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.

Mathematical concepts introduced include optimization for functions of several variables, optimization under constraints and further topics in probability theory, together with suitable examples from business and economics.

This course starts with an overview of concepts related to multivariate optimization, constrained optimization and linear programming. 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 and hypothesis testing (statistical inference).  Moreover, the participants will learn how to apply these concepts to data by using the built-in functionality from R. This will deepen their familiarity with R, enabling them to use this computing language for an independent analysis of quantitative problems in business and economics later in the program.

After completing the course, students should be familiar with basic concepts, methods and tools in optimization and descriptive and inferential statistics alongside their practical implementation using the open-source computer language R 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.

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 (VUE). 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

Course evaluation consists of four parts:

• Final exam (50 points)
• Midterm exam (20 points), 15.04.2020 during the lecture
• 7 Homework assignments (10 points in total)

             - practicals will be assessed as group work
             - for each exercise session we will distribute a set of 8 homework problems
             - out of the 56 problems all groups have the right to skip 8 problems without loss of any points
             - the groups should solve as many problems as possible and indicate until midday before the exercise session, which problems they have solved as well as upload their solutions on Learn@WU.
             - the group can pick a representative to present their solutions to a problem on the board; each time a different one, though.
             - the lecturers grade the board performance with a number between 0 (worst) and 1 (best) which counts for the whole group
             - 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

•  Case study (group work to be handed in in written form, 20 points)

             - 15 points group work + 5 points individual interview
             - will be assigned on 07.05.2020 and to be handed in on 27.05.2020
             - 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)

Successful completion of Quantitative Methods I is highly recommended.