As discussed in Chen et al. (2014), all the within-subject factors are flattened into ℝ1 under the multivariate model (MVM) formulation (1). Once the regression coefficient matrix A is estimated through solving the MVM system (1) with the least squares principle, each general linear test (GLT) can be expressed as a function of A, (A1) H0:Lu×q Aq×m Rm×v=0u×v, where the hypothesis matrix L, through premultiplying, specifies the weights among the rows of A that are associated with the between-subjects variables (groups or subject-specific quantitative covariates), and the response transformation matrix R, through postmultiplying, formulates the weighting among the columns of A that correspond to the m response variables. It is assumed that L and R are full of row- and column-rank respectively, and u ≤ q, v ≤ m. The matrix L (or R) plays a role of contrasting or weighted averaging among the groups of a between-subjects factor (or the levels of a within-subject factor).