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Overview
An extensive treatment of a key method in the statistician's toolboxFor more than two decades, the First Edition of Linear Regression Analysis has been an authoritative resource for one of the most common methods of handling statistical data. There have been many advances in the field over the last twenty years, including the development of more efficient and accurate regression computer programs, new ways of fitting regressions, and new methods of model selection and prediction. Linear Regression Analysis, Second Edition, revises and expands this standard text, providing extensive coverage of state-of-the-art theory and applications of linear regression analysis.
Requiring no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight-line regression and simple analysis of variance models, this new edition features:
- Up-to-date accounts of computational methods and algorithms currently in use without getting entrenched in minor computing details
- A careful and detailed survey of the research literature, making this a highly useful reference
- Expanded coverage of diagnostics, and more discussion of methods of model fitting, model selection and prediction
- More than 200 problems throughout the book plus outline solutions
Concise, mathematically clear, and comprehensive, Linear Regression Analysis, Second Edition, serves as both a reliable reference for the practitioner and a valuable textbook for the student.
Synopsis
Updating and expanding the original text, this new edition covers the current theory and applications of linear regression analysis. It provides a survey of the research literature and outlines the solutions to approximately 200 sample problems. Particular attention is given to diagnostics, model fitting, model selection, and prediction. Seber and Lee have each taught statistics at the University of Auckland. Annotation (c)2003 Book News, Inc., Portland, OR
Editorials
From the Publisher
"With excellent motivating and presenting style, this book is suitable for a beginning graduate level regression course." (Journal of Statistical Computation and Simulation, July 2005)"...revises and expands the standard text, providing extensive coverage of state-of-the-art theory..." (Zentralblatt Math, Vol. 1029, 2004)
"...largely rewritten...very useful for self-study...an excellent choice for a course in linear models and researchers who are interested in recent literature in the fields..." (Technometrics, Vol. 45, No. 4, November 2003)
“...rewritten to reflect current thinking, such as the major advances in computing during the past 25 years.” (Quarterly of Applied Mathematics, Vol. LXI, No. 3, September 2003)