Syllabus

Title
4904 Quantitative Methods II
Instructors
Jana Hlavinova, Ph.D., Assoz.Prof. PD Dr. Zehra Eksi-Altay, BSc.MSc.
Contact details
zehra.eksi@wu.ac.at, jana.hlavinova@wu.ac.at
Type
VUE
Weekly hours
2
Language of instruction
Englisch
Registration
02/23/26 to 02/26/26
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Quantitative Methods - Statistics and R Programming for Data Analysis
Dates
Day Date Time Room
Wednesday 03/11/26 10:00 AM - 12:00 PM D5.0.001
Wednesday 03/11/26 04:00 PM - 05:00 PM D5.1.002
Wednesday 03/18/26 10:00 AM - 12:00 PM D5.0.001
Wednesday 03/18/26 04:00 PM - 05:00 PM D5.1.002
Wednesday 03/25/26 10:00 AM - 12:00 PM D5.0.001
Wednesday 03/25/26 04:00 PM - 05:00 PM D5.1.002
Wednesday 04/08/26 10:00 AM - 12:00 PM D5.0.001
Wednesday 04/15/26 10:00 AM - 12:00 PM D5.0.001
Wednesday 04/15/26 04:00 PM - 05:00 PM D5.1.002
Friday 04/17/26 02:30 PM - 04:30 PM TC.0.10 Audimax
Wednesday 05/06/26 10:00 AM - 12:00 PM D5.0.001
Wednesday 05/06/26 04:00 PM - 05:00 PM D5.1.002
Wednesday 05/13/26 10:00 AM - 12:00 PM D5.0.001
Wednesday 05/13/26 04:00 PM - 05:00 PM D5.1.002
Wednesday 05/20/26 10:00 AM - 12:00 PM D5.0.001
Wednesday 05/20/26 04:00 PM - 05:00 PM D5.1.002
Friday 05/29/26 08:00 AM - 11:00 AM TC.0.10 Audimax

On this page:

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Contents

The course deepens the understanding of concepts, methods and tools from mathematics, statistics, and computing for the quantitative analysis of problems in modern business and economics.

This course starts with an overview of concepts related to probability theory. Then, the remaining part of the lecture deals mainly with statistics for business and economics. Students will become familiar with visualizing and summarizing data (descriptive statistics) as well as quantifying estimation uncertainty, hypothesis testing and the basics of linear regression (statistical inference). Moreover, the participants will learn how to apply these concepts to data by using the built-in functionalities of R. This will deepen their familiarity with R, enabling them to use this computer language for an independent analysis of quantitative problems in business and economics later in the program.

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Learning outcomes

After completing the course, students should be familiar with basic concepts, methods, and tools in probability and descriptive and inferential statistics that are necessary for the quantitative analysis of problems in modern business and economics. Moreover, students will have acquired intermediate programming skills in R, enabling them to independently administer, conduct, and interpret statistical analyses.

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Attendance requirements

The attendance and participation in all lectures and practical sessions are strongly recommended. Attendance will be checked during the practical sessions; two absences (whether excused or not) are allowed.

The participation in the in-class assignments in the practical sessions (see details below) is only allowed in person, no retake possibility in case of absence.

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Teaching/learning method(s)

The course will be taught as a lecture accompanied by practicals in small groups (VUE). There will be 8 on-campus lectures with 120 participants. Concerning the practicals, there will be 7 on-campus sessions, starting with the first week (the day of the first main lecture). The main focus of the practical sessions will be to cover the relevant R material and gain computational skills. Please make sure to always bring your computer with R installed to the practical session; this will be necessary to work on the in-class assignments. Additionally,  in order to support students for R programming, regular tutorials will be offered by the tutors.

Students are expected to be active in the class. We also encourage the use of the central forum.

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Assessment
Course evaluation consists of four parts:
  1. Midterm exam (30 points) (On campus)  
  2. Final exam (40 points) (On campus)
  3. 7 in-class assignments (15 points in total) - will take place in each practical session
    • In-class assignments will be assessed as individual work
    • Throughout the course, there will be a total of 7 assignments, each worth 3 points. At the end of the course, we take min(15, S) points as the result for this part, where S is the overall score. This means that missing up to two sessions still allows you to achieve the full 15 points for this part of the assessment.
  4. Case study (15 points)
    • 10 points group work to be handed in in written form + 5 points individual interview.
    • Any collaborations between different groups will be punished with severe point reductions; all groups members will be graded equally irrespective of their involvement in the misconduct and/or the internal division of tasks within the group.
    • If we detect any free-riding, the free rider will receive 0 points for the corresponding task. 
    • The use of AI to create a solution submission is not allowed.

In case of missing one of the exams due to properly documented sickness or legal reasons, there will be an oral retake option. However, it is not possible to retake both the midterm and the final exam. There will be no retake options for any of the individual components for those failing the class.

The following grading scale applies:

  • 89.00-100.00 - Excellent (1)
  • 78.00-88.99 - Good (2)
  • 67.00-77.99 - Satisfactory (3)
  • 56.00-66.99 -  Sufficient (4)
  • 0.00-55.99 -  Insufficient (5)
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Readings

Please log in with your WU account to use all functionalities of read!t. For off-campus access to our licensed electronic resources, remember to activate your VPN connection connection. In case you encounter any technical problems or have questions regarding read!t, please feel free to contact the library at readinglists@wu.ac.at.

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Recommended previous knowledge and skills

Basic knowledge in R programming is necessary. Successful completion of Quantitative Methods I is highly recommended.

Last edited: 2026-01-15



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