BPM_STA1 Statistics 1

Faculty of Economics and Administration
Autumn 2011
Extent and Intensity
2/2/0. 5 credit(s). Type of Completion: graded credit.
Teacher(s)
doc. Mgr. Maria Králová, Ph.D. (lecturer)
Mgr. Tomáš Lerch (seminar tutor)
Ing. Mgr. Markéta Matulová, Ph.D. (seminar tutor)
Mgr. Jan Orava (seminar tutor)
Mgr. Silvie Zlatošová, Ph.D. (seminar tutor)
Guaranteed by
RNDr. Luboš Bauer, CSc.
Department of Applied Mathematics and Computer Science - Faculty of Economics and Administration
Contact Person: Lenka Hráčková
Timetable
Mon 9:20–11:00 P102, Mon 9:20–11:00 P101
  • Timetable of Seminar Groups:
BPM_STA1/01: Wed 16:20–17:55 P312, T. Lerch
BPM_STA1/02: Tue 12:00–13:35 S311
BPM_STA1/03: Mon 12:50–14:30 S308
BPM_STA1/04: Wed 17:10–18:45 S313
BPM_STA1/05: Tue 8:30–10:05 S311
BPM_STA1/06: Tue 13:45–15:20 S307
BPM_STA1/07: Wed 14:35–16:15 P304, T. Lerch
BPM_STA1/08: Thu 14:35–16:15 P106, J. Orava
BPM_STA1/09: Thu 11:05–12:45 P201, J. Orava
BPM_STA1/10: Thu 12:50–14:30 P106, J. Orava
BPM_STA1/11: Wed 18:00–19:35 P312, T. Lerch
BPM_STA1/12: Thu 7:40–9:15 P103, M. Matulová
BPM_STA1/13: Thu 12:50–14:30 P104, S. Zlatošová
BPM_STA1/14: No timetable has been entered into IS.
BPM_STA1/15: Wed 12:50–14:30 P104, M. Matulová
BPM_STA1/16: No timetable has been entered into IS. M. Králová
BPM_STA1/17: Mon 16:20–17:55 P104, M. Králová
BPM_STA1/18: Fri 13:45–15:20 P304, M. Králová
BPM_STA1/19: Thu 14:35–16:15 P312, S. Zlatošová
BPM_STA1/20: Thu 9:20–11:00 P103, M. Matulová
BPM_STA1/21: Wed 8:30–10:05 S311
BPM_STA1/22: Wed 11:05–12:45 P104, M. Matulová
BPM_STA1/23: Thu 11:05–12:45 P303, M. Matulová
BPM_STA1/24: Thu 16:20–17:55 S310, S. Zlatošová
Prerequisites (in Czech)
( PMMAT2 Mathematics II || PMZMII Introduction to Mathematics II || BPM_MATE Mathematics ) && (! PMSTAI Statistics I )
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 21 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.
Syllabus
  • 1.Frequency and probability, properties of probability, examples.
  • 2.Independent events, properties of independent events, sequence of independent events.
  • 3. Conditional probability, total probability rule, examples.
  • 4. Prior and posterior probabilities, Bayes' theorem, examples.
  • 5. Descriptive statistics, quantitative variables, qualitative variables; frequency distributions in tables and graphs, examples of data sets.
  • 6. Functional characteristics and numerical descriptive measures for univariate and multivariate quantitative variables, examples.
  • 7. Random variable, distribution function and its properties, discrete and continous variable, transformation of random variable.
  • 8. Discrete probability distribution, probability function and its properties; continuous probability distribution, probability density and its properties; random vector and its functional characteristics.
  • 9. Simultaneous and marginal random vectors, independent random variables, sequence of Bernoulli trials.
  • 10. Examples of discrete and continuous probability distributions and their application in the field of economics.
  • 11. Numerical measures of probability distribution: expected value, variance, quantile, their properties and application in economics.
  • 12. Numerical measures of simultaneous probability distribution: covariance, correlation coefficient, their properties and application in economics.
  • 13. Characteristics of random vectors, inequality theorems (Markov inequality theorem, Cebysev inequality theorem).
Literature
    recommended 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
    not specified
  • 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
  • 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
Teaching methods
Theoretical lectures; practical seminar sessions;
Assessment methods
Lecture with a seminar
Graded credit requirements:
1. Adequately active participation at seminars
2. Success at progress test
3. Success at final test
A (90,100); B (80,89); C (70,79); D (60,69); E (50,59); F (0,49)
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
General note: Nezapisují si studenti, kteří absolvovali předmět PMSTAI.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2009, Autumn 2010, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, Autumn 2021.
  • Enrolment Statistics (Autumn 2011, recent)
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