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

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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. © Taylor & Francis Group, LLC.

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JASA

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