Items related to Linear Statistical Models
Synopsis
Linear Statistical Models
Developed and refined over a period of twenty years, the material in this book offers an especially lucid presentation of linear statistical models. These models lead to what is usually called "multiple regression" or "analysis of variance" methodology, which, in turn, opens up a wide range of applications to the physical, biological, and social sciences, as well as to business, agriculture, and engineering. Unlike similar books on this topic, Linear Statistical Models emphasizes the geometry of vector spaces because of the intuitive insights this approach brings to an understanding of the theory. While the focus is on theory, examples of applications, using the SAS and S-Plus packages, are included. Prerequisites include some familiarity with linear algebra, and probability and statistics at the postcalculus level.
Major topics covered include:
* Methods of study of random vectors, including the multivariate normal, chi-square, t and F distributions, central and noncentral
* The linear model and the basic theory of regression analysis and the analysis of variance
* Multiple regression methods, including transformations, analysis of residuals, and asymptotic theory for regression analysis. Separate sections are devoted to robust methods and to the bootstrap.
* Simultaneous confidence intervals: Bonferroni, Scheffe, Tukey, and Bechhofer
* Analysis of variance, with two- and three-way analysis of variance
* Random component models, nested designs, and balanced incomplete block designs
* Analysis of frequency data through log-linear models, with emphasis on vector space viewpoint. This chapter alone is sufficient for a course on the analysis of frequency data.
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From the Publisher
Emphasizes the geometry of vector spaces (the coordinate free approach) due to the fact that the author has found the intuition it provides is vital to understanding the theory. Methods of study of random vectors, multiple regression techniques, simultaneous confidence intervals and analysis of frequency data are among the subjects discussed. Includes examples of applications using SAS and S-Plus packages.
About the Author
JAMES H. STAPLETON teaches in the Department of Statistics and Probability at Michigan State University.
"About this title" may belong to another edition of this title.
- PublisherWiley-Interscience
- Publication date1995
- ISBN 10 0471571504
- ISBN 13 9780471571506
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages472
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Linear Statistical Models
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Linear Statistical Models (Wiley Series in Probability and Statistics)
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Wiley Series in Probability and Statistics: Linear Statistical Models
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