Analysis oj covariance might be one of the most misunderstood and inadequately taught of all applied statistical methods. Many methods books do not deal with it at all (Guttman et. a l . , 1982), or sparingly (Brownlee, 1965), and most of those that treat it substantially, such as Federer (1955), Snedecor and Cochran (1980), Steel and Torrie (1980), and Winer (1971), concentrate on balanced data, namely those which have equal numbers of observations in the subclasses. What happens if the data are not balanced and moreover if some of the observations are missing? The missing observations complicate computations and affect what is estimable. The analysis of covariance would become more complex. The application of geometry in the analysis of covariance may offer an understanding of the analysis as well as broaden the variety of methods that can be considered. When there are no missing observations on the repeated measures factor(s), computational algorithms can be used (see Henderson and Henderson, 1979).