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Statistics II



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Module name (EN):
Name of module in study programme. It should be precise and clear.
Statistics II
Degree programme:
Study Programme with validity of corresponding study regulations containing this module.
Industrial Engineering, Bachelor, regulation 01.10.2013
Module code: WIBASc-525-625-Ing28
Hours per semester week / Teaching method:
The count of hours per week is a combination of lecture (V for German Vorlesung), exercise (U for Übung), practice (P) oder project (PA). For example a course of the form 2V+2U has 2 hours of lecture and 2 hours of exercise per week.
2V+2U (4 hours per week)
ECTS credits:
European Credit Transfer System. Points for successful completion of a course. Each ECTS point represents a workload of 30 hours.
5
Semester: 6
Mandatory course: no
Language of instruction:
German
Assessment:
Written exam or written composition

[updated 29.10.2026]
Applicability / Curricular relevance:
All study programs (with year of the version of study regulations) containing the course.

WIBASc-525-625-Ing28 Industrial Engineering, Bachelor, regulation 01.10.2013 , semester 6, optional course, general subject
WIB21-WPM-T-114 (P420-0413) Industrial Engineering, Bachelor, regulation 01.10.2021 , semester 6, optional course, general subject
Workload:
Workload of student for successfully completing the course. Each ECTS credit represents 30 working hours. These are the combined effort of face-to-face time, post-processing the subject of the lecture, exercises and preparation for the exam.

The total workload is distributed on the semester (01.04.-30.09. during the summer term, 01.10.-31.03. during the winter term).
60 class hours (= 45 clock hours) over a 15-week period.
The total student study time is 150 hours (equivalent to 5 ECTS credits).
There are therefore 105 hours available for class preparation and follow-up work and exam preparation.
Recommended prerequisites (modules):
None.
Recommended as prerequisite for:
Module coordinator:
Prof. Dr. Susan Pulham
Lecturer:
Prof. Dr. Susan Pulham


[updated 11.03.2021]
Learning outcomes:
After successfully completing this module, students will be able to perform key statistical analyses using Excel and JASP. They will be able to select appropriate analysis methods for given research questions.

[updated 29.10.2026]
Module content:
1. Introduction to JASP and MS Excel analysis functions
2. Hypothesis tests in Excel und JASP
3. Regression analysis in Excel und JASP
4. Exploratory factor analysis in JASP
5. Cluster analysis in JASP

[updated 29.10.2026]
Teaching methods/Media:
Computer-based seminars, computer exercises

[updated 29.10.2026]
Recommended or required reading:
Peter P. Eckstein: Statistik für Wirtschaftswissenschaftler - Eine realdatenbasierte Einführung mit SPSS


[updated 29.10.2026]
 
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