Data Analytics and Computation 390R - Applied Linear Regression

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. We will also be using R, a statistical computing environment widely used by researchers and data scientists.

Students should have taken an introductory statistics class previously.

This is an UNDERGRADUATE class that CANNOT be used towards the DACSS MS.