## BPM_ST1A Statistics 1

Autumn 2021
Extent and Intensity
2/2/0. 6 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Mgr. Maria Králová, Ph.D. (lecturer)
Guaranteed by
doc. Mgr. Maria Králová, Ph.D.
Department of Applied Mathematics and Computer Science - Faculty of Economics and Administration
Supplier department: Department of Applied Mathematics and Computer Science - Faculty of Economics and Administration
Prerequisites
SOUHLAS
Knowledge of HE Mathematics
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 2 student(s).
Current registration and enrolment status: enrolled: 2/2, only registered: 0/2, only registered with preference (fields directly associated with the programme): 0/2
fields of study / plans the course is directly associated with
Course objectives
At the end of the course students should be able to:
- understand and explain the basic terms in the calculus of probability and in descriptive statistics;
- apply the probability terms and the descriptive statistics terms to the description of economic events and data;
- use the terminology in the follow-up course of mathematical statistics.
Learning outcomes
After graduation of the course student should be able to:
- use and interpret functional and quantitative characteristics within a framework of descriptive statistics
- describe types of variables concerning measurement scale
- via probability quantify randomness in elementary situations
- use and correctly interpret distribution function, probability function and density function
- determine appropriate distributions concerning the application context
Syllabus
• 1.Types of variables concerning measurement scale. Data visualisation.
• 2. Sampling, random sample
• 3. Fundamental of descriptive statistics.
• 4. Frequency and probability, probability properties, examples.
• 5. Independent events, properties of independent events, a sequence of independent events.
• 6. Conditional probability, total probability rule, Bayes' theorem, examples.
• 7. Random variable, a discrete and continuous variable, discrete probability distribution, probability function and its properties; continuous probability distribution, probability density function and its properties.
• 8. Distribution function, its properties and its application. 9. Quantitative measures of probability distribution: expected value, variance, quantile, their properties and applications in economics.
• 10. Quantitative measures of simultaneous probability.
• distribution: covariance, correlation coefficient, their properties and applications in economics.
• 11. Examples of discrete and continuous probability distributions and their applications in the field of economics.
• 12. Central limit theorem and its applications.
• 13. Review
Literature
required literature
• WEISS, N. A. Introductory statistics. Edited by Carol A. Weiss. 10th edition, global edition. Boston: Pearson, 2017. 763, 73. ISBN 9781292099729. info
recommended literature
• UBØE, Jan. Introductory statistics for business and economics : theory, exercises and solutions. Cham: Springer, 2017. xiv, 466. ISBN 9783319709352. info
• RAMACHANDRAN, K. M. and Chris P. TSOKOS. Mathematical statistics with applications in R. Second edition. Amsterdam: Elsevier/AP, 2015. xxiii, 789. ISBN 9780124171138. info
Teaching methods
The course consists of lectures and seminars.
Assessment methods
The course is completed by fulfilling the following requirements:
1. Adequately active participation at seminars
2. Success at ROPOT tests
3. Success at the final test
Language of instruction
English