BKM_STA1 Statistics I

Faculty of Economics and Administration
Autumn 2010
Extent and Intensity
0/0. 5 credit(s). Type of Completion: zk (examination).
Teacher(s)
Mgr. David Hampel, Ph.D. (lecturer)
RNDr. Marie Budíková, Dr. (alternate examiner)
Guaranteed by
RNDr. Luboš Bauer, CSc.
Department of Applied Mathematics and Computer Science – Faculty of Economics and Administration
Contact Person: Lenka Hráčková
Timetable
Sat 2. 10. 8:30–11:50 P101, Sun 24. 10. 8:30–11:50 aula Vinařská, Sat 13. 11. 8:30–11:50 aula Vinařská
Prerequisites (in Czech)
( KMMAT2 Mathematics 2 || KMMATB Mathematics B || C_KMMAT2 Mathematics || C_KMMAT2_T Mathematics || KMMAT2_T Mathematics 2 || BKM_MATE Mathematics 2 ) && (! KMSTAI Statistics I )
Course Enrolment Limitations
The course is only offered to the students of the study fields the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The course consists of descriptive statistics and principles of probability theory. The tutorials include motivation of the elementary concepts, key statements and calculation of typical examples. The topics follow a fixed procedure: descriptive statistical characteristics of nominal, ordinal, interval and proportional indicators; regression line; the basic properties of probability, stochastic independence of phenomena, conditional probability; random variables and vectors, their discrete and continuous type; joint distribution and stochastic independence of random variables; characteristics of random variables; asymptotic expressions; normal and other exact distributions.

At the end of this course, students should be able to:
understand terms from probability and statistics; correctly present real data; apply basics of probability to simple real situations.
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
  • BUDÍKOVÁ, Marie. Statistika. 1. vyd. Brno: Masarykova univerzita v Brně, 2004, 186 s. ISBN 8021034114. info
  • BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Popisná statistika (Descriptive Staistics). 2. dotisk 3. vydání. Brno: Masarykova univerzita v Brně, 2002, 52 pp. ISBN 80-210-1831-3. 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
  • BUDÍKOVÁ, Marie, Tomáš LERCH and Štěpán MIKOLÁŠ. Základní statistické metody. 1. vyd. Brno: Masarykova univerzita, 2005, 170 pp. ISBN 978-80-210-3886-8. info
  • Elementární statistická analýza. Edited by Lubomír Cyhelský - Jana Kahounová - Richard Hindls. 2. dopl. vyd. Praha: Management Press, 2001, 318 s. ISBN 80-7261-003-1. info
Teaching methods
Distance study: lectures, self study.
Assessment methods
Written exam consisting of theoretical and practical parts, POT (final project corrected by tutor).
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course can also be completed outside the examination period.
The course is taught annually.
General note: nezapisují si studenti, kteří absolvovali předmět KMSTAI.
The course is also listed under the following terms Autumn 2009, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023.
  • Enrolment Statistics (Autumn 2010, recent)
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