Statistical Linear Models

Catalog description

This is an applied statistics course with a focus on linear models. Building upon foundations of statistics, students will learn to specify and fit both ordinary linear models (OLS) and more advanced models. Specific topics beyond OLS include generalized linear models (GLM), two-way ANOVA, linear mixed models, and other extensions. Students will also learn model diagnostics as an important pillar of data science . Students will foster a deeper understanding of the flexibility of statistical modeling, which culminates in a course project with applications in real world data to address practical problems. Counts for the Interdisciplinary minor in Data Sciece. Satisfies Mathematical & Quantitative Thought in the Ways of Knowing. Prerequisites & Notes An introductory statistics course equivalent to MAT 104/105 (e.g., BIO 240, ECO 105, POL 182, PSY 300, SOC 201).