The corresponding diagrams are plotted in Fig. , including the graphical interpretation of the SMOC and WGS approaches. The confidence intervals, that are calculated similarly to the previous example, are summarised in Table : SMOC and MOC a result in the same confidence interval, and MOC b and both WGS a and WGS b result in the same confidence intervals, however different from the first one. This is expected, since SMOC and MOC a take only one parameter into account and fix the remaining, while MOC b and WGS take both parameters into account. The confidence intervals after Monte Carlo simulation are narrower than the WGS/MOC b confidence intervals, but broader than the SMOC and MOC a intervals. The graphical interpretation of SMOC and WGS are shown in Fig. (c) and 9(d). While all series again show a parabolic trend, the series for θ 1 or θ 2 differ slightly for both methods. The correlation between θ 1 and θ 2 can be analysed using the correlation coefficient and the scatter plots as shown in Fig. . Apart from the different confidence intervals, the ellipsoid after MOC is rotated with respect to the ellipsoid in Fig. and correlation can be observed (R 2 = 0.70). The scatter plot after WGS shows two lines again, where each line is assigned to the variation of one parameter. The correlation coefficient indicates a strong correlation (R 2 = 0.98), which however is an artefact since only the best-fit values are included but not all possible values that obey equation 35. Monte Carlo simulation on the other hand leads to a similar scattering of the parameters and a very similar correlation coefficient (R 2 = 0.70).