Regression and inference

Content for week of Tuesday, January 19, 2021–Friday, January 22, 2021


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.

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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:

  1. Joshua D. Angrist and Jörn-Steffen Pischke, Mastering ’Metrics: The Path from Cause to Effect (Princeton, NJ: Princeton University Press, 2015). ↩︎

  2. Scott Cunningham, Causal Inference: The Mixtape (New Haven, CT: Yale University Press, 2021), ↩︎

  3. Chester Ismay and Albert Y. Kim, ModernDive: An Introduction to Statistical and Data Sciences via R, 2018, ↩︎

  4. Ibid. ↩︎

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

  6. Ibid. ↩︎