Smith College SDS 291: Multiple Regression (Fall 2018)
I taught Multiple Regression at Smith College in Fall 2018,
building on materials originally written by Ben Baumer.
The course used Stat2: Building Models for a World of Data (Cannon et al.) and R with the mosaic package.
This is what is left of the course site: the lecture slides, the R labs and the first four homework assignments.
Back to Teaching.
Lectures
- Introduction
- Simple linear regression
- Conditions for regression
- Transformations
- Inference for simple linear regression
- Multiple regression
- Interpreting coefficients
- Second-order models
- Multicollinearity
- Exam 1 review
- Interaction plots
- Nested F-tests
- Chapter 3 cleanup
- Model selection
- The bootstrap
- Unusual points
- ANOVA
- More ANOVA
- Logistic regression
- More logistic regression
- Time series
- Wrap-up
Labs
- Introduction to R and RStudio
- Residuals
- Transformations
- Confidence and prediction intervals
- Regression summary output
- Multicollinearity, VIF and nested F-tests
- Added variable plots
- Diagnostics
- Stepwise regression
- Randomization and the bootstrap
- ANOVA
- Multiple testing
- Logistic regression
- Logistic regression fit
- Data wrangling, part I
- Data wrangling, part II
- Merge and reshape
- Panel data
- Time series