Linear Regression Analysis
2nd Edition
Published on 20. January 2012
582 pages
978-1-118-27442-2 (ISBN)
Description
Concise, mathematically clear, and comprehensive treatment of thesubject.
* Expanded coverage of diagnostics and methods of modelfitting.
* Requires no specialized knowledge beyond a good grasp of matrixalgebra and some acquaintance with straight-line regression andsimple analysis of variance models.
* More than 200 problems throughout the book plus outline solutionsfor the exercises.
* This revision has been extensively class-tested.
Reviews / Votes
"With excellent motivating and presenting style, this book issuitable for a beginning graduate level regression course."(Journal of Statistical Computation and Simulation, July2005) "...revises and expands the standard text, providing extensivecoverage of state-of-the-art theory..." (Zentralblatt Math,Vol. 1029, 2004) "...largely rewritten...very useful for self-study...anexcellent choice for a course in linear models and researchers whoare interested in recent literature in the fields..."(Technometrics, Vol. 45, No. 4, November 2003) "...rewritten to reflect current thinking, such as themajor advances in computing during the past 25 years."(Quarterly of Applied Mathematics, Vol. LXI, No. 3,September 2003)More details
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01/2012
Wiley-Blackwell
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George A. F. Seber | Alan J. Lee
Linear Regression Analysis
Book
02/2003
2nd Edition
Wiley
€187.50
Shipment within 10-20 days
Persons
GEORGE A. F. SEBER, PhD, is Professor Emeritus of Statistics at theUniversity of Auckland, New Zealand.
ALAN J. LEE, PhD, is the Chairman of the Department ofStatistics at the University of Auckland.
Content
Preface.
Vectors of Random Variables.
Multivariate Normal Distribution.
Linear Regression: Estimation and Distribution Theory.
Hypothesis Testing.
Confidence Intervals and Regions.
Straight-Line Regression.
Polynomial Regression.
Analysis of Variance.
Departures from Underlying Assumptions.
Departures from Assumptions: Diagnosis and Remedies.
Computational Algorithms for Fitting a Regression.
Prediction and Model Selection.
Appendix A. Some Matrix Algebra.
Appendix B. Orthogonal Projections.
Appendix C. Tables.
Outline Solutions to Selected Exercises.
References.
Index.
