0259 Quantitative Methods I
Kevin Kurt, MSc (WU), Assoz.Prof. PD Dr. Zehra Eksi-Altay, BSc.MSc.
Lectures and Exercise Session 1: Zehra Eksi-Altay (, Exercise Sessions 2-4: Kevin Kurt (
  • LV-Typ
  • Semesterstunden
  • Unterrichtssprache
03.10.2019 bis 07.10.2019
Anmeldung über LPIS
Hinweise zur LV
Die Lehrveranstaltung wird nur im Wintersemester angeboten.
Planpunkt(e) Bachelor
Wochentag Datum Uhrzeit Raum
Mittwoch 09.10.2019 09:00 - 10:30 TC.0.01 ERSTE
Mittwoch 09.10.2019 13:00 - 14:30 TC.5.15
Mittwoch 16.10.2019 09:00 - 10:30 TC.0.01 ERSTE
Mittwoch 16.10.2019 13:15 - 14:15 TC.5.14
Mittwoch 23.10.2019 09:00 - 10:30 TC.0.01 ERSTE
Mittwoch 23.10.2019 13:15 - 14:15 TC.5.14
Mittwoch 30.10.2019 09:00 - 10:30 TC.0.01 ERSTE
Mittwoch 30.10.2019 13:15 - 14:15 TC.5.14
Mittwoch 06.11.2019 09:00 - 10:30 TC.0.01 ERSTE
Mittwoch 06.11.2019 13:15 - 14:15 TC.5.14
Mittwoch 13.11.2019 09:00 - 10:30 TC.0.01 ERSTE
Mittwoch 13.11.2019 13:15 - 14:15 TC.5.14
Mittwoch 20.11.2019 09:00 - 10:30 TC.0.01 ERSTE
Mittwoch 20.11.2019 13:15 - 14:15 TC.5.14
Mittwoch 04.12.2019 09:00 - 10:30 TC.0.01 ERSTE
Mittwoch 04.12.2019 13:15 - 14:15 TC.5.14
Mittwoch 11.12.2019 09:00 - 11:00 TC.0.01 ERSTE
Mittwoch 18.12.2019 08:45 - 11:00 TC.0.10 Audimax

Inhalte der LV

Course contents:

  • introduction to the open source programming environment R, R as a calculator, named vectors in R
  • functions of one variable, defining and evaluating functions in R
  • brief introduction to functions of several variables
  • graphs of functions, graphing functions in R
  • special functions and their properties: linear, quadratic, polynomial, power, exponential, logarithmic
  • concatenation and composition of functions, inverse of functions
  • analytical and numerical rootfinding
  • elementary financial mathematics (discounting and compounding, simple annuities): computation and visualization using R
  • elementary matrix algebra and its usage in R
  • systems of linear equations and their representation using matrix algebra
  • analytical and numerical differentiation
  • basic concepts of probability
  • important probability distributions: uniform, binomial, Gaussian
  • simulation methods using R

Lernergebnisse (Learning Outcomes)

After completing the course, students should be familiar with basic concepts, methods and tools in mathematics and computing that are necessary for the quantitative analysis of problems in modern business and economics. Moreover, students will have acquired basic programming skills in the open-source computer language R, enabling them to independently conduct simple mathematical analyses.

Regelung zur Anwesenheit

100% physical, emotional, and intellectual participation is strongly recommended in both the lectures as well as the practical sessions. However, attendance in the lectures will not be formally checked. Note that there will be no chance to make up for any points which were lost due to missing practical sessions.


The course will be taught as a lecture accompanied by practicals in small groups (VÜ). There will be 10 lectures with 120 participants, lasting 90 minutes each. Concerning the practicals, there will be one introductory session (90 minutes, 4 x 30 participants) and 6 further exercise sessions (60 minutes, 4 x 30 participants), where students will use their own computers. Additionally, there will be tutorials held by senior students.

Leistung(en) für eine Beurteilung

Course evaluation consists of three parts:

  1. final exam (50 points)
  2. 7 homework assignments (30 points in total)
    • for each exercise session we will distribute a set of 8 homework problems
    • out of the 56 problems students have the right to skip 8 problems without loss of any points
    • students should solve as many problems as possible and indicate until midnight before the exercise session, which problems they have solved; a tick list on Learn@WU will be provided to do so
    • students must attend the exercise session, in order to present their solutions to a problem on the board; the lecturers grade the board performance with a number between 0 (worst) and 1 (best)
    • the average grade of the performances at the board multiplied with the total number of problems ticked gives the total points achieved in this part of the course evaluation
  3. case study (group work to be handed in in written form, 20 points)
    • 15 points group work + 5 points individual points
    • will be assigned on 21.11.2019 and to be handed in on 10.12.2019
    • any collaborations between different groups will be punished with severe point reductions

The following grading scale applies: 

  • 89.01-100.00 - Excellent (1)
  • 78.01-89.00 - Good (2)
  • 67.01-78.00 - Satisfactory (3)
  • 56.01-67.00 -  Sufficient (4)
  • 0.00-56.00 -  Insufficient (5) 


1 Autor/in: W. John Braun, Duncan J. Murdoch

A First Course in Statistical Programming with R

Prüfungsstoff: Ja
Empfehlung: Stark empfohlen (aber nicht absolute Kaufnotwendigkeit)
Art: Buch
2 Autor/in: Knut Sysdaeter, Peter Hammond

Essential Mathematics for Economic Analysis

Prüfungsstoff: Ja
Empfehlung: Stark empfohlen (aber nicht absolute Kaufnotwendigkeit)
Art: Buch
3 Autor/in: James T. McClave and Terry Sincich


Verlag: Pearson
Auflage: 12th / 13th
Anmerkungen: Both the 12th and 13th editions are fine as reference material for this course
Jahr: 2011
Diplomprüfungsstoff: Nein
Empfehlung: Stark empfohlen (aber nicht absolute Kaufnotwendigkeit)
Art: Buch

Empfohlene inhaltliche Vorkenntnisse

Mathematical skills and knowledge at high school level.

Zuletzt bearbeitet: 26.09.2019