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2020S, UE, 1.0h, 2.0EC

## Properties

• Semester hours: 1.0
• Credits: 2.0
• Type: UE 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

Theoretical and practical examples using R.

## Teaching methods

Theoretical and practical examples using R.

## Mode of examination

Immanent

The prerequisite for the course is

 105.596 VO Econometrics 1: Linear Models

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## Examination modalities

Continuous assessment via oral examination and regular homework tasks throughout the semester. A data analysis project, that will be presented at the end of the semester, will count for 1/3 of the grade.

## Course registration

Begin End Deregistration end
24.02.2020 09:00 31.03.2020 23:59 31.03.2020 23:59

## Curricula

Study CodeObligationSemesterPrecon.Info
066 645 Data Science Not specified

## Literature

No lecture notes are available.

## Previous knowledge

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

## Miscellaneous

• Attendance Required!

English