This is an applied regression analysis course that involves hands-on data analysis. Topics covered during the semester include simple and multiple linear regression models, model diagnostics and remedial measures, matrix representation of linear regression models, model comparison and selection, generalized linear regression models (e.g. binary logistic regression, multinomial logistic regression, ordinal logistic regression, and Poisson regression), and basic time-series autoregressive AR(p) models. Statistical language R will be used throughout the course to realize fitting linear (or generalized linear) regressions models, model diagnostics, model comparison and selection, and simulations.
Max Enrollment: 15
Prerequisites: STAT 218 and MATH 205 and MATH 206. (STAT 218 can be replaced by STAT 160, ECON 103, or STAT 260.)
Instructor: W. Wang (Fall); Joseph (Spring)
Distribution Requirements: MM - Mathematical Modeling and Problem Solving
Semesters Offered this Academic Year: Fall; Spring