Linear Regression Analysis
This course is an intensive introduction to linear models, with a focus on both principles and practice. Examples from finance, business, marketing and economics are emphasized. Large data sets are used frequently. Topics include simple and multiple linear regression; weighted, generalized, and outlier-resistant least squares regression; interaction terms; transformations; regression diagnostics and addressing violations of regression assumptions; variable selection techniques like backward elimination and forward selection, and logit/probit models. Statistical packages include R and SAS.