Abstract
ABSTRACTThis article generalizes Rubin's method of least squares estimation of missing values in any analysis of variance. The general method produces not only least squares estimates of all parameters and the residual mean square, but also the correct least squares standard error and t‐test of any contrast as well as the least squares sum of squares and F‐test due to any collection of contrasts. The method is noniterative and requires only those subroutines designed to handle complete data plus a subroutine to find the inverse of an m x m symmetric matrix, where m is the number of missing values.
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