Syllabus

Title
5142 Quantitative Methods II
Instructors
Jana Hlavinova, Ph.D., Tomas Masak, Ph.D.
Type
VUE
Weekly hours
2
Language of instruction
Englisch
Registration
02/24/25 to 02/27/25
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Quantitative Methods II
Dates
Day Date Time Room
Wednesday 03/12/25 10:00 AM - 12:00 PM TC.1.02
Wednesday 03/12/25 01:00 PM - 02:00 PM TC.5.02
Wednesday 03/19/25 10:00 AM - 12:00 PM TC.1.02
Wednesday 03/19/25 01:00 PM - 02:00 PM TC.5.02
Wednesday 03/26/25 10:00 AM - 12:00 PM TC.1.02
Wednesday 03/26/25 01:00 PM - 02:00 PM TC.5.02
Wednesday 04/02/25 10:00 AM - 12:00 PM TC.1.02
Wednesday 04/02/25 01:00 PM - 02:00 PM TC.5.02
Wednesday 04/09/25 10:00 AM - 12:00 PM TC.1.02
Wednesday 04/09/25 01:00 PM - 02:00 PM TC.5.02
Friday 04/11/25 11:00 AM - 01:00 PM TC.0.10 Audimax
Wednesday 05/07/25 10:00 AM - 12:00 PM TC.1.02
Wednesday 05/14/25 10:00 AM - 12:00 PM TC.1.02
Wednesday 05/14/25 01:00 PM - 02:00 PM TC.5.02
Wednesday 05/21/25 10:00 AM - 12:00 PM TC.1.02
Wednesday 05/21/25 01:00 PM - 02:00 PM TC.5.02
Wednesday 05/28/25 11:30 AM - 02:30 PM 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

100% physical, emotional, and intellectual participation is strongly recommended. Attendance on practical sessions is mandatory. This means, at most two sessions  can be missed.  Attendance in the main lectures will not be formally checked.

 

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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) where students will use their own computers. The main focus of the practical sessions will be to cover the relevant R material and gain computational skills. 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 Forum.

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Assessment
Course evaluation consists of four parts:
  1. Midterm exam (30 points) (On campus, will take place on April 11, 2025)  
  2. Final exam (40 points) (On campus, will take place on May 28, 2025)
  3. Homework assignments (10 points in total)
    • Homework assignments will be assessed as individual work
    • Throughout the course, there will be a total of 35 homework questions that are categorized into 1-star and 2-star exercises. 
    • Students should solve and submit as many problems as possible every week at Canvas, usually by 11:55 pm on Tuesday. Late submissions will not be accepted; please count with the possibility of technical problems and do not leave your submission to the last minute.
    • Each home assignment question is worth at most 1 point. At the end of the course, we take min(10, S/2.5) points as the result for this part, where S is the overall score. Dividing by 2.5 means that students can skip up to 10 exercises throughout the semester and still get full points. Taking the minimum with 10 is there because the highest achievable score in this part is 10 points.
  4. Case study (20 points)
    • 15 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 issues, the free rider will get 0 points for the corresponding task. 
    • The use of AI to create a solution submission is not allowed.

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: 2025-02-18



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