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.

The material will be presented in lecture slides in conjunction with derivations on board. Course related information and material, including notes and details about course assignments and exams will be posted in TUWEL.

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 statistical software we will use is R. It can be downloaded from the R home page . RStudio offers a GUI R platform.

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.