# 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"});}); 2024S 2023S 2022S 2021S 2020S

2023S, VU, 3.0h, 5.0EC

## Properties

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

## 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
Mon11:00 - 14:0006.03.2023 - 26.06.2023Sem.R. DA grün 04 General Regression Models
General Regression Models - Single appointments
DayDateTimeLocationDescription
Mon06.03.202311:00 - 14:00Sem.R. DA grün 04 General Regression Models
Mon13.03.202311:00 - 14:00Sem.R. DA grün 04 General Regression Models
Mon20.03.202311:00 - 14:00Sem.R. DA grün 04 General Regression Models
Mon27.03.202311:00 - 14:00Sem.R. DA grün 04 General Regression Models
Mon17.04.202311:00 - 14:00Sem.R. DA grün 04 General Regression Models
Mon24.04.202311:00 - 14:00Sem.R. DA grün 04 General Regression Models
Mon08.05.202311:00 - 14:00Sem.R. DA grün 04 General Regression Models
Mon15.05.202311:00 - 14:00Sem.R. DA grün 04 General Regression Models
Mon22.05.202311:00 - 14:00Sem.R. DA grün 04 General Regression Models
Mon05.06.202311:00 - 14:00Sem.R. DA grün 04 General Regression Models
Mon12.06.202311:00 - 14:00Sem.R. DA grün 04 General Regression Models
Mon19.06.202311:00 - 14:00Sem.R. DA grün 04 General Regression Models
Mon26.06.202311:00 - 14:00Sem.R. DA grün 04 General Regression Models

## Examination modalities

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

## Course registration

Begin End Deregistration end
21.02.2023 17:00 18.03.2023 23:59 27.03.2023 23:59

## Literature

No lecture notes are available.

## Previous knowledge

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

## Miscellaneous

• Attendance Required!

English