MV011 Statistics I

Faculty of Informatics
Spring 2020
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).
doc. Mgr. Jan Koláček, Ph.D. (lecturer)
Mgr. et Mgr. Daniela Kuruczová, Ph.D. (seminar tutor)
RNDr. Radim Navrátil, Ph.D. (seminar tutor)
Guaranteed by
doc. PaedDr. RNDr. Stanislav Katina, Ph.D.
Faculty of Informatics
Supplier department: Faculty of Science
Mon 17. 2. to Fri 15. 5. Tue 8:00–9:50 A318
  • Timetable of Seminar Groups:
MV011/01: Mon 17. 2. to Fri 15. 5. Mon 12:00–13:50 A215, D. Kuruczová
MV011/02: Mon 17. 2. to Fri 15. 5. Mon 14:00–15:50 A215, D. Kuruczová
Prerequisites: calculus in one and several variables, basics of linear algebra.
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 37 fields of study the course is directly associated with, display
Course objectives
Upon completing this course, students will be able to perform basic computer aided statistical data set analysis in R language, 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; has basic knowledge of statistical hypothesis testing, will be able carry out tests in statistical software and interpret the results.
  • Introduction to the probability theory.
  • 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.
  • Data files, empirical characteristics and graphs, numerical characteristics. Descriptive statistics in R language.
  • Random sample, point and interval estimators.
  • Basics of testing hypothesis. Testing hypothesis in R language.
  • Regression analysis in R language.
    recommended literature
  • FORBELSKÁ, Marie and Jan KOLÁČEK. Pravděpodobnost a statistika I. 1. vyd. Brno: Masarykova univerzita, 2013. Elportál. ISBN 978-80-210-6710-3. url info
  • FORBELSKÁ, Marie and Jan KOLÁČEK. Pravděpodobnost a statistika II. 1. vyd. Brno: Masarykova univerzita, 2013. Elportál. ISBN 978-80-210-6711-0. 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.). 3rd ed. Brno: Masarykova univerzita, 2004. 127 pp. ISBN 80-210-3313-4. info
  • 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
  • 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 fill in question sets and solve practical task in R. The examination is written: theory and examples. Final grade: A ... 46 - 50 points B ... 41 - 45 points C ... 36 - 40 points D ... 31 - 35 points E ... 26 - 30 points F ... 0 - 25 points
Language of instruction
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
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 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019.
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