Regression

Catalog description

Regression (3) Regression uses data to study mathematical relations among two or more variables, with the purpose of understanding trends, identifying significant predictors, and forecasting. The course covers simple and multiple regression, the method of least squares, analysis of variance, model building, regression diagnostics, and prediction. Students estimate and test significance of regression slopes, evaluate the goodness of fit, build optimal models, verify regression assumptions, suggest remedies, and apply regression methods to real datasets using statistical software. The course introduces concepts and methods that are fundamental for modern machine learning and artificial intelligence (AI) including supervised model fitting, prediction, bias-variance tradeoff, model complexity, performance evaluation, and matrix representation of data and coefficients. Crosslist: STAT-415. Usually Offered: fall, spring, and summer. Prerequisite: STAT-614.