Statistical Methods and Risk Analysis
FP-smarPAcad. year: 2020/2021
The course deals with basic ideas and methods of mathematical statistic: methods of analysis of multidimensional data files, methods of regression and correlation analysis, methods of regression analysis for description of a trend in time series, and characteristics of time series describing economic and social events, index analysis.
Learning outcomes of the course unit
Students will be made familiar with the methods of mathematical statistics, methods of analysis of multidimensional data files, methods of regression and correlation analysis, methods of analysis of time series analysis and methods of index analysis and will learn how to use the respective methods when solving economic problems. After completion of this course students will be prepared to use these methods in economic courses.
Fundamentals of probability theory and fundamentals of mathematical statistics
(theory of probability: random events, classical probability, conditional probability;
discret and continuous random variables: distribution function, density, Binomical and Poisson distribution, normal distribution;
estimation of confidence interval , hypothesis testing)
Recommended optional programme components
Recommended or required reading
KROPÁČ, J. Statistika B. 3. vyd. CERM, Brno: FP VUT, 2012. ISBN 978-80-7204-822-9. (CS)
WONNACOT, T.H. aj. Statistika pro obchod a hospodářství. 1. vyd. Praha: Victoria Publishing, 1995. ISBN 80-85605-09-0. (CS)
SEGER, J. aj. Statistické metody v tržním hospodářství. 1. vyd. Praha: Victoria Publishing, 1995. ISBN 80-7187-058.7. (CS)
KROPÁČ, J. Statistika. 5. vyd. CERM, Brno: 2013. ISBN 978-80-7204-835-9. (CS)
HINDLS, R. aj. Analýza dat v manažerském rozhodování. Praha: Grada Publishing, 1999. ISBN 80-7169-255-7. (CS)
Planned learning activities and teaching methods
The course contains lectures that explain basic principles, problems and methodology of the discipline, and exercises that promote the practical knowledge of the subject presented in the lectures.
Assesment methods and criteria linked to learning outcomes
in case of full-time (contact) teaching:
CREDIT: Credit is awarded on the basis of:
- points for tests
- tests cannot be repeated
EXAM: The exam is written, it consists of theory (time about 20 minutes)
The grades, corresponding to the sum (max. 100 points), consists of:
- points for tests (30 + 30 + 25 for 3 tests)
- points for the exam (15 points)
- there is a requirement for a minimum score for the test
Marks and corresponding points:
A (100-90), B (89-80), C (79-70), D (69-60), E (59-50), F (49-0)
In the case of distance learning:
- credit and exam will be solved on the basis of how long the full-time form of teaching will be maintained
- the effort will be that it is possible to carry out credit tests in person at the faculty (even if the teaching takes place online), even within one day at the end of the semester
- if the exam and credit tests had to be distance, then probably 1 comprehensive credit test would take place and then an oral exam via Microsoft Teams
Language of instruction
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 economic problems. After completion of this course students will be prepared to use these methods in economic courses.
week 1: Discrete and continuous random variables, distribution function, density, special types of probability distrubiutions
week 2: Construction of confidence intervals; hypothesis testing
week 3: Methods of measuring the relationship between quantitative variables: covariance coefficient, correlation coefficient
week 4: Methods of measuring the relationship between quantitative variables: test of independence
week 5: Methods of measuring the relationship between qualitative variables: test of independence
week 6: 2x2 tables, NcNemar's test
week 7: Regression and correlation analysis. Regression line.
week 8: Regression analysis: another types of regression functions.
week 9: Times series - introduction. Characteristics of time series. Decomposition of time series.
week 10: Time series - estimation of trend in time series. Seasonal and cyclical component.
week 11: Index analysis - simple and complex individual indexes, agregate indexes.
week 12: Decision-making under risk.
week 13: Decision trees.
The objective of the course is to make students familiar with the fundamental ideas and methods used by these mathematical disciplines which enable students to apply this knowledge when solving problems related to the economic areas where these methods are used.
Specification of controlled education, way of implementation and compensation for absences
Attendance at lectures is not mandatory, but it is recommended. Attendance at seminars is not mandatory, but recommended. Due to the low number of students, lectures and exercises will be strongly intertwined.