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.



1 Answer 1


I think two excellent books for R are: John Fox's Companion to Applied Regression https://us.sagepub.com/en-us/nam/applied-regression-analysis-and-generalized-linear-models/book237254 and Julian Faraway's Linear Models with R https://rdrr.io/cran/faraway/

Simon Wood (also at Bath) has a book on GAMs https://www.crcpress.com/Generalized-Additive-Models-An-Introduction-with-R-Second-Edition/Wood/p/book/9781498728331

  • $\begingroup$ Paul, what is the title of Faraway's book on GAMs? $\endgroup$ Nov 25, 2019 at 15:56
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    $\begingroup$ Hi Isabella, I should have said Simon Wood, sorry, I've corrected the answer. $\endgroup$ Nov 25, 2019 at 16:38
  • $\begingroup$ Thanks, Paul! That's what I suspected but thought there may be a new book out by Julian Faraway I am not familiar with. I love all 3 authors you listed. $\endgroup$ Nov 26, 2019 at 1:45

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