Linear Regression Analysis

Concise, mathematically clear, and comprehensive treatment of the subject. * Expanded coverage of diagnostics and methods of model fitting. * Requires no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight-line regression and simple analysis of variance models. * More than 200 problems throughout the book plus outline solutions for the exercises. * This revision has been extensively class-tested.

GEORGE A. F. SEBER, PhD, is Professor Emeritus of Statistics at the University of Auckland, New Zealand. <BR>

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.

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Linear Regression Analysis

Summary

Author Biography

Table of Contents

Supplemental Materials