Three main topics are covered in this report: (i) outlier detection, (ii) selection of the most suited regression algorithm and (iii) alternative methods for confidence interval determination. Regarding outlier detection a 3-step procedure is recommended including the Inter-Quartile Range approach to search for extreme values in the dataset, followed by methods to reduce the dataset. Regarding the selection of the most suited regression algorithm, a hybrid approach was proposed and tested, in which a gradient-free algorithm starts the parameter search, and is followed by the Levenberg-Marquardt algorithm. Finally, two methods for confidence interval determination have been explored: Bootstrapping and Bayesian inference. Both result in a more robust determination of confidence intervals for the parameters.