An excellent detailed but practical introduction to Regression from a largely Bayesian point of view with great examples and R code. There are many interesting asides, e.g. regression to the mean, and some key topics are explained in 2 or 3 different ways to aid your understanding. Also, by doing things in both a traditional frequentist - maximum likelihood way and then using stan_glm, the benefits of the Bayesian approach are seen.