Applied Statistics in R
Lecturer (assistant) | |
---|---|
Number | MGT001438S |
Term | Sommersemester 2024 |
Language of instruction | English |
Position within curricula | See TUMonline |
- 15.04.2024 13:15-14:45 Z510, CIP-Pool
- 22.04.2024 13:15-14:45 Z510, CIP-Pool
- 29.04.2024 13:15-14:45 Z510, CIP-Pool
- 06.05.2024 13:15-14:45 Z510, CIP-Pool
- 13.05.2024 13:15-14:45 Z510, CIP-Pool
- 27.05.2024 13:15-14:45 Z510, CIP-Pool
- 03.06.2024 13:15-14:45 Z510, CIP-Pool
- 10.06.2024 13:15-14:45 Z510, CIP-Pool
- 17.06.2024 13:15-14:45 Z510, CIP-Pool
- 24.06.2024 13:15-14:45 Z510, CIP-Pool
- 01.07.2024 13:15-14:45 Z510, CIP-Pool
- 08.07.2024 13:15-14:45 Z510, CIP-Pool
- 15.07.2024 13:15-14:45 Z510, CIP-Pool
Objectives
At the end of the course, students are able to
- solve practical problems by selecting adequate, advanced statistical methods,
- applying statistical methods in a suitable analysis software, and
- present the obtained results well using advanced visualization methods for statistical data (e.g. of spatial data)
- solve practical problems by selecting adequate, advanced statistical methods,
- applying statistical methods in a suitable analysis software, and
- present the obtained results well using advanced visualization methods for statistical data (e.g. of spatial data)
Description
The diversity types of data in real life scenarios leads to a manifold of statistical methods one has to master.In this course, the students will learn about advanced statistical techniques (e.g. nonlinear and logistic regression, penalized regression, and time series analysis) for different research questions. The course consists of a practical part including an introduction to the statistical programming language R, visualization techniques, and applying a selection of statistical methods to real life data, and a complementary lecture about the statistical backgrounds of the selected methods. The main focus is on the practical aspects and the application in R.
Teaching and learning methods
Seminar session will be partly structured as interactive lectures, introducing and discussing new tools in R. Further, in short presentations, students recapitulate and reflect on the most important learning outcomes of the previous session. In the following exercises, the methods learned are applied to new problems and questions.