FI:MV013 Statistics - Course Information
MV013 Statistics for Computer Science
Faculty of InformaticsAutumn 2016
- 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)
- doc. PaedDr. RNDr. Stanislav Katina, Ph.D. (lecturer)
Mgr. Mojmír Vinkler (seminar tutor) - Guaranteed by
- doc. PaedDr. RNDr. Stanislav Katina, Ph.D.
Faculty of Informatics
Supplier department: Faculty of Science - Timetable
- Mon 8:00–9:50 A319
- Timetable of Seminar Groups:
MV013/01: Mon 10:00–11:50 B117, M. Vinkler
MV013/02: Mon 12:00–13:50 B117, M. Vinkler - 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 22 fields of study the course is directly associated with, display
- Syllabus
- Why computer scientists should study statistics?
- Computer science related problems with analysed data
- Why the thought study based on data is useful?
- Data types
- Sampling
- Parametric probabilistic and statistical models
- Likelihood principle and parameter estimation using numerical methods
- Descriptive statistics (tables, listings, figures)
- From description to statistical inference
- Hypothesis testing and parameters of a model
- Goodness-of-fit tests
- Testing hypotheses about one-sample
- Testing hypotheses about two-samples
- Testing hypotheses about more than to sample problems
- Interpretation of statistical findings
- Literature
- CASELLA, George and Roger L. BERGER. Statistical inference. 2nd ed. Pacific Grove, Calif.: Duxbury, 2002, xxviii, 66. ISBN 0534243126. info
- Language of instruction
- English
- Further Comments
- Study Materials
The course is taught annually.
- Enrolment Statistics (Autumn 2016, recent)
- Permalink: https://is.muni.cz/course/fi/autumn2016/MV013