FP-EasPAcad. year: 2020/2021
The course deals with main ideas and methods of mathematical statistics, methods of regression analysis for description of a trend in time series and characteristics of time series describing economics and social events.
Learning outcomes of the course unit
Students will be made familiar with the methods of mathematical statistics, regression analysis, and time series analysis and will learn how to use the respective methods when solving economics problems. After completion of this course students will be prepared to use these methods in economics courses.
Recommended optional programme components
Recommended or required reading
GUJARATI, D. N., PORTER, D. C. Basic econometrics. 5. vyd. Boston : McGraw-Hill, 2009. 922s. ISBN 978007127625
KOOP, G. Introduction to econometrics. Chichester : John Wiley & Sons, 2008. 371s. ISBN 978047003270
MATHEWS, P. Design of Experiment with Minitab. Milwaukee: ASQ Quality Press, 2005. ISBN 9780873896375
KARPÍŠEK, Z. and M. DRDLA. Applied statisitcs. 1. ed. Brno: PC-DIR Real, 1999. ISBN 8021414936
Planned learning activities and teaching methods
Teaching consists of lectures that have an explanation of basic principles and methodology of the discipline, practical problems and their sample solutions.
Exercise promote the practical knowledge of the subject presented in the lectures.
Assesment methods and criteria linked to learning outcomes
The mark, which corresponds to the total sum of points achieved (max 100 points), consists of:
- points achieved by answering theoretical questions,
- points achieved by computer-aided calculation of projects.
Student obtains the assessment after having a short talk with the tutor where his/her work is evaluated.
The grades and corresponding points:
A (100-91), B (90-81), C (80-71), D (70-61), E (60-50), F (49-0).
Language of instruction
Students will obtain basic knowledge and skills of point and interval estimates, the most used parametric and nonparametric tests, good fit tests, an analysis of variance, a categorial analysis, linear and nonlinear multiple regression models and time series analysis.
Topics lectures are as follows:
1. Basic concepts of statistical testing.
2. Parametric statistical tests – t-test.
3. Parametric statistical tests – two sample t-test and F-test.
4. Kolmogorov-Smirnov test, Pearson test and Shapiro-Wilk test.
5. Analysis of variance (ANOVA).
6. Nonparametric statistical tests – Sign test, Wilcoxon rank sum test.
7. Nonparametric statistical tests –Kruskal-Wallis test, Friedman test, Spearman's correlation coefficient.
8. Categorical analysis – contingency table and Chi square test.
9. Univariate regression model.
10. Multivariate regression models.
11. The release of the classical assumptions – heteroscedasticity, multicollinearity and autocorrelation of random components.
12. Nonlinear regression models – linearizable regression model and S-curve.
13. Panel data аnalysis.
The objective of this course is to familiar students with ideas and methods of mathematical statistics, methods of regression analysis for description of a trend in time series and characteristics of time series describing economics and social events.
Specification of controlled education, way of implementation and compensation for absences
Attendance at lectures is not compulsory, but is recommended.
Classification of course in study plans
- Programme MGR-EBF Master's, 1. year of study, winter semester, 5 credits, compulsory