MA237 - Statistical Methods I
Covers statistical methods for learning from data beyond those typically learned in introductory courses. Emphasis on statistical modeling, including multiple linear regression, classification models, and other methods for supervised learning and statistical inference. Additional techniques include non-parametric methods, bootstrap estimation, and analysis of model fit via cross-validation. Includes a strong computational component and will make use of the statistical programing language R for data analysis and simulations. Meets the Critical Learning: FRL requirement. Meets the Critical Learning: SA requirement.
Prerequisite: Mathematics 217 or (Mathematics 117 and either Mathematics 125 or Mathematics 126).
Degree requirement — Critical Learning: FRL, Critical Learning: SA
1 unit — Kim, Sancier-Barbosa
Offerings
| Term | Block | Title | Instructor | Location | Student Limit/Available | Updated |
|---|---|---|---|---|---|---|
| Fall 2024 | Block 1 | Statistical Methods I | Flavia Sancier-Barbosa | Armstrong Hall 2 | 25 / 14 | 12/23/2024 |
| Spring 2025 | Block 6 | Statistical Methods I | Minho Kim | TBA | 25 / 8 | 12/23/2024 |