Linear Statistical Models

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, etc. The theory will be approached mainly from the frequentist perspective and use of the computer (mostly R) to analyze data will be emphasized. Prerequisite: CSE 131 or 200; a course in linear algebra (such as Math 309 or 429); Math/SDS 3211 or Math/SDS 3200 and Math/SDS 493 (493 can be taken concurrently). If Math/SDS 3211 is taken, Math/SDS 493 is not required.
Course Attributes: FA NSM; AS NSM

Linear Statistical Models

STATISTICS AND DATA SCIENCE 439

Section 01

Section 02