Czech

Not applicable.

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 completing the course, students will be prepared to practically apply these methods in a Master's degree courses and in real environments.

Fundamentals of a probability theory and a random variable.

Not applicable.

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.

The course-unit credit is awarded on the following conditions:
- passing control tests,
- submitting answers to calculating problems and theoretical questions.

The exam has a written form.
In the first part of the exam student solves 4 examples within 80 minutes. In the second part of the exam student works out answers to 3 theoretical questions within 15 minutes.

The mark, which corresponds to the total sum of points achieved (max. 100 points), consists of:
- points achieved in control tests, points achieved to calculating questions and theoretical questions,
- points achieved by solving examples,
- points achieved by answering theoretical questions.

The grades and corresponding points:
A (100-90), B (89-83), C (82-76), D (75-69), E (68-60), F (59-0).

Descriptive statistics (week 1)
Sample characteristics
Empirical distribution function
Analysis of big data samples
Mathematical statistics (week 2 - 4)
Point and interval estimates
Testing of statistical hypothesis
Analysis of bivariate data sample (week 5 - 7)
Correlation analysis
Categorial analysis
Analysis of variance
Regression analysis (week 8 - 10)
Linear models
Nonlinear models
Time series analysis (week 11 - 13)
Time series characteristics
Decomposition of time series

Not applicable.

The objective of this course is to familiar students with ideas and methods of point and interval estimates, the most used parametric tests, good fit tests, an analysis of variance, a categorial analysis, linear and nonlinear multiple regression models and time series analysis.

Attendance at lectures is not compulsory, but is recommended.

Not applicable.

Not applicable.

KROPÁČ, J. STATISTIKA B. 3. vyd. Brno: Akademické nakladatelství CERM, 2012. 152 s. ISBN 978-80-7204-822-9.
KROPÁČ, J. STATISTIKA. 2. vyd. Brno: Akademické nakladatelství CERM, 2012. 138 s. ISBN 978-80-7204-788-8.
Studijní materiály vystavené na e-learningu.

BUDÍKOVÁ, M., T. LERCH a Š. MIKOLÁŠ. Základní statistické metody. 1. vyd. Brno: Masarykova univerzita v Brně, 2005. ISBN 80-210-3886-1.
FIELD, A., J. MILES and Z. FIELD. Discovering Statistics Using R. 1 edition. Los Angeles, Calif.: SAGE Publications Ltd., 2012. ISBN 978-1-4462-0046-9.
JAMES, G., D. WITTEN, T. HASTIE a R. TIBSHIRANI. An Introduction to Statistical Learning: with Applications in R. New York: Springer New York, 2014. 426 s. ISBN 978-1-4614-7137-0.