Another way of evaluating the quality of the data fits is to examine the standard errors of the fitted parameters, which are calculated from the inverse of Jacobian matrix for the model (partial derivatives with respect to the parameters) and the standard errors of the fit. We focused our attention on those parameters/cases with % relative standard errors (% RSE) greater than 50 %. There were seven cases (stationary phase/solute combinations) where one or more parameters exhibited these larger % RSEs. For six cases, it was found that the error in the In order to demonstrate the improved performance of the re-parameterized model, we fit these same data (after outlier removal) using the conventional NK model (i.e., ref = 0). The resulting fit qualities were identical, but for 33 cases out of 315 fits, the code reported warnings that the problem was ill-conditioned, indicating that the parameter estimates might not be as reliable. The distributions for the % RSE values for the conventional NK parameters analogous to those shown in Fig. are provided in Fig. in the supplemental material. The average % RSE values for these parameters were 2.2 %, 4.2 % and 3.2 %, for S1, S2 and kw, respectively. These values are 3.0, 1.1, and 4.6 times worse than the values obtained for the re-parameterized model, respectively. This result indicates that the performance of re-parameterized fitting of the isocratic data is improved relative to the conventional NK model.