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PřF:M8986 Statistical inferences I - Course Information

## 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:

*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**- Applied Mathematics for Multi-Branches Study (programme PřF, N-MA)
- Mathematical Modelling and Numeric Methods (programme PřF, N-MA)
- Statistics and Data Analysis (programme PřF, N-MA)

**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 in one-, two-, and multi-sample experiments,
- design for contingency tables,
- design in linear regression model for categorical data,

**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

*recommended literature*- KATINA, Stanislav, Miroslav KRÁLÍK and Adéla HUPKOVÁ.
**Teaching methods**- Lectures, practicals.
**Assessment methods**- Homework, oral exam.
**Language of instruction**- Czech
**Further Comments**- Study Materials

The course is taught annually.

- Enrolment Statistics (spring 2018, recent)
- Permalink: https://is.muni.cz/course/sci/spring2018/M8986