title: Multivariate multiple regression analyses: a permutation method for linear models.
abstract: A multivariate extension of a univariate procedure for the analysis of experimental designs is presented. A Euclidean-distance permutation procedure is used to evaluate multivariate residuals obtained from a regression algorithm, also based on Euclidean distances. Applications include various completely randomized and randomized block experimental designs such as one-way, Latin square, factorial, nested, and split-plot designs, with and without covariates. Unlike parametric procedures, the only required assumption is the randomization of subjects to treatments.
mesh_terms: D002648:Child; D006801:Humans; D016014:Linear Models; D008960:Models, Psychological; D011584:Psychology / Q000379:methods; D012044:Regression Analysis; D012649:Self Concept