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

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 here presented and discussed. The procedure, which is aimed at obtaining highly accurate estimation of the fitting parameters, consists in the identification of the outliers that remarkably impair the fitting by means of the so called 'leverage analysis' and some related diagnostics, allowing 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 and to reduce the bias introduced into the parameter estimates. It has been found that the proposed procedure, indeed applied to experimental kinetic data, does yield to a significant improvement of the regression results.