After successful completion of the course, students are able to (i) apply modern regression/statistical learning methods to build predictive models, (ii) select and validate statistical learning models, (iii) assess model fit and error and (iv) use the R language for modern regression and data analysis.
Simple Linear Regression, Multiple Regression, Regression Diagnostics, Generalized Linear Models, Nonparametric Regression, Penalized Regression, Model Selection and Dimension Reduction.
Lecture notes by Dr. Gurker can be downloaded from the class website. Reference books are Applied Linear Statistical Models, 5th Edition by Kutner et al.; Practical Regression and ANOVA Using R at ftp://cran.r-project.org/pub/R/doc/contrib/Faraway-PRA.pdf ; Introduction to Statistical Learning with applications in R by James, Witten, Tibshirani & Hastie; Elements of Statistical Learning by Hastie, Tibshirani & Friedman.
The prerequisite for the course is
The final exam will be written and will cover all topics presented in class.
Basic probability and statistics; Linear algebra; Econometrics 1: Linear Models.
