Instruction
Group size: 1-3 students | Language: R or Python | Course grade: 30%
Choose one statistical method, implement its main algorithm, test it, and explain what you learned. One well-developed, established method is enough.
What you will produce
Use the same method for all three components. Follow the links for full requirements and rubrics.
| Component | Deliverable | Weight |
|---|---|---|
| Report | An 8-10 page PDF explaining the method and results, plus source files | 10% |
| Package | An installable R or Python package with documentation, an example, and tests | 10% |
| Presentation | An oral presentation explaining the method, implementation, and findings | 10% |
Python option: You may build a Python package; see the Python requirements.
How to get started
- Choose a topic. Use the instructor’s list (TBA), or propose your own method for instructor approval.
- Propose your plan. Send member names, the method, language, core algorithm, and a planned experiment. See approval details.
- Build and compare. Test two settings against a known answer or reference method; explain accuracy, computing time, and limitations.
A few ideas
- Bootstrap intervals: how often do intervals contain the true population mean?
- Gradient descent: how does step size affect convergence when fitting a regression?
- Monte Carlo integration: how do more random draws improve accuracy?
Due on December 13, 2026: report and package. Presentation date, time, and time limit: TBA. All members contribute and present. No slides or other presentation files need to be submitted.