Stud.IP Uni Oldenburg
Universität Oldenburg
17.05.2022 13:01:09
neu725 - Multivariate Statistics and Applications in R (Vollständige Modulbeschreibung)
Originalfassung Englisch PDF Download
Modulbezeichnung Multivariate Statistics and Applications in R
Modulkürzel neu725
Kreditpunkte 6.0 KP
Workload 180 h
2 SWS Lecture: 30h contact / 60h self-studies and exam preparation
2 SWS Seminar: (30h contact / 60h statistical data analysis in R
Einrichtungsverzeichnis Department für Neurowissenschaften
Verwendbarkeit des Moduls
  • Master Biology (Master) > Skills Modules
  • Master Neuroscience (Master) > Skills Modules
Zuständige Personen
Hildebrandt, Andrea (Modulverantwortung)
Hildebrandt, Andrea (Prüfungsberechtigt)
recommended in semester 1/3
weeks 11-13 of summer semester
Students will acquire basic knowledge in planning empirical investigations, managing and understanding quantitative data and conducting a wide variety of multivariate statistical analyses. They will learn how to use the statistical methodology in terms of good scientific practice and how to interpret, evaluate and synthesize empirical results from the perspective of statistical modeling in basic and applied research context. The courses in this module will additionally point out statistical misconceptions and help students to overcome them.

+ Independent research
+ Scient. Literature
+ Social skills
++ Interdiscipl. knowledge
++ Maths/Stats/Progr.
++ Data preset./disc.
+ Scient. English
++ Ethics

Part 1: Multivariate Statistics I (lecture):
Graphical representation of multivariate data
The Generalized Linear Modeling (GLM) framework
Multiple and moderated linear regression with quantitative and qualitative predictors
Logistic regression
Multilevel regression (Generalized Linear Mixed Effects Modeling – GLMM)
Non-linear regression models
Path modeling
Factor analysis (exploratory & confirmatory)
(Multilevel) Structural equation modeling (SEM linear and non-linear)

Part 2: Analysis Methods with R (seminar)
Data examples and applications of GLM, GLMM, polynomial, spline and local regression, path
modeling, factor analyses and SEM
Course material will be available in Stud.IP
Unterrichtssprache Englisch
Dauer in Semestern 1 Semester
Angebotsrhythmus Modul winter term, annually
Aufnahmekapazität Modul unbegrenzt ()
Modullevel / module level MM (Mastermodul / Master module)
Modulart / typ of module Wahlpflicht / Elective
Lehr-/Lernform / Teaching/Learning method
Vorkenntnisse / Previous knowledge
Lehrveranstaltungsform Kommentar SWS Angebotsrhythmus Workload Präsenz
2 SoSe oder WiSe 28
2 SoSe oder WiSe 28
Präsenzzeit Modul insgesamt 56 h
Prüfung Prüfungszeiten Prüfungsform
End of winter semester
written exam
attendance of at least 70% in the seminars (in addition, mandatory but ungraded)