A Theorem on Least Squares and Vector Correlation in Multivariate Linear Regression

Abstract

This paper shows that the least-squares estimate of the matrix of coefficients in a multivariate linear regression model maximizes the squared vector correlation coefficient between the dependent variables and a linear transformation of the explanatory variables.