volume of this book, "Extending the Linear Model with R" as needs arise.
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10 of 13 people found the following review helpful By John Student on January 18, 2006
Format: Hardcover Verified Purchase
Very clear, very concise. The author moves from important point to important point in a smooth manner. Though, there is definitely room for some more detail, that's not what I bought it for. I wanted a good summary of linear models and how to use R to fit and analyze them. That's exactly what I got. If I want more detail, the author places references to appropriate books throughout the book.
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By Belinda on October 16, 2013
Format: Hardcover Verified Purchase
It's good with this price. It's the recommended book for one of my courses, and I feel it should be useful.
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By Madison Hansen on October 2, 2013
Format: Hardcover Verified Purchase
Helped me complete my homework, which is all I can ask for! I recommend this book as a brief guide to learning stats.
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4 of 7 people found the following review helpful By James Brownlow on August 19, 2007
Format: Hardcover Verified Purchase
I got this book because I needed to use robust techniques in regression and ANOVA. I could not find much in the R-documentation to help with this.

The book is excellent in the sense that it guides the reader through a number of fairly useful techniques using linear models and generalized linear models. I adopted the strategy of starting with the first chapter and working each example in R as the author presents it. I've learned a lot about how to use R doing this. It took me a while to get used to the "unified" linear model idea- viz., regression and ANOVA are based on the same linear model. The book has a number of excellent examples that demonstrate the power of R and show the reader how to exploit various library functions.

Overall I like the book. It is an excellent introduction to how to use R in one's linear model analyses. It is long on "here, type this in and you'll get....." and short on the theory/principles behind each technique.

One aspect of R I have not yet cracked- how do I (easily) specify a linear model that includes only interactions up to level 2 or 3? I currently enter the formula as Y~A*B+A*C + ... and so on. Must be an easier way.

The discussions on robust methods are good, but (understandably) short. Use of the trimmed least squares and quantile regression techniques as well as a discussion of using bootstrap methods to get a confidence interval are, again presented as "here, type this in and..." At least this book got me headed in the right direction. There is also a 2- or 3-page summary of basic R commands to help the neophyte get started with R.

A good book, but could be better with some presentation of the theory behind each of the techniques presented.
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