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Regression Graphics: Ideas for Studying Regressions Through Graphics
 
 
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Regression Graphics: Ideas for Studying Regressions Through Graphics [Hardcover]

R. Dennis Cook (Author)
5.0 out of 5 stars  See all reviews (1 customer review)

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Book Description

0471193658 978-0471193654 September 16, 1998 1
An exploration of regression graphics through computer graphics.

Recent developments in computer technology have stimulated new and exciting uses for graphics in statistical analyses. Regression Graphics, one of the first graduate-level textbooks on the subject, demonstrates how statisticians, both theoretical and applied, can use these exciting innovations. After developing a relatively new regression context that requires few scope-limiting conditions, Regression Graphics guides readers through the process of analyzing regressions graphically and assessing and selecting models. This innovative reference makes use of a wide range of graphical tools, including 2D and 3D scatterplots, 3D binary response plots, and scatterplot matrices. Supplemented by a companion ftp site, it features numerous data sets and applied examples that are used to elucidate the theory.

Other important features of this book include:
* Extensive coverage of a relatively new regression context based on dimension-reduction subspaces and sufficient summary plots
* Graphical regression, an iterative visualization process for constructing sufficient regression views
* Graphics for regressions with a binary response
* Graphics for model assessment, including residual plots
* Net-effects plots for assessing predictor contributions
* Graphics for predictor and response transformations
* Inverse regression methods
* Access to a Web site of supplemental plots, data sets, and 3D color displays.

An ideal text for students in graduate-level courses on statistical analysis, Regression Graphics is also an excellent reference for professional statisticians.

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Editorial Reviews

Review

In summary, it is a very well-written book with a good blend of theory and application. Some of the chapters in the book are very theoretical and very extensive so that a researcher in this area will benefit much from reading this book. Practitioners will also benefit by getting the basic ideas of the various concepts and understanding them through the abundant number of examples in the book. (Statistical Methods in Medical Research, 9: 602-604, 2000)

From the Back Cover

An exploration of regression graphics through computer graphics.

Recent developments in computer technology have stimulated new and exciting uses for graphics in statistical analyses. Regression Graphics, one of the first graduate-level textbooks on the subject, demonstrates how statisticians, both theoretical and applied, can use these exciting innovations. After developing a relatively new regression context that requires few scope-limiting conditions, Regression Graphics guides readers through the process of analyzing regressions graphically and assessing and selecting models. This innovative reference makes use of a wide range of graphical tools, including 2D and 3D scatterplots, 3D binary response plots, and scatterplot matrices. Supplemented by a companion ftp site, it features numerous data sets and applied examples that are used to elucidate the theory.

Other important features of this book include:
* Extensive coverage of a relatively new regression context based on dimension-reduction subspaces and sufficient summary plots
* Graphical regression, an iterative visualization process for constructing sufficient regression views
* Graphics for regressions with a binary response
* Graphics for model assessment, including residual plots
* Net-effects plots for assessing predictor contributions
* Graphics for predictor and response transformations
* Inverse regression methods
* Access to a Web site of supplemental plots, data sets, and 3D color displays.

An ideal text for students in graduate-level courses on statistical analysis, Regression Graphics is also an excellent reference for professional statisticians.

Product Details

  • Hardcover: 349 pages
  • Publisher: Wiley-Interscience; 1 edition (September 16, 1998)
  • Language: English
  • ISBN-10: 0471193658
  • ISBN-13: 978-0471193654
  • Product Dimensions: 9.7 x 6.2 x 0.9 inches
  • Shipping Weight: 1.5 pounds (View shipping rates and policies)
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Best Sellers Rank: #2,602,699 in Books (See Top 100 in Books)

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24 of 24 people found the following review helpful:
5.0 out of 5 stars intelligent use of graphics in regression, February 9, 2008
This review is from: Regression Graphics: Ideas for Studying Regressions Through Graphics (Hardcover)
This is one of a series of books Dennis Cook has written on regression graphics. This is the most theoretical of the three. It is a companion to the recent book with Sandy Weisberg. The other book emphasizes applications while this provides some theory to support a complete regression analysis through graphical methods. It provides a modern and up-to-date treatment. This work was summarized in a recent tutorial I attended that Cook gave at the Deming Conference last year. It includes several examples and covers asymptotic theory for such topics as sliced inverse regression. It is a great reference source also.
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Inside This Book (learn more)
First Sentence:
The focus of this book is fairly narrow relative to what could be included under the umbrella of statistical graphics. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
sufficient summary plot, inverse response plot, mean checking condition, variance checking condition, central subspace, kyphosis data, contoured random variable, horizontal screen axis, inverse variance function, reaction yield data, marginal regression functions, naphthalene data, mussel data, possible structural dimensions, regression subspace, kyphosis cases, predictor transformations, haystack data, structural dimensionality, isomerization data, transformed predictors, sliced inverse regression, graphical regression, lowess smooth, approximate multivariate normality
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Li-Duan Proposition, Big Mac, Conditional Independence Proposition, Australian Institute of Sport, Monte Carlo, Australian Sport
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