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.

Categories: Administrativia Tutorials

Tagged as: data science linear regression Logistic Regression Practical Data Science Pragmatic Machine Learning R

Nina Zumel

Data scientist with Win Vector LLC. I also dance, read ghost stories and folklore, and sometimes blog about it all.