## M8986 Statistical inferences II

Faculty of Science
spring 2018
Extent and Intensity
2/2/0. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
doc. PaedDr. RNDr. Stanislav Katina, Ph.D. (lecturer)
Mgr. Veronika Horská (seminar tutor)
Mgr. Markéta Janošová (assistant)
Guaranteed by
doc. PaedDr. RNDr. Stanislav Katina, Ph.D.
Department of Mathematics and Statistics - Departments - Faculty of Science
Contact Person: doc. PaedDr. RNDr. Stanislav Katina, Ph.D.
Supplier department: Department of Mathematics and Statistics - Departments - Faculty of Science
Timetable
Mon 10:00–11:50 M3,01023
• Timetable of Seminar Groups:
M8986/01: Mon 18:00–19:50 MP1,01014, V. Horská
Prerequisites (in Czech)
M7986 Statistical inferences 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
The main goal of the course is to become familiar with some basic principles of testing statistical hypotheses base on Wald principle, likelihood and score principle connecting the statistical theory with MC simulations, implementation in R, geometry, and statistical graphics; to understand and explain basic principles of parametric statistical inference for categorical data; to implement these techniques in R language; to be able to apply them to real data.
Learning outcomes
Student will be able:
- to understand principles of likelihood and statistical inference for discrete data;
- to select suitable probabilistic and statistical model in statistical inference for discrete data;
- to build up and explain suitable simulation study for selected statistical test or confidence for discrete data;
- to build up and explain suitable statistical test for discrete data;
- to apply statistical inference for discrete data;
- to implement methods of statistical inference for discrete data in R.
Syllabus
• discrete probability distributions, maximum likelihood estimates of their parameters,
• principles of MC simulations in testing statistical hypotheses,
• design for contingency tables,
Literature
recommended literature
• KATINA, Stanislav, Miroslav KRÁLÍK and Adéla HUPKOVÁ. Aplikovaná štatistická inferencia I. Biologická antropológia očami matematickej štatistiky (Applied statistical inference I). 1. vyd. Brno: Masarykova univerzita, 2015. 320 pp. ISBN 978-80-210-7752-2. info
• COX, D. R. Principles of statistical inference. 1st ed. Cambridge: Cambridge University Press, 2006. xv, 219. ISBN 0521685672. info
• CASELLA, George and Roger L. BERGER. Statistical inference. 2nd ed. Pacific Grove, Calif.: Duxbury, 2002. xxviii, 66. ISBN 0534243126. info
Teaching methods
Lectures, practicals.
Assessment methods
Homework, oral exam.
Language of instruction
Czech