Seminar 3: Applied Regression Analysis

There are no lecture slides; 100% activity today!

To get started:

1. Download this zipped project scaffold on to your computer
2. Unzip it where you keep your coursework
3. Double-click the applied-regression_activity.Rproj to open the project
4. Run the processing scripts in the project’s syntax/ folder to get the data download and assembled

Scripts from in-class:

• 4_join-data.R
• 5_visualisation.R
• 6_modeling.R

Required reading:

• Huntington-Klein, N. (2022) The Effect: An Introduction to Research Design and Causality. Read chapter 13: Regression (https://theeffectbook.net/ch-StatisticalAdjustment.html)

• Altman, N. and Krzywinski (2016) Regression diagnostics. Nature Methods, 13:385-386 https://www.nature.com/articles/nmeth.3854

Further reading:

• Kassambara, A. (2018) “Linear regression assumptions and diagnostics in R: Essentials.” http://www.sthda.com/english/articles/39-regression-model-diagnostics/161-linear-regression-assumptions-and-diagnostics-in-r-essentials/
  • A good very quick overview of basic R diagnostics for linear models
• Gelman, A. and Hill, J. (2007) Data Analysis using Regression and Multilevel/Hierarchical Models. Cambridge University Press.
  • The best general regression textbook, with detailed discussion of model diagnostics