STSCI 4030

Linear Models with Matrices

Course Description

Course information provided by the 2026-2027 Catalog.

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.

Prerequisites STSCI 2150 or STSCI 2200 or STSCI 3080, MATH 1920, MATH 2210 or their equivalents, STSCI 3200 or STSCI 6020.

Distribution Requirements (DLS-AG, OPHLS-AG), (SDS-AS), (STA-IL)

Last 4 Terms Offered 2025FA, 2024FA, 2023FA, 2022FA

Learning Outcomes

• Students will be able to discuss the mathematical foundations of linear statistical models using matrix algebra.
• Students will be able to use diagnostic measures to assess the validity of a given statistical model.
• Students will be able to analyze data involving both fixed and random factors.

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