Description
Book SynopsisRequiring no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight line regression and simple analysis of variance models, this work covers the diagnostics and methods of model fitting.
Trade Review"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)
Table of ContentsPreface.
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.
