Stud.IP Uni Oldenburg
Universität Oldenburg
22.01.2022 16:31:37
neu725 - Multivariate Statistics and Applications in R (Vollständige Modulbeschreibung)
 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) Teilnahmevoraussetzungen recommended in semester 1/3 weeks 11-13 of summer semester Kompetenzziele 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 Modulinhalte 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 Literaturempfehlungen Course material will be available in Stud.IP Links 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
Vorlesung
2 SoSe oder WiSe 28
Übung
2 SoSe oder WiSe 28
Präsenzzeit Modul insgesamt 56 h
Prüfung Prüfungszeiten Prüfungsform
Gesamtmodul
End of winter semester
written exam
attendance of at least 70% in the seminars (in addition, mandatory but ungraded)