ABSTRACT
This chapter describes fitting and providing a worked example. It discusses parameter uncertainty estimations and how to evaluate and optimize the resulting fit. Modern day experiments generate large amounts of data, but humans generally cannot operate simultaneously with more than a handful of parameters or ideas. The fitting is the procedure that finds the best values of the free parameters. The process of the model selection is outside of the domain of the fitting algorithm. From a scientific point of view, the model is actually the most important part of the data reduction procedure. The outcome of the fitting procedure is the set of important parameter values as well as the ability to judge whether the selected model is a good one. Well-established models that are proved to be true by many generations of scientists are promoted to the status of laws of nature.
