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Statistics and Data Analysis

Module name (EN):
Name of module in study programme. It should be precise and clear.
Statistics and Data Analysis
Degree programme:
Study Programme with validity of corresponding study regulations containing this module.
Industrial Engineering, Bachelor, ASPO 01.10.2021
Module code: WIBb21-420
SAP-Submodule-No.:
The exam administration creates a SAP-Submodule-No for every exam type in every module. The SAP-Submodule-No is equal for the same module in different study programs.
P450-0342
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.
30VS (30 hours)
ECTS credits:
European Credit Transfer System. Points for successful completion of a course. Each ECTS point represents a workload of 30 hours.
5
Semester: 4
Mandatory course: yes
Language of instruction:
German
Assessment:
Exam

[updated 18.06.2025]
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).
The total student study time for this course is 150 hours.
Recommended prerequisites (modules):
None.
Recommended as prerequisite for:
WIBb21-730 Systems Engineering/ X in the Loop (HiL, SiL, MiL)


[updated 30.05.2025]
Module coordinator:
Studienleitung
Lecturer: Studienleitung

[updated 08.10.2021]
Learning outcomes:
After successfully completing this module, students will:
• be able to prepare quantitative and qualitative data using descriptive statistical methods, and
• interpret results
• be able to recognize stochastic situations as such and analyze them using stochastic models
• They will, in particular, be able to calculate probabilities, determine suitable distribution forms, and estimate distribution parameters
• Students will be able to demonstrate a basic understanding of inductive statistics, in particular methods of parameter estimation and hypothesis testing
• They will be able to select and carry out appropriate test procedures for empirical questions and interpret the results adequately.


[updated 18.06.2025]
Module content:
1.      Descriptive statistics
        1.1     Basic terms
        1.2     One- and two-dimensional frequency distributions
        1.3     Measures of location and measures of spread/dispersion
        1.4     Calculating correlation and regression
        1.5     Contingency calculation
   
2.      Probability calculus
        2.1     Basic terms: random experiment, events, probability
        2.2     Modeling random experiments
        2.3     Multi-stage random experiments
        2.4     Conditional probability and independence
        2.5     Random variables, expected value, variance
        2.6     Calculation rules for expected values, variances, and covariances
        2.7     Important distributions and limit theorems
   
3.      Basic elements of inferential statistics
        3.1     Problems with inferential statistics
        3.2     Point and interval estimates
        3.3     Hypothesis testing (parametric and non-parametric)

[updated 18.06.2025]
Teaching methods/Media:
Lecture, digitally supported teaching, self-study

[updated 18.06.2025]
Recommended or required reading:
Eckstein, Peter: Statistik für Wirtschaftswissenschaftler, 6. Auflage, Gabler, Wiesbaden, 2018
Eckstein, Peter: Klausurtraining Statistik, 4. Auflage, Gabler, Wiesbaden, 2005
Göllmann, Laurenz; Hübl, Reinhold; Pulham, Susan; Ritter, Stefan; Schon, Henning; Schüffler, Karlheinz; Voß, Ursula; Vossen, Georg: Mathematik für Ingenieure: Verstehen – Rechnen – Anwenden: Band 1: Vorkurs, Analysis in einer Variablen, Lineare Algebra, Statistik, Springer Vieweg Verlag, Wiesbaden, 2017
Pulham, Susan: Statistik leicht gemacht, Gabler, Wiesbaden, 2011
Sachs, Michael: Wahrscheinlichkeitsrechnung und Statistik für Ingenieurstudenten an Fachhochschulen; 5. Auflage, Carl Hanser Verlag, 2018


[updated 18.06.2025]
 
[Wed Jul  9 20:52:17 CEST 2025, CKEY=wsuda, BKEY=wit, CID=WIBb21-420, LANGUAGE=en, DATE=09.07.2025]