Regression diagnostics applied in kinetic data processing: Outlier recognition and robust weighting procedures

Abstract

Abstract An efficient protocol, based on advanced statistical diagnostics and robust fitting techniques applied to the least‐squares processing of kinetic data of chemical reactions, is presented and discussed. The procedure, which is aimed at obtaining highly accurate estimation of the fitting parameters, consists of the identification of the outliers that remarkably impair the fitting by means of the so‐called “leverage analysis” and some related diagnostics. This approach allows the elimination of the actually aberrant observations from the data set and/or their robust weighting to inhibit the negative effects induced on the data fitting, with consequent reduction of the bias introduced into the parameter estimates. It has been found that the proposed procedure, applied to experimental kinetic data, does yield to a significant improvement in the regression results. © 2010 Wiley Periodicals, Inc. Int J Chem Kinet 42: 587–607, 2010