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.

Klappentext

Systematic treatment of the commonly employed crossed and nested classification models used in analysis of variance designs with a detailed and thorough discussion of certain random effects models not commonly found in texts at the introductory or intermediate level. It also includes numerical examples to analyze data from a wide variety of disciplines as well as any worked examples containing computer outputs from standard software packages such as SAS, SPSS, and BMDP for each numerical example.

Inhalt

List of Figures List of Tables Preface Matrix Preliminaries and General Linear Model Some General Methods of 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 Appendices General Bibliography Author Index Subject Index