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.
The linear regression material and some nonparametric regression material is covered in Linear Regression Analysis by Seber and Lee https://onlinelibrary.wiley.com/doi/book/10.1002/9780471722199. Other 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.