BPM_STA1 Statistics 1

Faculty of Economics and Administration
Autumn 2019
Extent and Intensity
2/2/0. 5 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Mgr. Maria Králová, Ph.D. (lecturer)
Mgr. Jan Böhm (seminar tutor)
Ing. Matouš Cabalka (seminar tutor)
Mgr. Terézia Černá (seminar tutor)
Mgr. Ondřej Černý (seminar tutor)
Mgr. Monika Filová (seminar tutor)
Lenka Hráčková (seminar tutor)
Ing. Mgr. Markéta Matulová, Ph.D. (seminar tutor)
Mgr. Petra Ráboňová, Ph.D. (seminar tutor)
Ing. Mgr. Vlastimil Reichel, Ph.D. (seminar tutor)
Ing. Lukáš Kokrda (assistant)
Guaranteed by
doc. Mgr. Maria Králová, Ph.D.
Department of Applied Mathematics and Computer Science – Faculty of Economics and Administration
Contact Person: Lenka Hráčková
Supplier department: Department of Applied Mathematics and Computer Science – Faculty of Economics and Administration
Timetable
Tue 10:00–11:50 P101
  • Timetable of Seminar Groups:
BPM_STA1/01: Tue 12:00–13:50 VT105, M. Králová
BPM_STA1/02: Thu 8:00–9:50 VT204, M. Matulová
BPM_STA1/03: Wed 8:00–9:50 VT105, J. Böhm
BPM_STA1/04: Thu 16:00–17:50 VT202, P. Ráboňová
BPM_STA1/05: Wed 12:00–13:50 VT105, J. Böhm
BPM_STA1/06: Wed 14:00–15:50 VT105, J. Böhm
BPM_STA1/07: Wed 16:00–17:50 VT105, T. Černá
BPM_STA1/08: Wed 18:00–19:50 VT105, T. Černá
BPM_STA1/09: Thu 12:00–13:50 VT105, P. Ráboňová
BPM_STA1/10: Thu 18:00–19:50 VT105, P. Ráboňová
BPM_STA1/11: Thu 14:00–15:50 VT202, P. Ráboňová
BPM_STA1/12: Thu 16:00–17:50 VT105, M. Matulová
BPM_STA1/13: Tue 12:00–13:50 VT206, M. Filová
BPM_STA1/14: Tue 14:00–15:50 VT202, M. Filová
BPM_STA1/15: Thu 18:00–19:50 VT206, T. Černá
BPM_STA1/17: Wed 18:00–19:50 VT202, J. Böhm
Prerequisites (in Czech)
( BPM_MATE Mathematics )
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 22 fields of study the course is directly associated with, display
Course objectives
At the end of the course students should be able to:
- understand and explain the basic terms in 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 numeric characteristics within a framework of descriptive statistics
- describe types of variables with respect to measurement scale
- quantify randomness in elementary setting by probability
- use and properly interpret distributional function, probability function and density function
- determine in mathematical statistics popular distributions with respect to the application context
Syllabus
  • 1.Types of variables with respect to measurement scale. Data visualisation.
  • 2. Sampling, random sample
  • 3. Basic of descriptive statistics.
  • 4. Frequency and probability, probability properties, examples.
  • 5. Independent events, properties of independent events, 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. Numerical measures of probability distribution: expected value, variance, quantile, their properties and application in economics.
  • 10. Numerical measures of simultaneous probability distribution: covariance, correlation coefficient, their properties and application in economics.
  • 11. Examples of discrete and continuous probability distributions and their application in the field of economics.
  • 12. Central limit theorem and its applications.
  • 13. Review
Literature
    required literature
  • BUDÍKOVÁ, Marie, Maria KRÁLOVÁ and Bohumil MAROŠ. Průvodce základními statistickými metodami (Guide to basic statistical methods). vydání první. Praha: Grada Publishing, a.s., 2010, 272 pp. edice Expert. ISBN 978-80-247-3243-5. URL info
    recommended literature
  • WEISS, N. A. Introductory statistics. Edited by Carol A. Weiss. 10th edition, global edition. Boston: Pearson, 2017, 763, 73. ISBN 9781292099729. info
  • HANOUSEK, Jan and Pavel CHARAMZA. Moderní metody zpracování dat :matematická statistika pro každého. 1. vyd. Praha: Grada, 1992, 210 s. ISBN 80-85623-31-5. info
  • HINDLS, Richard, Stanislava HRONOVÁ and Jan SEGER. Statistika pro ekonomy. 4. vyd. Praha: Professional publishing, 2003, 415 s. ISBN 8086419525. info
  • BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Teorie pravděpodobnosti a matematická statistika.Sbírka příkladů. (Probability Theory and Mathematical Statistics.Collection of Tasks.). 2.dotisk 2.přeprac.vyd. Brno: Masarykova univerzita Brno, 2002, 127 pp. ISBN 80-210-1832-1. info
Teaching methods
Theoretical lectures; practical computer-aided seminar sessions;
Assessment methods
Lecture with a seminar
Test requirements:
1. Adequately active participation at seminars
2. Success at ROPOT tests
3. Success at progress test
4. Success at final test
Any copying, recording or leaking tests, use of unauthorized tools, aids and communication devices, or other disruptions of objectivity of exams (credit tests) will be considered non-compliance with the conditions for course completion as well as a severe violation of the study rules. Consequently, the teacher will finish the exam (credit test) by awarding grade "F" in the Information System, and the Dean will initiate disciplinary proceedings that may result in study termination.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
General note: Přednášky jsou dostupné online a ze záznamu.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023.
  • Enrolment Statistics (Autumn 2019, recent)
  • Permalink: https://is.muni.cz/course/econ/autumn2019/BPM_STA1