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Applied Regression Analysis (Wiley Series in Probability and Statistics) Third Edition

4.3 out of 5 stars 13 customer reviews
ISBN-13: 978-0471170822
ISBN-10: 0471170828
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Editorial Reviews

Review

"I would wholeheartedly recommend this book to any statistician. The third edition has many advantages over the second." (Statistical Methods in Medical Research, Vol. 9, 5)

"this is an excellently written book" (Statistics & Decisions, Vol. 19, No.3, 2001)

From the Back Cover

An outstanding introduction to the fundamentals of regression analysis-updated and expanded The methods of regression analysis are the most widely used statistical tools for discovering the relationships among variables. This classic text, with its emphasis on clear, thorough presentation of concepts and applications, offers a complete, easily accessible introduction to the fundamentals of regression analysis. Assuming only a basic knowledge of elementary statistics, Applied Regression Analysis, Third Edition focuses on the fitting and checking of both linear and nonlinear regression models, using small and large data sets, with pocket calculators or computers. This Third Edition features separate chapters on multicollinearity, generalized linear models, mixture ingredients, geometry of regression, robust regression, and resampling procedures. Extensive support materials include sets of carefully designed exercises with full or partial solutions and a series of true/false questions with answers. All data sets used in both the text and the exercises can be found on the companion disk at the back of the book. For analysts, researchers, and students in university, industrial, and government courses on regression, this text is an excellent introduction to the subject and an efficient means of learning how to use a valuable analytical tool. It will also prove an invaluable reference resource for applied scientists and statisticians.
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Product Details

  • Hardcover: 736 pages
  • Publisher: Wiley-Interscience; Third edition (April 23, 1998)
  • Language: English
  • ISBN-10: 0471170828
  • ISBN-13: 978-0471170822
  • Product Dimensions: 7.2 x 1.6 x 10.3 inches
  • Shipping Weight: 3 pounds (View shipping rates and policies)
  • Average Customer Review: 4.3 out of 5 stars  See all reviews (13 customer reviews)
  • Amazon Best Sellers Rank: #788,332 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

By Michael R. Chernick on February 12, 2008
Format: Hardcover
Draper and Smith have long had a reputation for an outstanding book on regression analysis written at an elementary to intermediate level. I have long had a copy on my bookshelf and continue to purchase the revisions. They are careful to keep the book current by always incorporating new advances. This edition includes many of the recent advances in regression diagnostics as well as a description of the bootstrap approach to regression problems. Those interested in regression graphics should consult the book by R. Dennis Cook. More on the bootstrap can be found in my book "Bootstrap Methods: A Practitioner's Guide" or the other fine books by Efron and Tibshirani, Davison and Hinkley, and Lunneborg.
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Format: Hardcover
I used this book for an undergraduate class in college. It was on the bookshelves of most of my Statistics professors (older editions of course). I found it to be a very intuitive and strait forward introduction in Regression Analysis. It contains all the basics needed to understand the bread and butter of Regression.
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Format: Hardcover Verified Purchase
I used this book as a reference for an advanced statistics for finance class that I took. Applied Regression has a really good balance between the theoretical foundation of least squares and the application. The introduction to the linear algebra of least squares is particularly gentle. The authors introduce all of the linear algebra that you'll need to know. They also provide some background for least squares as a mapping into the column space of X. But the theory never becomes over whelming.

As the title suggests, the book is about regression analysis. In addition to multivariate least squares, the book covers advanced linear regression topics like ridge regression.

The book could be criticized for being dated in that it does not give examples in R or Matlab. However, the material that is covered is timeless. Regardless of the mathematics software you use, the foundation is the same and this book provides a good in-depth introduction to the regression analysis.

A good complement to this book (which I also used in my advanced statistics class) is Linear Models with R which shows how to build most of the models discussed Applied Regression in R.
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Format: Hardcover
This book falls into some strange no-mans-land where definitions, proofs, and the underlying statistical theory are skipped, but formulae are stated in a symbolic language complex enough that it is hard to imagine any readers that could understand the book yet not desire more mathematical rigor. The authors do give extensive references to material they don't explain--on the other hand, for $90 most readers probably want the explanations. The authors also present many of their own opinions about the relative merits of certain statistical quantities. While their opinions, based on their years of experience as researchers, may have some merit, all too often they are forced to admit that other researchers, with comparable credentials, have opposing opinions! In that case, why include the opinions _in a textbook_? Debate in scientific journals. Explain in textbooks.
The organization at a chapter level is clear enough, but within chapters the exposition is ad-hoc. This is probably a side effect of the authors' commitment to eliminating explanations and proofs--there's no need to organize since they are just enumerating formulae and opinions.
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Format: Hardcover
This book is severely outdated to the point that it cannot serve any audience. Simply reading this book and noting phrases such as computations which can be carried out on a "pocket calculator" or these procedures typically require specialized programs to be "run on a computer" indicate how severely outdated this book is.

Those wanting a modern applied book focusing in data analysis (using R or SAS) will be disappointed, and those looking for a baby version of Seber (and Lee) will also be disappointed. A significant disappointment of the book is the lack of focus in setting up ordinary least squares regression, and hence the lack of step-by-step generalization that a fundamental presentation of regression should entail (least squares -> generalized least squares -> iteratively weighted least squares -> robust regression -> generalized linear models).

The book also lacks "good" problems. Most of the problems focus on computations using small matrices and do not focus on proving the easily proven fundamental results. In addition the book takes the habit of stating formulas and not deriving them from the fundamental problem.
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Format: Hardcover
Presents regression and the associated statistics in the framework of linear algebra. Useful in its own right; a necessary primer for more advanced material like Kalman filters.
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Format: Hardcover Verified Purchase
Is a kind of book nice to have because review all topics in regresión and plus, Furthermore a great examples are used
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