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This tutorial illustrates how to interpret the more advanced output and to set different prior specifications in performing Bayesian regression analyses in JASP (JASP Team, 2020). We explain various options in the control panel and introduce such concepts as Bayesian model averaging, posterior model probability, prior model probability, inclusion Bayes factor, and posterior exclusion probability. After the tutorial, we expect readers can deeply comprehend the Bayesian regression and perform it to answer substantive research questions. For readers who need fundamentals of JASP, we recommend reading JASP for beginners. If readers need nuts and bolts of Bayesian analyses in JASP, we suggest following JASP for Bayesian analyses with default priors. The current tutorial assumes that readers are equipped with the knowledge necessary for advanced Bayesian regression analysis.

Since we continuously improve the tutorials, let us know via Github (https://github.com/Rensvandeschoot/Tutorials) if you discover mistakes, or if you have additional resources we can refer to.