Content (Syllabus outline)
- Introduction to decision making: principles of decision-making (problem formulation, types of the models, construction of models, analysis of the results), types of models, feasible and optimal solutions.
- Linear programming: concept of a linear program, the dual linear program, sensitivity analysis.
- Multi-stage decision-making: the basic elements of discrete deterministic dynamic programming, graphical solution by applying Bellman’s principle of optimality, stock problem, knapsack problem.
- Multiple-criteria decision making: goal programming, analytic hierarchy process, DEX.
- Surveys: sampling methods, construction of questionnaires.
- Univariate and bivariate methods: overview of parametric tests, ANOVA, contingency tables.
- Multivariate methods: principal component analysis, cluster analysis, discriminant analysis.
- Computer programs for statistics and decision-making.
Prerequisites
1. Prerequisites for course work:
- Enrolment in the corresponding year of the master study program.
- Completed exam of at least one subject with mathematical contents and basic statistics at BSc (1. Bologna level).
2. Prerequisites to attend the final exam:
- Attendance at tutorials.
- Seminar work from tutorials.