|Course code module||FTEMAJ0130|
|Study load (hours)||168|
|Language of instruction:||English|
|Semester exam information:||exam in the 1st semester|
|Contract restriction information:|
Students are assumed to be familiar with basic statistical theory and applicatons (probability theory, hypothesis testing and related subjects). In addtion the have a sufficient insight in linear algebra and matrix operations. The course is complementary to the course of econometrics. Econometric modelling and regression analysis are therefore not covered. De course is also complementary to the course 'research methodology', which covers research design, hypothesis formulation and methods of collecting primary data.
Statistics with managerial application, mathematics with managerial applicatons, introduction to econometrics
2. Objectives (expected learning outcomes)
Statitiscal processing and decision making in management research based on multivariate statistical analysis. Topics covered in this respect are exploratory factor analysis, structural equations, scaling and unfolding. The course provides an introduction to the most widely used methods in multivariate data analysis. It covers the material at an intermediate level of mathematical analysis.
Students are requested to familiarize themselves with one of the mainstream statistical packages (SPSS, SAS, R). They develop a sufficient insight into the appropriate choice and the requirements related to the methods covered, with a view of obtaining relevant results and conclusions from a research or managerial perspective. In the term paper, assigned as a team project, they analyse a substantial data set and report their findings.
3. Course content
Traditionally, multivariate data analyis makes a distinction between dependency methods and interdependency methods. This course focusses on interdependency methods, in view of the emphasis put on dependency methods in econometrics classes. Some topics in the depencdency contect are included however (discriminant analysis and logistic regression).
Exploratory Factor Analysis
Measurement, validity, reliabiltiy
Structural Equations (introduction)
Structrual Equations (evaluation)
4. Teaching method
Direct contact: Lectures
Personal work: ExercisesAssignments - in groupProject-based work - in group
5. Assessment method
Exam: Written, without oral presentationWritten, with oral presentationOpen book
Written assignment: With oral presentationWithout oral presentation
6. Compulsory reading – study material
Hair et al: Multivariate Data analysis (chapters covered)
Additional readings in course outline
7. Recommended reading - study material