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.

Ausnahmen:

Module MV-LTD-M187-M-7

Data evaluation and design of experiments (M, 3.0 LP)

Module Identification

Module Number Module Name CP (Effort)
MV-LTD-M187-M-7 Data evaluation and design of experiments 3.0 CP (90 h)

Basedata

CP, Effort 3.0 CP = 90 h
Position of the semester 1 Sem. in SuSe
Level [7] Master (Advanced)
Language [DE] German
Module Manager
Lecturers
Area of study [MV-LTD] Engineering Thermodynamics
Reference course of study [MV-88.B78-SG] M.Sc. Production Engineering in Mechanical Engineering
Livecycle-State [NORM] Active

Courses

Type/SWS Course Number Title Choice in
Module-Part
Presence-Time /
Self-Study
SL SL is
required for exa.
PL CP Sem.
2V MV-LTD-86081-K-7
Data evaluation and design of experiments
P 28 h 62 h
TESTAT
ja PL1 3.0 SuSe
  • About [MV-LTD-86081-K-7]: Title: "Data evaluation and design of experiments"; Presence-Time: 28 h; Self-Study: 62 h
  • About [MV-LTD-86081-K-7]: The study achievement "[TESTAT] tests / audited elaborations" must be obtained.
    • It is a prerequisite for the examination for PL1.

Examination achievement PL1

  • Form of examination: oral examination (20-30 Min.)
  • Examination Frequency: each semester
  • Examination number: 10196 ("Data evaluation and design of experiments")

Evaluation of grades

The grade of the module examination is also the module grade.


Contents

  • 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

Literature

  • 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

Requirements for attendance of the module (informal)

Previous knowledge of higher mathematics

Requirements for attendance of the module (formal)

None

References to Module / Module Number [MV-LTD-M187-M-7]

Course of Study Section Choice/Obligation
[MV-88.A29-SG] M.Sc. Biological and Chemical Engineering [Compulsory Modules] Studienschwerpunkt II [WP] Compulsory Elective
Module-Pool Name
[MV-ALLG-2022-MPOOL-6] Wahlpflichtmodule Master allgemein 2022
[MV-ALL-MPOOL-6] Wahlpflichtmodule allgemein
[MV-BioVT-MPOOL-6] Wahlpflichtmodule Bioverfahrenstechnik
[MV-CE-2022-MPOOL-6] Wahlpflichtmodule M.Sc. Computational Engineering 2022
[MV-CE-MPOOL-6] Wahlpflichtmodule Computational Engineering
[MV-EVT-2022-MPOOL-6] Wahlpflichtmodule M.Sc. EVT 2022
[MV-EVT-MPOOL-6] Wahlplichtmodule Energie- und Verfahrenstechnik
[MV-PE-2022-MPOOL-6] Wahlpflichtmodule M.Sc. Produktentwicklung 2022
[MV-PE-MPOOL-6] Wahlpflichtmodule Produktentwicklung im Maschinenbau