STATISTICAL INFERENCE FOR DATA SCIENCE

Prerequisites & course information

Requisites: STAT 611 Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Spring 2026 Learning Outcomes: 1. Construct point estimators including maximum likelihood estimators, understand the theoretical properties of point estimation methods, evaluate their performance using mathematical derivations and simulation-based techniques, and identify optimal point estimators Audience: Graduate 2. Describe the Bayesian approach to point estimation and contrast it with the frequentist approach Audience: Graduate 3. Construct and evaluate hypothesis tests (such as likelihood ratio tests) using mathematical derivations or simulation-based techniques, interpret their results, understand theoretical properties of hypothesis testing methods, and identify optimal hypothesis tests Audience: Graduate 4. Construct and evaluate interval estimators using mathematical and simulation-based techniques, understand the theoretical properties of interval estimation methods, and interpret their results Audience: Graduate 5. Identify and describe the assumptions underlying methods of statistical inference and explain their importance Audience: Graduate 6. Fit models and carry out statistical inference in classical situations: namely: the comparison of two or more samples (ANOVA), the analysis of categorical data, linear regression, generalized linear models, and random and mixed effects models Audience: Graduate Learning Outcomes: 1. Construct point estimators including maximum likelihood estimators, understand the theoretical properties of point estimation methods, evaluate their performance using mathematical derivations and simulation-based techniques, and identify optimal point estimators Audience: Graduate 2. Describe the Bayesian approach to point estimation and contrast it with the frequentist approach Audience: Graduate 3. Construct and evaluate hypothesis tests (such as likelihood ratio tests) using mathematical derivations or simulation-based techniques, interpret their results, understand theoretical properties of hypothesis testing methods, and identify optimal hypothesis tests Audience: Graduate 4. Construct and evaluate interval estimators using mathematical and simulation-based techniques, understand the theoretical properties of interval estimation methods, and interpret their results Audience: Graduate 5. Identify and describe the assumptions underlying methods of statistical inference and explain their importance Audience: Graduate 6. Fit models and carry out statistical inference in classical situations: namely: the comparison of two or more samples (ANOVA), the analysis of categorical data, linear regression, generalized linear models, and random and mixed effects models Audience: Graduate