ADVANCED STATISTICAL METHODS
Gain thorough grounding in modern statistical methods and theory. Classical and modern statistical techniques, including linear regression models, diagnostic tools, prediction, generalized linear regression models, random effect models, experimental design, and model selection. Apply these techniques to dataset in specific contexts.
Prerequisites & course information
Requisites: Declared in Statistics MS, Statistics PhD, Biomedical Data Science PhD, Biomedical Data Science MS, Business PhD, or Statistics Doctoral Minor Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Build appropriate regression models given specific contexts. Audience: Graduate 2. Conduct estimation and inference for regression models: Calculate important quantities, such as MLE and test statistics. Derive important statistical properties, such as distributions of statistics and conducting hypothesis testing Audience: Graduate 3. Analyze the performance of linear models: The use of goodness-of-fit measurements of models. Describe common issues of model building. Use statistical methods to select between models.Provide interpretation of the built models. Audience: Graduate 4. Master the concepts and relevant theory for general linear model, especially the inference procedure for binary, multinomial, count and categorical data. Audience: Graduate 5. Understand the theory and methods for random effect models and experiment design including blocking, nesting, replication. Audience: Graduate Learning Outcomes: 1. Build appropriate regression models given specific contexts. Audience: Graduate 2. Conduct estimation and inference for regression models: Calculate important quantities, such as MLE and test statistics. Derive important statistical properties, such as distributions of statistics and conducting hypothesis testing Audience: Graduate 3. Analyze the performance of linear models: The use of goodness-of-fit measurements of models. Describe common issues of model building. Use statistical methods to select between models.Provide interpretation of the built models. Audience: Graduate 4. Master the concepts and relevant theory for general linear model, especially the inference procedure for binary, multinomial, count and categorical data. Audience: Graduate 5. Understand the theory and methods for random effect models and experiment design including blocking, nesting, replication. Audience: Graduate
Sources
- Course catalogue ↗2026-2027