Quantitative Methods for Business
The module teaches knowledge and skills in quantitative methods for business and the use of mathematical and statistical tools and applications. These methods solve complex business problems, take full advantage of the knowledge represented in mass data and quantify the uncertainty to be taken into account for decisions.
The course on statistical methods teaches estimation and hypothesis testing, data collection, acquisition and preparation, statistical data analysis, regression models and survey research methods. Topics of the course on mathematical methods are optimisation including linear programming, sensitivity analysis, queuing processes, dynamic systems and simulation. At the end of the course the participants will be able to use data and models to support management and research.
|Should be visited:||FT: 1st
PT: 1st or 3rd
|Academic Module Coordinator||Beat Hulliger|
|Lecturers||Prof. Dr. Beat Hulliger, Prof. Dr. Thomas Hanne|
Basic algebra (solving equations in one variable, solving simultaneous equations in several variables) and calculus (analysis of the features of functions and derivatives), basic probability theory, descriptive statistics (measures of location and dispersion, tables and graphs), confidence interval and t-test for the mean of a normal random variable. This material is normally covered in a Bachelor of Business Administration curriculum.
|Overall hours||180 h
Including contact hours: 56 h
|Outline Content||Statistical methods: The concepts of decision making under uncertainty and statistical knowledge formation are introduced. Estimation and Hypotheses testing under parametric models and non-parametric methods are explained. Data collection and data preparation, in particular survey research methods, are discussed. Statistical data analysis, model building and assessment with linear models are the core of the course. An outlook on further statistical methods (time series and forecasting, generalized linear models and multivariate statistics) is given.
Mathematical methods: Matrix calculus and linear optimisation with constraints with practical applications is treated. An introduction to queuing theory and discrete event simulation is given.
The module will use practical training, case studies and homework with appropriate software, in particular the software package R for statistical data analysis and AutoMod for simulations.
Data analysis and the communication of results of application of mathematical and statistical methods will be trained.
|Teaching and Learning Methods||The module is taught through plenary lectures with discussions and exercises. Class participation is actively encouraged.
Exercises comprise statistical data analysis of data sets including modeling, interpretation and presentation. Students work through exercises independently. The exercises are discussed in classroom regularly.
|Indicative Learning Resources||