Linear Statistical Models Grad

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, collinearity and confounding, and model selection. The theory will be approached mainly from the frequentist perspective and use of statistical software (mostly R) to analyze data will emphasized. Prerequisites: an introductory statistics course at the level of Math 3200; a course in linear algebra at the level of Math 309 or 429; some acquaintance with fundamentals of computer programming (CSE 131); Math 493, or permission of instructor.
Course Attributes:

Linear Statistical Models Grad

MATHEMATICS AND STATISTICS 5071

Section 01