The current state of usage of regression models in analysis of variance (ANOVA) designs is empirically examined, and examples of several statistical errors made in usage are presented. The assumptions of the general linear model are that all predictors are known without error of measurement and are fixed with no replication or sample variation; in the population, errors are normally distributed independently with variance, and errors are independent of all predictors. The rules for construction of the ANOVA allow the expected mean squares to hold just as if the levels of each factor had been randomly sampled. Analysis of Covariance (ANCOVA) combines the elements of regression analysis with design, albeit in a restricted manner. The homogeneity of regression coefficients is a parameter restriction from the design view point. The regression weight associated with a given covariate level is discussed. Most regression approaches to ANOVA and ANCOVA assume a fixed factor model under all design specifications. A major oversight of expected mean squares has contributed to the current lack of concern for the level of generalizability warranted from the design specification. Aptitude treatment interaction models are examined. (Author/CM)