Linear Models for Unbalanced Data

This presentation of the fundamentals of linear statistical models is devoted 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. References and statistical tables are included.