neu725 - Multivariate Statistics and Applications in R (Complete module description)

# neu725 - Multivariate Statistics and Applications in R (Complete module description)

 Module label Multivariate Statistics and Applications in R Modulkürzel neu725 Credit points 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)) Institute directory Department of Neurosciences Verwendbarkeit des Moduls Master's Programme Biology (Master) > Skills Modules Master's Programme Neuroscience (Master) > Skills Modules Zuständige Personen Hildebrandt, Andrea (module responsibility) Hildebrandt, Andrea (Prüfungsberechtigt) Prerequisites recommended in semester 1/3 weeks 11-13 of summer semester Skills to be acquired in this module 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 Module contents 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 Language of instruction English Duration (semesters) 1 Semester Module frequency winter term, annually Module capacity unlimited (recommended in semester 1/3 weeks 11-13 of summer semester) Type of module Wahlpflicht / Elective Module level MM (Mastermodul / Master module)
Lehrveranstaltungsform Comment SWS Frequency Workload of compulsory attendance
Lecture 2 SoSe oder WiSe 28
Exercises 2 SoSe oder WiSe 28
Präsenzzeit Modul insgesamt 56 h
Examination Prüfungszeiten Type of examination
Final exam of module
End of winter semester
written exam
attendance of at least 70% in the seminars (in addition, mandatory but ungraded)