Selected Courses

• STAT 245: Introduction to Statistical Methods
• STAT 342: Mathematical Statistics
• STAT 345: Design and Analysis of Experiments
• STAT 348: Sampling Techniques
• STAT 812/420: Computational Statistics
• STAT 851/443: Linear Statistical Models

All Courses

Prof. Li has taught a diverse array of statistics courses at the University of Saskatchewan, organized into four main areas:

Introductory Statistics: STAT 242: Statistical Theory and Methodology STAT 244: Elementary Statistical Concepts STAT 245: Introduction to Statistical Methods	Applied Statistics: STAT 345: Design and Analysis of Experiments STAT 348: Sampling Techniques STAT 845: Statistical Methods for Research STAT 834: Advanced Experimental Design STAT 848: Multivariate Data Analysis
Probability/Statistical Theory: STAT 241: Probability Theory STAT 342: Probability and Mathematical Statistics STAT 442: Statistical Inference STAT 443: Linear Statistical Models STAT 851: Linear Models STAT 841: Probability Theory	Computational Statistics: STAT 812: Computational Statistics STAT 420: Topics in Computational Statistics

Features of Longhai Li's Teaching

His teaching is featured by incorporating cutting-edge computational/data science techniques and real-world examples into classes.

• He introduces his students to computational tools such as R, R Markdown, python, and web-based computation for analyzing real datasets, developing statistical packages, and sharing analysis results. He has created web pages using R Markdown for all his classes on introductory and applied statistics and provided the source code for students to learn these tools.
• He employs computational simulation and animation tools to elucidate statistical theorems and computer algorithms.
• He is also committed to using real-world examples in his classes. For instance, he used data on SK’s COVID-19 vaccination and hospitalization rates to demonstrate the power of Bayes' rule for understanding the efficacy of vaccination.
• He actively involves undergraduate students in his research. In 2021, he led a team supported by MITACS to develop a public website that provides real-time reproduction rates for Canada’s national and provincial jurisdictions. He has also involved undergraduate students in building R packages (e.g. HTLR) for statistical machine learning.