Module Handbook

  • Dynamischer Default-Fachbereich geändert auf MV

Notes on the module handbook of the department Mechanical and Process Engineering

Die hier dargestellten veröffentlichten Studiengang-, Modul- und Kursdaten des Fachbereichs Maschinenbau und Verfahrenstechnik ersetzen die Modulbeschreibungen im KIS und wuden mit Ausnahme folgender Studiengänge am 28.10.2020, bzw. am 13.01.2021 verabschiedet.


Course MV-LTD-86081-K-7

Data evaluation and design of experiments (2V, 3.0 LP)

Course Type

SWS Type Course Form CP (Effort) Presence-Time / Self-Study
2 V Lecture with integrated exercises 3.0 CP 28 h 62 h
(2V) 3.0 CP 28 h 62 h


CP, Effort 3.0 CP = 90 h
Position of the semester 1 Sem. in SuSe
Level [7] Master (Advanced)
Language [DE] German
Area of study [MV-LTD] Engineering Thermodynamics
Additional informations
Livecycle-State [NORM] Active

Possible Study achievement

  • Verification of study performance: tests / audited elaborations
  • Details of the examination (type, duration, criteria) will be announced at the beginning of the course.


  • Basics of statistics:
    • Hypothesis testing
    • Analysis of variance
  • Data analysis:
    • Correlation analysis
    • Principal Component Analysis (PCA)
  • Data-based modeling:
    • linear regression
    • partial least squares
    • non-linear regression
    • model evaluation and discrimination
  • Design of experiments:
    • factorial experiments
    • model-based experimental design
  • independent application of theory in practical computer exercises

Competencies / intended learning achievements

1. Lecture

The students are able to

  • describe basic methods of statistics, parameter estimation and experimental design
  • estimate parameters
  • evaluate models using statistical methods

2. Exercise

The students are able to

  • apply the methods discussed in the lecture independently in small groups
  • estimate parameters for process engineering models
  • evaluate the developed models by statistical means
  • create statistical experimental designs and identify optimal experimental conditions


  • D. C. Montgomery, Design and Analysis of Experiments, Wiley
  • A. Rasmuson et al., Mathematical Modeling in Chemical Engineering, Cambridge University Press
  • Peter Goos, Bradley Jones; Optimal Design of Experiments: A Case Study Approach


Electronic blackboard, slides, interactive computer exercise (Matlab)

Requirements for attendance (informal)

Previous knowledge of higher mathematics

Requirements for attendance (formal)


References to Course [MV-LTD-86081-K-7]

Module Name Context
[MV-LTD-M187-M-7] Data evaluation and design of experiments P: Obligatory 2V, 3.0 LP