Practical demonstrations of linear regression, 2023

This video series features demonstrations of fitting, interpreting, and evaluating linear regression models in R. It was created as part of a statistics class for first-year psychology PhD students in 2023. It consists of ten videos listed on this page and in a Youtube playlist.

These videos walk students through several models to practice the concepts from the conceptual introduction to linear regression before moving on to new topics including unitless measures of association (effect sizes) and regression diagnostics. Part of their purpose is to build confidence using R and interpreting regression results.

These videos are designed to be viewed in order. For example, the first video introduces hypothetical data used in nearly all of the subsequent examples. These videos also assume that students have some exposure to simple linear regression, particularly the ideas covered in the conceptual intro linked above.

The main R code files used in this video series are available here: Code for demos and hypothetical data [zip]. The sixth video (unitless measures of association, continued) uses some functions from my collection of miscellaneous general-purpose R functions. To run the corresponding part of the main code file, download the latest version of MiscFunctions.r from the linked page and place it in your working directory for these demos.

I also show some slides in several of the videos. Those slides are available here: Misc slides for regression demos [zip]. Some of the slides will not make sense without the context given in the videos, and you do not need the slides to watch the videos. A copy of the practice questions from the end of the final video is included with the slides.

List of Videos

The videos linked below are also available in a single Youtube playlist.

Part 1: Hypothetical data; Linear regression with one continuous predictor

Part 2: Linear regression with one binary predictor; Factors in R

Part 3: Multicategorical predictor

Part 4: Multiple predictors

Part 5: Unitless measures of association (effect sizes)

Part 6: Unitless measures of association (effect sizes), continued

Part 7: F-tests for model comparison

Part 8: Assumptions and diagnostics

Part 9: Assumptions and diagnostics, continued

Part 10: Comparison with Jamovi and SPSS