Author: Ziemann, Volker
Title: Regression Models and Hypothesis Testing Cord-id: cj0rz4fv Document date: 2021_1_19
ID: cj0rz4fv
Snippet: 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. Th
Document: 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.
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