The Use of Analysis of Covariance to Analyse Data from Designed Experiments with Missing or Mixed‐Up Values

Patricia L. Smith

Journal of the Royal Statistical Society Series C, 1981, vol. 30, issue 1, 1-8

Abstract: Analysis of covariance is a well‐known technique for obtaining estimates and analysing data from designed experiments with missing values. In this article, we show that a covariance model also yields an exact analysis in the case of mixed‐up values and, in fact, is easier to implement on a computer than the correct model. In addition, while estimators of the coefficients of dummy variables introduced as covariates may be used as estimators of missing or mixed‐up values, we show using the technique of John and Lewis (1976) that only for the case of missing values are these covariate coefficient estimators the best linear unbiased estimators (BLUE's) of the means of these values.

Date: 1981
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