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One of the chapters that we are especially proud of in Practical Data Science with R is Chapter 7, “Linear and Logistic Regression.” We worked really hard to explain the fundamental principles behind both methods in a clear and easy-to-understand form, and to document diagnostics returned by the R implementations of lm and glm.
For the second edition, we added a new section on regularization of linear models, and how to fit regularized linear models with glmnet.
So if you are looking for a good introduction to the principles and practice of linear models in R, we hope you check out Practical Data Science with R.
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