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Regression Analysis by Example 4th Edition

4.6 out of 5 stars 7 customer reviews
ISBN-13: 978-0471746966
ISBN-10: 0471746967
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Editorial Reviews

Review

"This book is now well established as an excellent source of examples for regression analysis. It has been and still is readily readable and understandable to those with a minimum of data analytic experience.... It is an excellent source of information and example analyses concerning regression modeling for the beginning to moderately trained data analyst." (Journal of the American Statistical Association, March 2009)

"This book is now well established as an excellent source of examples for regression analysis.  It has been and still is readily readable and understandable to those with a minimum of data analytic experience … It is an excellent source of information and example analyses concerning regression modeling for the beginning to moderately trained data analyst." (Journal of the American Statistical Association, March 2009)

"I would like to have the new edition on my desk and suggest you do as well!" (Technometrics, May 2007)

"…I would recommend this book for all students…interested in regression modeling…" (MAA Reviews, December 12, 2006)

From the Publisher

A variety of ideas and methods of regression analysis are explored with the aid of realistic examples that highlight the analysis of data and include irregularities similar to those encountered in practice. Recent advances in regression diagnostics are covered with emphasis on plots such as component plus residual, added variable, sequence, along with index plots for leverage and function. The authors utilize standard and some not so standard summary statistics on the basis of their intuitive appeal to demonstrate concepts. The majority of analyses described are available in software packages on the market today. --This text refers to an out of print or unavailable edition of this title.
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Product Details

  • Hardcover: 408 pages
  • Publisher: Wiley-Interscience; 4 edition (July 21, 2006)
  • Language: English
  • ISBN-10: 0471746967
  • ISBN-13: 978-0471746966
  • Product Dimensions: 6.4 x 1 x 9.2 inches
  • Shipping Weight: 1.6 pounds
  • Average Customer Review: 4.6 out of 5 stars  See all reviews (7 customer reviews)
  • Amazon Best Sellers Rank: #873,247 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

Format: Hardcover
Its strength is in the examples. It is true that the book also employs formulas, but most of them are informative and fairly easy to follow if you have a moderate background in statistics. I would recommend this book for an intermediate course in econometrics, mainly because it offers a comprehensive view of the subject and ways to apply analysis tools. I give it five stars for the presence of applied data.
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Format: Hardcover Verified Purchase
Over the years, I have read many books on regression analysis. This book is one of the most lucidly written book on the subject. It achieves a fine balance between theoretical explanation and application of the method to real world problems. It requires some background knowledge, i.e. first college course of statistics, but it is not too dense. I am using this as a companion to a book on regression analysis by Douglas Montgomery (another excellent text). The best part is that the data and computer programs in SAS, R etc. are available on the UCLA website. All in all, a great resource.
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Format: Hardcover Verified Purchase
I used the first edition of this book (Chatterjee and Price, 1975) when I was a graduate student in the late '70's. The text was not as easy to read as the title might suggest, but it was much more accessible than commonly used alternatives, such as Johnston's introduction to econometrics and Draper and Smith's regression book. Chatterjee and Price's last chapter, however, seemed misplaced and was a bit of a mystery. It dealt with multicollinearity and leaned heavily on the work of a British statistician named Silvey. His prescription for dealing with multicollinearity was (to closely paraphrase) collect additional data in the direction of an eigenvalue that is small but not equal to zero. Fortunately, it was not necessary to master the last chapter to take nearly the full measure of Chatterjee and Price's offering.

In subsequent editions nothing quite so formidable as Silvey's account and prescripton has been included. The book remains accessible and informative. It includes useful examples, though for students of the social and behvaioral sciences, the examples may seem substantively unfamiliar. Nevertheless, the examples are understandable, and they do a reasonbly good job of clarifying the authors' explanations. My only concern is that some of the illustrative material inclued early in the book seems better served through use of time-series analysis rather than cross-sectional ordinary least squares, as applied by the authors.

Some readers may like the authors' intuitive approach to regression diagnostics. For example, rather than employ the usual formula to identify a Cook's Distance value that betokens an unduly influential observation, the authors' suggest visual examination of distributions of the Cook's Distance statistic to find unusually large values.
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Format: Hardcover
I used this book for an introductory/intermediate course in regression.

Although the authors presented a lot of material and I did find it informative, I found myself having to refer to other texts for a better explanation of about half of the material presented.

To me it read like an academic paper. It seemed that all the variables were explained only once in the text and used throughout the book without a central glossary or formula page. There also seemed to be a lot of subscripts and superscripts to the variables, as well as bouncing back and forth between data examples, which made following the concepts difficult.

There was a lot of information packed in the book's pages, but it was real difficult reading and trying to comprehend past the second chapter.
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