Regression and Other Stories is available!

This will be, without a doubt, the most fun you’ll have ever had reading a statistics book. Also I think you’ll learn a few things reading it. I know that we learned a lot writing it.

Regression and Other Stories started out as the first half of Data Analysis Using Regression and Multilevel/Hierarchical Models, but then we added a lot more and we ended up rewriting and rearranging just about all of what we had before. So this is basically an entirely new book. Lots has happened since 2007, so there was much new to be said. Jennifer and Aki are great collaborators. And we put lots of effort into every example.

Here’s the Table of Contents.

The chapter titles in the book are descriptive. Here are more dramatic titles intended to evoke some of the surprise you should feel when working through this material:

• Part 1:
– Chapter 1: Prediction as a unifying theme in statistics and causal inference.
– Chapter 2: Data collection and visualization are important.
– Chapter 3: Here’s the math you actually need to know.
– Chapter 4: Time to unlearn what you thought you knew about statistics.
– Chapter 5: You don’t understand your model until you can simulate from it.

• Part 2:
– Chapter 6: Let’s think deeply about regression.
– Chapter 7: You can’t just do regression, you have to understand regression.
– Chapter 8: Least squares and all that.
– Chapter 9: Let’s be clear about our uncertainty and about our prior knowledge.
– Chapter 10: You don’t just fit models, you build models.
– Chapter 11: Can you convince me to trust your model?
– Chapter 12: Only fools work on the raw scale.

• Part 3:
– Chapter 13: Modeling probabilities.
– Chapter 14: Logistic regression pro tips.
– Chapter 15: Building models from the inside out.

• Part 4:
– Chapter 16: To understand the past, you must first know the future.
– Chapter 17: Enough about your data. Tell me about the population.

• Part 5:
– Chapter 18: How can flipping a coin help you estimate causal effects?
– Chapter 19: Using correlation and assumptions to infer causation.
– Chapter 20: Causal inference is just a kind of prediction.
– Chapter 21: More assumptions, more problems.

• Part 6:
– Chapter 22: Who’s got next?

• Appendixes:
– Appendix A: R quick start.
– Appendix B: These are our favorite workflow tips; what are yours?

Here’s the preface, which among other things gives some suggestions of how to use this book as a text for a course, and here’s the first chapter.

The book concludes with a list of 10 quick tips to improve your regression modeling. Here’s the chapter, and these are the tips:

– 1. Think about variation and replication.

– 2. Forget about statistical significance.

– 3. Graph the relevant and not the irrelevant.

– 4. Interpret regression coefficients as comparisons.

– 5. Understand statistical methods using fake-data simulation.

– 6. Fit many models.

– 7. Set up a computational workflow.

– 8. Use transformations.

– 9. Do causal inference in a targeted way, not as a byproduct of a large regression.

– 10. Learn methods through live examples.

And here’s the index.

You can order the book here. Enjoy.

P.S. I saved the best for last. All the data and code for the book are on this beautiful Github page that Aki put together. You can run and modify all the examples!