Prerequisites: A two-semester sequence on statistical methods (e.g. BTRY 3010-BTRY 3020), a course on probability and distribution theory (e.g. BTRY 3080), multivariable calculus, and linear/matrix algebra.
Course Description: The focus of this course is the theory and application of the general linear model expressed in its matrix form. Topics will include: least squares estimation, multiple linear regression, coding for categorical predictors, residual diagnostics, anova decomposition, polynomial regression, model selection techniques, random effects and mixed models, maximum likelihood estimation and distributional theory assuming normal errors. Homework assignments will involve computation using the R statistical package.
Outcome 1: Students will be able to discuss the mathematical foundations of linear statistical models using matrix algebra.
Outcome 2: Students will be able to use diagnostic measures to assess the validity of a given statistical model.
Outcome 3: Students will be able to analyze data involving both fixed and random factors.