Premium
The Use of Analysis of Covariance to Analyse Data from Designed Experiments with Missing or Mixed‐Up Values
Author(s) -
Smith Patricia L.
Publication year - 1981
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.2307/2346651
Subject(s) - missing data , covariance , statistics , analysis of covariance , econometrics , mathematics , computer science
S ummary 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.