Universität Wien

040713 UK Applied Statistics (2018W)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 50 participants
Language: German

Lecturers

Classes (iCal) - next class is marked with N

  • Monday 01.10. 16:45 - 18:15 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 08.10. 16:45 - 18:15 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 15.10. 16:45 - 18:15 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 22.10. 16:45 - 18:15 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 29.10. 16:45 - 18:15 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 05.11. 16:45 - 18:15 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 12.11. 16:45 - 18:15 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 19.11. 16:45 - 18:15 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 26.11. 16:45 - 18:15 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 03.12. 16:45 - 18:15 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 10.12. 16:45 - 18:15 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 07.01. 16:45 - 18:15 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 14.01. 16:45 - 18:15 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 21.01. 16:45 - 18:15 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 28.01. 16:45 - 18:15 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock

Information

Aims, contents and method of the course

The lecture treats a number of basic issues in application of statistics. In particular the following topics will be discussed:
1. Basic concepts, Types of data, Methods of data capture
2. Principle of classical Statistical Inference: Frequency approach, Likelihood approach, Bayes approach
3. Extensions of the classical approaches: Empirical Bayes, Model selection and prediction, Causal inference, Meta anaylysis
4. Computer intensive methods: Large-scale hypothesis testing, Neural networks and Deep learning

Assessment and permitted materials

Attendance of lectures (3 missing classes allowed)
Solving practical exercises with R
Writing a report
Final exam

Minimum requirements and assessment criteria

Execises:
There are three exercise sheets with practical exercises. Each sheet covers different topics of the lecture. The exercises are solved using R and uploaded on Moodle.
Reports:
The reports cover either more elaborated data analysis or additional material in the different topics and is workedroups of two students.
Written exam:
In each part a positive assessment must be achieved.
The overall grade is 30 % execises, 30 % report, 40% written exam

Examination topics

The written exam is based on the contents of the lecture (theoretical questions)

Reading list

The concept of the lecture is based on:
B. Efron, T. Hastie: Computer Age Statistical Inference- Algorithms, Evidence and Data Science. Cambridge University Press, 2016
Further References to special topics will be announced in Moodle

Association in the course directory

Last modified: Mo 07.09.2020 15:29