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University of Oldenburg
07.12.2021 03:21:29
neu725 - Multivariate Statistics and Applications in R
Original version English Download as PDF
Module label Multivariate Statistics and Applications in R
Module code neu725
Credit points 6.0 KP
Workload 180 h
(
1,5 SWS Lecture (VO)
Total workload 68h: 28h contact / 20h background reading / 20h exam preparation
2,5 SWS Supervised exercise (UE):
Total workload 113h: 28h contact / 20h background reading / 65h exercise solving
)
Institute directory Department of Neurosciences
Applicability of the module
  • Master's Programme Neuroscience (Master) > Skills Modules
Responsible persons
Otto-Sobotka, Fabian (Module responsibility)
Otto-Sobotka, Fabian (Authorized examiners)
Prerequisites
Skills to be acquired in this module

+ Social skills
+ Interdiscipl. knowlg.
++ Maths/Stats/Progr.
+ Scientific English

students learn the use of the software R in application scenarios
students learn to actively "speak" the programming language R
students practice statistical data analysis with R
Module contents
The lecture gives an intuitive introduction into the use of the statistics software R. We start by introducing the basic handling of R and the syntax of its programming language. We use those to obtain the first statistical analyses from R. The next important step is to create informative graphics to represent the statistical results. Finally, we look into programming concepts that allow for more complex statistical analyses.
Reader's advisory
Uwe Ligges - Programmieren mit R (2008) Springer.
R Core Team - R: A language and environment for statistical computing (Reference Manual)
Simon N. Wood - Generalized Additive Models: An Introduction with R (2006) Chapman & Hall
Links
Language of instruction English
Duration (semesters) 1 Semester
Module frequency annually , summer term
Module capacity 24
Reference text
Recommended previous knowledge / skills: basic statistical knowledge including regression analysis
Modullevel / module level
Modulart / typ of module Wahlpflicht / Elective
Lehr-/Lernform / Teaching/Learning method
Vorkenntnisse / Previous knowledge
Course type Comment SWS Frequency Workload of compulsory attendance
Lecture
2 SuSe 28
Exercises
2 SuSe 28
Total time of attendance for the module 56 h
Examination Time of examination Type of examination
Final exam of module
after the course
practical exercise

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