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.
Autorentext
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.
Zusammenfassung
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.
Inhalt
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.