ABSTRACT

The analysis of covariance deals with one or more added variables, referred to as covariates, used to measure variability in the uncontrolled variables. The analysis of covariance makes use of both the techniques of regression and analysis of variance to account for the variation associated with the covariate. It should be obvious to the reader that considerable variation exists in the initial weight of the rabbits. The linear additive model for an analysis of covariance simply involves the addition of a regression term to the model. If the assumption that the X's are not affected by treatment is violated, the researcher must cautiously interpret the analysis. One additional factor should be considered in evaluating the effectiveness of the two procedures, that is, the loss of 1 degree of freedom from the covariance MSE. To many, the analysis is poorly understood and to some appears to involve data manipulation.