Linear Statistical Models

This is a lecture note for Linear Statistical Models (208780). The objective of this course is to help students gain hands-on experience in R programming for Bayesian regression and its application on statistical modeling and causal inference.


Donlapark Ponnoprat


March 6, 2022


This is lecture notes that I wrote for the Linear Statistical Models (208780) course in Winter 2022. Any comments and suggestions are welcome.



Linear regression

  1. Basic regression
  2. Linear regression with a single predictor
  3. Fitting linear regression
  4. Prediction and Bayesian inference
  5. Linear regression with multiple predictors
  6. Model diagnostics and evaluation
  7. Logarithmic transformations
  8. Comparing regression models

Generalized linear models

  1. Logistic regression
  2. Logistic regression with multiple predictors
  3. Diagnostics of logistic regression models
  4. Generalized linear models
  5. Poststratification: regression with non-representative sample

Causal inference

  1. Basics of causal inference
  2. Causal inference with regression
  3. Causal inference with observational data
  4. Subclassification and propensity score matching
  5. Instrumental variables
  6. Regression discontinuity
  7. Difference-in-differences
  8. Panel data
  9. Synthetic control

Conformal Prediction

  1. Full & split conformal prediction
  2. Jackknife+, CV+ and Quantile regression
  3. Conformal prediction for classification