Not sure if there's a book recommendation section. I am looking for two books, one for implementation in R and one for implementation in Python. I'm hoping the books go pretty in depth on building linear models (from start to finish). Also, I'm hoping to learn this from a frequentist perspective.
I'm hoping good portion of the book devoted to the classical linear model, which should cover simple and multiple OLS linear regression with Gaussian assumption. Hoping to learn from start to finish (do we need to transform data, should we add interaction terms, how to deal with/interpret heteroskedasticity, multicollinearity, how to view diagnostic plots like QQplots, model selection, etc...)
And then hoping another portion of the book covers Generalized Linear Models, and also going pretty in depth on setting up the model, and then running diagnostics on it.