M011 Statistics I

Faculty of Informatics
Spring 2002
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
RNDr. Marie Budíková, Dr. (lecturer)
doc. RNDr. Jaroslav Michálek, CSc. (lecturer)
Mgr. Martin Ander, Ph.D. (seminar tutor)
Mgr. Petra Hobstová (seminar tutor)
RNDr. Štěpán Mikoláš (seminar tutor), Mgr. Martin Ander, Ph.D. (deputy)
Guaranteed by
doc. RNDr. Jiří Kaďourek, CSc.
Departments – Faculty of Science
Contact Person: RNDr. Marie Budíková, Dr.
Timetable
Tue 8:00–9:50 D1
  • Timetable of Seminar Groups:
M011/01: Mon 9:00–9:50 A104, Mon 9:00–10:50 B003, Š. Mikoláš
M011/02: Mon 11:00–12:50 B003, Mon 12:00–12:50 A104, Š. Mikoláš
M011/03: Thu 13:00–14:50 B007, M. Ander
M011/04: Thu 15:00–16:50 B007, M. Ander
M011/50: No timetable has been entered into IS. M. Ander
M011/06: Fri 8:00–9:50 A104, Fri 8:00–9:50 B007, P. Hobstová
M011/07: Fri 10:00–11:50 A104, Fri 10:00–11:50 B007, P. Hobstová
Prerequisites (in Czech)
( M000 Calculus I || M500 Calculus I )&&( M003 Linear Algebra and Geometry I || M503 Linear Algebra and Geometry 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
Course objectives
Data files, empirical characteristics and graphs, numerical characteristics.
Probability space, property of probability, conditional probability, stochastic independence of events.
Random variables and vectors, functional and numerical characteristics.
Weak law of large number and central limit theorem.
Syllabus
  • Data files, empirical characteristics and graphs, numerical characteristics.
  • Probability space, property of probability, conditional probability, Bayes' theorem, stochastic independence of events.
  • Construction of classical probability and of probability distributions using probability function and density.
  • Random variables and vectors. Probability distribution and distribution function.
  • Discrete and continuous random variables and vectors. Typical distribution laws. Simultaneous and marginal distributions.
  • Stochastic independence of random variables and vectors. The sequence of independent trials.
  • Quantiles, expectation, variance, covariance, correlation coeficient and their properties.
  • Weak law of large number and central limit theorem.
Literature
  • BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Popisná statistika (Descriptive Statistics). 3., doplněné vyd. Brno: Masarykova univerzita, 1998, 52 pp. ISBN 80-210-1831-3. info
  • BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Teorie pravděpodobnosti a matematická statistika : sbírka příkladů. 2. vyd. Brno: Masarykova univerzita v Brně, 1998, viii, 116. ISBN 8021018321. info
  • OSECKÝ, Pavel. Statistické vzorce a věty. 1. vyd. Brno: Masarykova univerzita, 1998, [29] list. ISBN 8021017589. info
  • ANDĚL, Jiří. Statistické metody. 1. vyd. Praha: Matfyzpress, 1993, 246 s. info
Assessment methods (in Czech)
Výuka probíhá každý týden v rozsahu 2 hodiny přednášek, 2 hodiny cvičení. Nutnou podmínkou zápočtu je vypracování zápočtového příkladu. Zkouška je písemná, obsahuje část testovou a část s příklady.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course is taught annually.
The course is also listed under the following terms Spring 1996, Spring 1997, Spring 1998, Autumn 1998, Spring 1999, Spring 2000, Spring 2001.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/fi/spring2002/M011