Inhaltspezifische Aktionen

(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.

  • (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.
Wann

06.12.2023 von 09:00 bis 12:00 (Europe/Berlin / UTC100)

Alle Termine

06.12.2023 von 09:00 bis 12:00
13.12.2023 von 09:00 bis 12:00

Wo

Seminarraum 32, Mathematisches Institut, Arndtstraße 2

Telefon des Kontakts

0641-99-18300

Termin zum Kalender hinzufügen

iCal

 

 

Überblick

  • Dez 6 + Dez 13, 9:00 - 12:00 + tba + tba (4 Termine á 3h)
  •  Nachwuchswissenschaftlerinnen und -wissenschaftler aller Disziplinen
  • Dr. Gerrit Eichner

 
  • Englisch

 

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).

 

 

Anmeldung via

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.