Publisher description for Linear models for unbalanced data / Shayle R. Searle.


Bibliographic record and links to related information available from the Library of Congress catalog


Information from electronic data provided by the publisher. May be incomplete or contain other coding.


Counter This presentation of the fundamentals of linear statistical models is unique in its total devotion to unbalanced data (data having unequal numbers of observations in the subclasses) and its emphasis on the up-to-date cell means model approach to linear models for unbalanced data. Unbalanced data are harder to analyze and understand than balanced data, but are becoming more prevalent through computer storage of data. Topics covered include cell means models, 1-way classification, nested classifications, 2-way classification with some-cells-empty data, models with covariables, matrix algebra and quadratic forms, linear model theory, comments on computing packages, and much more. Includes references and statistical tables.

Library of Congress subject headings for this publication: