Linear Regression Analysis, Second Edition
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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.

ALAN J. LEE, PhD, is the Chairman of the Department of Statistics at the University of Auckland.



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.




"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 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)