Abstract
Abstract This note shows that the least squares estimate of the matrix of coefficients in multivariate linear regression model maximizes all the sample canonical correlations between the dependent variables and the linear transformations of the explanatory variables, and it maximizes all the characteristic roots of the sample regression sum of squares and products matrix due to regression of the dependent variables on the linear transformations of the explanatory variables.
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