The course provides an overview of the general linear regression model - one of the most widely used inferential tools in the social sciences. This course first focuses on the model and its statistical properties. We will then consider generalizations or extensions of the model that have been designed to handle violations of the basic model's assumptions. Topics typically include the general linear model, hypothesis testing, nonlinearities in variables, interactions, diagnostics, heteroscedastic residuals, limited dependent variables, measurement error, and causal inference.

In this course, students build on their fall experience by translating self-discovery into professional readiness. They refine resumes, craft LinkedIn profiles, and practice elevator pitches while meeting with career coaches and peers to explore majors and industries. Emphasizing communication, collaboration, and professionalism, the course prepares students to navigate career conversations confidently and engage more fully in the Isenberg community.

In this course, students build on their fall experience by translating self-discovery into professional readiness. They refine resumes, craft LinkedIn profiles, and practice elevator pitches while meeting with career coaches and peers to explore majors and industries. Emphasizing communication, collaboration, and professionalism, the course prepares students to navigate career conversations confidently and engage more fully in the Isenberg community.