A Distribution-Free Measure of the Significance of CER Regression Fit Parameters Established Using General Error Regression Methods

Abstract

Abstract General error regression methods (GERM) have given rise to a wide variety of functional forms for cost-estimating relationships but have so far lacked a means for evaluating the “significance” of the individual regression fit parameters in a way that is analogous to the roles played by the t-statistic and associated p-value in ordinary least squares (OLS) regression. This article attempts to remedy that situation by developing and describing an analogous “significance” metric for GERM regression fit parameters that is independent of the nature of the underlying error distribution. Significance metrics developed herein are comparable across CERs, regardless of the functional form of the regression equation or the underlying error specification. Moreover, they are developed heuristically, require no distributional assumptions, and provide a collection of simple metrics by which to judge the “significance” of the individual regression fit parameters. These metrics will be of benefit to anyone who uses GERM to develop CERs. The author is willing to share any data involved in thisd study.