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The aim of this work is based in the reduction of independent variables in multivariate regression analysis to one by means a vector dot product (E 3 ). By this way, it is omit the orthogonalized procedure to obtained valid regression equation without co-linearity variables and valid signs supporting each independent variables factor, also by this procedure (E 3 ) it is possible to omit variable reduction process by means the Principal Components Analysis (PCA) and the used of others calibrations techniques in order to reach simples valid regressions functions. The reduction of three independent variables to one by (E 3 ) method, permit to applied linear regression (y = m*x + n) with clear signicance on m and n parameters, this not occur in the original three-independent variable parameters regression, if it is not properly treatise. INTRODUCTION In the QSPR multivariate regression equations, the real signicance of all factors and signs affecting each independent variable are obtained if orthogonal procedure

journal of the chilean chemical society

VECTOR DOT PRODUCT USED FOR REDUCED THREE INDEPENDENT VARIABLES OF MULTIVARIATE REGRESSION TO A LINEAR REGRESSION WITH ONE INDEPENDENT VARIABLE: ALCOHOLS USED LIKE A MODEL