analysis of covariance might be one of the most miS4nderstood and inadequately taught of all
applied statistical methods. Many methods books do not deQl with it at all (Cullman et. aI., 1982), or
sparingly (Brownlee, J965), and most of those that treat it substantially, such as Federer (1955),
Snedecor and Cochran (J 980), 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 of fer 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)
