Description
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. Introduction 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 and Higher-Order Nested Classifications General Balanced Random Effects Model General Bibliography Author Index Subject Index
