Applied Analytical Statistics
FP-BAASEAcad. year: 2019/2020
Students will gain knowledge of random variable, mathematical statistics, categorical analysis, methods of regression analysis and analysis of time series describing economics and social events.
Offered to foreign students
Of all faculties
Learning outcomes of the course unit
Students acquire basic knowledge of random variables and important types of their distribution, processing data sets of quantitative and qualitative character, point and interval estimation, the most widely used parametric tests and tests of goodness of fit, simple and complex indices, linear and nonlinear regression models and analysis of time series, and will be able to use this knowledge in real business environment so that they are able to receive relevant information needed to support the management of business activities.
Fundamentals of linear algebra (sets, set operations), mathematical analysis (derivation, integral, combinatorials scheme) and probability theory (probability, conditional probability).
Recommended optional programme components
Recommended or required reading
KARPÍŠEK, Z. a M. DRDLA. Applied Statistics. Brno University of Technology, Faculty of Business and Management. Brno, 1999. ISBN 80-214-1493-6. (EN)
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. (EN)
BOX, George E. P., William Gordon HUNTER a J. Stuart HUNTER, 1978. Statistics for experimenters: an introduction to design, data analysis, and model building. B.m.: Wiley. ISBN 978-0-471-09315-2. (EN)
MATHEWS, P. Design of Experiments with Minitab. Milwaukee: ASQ Quality Press, 2005. ISBN 978-08-738-9637-5. (EN)
MONTGOMERY, Douglas C., 2008. Design and Analysis of Experiments. B.m.: John Wiley & Sons. ISBN 978-0-470-12866-4. (EN)
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
1. Discrete and continuous random variable (basic concepts, empirical and function characteristics)
2. Important type of distributions (Binomial distribution, Poission distribution, Gauss distribution, Exponential distribution...)
3. Bivariate random variables (correlation)
4. Descriptive statistics (basic concepts, empirical characteristics, empirical distribution function)
5. Data sample analysis
6. Parameters’ estimation (point and interval estimates)
7. Test of statistical hypothesis (basic concepts and procedure)
8. Basic parametric tests (t-test, F-test, ANOVA)
9. Index analysis
10. Individual and composite indexes
11. Linear regression model (basic concepts, the least square method)
12. Non-linear regression model (linearizable and non-linearizable regression models)
13. Time series analysis (basic characteristics, decomposition)
Students will be introduced to basic concepts of discrete and continuous random variables and their major distribution, processing data files, point and interval estimation, hypothesis testing, linear and nonlinear regression models and time series analysis. Students will be able to use appropriate methods in dealing with informatics and economic problems. After completing the course, students will be prepared to practically apply these methods in ICT and related economic subjects using statistical programs.
Specification of controlled education, way of implementation and compensation for absences
Attendance at lectures is not compulsory, but is recommended. Attendance at seminars is controlled.