Abstract
Repeated measures design with error serial dependence. Two analysis procedures for a random experimental design with several consecutives measures through time in each of the experimental units, are examinated in this paper. One of them consists in facing data applying AVAR mixed model with modelated error structure by AR processes. On the other side, the problem also is faced from a more general perspective, using a repeated measures multivariate approach. Data were simulated by Monte Carlo procedures to investigate the effect that have no satisfaction of independence and sphericity assumptions on the biases degree of estimated parametres, on the empirical probability to make type I errors and, on the power of statistical test of the two procedures.
