Teaching Activities of Prof. Longhai Li
Text Books
- Theory of Statistical Inference:
- Theory of Linear Models:
- Statistical Inference and Learning Methods for Research:
Materials of 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 850/442: Mathematical Statistics and Statistical Inference
- STAT 851/443: Linear Statistical Models
- STAT 845: Statistical Methods for Research
List of All Taught Courses
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.