Determination of confidence ellipses in parameter space is a helpful tool to compare and classify the determined estimators. By visualization of the confidence intervals in terms of the estimators, different evaluation procedures (models) may be compared and judged according to their overlap in 2D-parameter space. Overall, the complexity of determining confidence ellipses in parameter space is a function of the respective model (M) and the error structure of the data. Hereby, model denotes the type of function to be used in the fitting procedure (e.g. linear, polynomial, non-linear). The error structure of the data must also be accounted for, as data points may need to be weighted differently. The procedure of determining confidence ellipsis for non-linear estimations is a statistically complex task. For polymer chemists, such models are relevant in determining copolymerization reactivity ratios. Here, the nonlinear Mayo-Lewis Terminal-model (ML) is most often employed. It is given by