Overview
STAT305 develops advanced theoretical and applied methods for statistical inference, modeling, and prediction. Topics include generalized linear and additive models, mixed-effects and hierarchical models, multivariate analysis, dimension reduction, discriminant analysis, nonparametric and resampling methods, robust estimation, regularization, model selection, Bayesian inference, missing-data strategies, and multiple testing.
The course emphasizes likelihood-based reasoning, estimation, uncertainty quantification, model assumptions, identifiability, computation, and reproducible analysis. Students evaluate model adequacy, compare explanatory and predictive approaches, address overfitting and data leakage, and communicate statistical conclusions responsibly using realistic datasets and statistical software.
Learning Outcomes
- Derive and explain key estimators, likelihood procedures, and inferential methods used in advanced statistical modeling.
- Evaluate the assumptions, identifiability, and computational requirements of competing statistical models.
- Select appropriate generalized, multilevel, multivariate, nonparametric, Bayesian, and regularized methods for defined research objectives.
- Diagnose model inadequacy, influential observations, missing-data mechanisms, overfitting, and data leakage using appropriate statistical and graphical procedures.
- Implement reproducible statistical analyses using software, including resampling, model comparison, uncertainty quantification, and predictive evaluation.
- Synthesize statistical evidence into clear technical reports that distinguish explanatory inference from prediction and address ethical interpretation.
Timetable
| Type | Length | Frequency | Period |
|---|---|---|---|
| Lecture | 3 hours | Weekly | All semester |
| Lab | 2 hours | Weekly | All semester |
| Tutorial | 1 hour | Fortnightly | All semester |
Assessment Schedule
| Type | Description | Weighting |
|---|---|---|
| Assignment | Problem sets (5 × 6%) | 30.00% |
| Deliverable | Computational data analysis project | 20.00% |
| Test | Practical statistical methods test | 15.00% |
| Assignment | Research communication assignment | 10.00% |
| Exam | Final examination | 25.00% |
Prerequisites
Teaching Staff & Programs
This course is delivered jointly by faculty from the participating programs listed below. In line with the Douchewater Way, the University of Sexology tailors core instruction directly to each cohort's specific discipline — adapting curriculum to program needs rather than forcing students into a one-size-fits-all model. Learn more about our approach at The Douchewater Way.