This 1971 classic on linear models is once again available–as a Wiley Classics Library Edition. It features material that can be understood by any statistician who understands matrix algebra and basic statistical methods.
Table of Content
Generalized Inverse Matrices.
Distributions and Quadratic Forms.
Regression, or the Full Rank Model.
Introducing Linear Models: Regression on Dummy Variables.
Models Not of Full Rank.
Two Elementary Models.
The 2-Way Crossed Classification.
Some Other Analyses.
Introduction to Variance Components.
Methods of Estimating Variance Components from Unbalanced Data.
Variance Component Estimation from Unbalanced Data: Formulae.
Literature Cited.
Statistical Tables.
Index.
About the author
Shayle R. Searle, Ph D, is Professor Emeritus in the Department of Biological Statistics and Computational Biology at Cornell University. Dr. Searle is the author of Linear Models, Linear Models for Unbalanced Data, Matrix Algebra Useful for Statistics, and Variance Components, all published by Wiley.
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