ANOVA models involving random effects have found widespread application to experimental design in varied fields such as biology, econometrics, and engineering. Volume I of this two-part work is a comprehensive presentation of methods and techniques for point estimation, interval estimation, and hypotheses tests for linear models involving random effects. Volume I examines models with balanced data (orthogonal models); Volume II studies models with unbalanced data (non-orthogonal models).

Accessible to readers with a modest mathematical and statistical background, the work will appeal to a broad audience of graduate students, researchers, and practitioners. It can be used as a graduate text or as a self-study reference.

Systematic treatment of the commonly employed crossed and nested classification models used in analysis of variance designs Detailed and thorough discussion of certain random effects models not commonly found in texts at the introductory or intermediate level Numerical examples to analyze data from a wide variety of disciplines Many worked examples containing computer outputs from standard software packages such as SAS, SPSS, and BMDP for each numerical example Extensive exercise sets at the end of each chapter Numerous appendices with background reference concepts, terms, and results Balanced coverage of theory, methods, and practical applications Complete citations of important and related works at the end of each chapter, as well as an extensive general bibliography Includes supplementary material: sn.pub/extras

Inhalt
Matrix Preliminaries and General Linear Model.- Some General Methods for Making Inferences about Variance Components.- One-Way Classification.- Two-Way Crossed Classification without Interaction.- Two-Way Crossed Classification with Interaction.- Three-Way and Higher-Order Crossed Classifications.- Two-Way Nested Classification.- Three-Way Nested Classification.- General r-Way Nested Classification.