Module Handbook

  • Dynamischer Default-Fachbereich geändert auf WIW

Module WIW-KM-QTM-M-6

Quantitative Methods (M, 6.0 LP)

Module Identification

Module Number Module Name CP (Effort)
WIW-KM-QTM-M-6 Quantitative Methods 6.0 CP (180 h)
WIW-KM-QTM4-M-6 Quantitative Methods 4.0 CP (120 h)


CP, Effort 6.0 CP = 180 h
Position of the semester 2 Sem. from WiSe/SuSe
Level [6] Master (General)
Language [EN] English
Module Manager
Area of study [WIW-WIN] Business Information Systems and Operations Research
Reference course of study [WIW-88.21-SG#2009] M.Sc. Business Studies (2009) [2009]
Livecycle-State [NORM] Active


In der neuen Studiengangsstruktur ab dem WS 2021/22 wird das Modul Quantitative Methoden parallel zum 6 LP-Modul der alten Struktur (das unverändert bleibt) auch als 4 LP-Modul angeboten. Es ist dann nur einer der 3 Kurse zu wählen:

1. Quantitative Methods in Economics

2. Multivariate and Nonlinear Models

3. Quantitative Methods in Operations Management


Type/SWS Course Number Title Choice in
Presence-Time /
SL SL is
required for exa.
PL CP Sem.
Quantitative Methods in Economics
WP 30 h 60 h - - PL1 3.0 SuSe
Multivariate and Nonlinear Models
WP 30 h 60 h - - PL1 3.0 WiSe
Quantitative Methods in Operations Management
WP 30 h 90 h - - PL1 3.0 WiSe
  • About [WIW-FE-WME-K-6]: Title: "Quantitative Methods in Economics"; Presence-Time: 30 h; Self-Study: 60 h
  • About [WIW-KM-MNM-K-6]: Title: "Multivariate and Nonlinear Models"; Presence-Time: 30 h; Self-Study: 60 h
  • About [WIW-POM-QMP-K-6]: Title: "Quantitative Methods in Operations Management"; Presence-Time: 30 h; Self-Study: 90 h

Examination achievement PL1

  • Form of examination: written exam (Klausur) (180 Min.)
  • Examination Frequency: each semester

Evaluation of grades

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

Average of the result of individual subjects. Individual courses might offer the possibility to earn bonus points to improve a passing grade.


The lecture consists of two parts: Dynamical Systems and Statistics. First, we’ll discuss linear parametric regression models. We’ll start with Simple Regression, which is generalized later on, when we talk about the General Linear Regression Model. After this first chapter, we’ll go on to Generalized Linear Models, and we’ll discuss Binary Regression.

In the Dynamical Systems part, we’ll start with examining a linear one-dimensional system, i.e. we’ll define and analyze steady states and their stability. Then, we’ll consider non-linear non-dimensional systems and extend our results on existence and stability to the non-linear case. We’ll use the same approach when discussing multidimensional dynamics.

The lecture provides an overview of problems and state-of-the-art techniques of generalizing models from (small or large) data sets with known or unknown hypotheses regarding the underlying functional dependencies. Data sets from various application domains are analyzed and appropriate software tools introduced. Students will be able to work on an independent analysis on a self-chosen dataset to earn bonus points in a small group.

Methods covered:

  • Data Analysis and Graphical Presentation
  • Multivariate Linear Models
    • Multivariate analysis of variance
    • Multiple Regression
    • Factor Analysis
    • Multidimensional scaling
  • Multivariate Nonlinear Models
    • Artificial Neural Networks
    • Kernel-based Estimators and Support Vector Machines
  • Introduction in modelling software GAMS
  • Modelling mixed integer programs
  • Matheuristics, e.g. Fix-and-Relax, Fix-and-Optimize
  • Column Generation

Competencies / intended learning achievements

After completing the module, students should be able to
  • know how decision problems arising in Operation Management can be modelled in GAMS.
  • solve these problems using suitable exact and/or heuristic solution approaches
  • know the difference between univariate and multivariate statistical techniques.
  • to contrast linear and non-linear methods for data analysis methods
  • understand basics of artificial neural networks and statistical learning.
  • Interpret and justify the results of data analysis for a given or self-developed hypothesis.
  • understand and assess macroeconomic models.


Jan Wenzelburger: Dynamische Systeme, eine Einführung, Manuscript

Oded Galor: Discrete Dynamical Systems, Springer Verlag, Skript

Lecture Notes, Problem Sets

Hair, J.F. et al., Multivariate Data Analysis, 7th Edition, Pearson 2014

Christianini, N.; Shawe-Taylor, J.: An Introduction to Support Vector Machines and other kernel-based learning methods, Cambridge University Press 2013

Additionally, slides are made available that contain a list of complementary literature.

Slides are made available that contain a list of complementary literature.

Requirements for attendance of the module (informal)


Requirements for attendance of the module (formal)


References to Module / Module Number [WIW-KM-QTM4-M-6]

References to Module / Module Number [WIW-KM-QTM-M-6]

Course of Study Section Choice/Obligation
[WIW-88.21-SG#2009] M.Sc. Business Studies (2009) [2009] [Fundamentals] Master Business Studies: Compulsory [P] Compulsory
[WIW-88.789-SG#2009] M.Sc. Business Studies with Technical Qualifications (2009) [2009] [Fundamentals] Master Business Studies: Compulsory [P] Compulsory