M8986 Statistical inference II

Faculty of Science
Spring 2022
Extent and Intensity
2/2/0. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Taught in person.
Teacher(s)
doc. PaedDr. RNDr. Stanislav Katina, Ph.D. (lecturer)
Mgr. Veronika Horská (seminar tutor)
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
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 intervals 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
    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. On-line using MS Teams or full-time according to the according to the development of the epidemiological situation and the applicable restrictions.
Assessment methods
Homework, oral exam. The conditions may be specified according to the development of the epidemiological situation and the applicable restrictions.
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is taught: every week.
Teacher's information
The lectures will take place online at MS Teams at the time of the normal lectures according to the schedule. Due to the possible low signal quality, I recommend students not to use the camera. Questions during the lecture will not be possible to ask by voice, but by chat.

The recording from the lecture will be uploaded in the IS sequentially and not in advance, so the recording will be uploaded only after the given lecture and before the next lecture. The recordnig does not have to contain a complete lecture, it is up to a teacher what to share from the record and share it with the students. What is a lecture recording? It can be a PDF of text written by the lecturer on the screen with an electronic pen during the lecture, and this can be supplemented by the voice (or voice and video) of the lecturer. Slides in PDF with TeX-ed text will always be available in the IS and will be shared only after the given lecture and before the next lecture.

Consultations about the lectures will take place through a discussion forum, where the lecturer / instructor moderates this discussion and new discussion forums established by students will not be taken into account. Discussion forums will be based on individual lectures and practicals (if the course has practicals) and about homework. Discussions by e-mail will not take place.

To obtain the credit, active participation in seminars is required (2 unexcused absences are allowed). An excused absence is considered exclusively an absence excused at the study department and uploaded into the information system in due time (within 5 working days from the date of the course). This is in accordance with the study regulations, where Article 9 paragraph (7) states that (7) The student is obliged to apologize in writing to the study department of the faculty within 5 working days from the date of the course being excused.

The course is also listed under the following terms Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021.
  • Enrolment Statistics (Spring 2022, recent)
  • Permalink: https://is.muni.cz/course/sci/spring2022/M8986