MV011 Statistics I

Faculty of Informatics
Spring 2013
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)
Mgr. Martin Řezáč, Ph.D. (lecturer)
Mgr. Eva Janoušková, Ph.D. (seminar tutor)
Mgr. Michal Theuer, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Faculty of Informatics
Contact Person: Mgr. Martin Řezáč, Ph.D.
Supplier department: Faculty of Science
Timetable
Mon 10:00–11:50 G101
  • Timetable of Seminar Groups:
MV011/T01: Wed 10:00–11:55 Učebna S7 (18), Fri 10:00–11:55 Učebna S7 (18), E. Janoušková
MV011/01: Wed 12:00–13:50 G124, E. Janoušková
MV011/02: Wed 14:00–15:50 G124, E. Janoušková
MV011/03: Tue 8:00–9:50 G125, M. Theuer
MV011/04: Tue 10:00–11:50 G125, M. Theuer
Prerequisites (in Czech)
Předpokládá se znalost diferenciálního a integrálního počtu jedné a více proměnných a znalost lineární algebry.
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
Upon completing this course, students will be able to perform basic computer aided statistical data set analysis, resulting in tables, graphs and numerical characteristics; will understand basic probability concepts; will be able to solve probability tasks related to explained theory (in some cases using statistical software); will be able to generate realizations of selected types random variables using statistical software.
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, 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
  • 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. (Probability Theory and Mathematical Statistics. Collection of Tasks.). 3rd ed. Brno: Masarykova univerzita, 2004, 127 pp. ISBN 80-210-3313-4. 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
Teaching methods
Lectures, Exercises
Assessment methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises. Throughout semester, students elaborate a semester project and write a test. The examination is written, consisting of test part and exercises part.
Language of instruction
Czech
Follow-Up Courses
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020.
  • Enrolment Statistics (Spring 2013, recent)
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