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	11/04/2024
Spring 2025	Block 6	Statistical Methods I	Minho Kim	TBA	25 / 25	11/04/2024