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Covariance analysis of multiple linear regression equations Eekman, Gordon Clifford Duncan

Abstract

A covariance analysis procedure which compares multiple linear regression equations is developed by extending the general linear hypothesis model of full rank to encompass heterogeneous data. A FORTRAN IV computer program tests parallelism and coincidence amongst sets of regression equations. By a practical example both the theory and the computer program are demonstrated.

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