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Abstract
For small and balanced analysis of variance problems, standard computer programs are convenient and efficient. For large problems, regression pro- grams are at least competitive with analysis of variance programs; and, when a problem is unbalanced, they usually provide the only reasonable solution. This paper discusses procedures for using regression programs for the computing of analyses of variance. A procedure for coding matrices is described for experimental designs having nested and crossed factors. Several illustrations are given, and the limitation of the procedure with large repeated measures designs is discussed. A second algorithm is offered for obtaining the sums of squares for nested factors and their interactions in such designs.