This chapter covers the basics of adapting regression models, also known as linear fits in physics, to find the parameters that best explain data in a model and then estimate the error bars of the parameters. The analysis of the model’s reliability stimulates a discussion of [Formula: see text] and t-distributions and their role in testing hypotheses regarding the parameters; for example, whether a parameter can be omitted from the fit. A more elaborate method, based on the F-test, follows. The chapter closes with a discussion of parsimony as a guiding principle when building models. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this chapter (10.1007/978-3-030-63643-2_7) contains supplementary material, which is available to authorized users.
Regression Models and Hypothesis Testing