The 95% confidence intervals using F-Test based methods applying the (Simplified) Model Comparison and Weakened Grid Search approach are given in Table . Both have been applied to either parameters individually (MOC a , WGS a ) or to both (MOC b , WGS b) ) together. The F-Test confidence intervals are effectively the same, independent of the approach, with some numerical differences due to step size during the evaluation. Using Monte Carlo simulation (ε = SEy) with the percentile method, the confidence interval is much narrower than these obtained with the F-Test approach. Those differences were already pointed out by Motulsky and Christopoulos. The variation of the individual parameters θ i by ±δ θ ,i and the corresponding SSE for SMOC and WGS are shown in Fig. (c) and 7(d). In both charts, the series show a parabolic trend, indicating that the SSE max can be reached during variation. The correlation coefficient for θ 1 and θ 2 and the scatter plots (Fig. ) after MOC, WGS and MC clearly indicate that there is no dependency between both parameters, which is in agreement with the given function. The obtained correlation coefficient for θ 1 and θ 2 is 3.6 • 10 -5 after Model Comparison. Using WGS the accepted values for θ 1 and θ 2 show a correlation coefficient of zero. The lines display two sets of accepted values for θ 1 and θ 2 , where one parameter is not affected by changing the other. The model parameters after Monte Carlo simulation indicate no correlation (R 2 = 1.9 • 10 -4 ) as well, but the pairs of θ 1 and θ 2 do not form a complete ellipse as obtained after Model Comparison. However, in case of functions or models with uncorrelated parameters the implemented F-test based approaches lead to practically identical results, which differ from the Monte Carlo simulation based results.