Multivariate Quantitative Methods for Economics


Zaradený v študijných programoch

Výsledky vzdelávania

The main educational goal of the course is:
• acquaint students with the principles of multivariate quantitative analysis suitable for business and/or economics,
• teach students to apply appropriate sophisticated multivariate tools in solving practical problems in the field of economic practice,
• to support students' awareness in the selection, evaluation, identification and interpretation of the results of applied multivariate quantitative methods used.
The successful graduate of the course will gain knowledge from the application of multivariate quantitative methods, which can be applied in decision-making in various areas of economic practice.
After completing the course, the student is able to solve and analyze the problems of business and economic practice by applying appropriate multivariate statistical methods and procedures, interprets the results in a suitable way and draws conclusions based on empirical results.
The graduate can implement, perform sophisticated multivariate statistical analysis, construct multivariate models, and draw relevant conclusions from applied multivariate statistical procedures and techniques. The student applies the acquired theoretical knowledge to solve specific business and economic problems.

Stručná osnova predmetu

• Basic concept of multivariate quantitative methods in economics.
• Multivariate regression analysis.
• Correlation. Multicollinearity.
• Generalized linear model (GLM).
• Regression trees.
• Factor and principal component analysis.
• Cluster analysis.
• Discriminant analysis.
• Logistic regression.

Odporúčaná literatúra

1. CLEFF, T. (2019). Applied Statistics and Multivariate Data Analysis for Business and Economics: A Modern Approach Using SPSS, Stata, and Excel. Springer, 2019. ISBN-13: 978-3030177669.
2. PITUCH, K.A. (2016). Applied Multivariate Statistics for the Social Sciences. Routledge, 2016. ISBN-13: 978-0415836661.
3. TABACHNICK, B.G. – FIDELL, L.S. (2013). Using Multivariate Statistics. Microsoft Press, 2013. ISBN-13: 978-1292021317.
4. SHARMA, S.(1996). Applied multivariate techniques. New York, John Wiley & Sons. 1996. ISBN 0-471-31064-6.
5. KHATTREE, R. – NAIK, D. N.(2000). Multivariate data reduction and discrimination with SAS® Software. Cary, NC: SAS Institute Inc., 2000. ISBN 1-58025-696-1.
6. IZENMAN, A.L. (2008). Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning. Springer, 2008. ISBN-13: 978-0387781884.

Sylabus predmetu

• Familiarity with the use of multidimensional quantitative methods in economics. Choosing a software product to solve tasks. • Multidimensional regression analysis. Classic linear regression model. Estimates of model parameters, significance of the model, contribution of explanatory variables. • Correlation. Multicollinearity. Ways to select variables. Verification of conditions. • Generalized Linear Model (GLM). • Regression trees: CHAID (Chi-squared automatic interaction detection), CRT (Classification and regression). • Factor analysis and principal components analysis. Determination of the number of main components. • Cluster analysis. Measures of similarity of objects. Hierarchical and non-hierarchical clustering procedures. • Discriminant analysis. Assumptions, descriptive task of discriminant analysis, discriminant functions. • Logistic regression. Estimation of model parameters, model testing, estimation of odds ratios, evaluation of logistic model quality.

Podmienky na absolvovanie predmetu

continuous written work, active self-study of the topics
presentation of the written work, defense of written work and oral exam
• semester assignment - 40 %
• presentation of the final written work – 60 %

Pracovné zaťaženie študenta

• Participation in colloquia: 16 hours
• Preparation for colloquia: 44 hours
• Elaboration of a research study: 100 hours
• Preparation for the final exam: 100 hours

Jazyk, ktorého znalosť je potrebná na absolvovanie predmetu

English language

Dátum schválenia: 20.12.2022

Dátum poslednej zmeny: 20.12.2022

Dátum schválenia: 20.12.2022

Dátum poslednej zmeny: 20.12.2022