ABSTRACT
Modern Bayesian inference can be as simple as pressing one button, but effective model building and analysis can take some more work. As we progress through this book we will see many different models. One of the building blocks of such models is linear regression, that we will introduce in this chapter. A strong understanding of how to fit and interpret linear models is a strong foundation for the model that will follow, that will also help us to consolidate the fundamentals of model inference and exploratory analysis of bayesian models. We will first fit an intercept only model, that is a model with no covariates, and then we will add extra complexity by adding one or more covariates
