Kai Li

Researcher and educator working in statistics, reinforcement learning, modeling, and simulation.

Teaching connection: Graduate instructor in Spring 2023, Spring 2024, and Spring 2025.

This page is part of my personal academic record, not an official course website.

Course description

Estimation, confidence intervals, Neyman-Pearson lemma, likelihood ratio test, hypothesis testing, chi-square test, regression, analysis of variance, and nonparametric methods.

Textbooks and resources

Supplementary textbook

  • John E. Freund’s Mathematical Statistics with Applications by Irwin Miller and Marylees Miller, 8th edition, 2018, Pearson

Learning outcomes

By the end of this course, students should successfully be able to:

  1. Calculate and evaluate point estimators.
  2. Formulate and construct confidence intervals for parameters in a statistical model, and interpret the results.
  3. Formulate statistical hypotheses, construct appropriate hypothesis tests, and interpret the results.
  4. Formulate linear regression models, fit these models, and interpret the results.
  5. Formulate one-way ANOVA models, fit these models, and interpret the results.
  6. Construct distribution-free hypothesis testing procedures and interpret the results.

Spring 2025 syllabus · Back to Teaching & Mentoring · Back to Graduate Studies