99. Statistical Constants in Predicting Equations
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Paper
Abstract
A linear predicting equation is presented for which the coefficients are obtained by minimizing the sum of the squares of the percentage errors. Comparison with the ordinary least-squares regression equation (based on actual errors) shows that the two equations can yield substantially different estimates when the range of the variables in the sample to be fitted is large. If percentage errors are more important to the experimenter than actual errors, and if the range of variables is large, the predicting equation presented in this paper is preferable to the ordinary regression equation.
