Regression and inference

Content for Thursday, January 20, 2022


  • Chapters 3 and 4 in The Effect1

Look through your notes on regression from your last stats class. Also, you can skim through these resources:

We’ll review all this regression stuff in the videos, so don’t panic if this all looks terrifying! Also, take advantage of the videos that accompany the OpenIntro chapters. And also, the OpenIntro chapters are heavier on the math—don’t worry if you don’t understand everything.


The slides for today’s lesson are available online as an HTML file. Use the buttons below to open the slides either as an interactive website or as a static PDF (for printing or storing for later). You can also click in the slides below and navigate through them with your left and right arrow keys.

View all slides in new window Download PDF of all slides

Fun fact: If you type ? (or shift + /) while going through the slides, you can see a list of special slide-specific commands.


Videos for each section of the lecture are available at this YouTube playlist.

You can also watch the playlist (and skip around to different sections) here:

In-class stuff

Here are all the materials we’ll use in class:

Hands-on R materials:

Bayesian statistics resources

In class I briefly mentioned the difference between frequentist and Bayesian statistics. You can see a bunch of additional resources and examples of these two approaches to statistics here. This huge blog post also shows how to do multilevel models with Bayesian models.

  1. Nick Huntington-Klein, The Effect: An Introduction to Research Design and Causality (Boca Raton, Florida: Chapman and Hall / CRC, 2021), ↩︎

  2. Chester Ismay and Albert Y. Kim, Statistical Inference via Data Science: A ModernDive into R and the Tidyverse (Chapman and Hall / CRC, 2019), ↩︎

  3. Ibid. ↩︎

  4. David M. Diez, Christopher D. Barr, and Mine Çetinkaya-Rundel, OpenIntro Statistics, 3rd ed., 2017, ↩︎

  5. Ibid. ↩︎