The aim of the paper is to determine maximum-likelihood estimation equations. Observations follow a multivariate normal distribution, , where , and describe the unknown covariance structure between rows and columns of , respectively. Imposing restrictions on and four types of covariance structures will be considered.

The aim of this paper is to consider the optimality in the growth curve model with respect to two aspects: time and the block design and to show some relations between information functions for different designs. The A-, D- and E- optimality are studied.

In the article, properties of the Bennett test and Miller test are analyzed. Assuming that the sample size is the same for each sample and considering the null hypothesis that the coefficients of variation for k populations are equal against the hypothesis that k − 1 coefficients of variation are the same but differ from the coefficient of variation for the kth population, the empirical significance level and the power of the test are studied. Moreover, the dependence of the test statistic and the power of the test on the ratio of coefficients of variation are considered. The analyses are performed on simulated data.