(R-) Crash Course on Nonparametric/Distribution-free Statistical Methods
The course is designed to be a mix of theoretical reviews and practical work on examples for selected data sets using the open-source statistics software R.
- https://www.uni-giessen.de/de/fbz/ggn/events/workshops/nonparametric
- (R-) Crash Course on Nonparametric/Distribution-free Statistical Methods
- 2023-12-06T09:00:00+01:00
- 2023-12-06T12:00:00+01:00
- The course is designed to be a mix of theoretical reviews and practical work on examples for selected data sets using the open-source statistics software R.
06.12.2023 von 09:00 bis 12:00 (Europe/Berlin / UTC100)
06.12.2023 von 09:00 bis 12:00
13.12.2023 von 09:00 bis 12:00
Seminarraum 32, Mathematisches Institut, Arndtstraße 2
Gießener Graduiertenzentrum Naturwissenschaften und Psychologie
Überblick
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Kursbeschreibung
Aim: Depending on your knowledge, the course will be a combination of a refresher or a crash course in nonparametric/distribution-free statistics.
Requirements: Basic proficiency in applied inferential statistics and a basic working knowledge of R.
Intended content: A selection of topics on nonparametric/distribution-free statistics:
- Univariate tests for location or scale alternatives for one sample or two (independent or paired) samples, e.g. rank-based tests according to Wilcoxon, Ansari-Bradley, etc.;
- Univariate tests for a broad alternative for two independent samples, e.g. according to Kolmogorov & Smirnov;
- Univariate tests in one- or two-factorial designs, e.g. rank-based tests according to Kruskal & Wallis, Jonckheere & Terpstra, Friedman, Page, etc.;
- Univariate tests in one- or two-factorial designs for longitudinal dara, e.g. tests based on so-called relative effects according to Brunner, Langer & et al.;
- Tests for bivariate independence, e.g. according to Spearman, Kendall, Hoeffding;
- Tests for the slope/s in a regression problem for one-sample (due to Theil) and for two-samples (due to Holland).
Anmeldelink für Externe
Der Referent
Dr. Gerrit Eichner studied Mathematics at the JLU Giessen and wrote his doctoral
thesis on nonparametric estimation in survival/sacrifice experiments. He spent part of his
education at the Department of Statistics at the University of Washington in Seattle/
Washington, USA. Since 1998, he has been a statistical consultant for many interdisciplinary
research projects in life sciences at the JLU as well as for private companies. He teaches
routinely a 4-semester course “Applied Statistics with R” at the Mathematical Institute of the
JLU for mathematics students and other interested faculties, and has conducted several
statistics workshops since 2008.