OPF:INMDAMMA Mathematical-Statistical Metho - Course Information

INMDAMMA Mathematical-Statistical Methods

Extent and Intensity

0/0/0. 15 credit(s). Type of Completion: dzk.

Teacher(s)

prof. RNDr. Jaroslav Ramík, CSc. (lecturer)

Guaranteed by

prof. RNDr. Jaroslav Ramík, CSc.
Department of Informatics and Mathematics – School of Business Administration in Karvina
Contact Person: prof. RNDr. Jaroslav Ramík, CSc.

Prerequisites

The completion of the course does not require any extra conditions and the course can be enrolled independently of the other courses.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

• Business Economics and Management (programme OPF, D_EKOMAN4)

Course objectives

The aim of this course is to provide students with the necessary knowledge in modern mathematical-statistical methods, multi-criteria decision methods and methods of knowledge engineering. The subject is presented with respect to applications in economical area, particularly in marketing, management and finance. The aim is also to manage computational skills with the adequate software tools on PC, (i.e. Excel-Solver, SPSS, Expert Choice, DAME etc.). The students have the corresponding e-learning course at disposition and they have to solve three given case studies.

Syllabus

• Statistical methods and methods of economical data analysis, statistical methods of quality management.
  Analysis of data matrix. Analysis of variance (ANOVA). Multi-dimensional regression and correlation analysis. Analysis of main components, factor analysis. Evaluation of production process.
  Econometric modeling.
  Classical linear regression model. Special problems of the linear regression model (dummy variables, generalized model). Regression models with delayed variables, multicollinearity, heteroskedasticity. Case studies with using PC.
  Analysis of economical time series.
  Besic approaches to time series analysis, specific problems, decomposition of time series analysis (TSA). Box - Jenkins methodology: ARIMA a SARIMA models. Multi-dimensional TSA: VARMA. Cointegration, Error-Correction models. TSA by the SARIMA model and SPSS SW.
  Multi-criteria decision making methods.
  Multi-criteria decision making (DM). Analytic hierarchy process (AHP). Methods of DM under uncertainty and risk. Application of Excel and DAME when solving problems of DM. Case study with using Excel.
  Methods of knowledge engineering and data mining.
  Typical problems of knowledge engineering (KE) and data mining (DM). General methodology, questions, goals, applications. Expert and knowledge systems. Main principles of methods of KE and DM: neural networks, induction rules, association rules, grouping methods, statistical models, visualization. Practical applications.

Literature

required literature
• SCHREIBER, A. T., AKKERMANS, H., ET. AL. Knowledge engineering and management: the Common KADS methodology (1st ed.). Cambridge, MA: The MIT Press, 2000. ISBN 978-0-262-19300-9. info
• GURAJATI, D. Essentials of econometrics. New York: McGraw-Hill, 1992. ISBN 0-13-844143-X. info
• SAATY, T. L. Multicriteria decision making - the Analytical Hierarchy Process. Pittsburgh: RWS Publications, 1991. info

recommended literature
• RUD, O. Data mining. Praha: Computer Press, 2001. ISBN 80-7226-577-6. info
• ENDERS, W. Applied Economic Time Series. New York etc.: John Wiley&Sons, 1995. ISBN 0-314-77818-7. info
• BERENSON, M., LEVINE, M. Basic Business Statistics, Concepts and Applications. New Jersey: Prentice Hall, 1992. ISBN 0-13-065780-8. info

Teaching methods

One-to-One tutorial
Skills demonstration

Assessment methods

Written exam

Language of instruction

English

Further comments (probably available only in Czech)

The course can also be completed outside the examination period.
Information on the extent and intensity of the course: Přednáška 32 HOD/SEM.

Teacher's information

attendance in all 4 tutorials, presentation of written case studies, final written/oral exam

Activity	Difficulty [h]
Ostatní studijní zátěž	346
Přednáška	72
Zkouška	32
Summary	450

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