# 105.725 General Regression Models This course is in all assigned curricula part of the STEOP.\$(function(){PrimeFaces.cw("Tooltip","widget_j_id_21",{id:"j_id_21",showEffect:"fade",hideEffect:"fade",target:"isAllSteop"});});This course is in at least 1 assigned curriculum part of the STEOP.\$(function(){PrimeFaces.cw("Tooltip","widget_j_id_23",{id:"j_id_23",showEffect:"fade",hideEffect:"fade",target:"isAnySteop"});});

2020S, VU, 3.0h, 5.0EC

## Properties

• Semester hours: 3.0
• Credits: 5.0
• Type: VU Lecture and Exercise

## Learning outcomes

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.

## Subject of course

Simple Linear Regression, Multiple Regression, Regression Diagnostics, Generalized Linear Models, Nonparametric Regression, Penalized Regression, Model Selection and Dimension Reduction.

## Teaching methods

### 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.

## Mode of examination

Written

The prerequisite for the course is

 105.596 VO Econometrics 1: Linear Models

## Course dates

DayTimeDateLocationDescription
Thu09:00 - 11:3012.03.2020FAV Hörsaal 3 Zemanek (Seminarraum Zemanek) Allgemeine Regressionsmodelle

## Examination modalities

The final exam will be written and will cover all topics presented in class.

## Course registration

Begin End Deregistration end
05.03.2020 15:00 25.03.2020 23:59 25.03.2020 23:59

## Literature

No lecture notes are available.

## Previous knowledge

Basic probability and statistics; Linear algebra; Econometrics 1: Linear Models.

## Miscellaneous

• Attendance Required!

English