Teaching

STAT 331: Applied Linear Models

Course notes.

• Review of Simple Linear Regression

  Relevant distributions; model assumptions; inference; statistical significance; prediction; model checking.

• Multiple Linear Regression: Theory

  Relevant matrix manipulations; model assumptions; inference; statistical significance; prediction.

• Multiple Linear Regression: Case Studies

  Elements of a regression output; nonlinear modeling; categorical predictors; interaction effects; heteroscedasticity.

• Model Checking

  Colinearity; residual analysis; outliers.

• Model Selection

  Stepwise regression; AIC; cross-validation.

STAT 440: Computational Inference

• Review: The Multivariate Normal Distribution.

• Stochastic Differential Equations

• Profile Likelihood

• Exponential Families

• Variable Selection

• The Bootstrap Method

• The Expectation-Maximization Algorithm

• Introduction to Bayesian Inference

• Basics of Markov Chain Monte Carlo

• MCMC: Intermediate Examples

STAT 946: Advanced Bayesian Computing

Course notes written by the class of Spring 2020.

Graphical course outline.

Topics in Statistical Consulting

Collection of modules written by the student consultant RAs at the University of Waterloo Statistical Consulting and Survey Research Unit.