Theory and practice of linear regression, analysis of variance (ANOVA) and their extensions, including testing, estimation, confidence interval procedures, modeling, regression diagnostics and plots, polynomial regression, colinearity and confounding, model selection, geometry of least squares. The theory will be approached mainly from the frequentist perspective and use of the computer (mostly R) to analyze data will be emphasized.
Prerequisite: Math 3200, a course in linear algebra (Math 309 or 429); some acquaintance with fundamentals of computer programming (CSE 131) and Math 493 (concurrently is okay), or permission of instructor.
Course Attributes: FA NSMAR NSMAS NSM
Section 01
Linear Statistical Models
INSTRUCTOR: [TBA]
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