M7986 Statistical inferences I

Faculty of Science
Autumn 2023
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, Ph.D. (seminar tutor)
Mgr. Tereza Juchelková (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
Timetable
Mon 8:00–9:50 M2,01021
  • Timetable of Seminar Groups:
M7986/01: Mon 18:00–19:50 MP1,01014, T. Juchelková
M7986/02: Mon 16:00–17:50 MP1,01014, V. Horská
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 for continuous data; to understand and explain basic principles of parametric statistical inference for continuous data; to implement these techniques into 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 continuous data;
- to select suitable probabilistic and statistical model in statistical inference for continuous data;
- to build up and explain suitable simulation study for selected statistical test or confidence interval for continuous data;
- to build up and explain suitable statistical test for continuous data;
- to apply statistical inference on real continuous data;
- to implement methods of statistical inference for continuous data in R.
Syllabus
  • probabilistic and statistical model,
  • likelihood function and its maximisation,
  • basic principles of testing statistical hypotheses,
  • types of test statistics,
  • principles of MC simulations for testing statistical hypotheses,
  • design in one-, two-, and multi-sample experiments,
  • examples in R,
  • applications on real 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 2 hours per week.
Practicals 2 hours per week.
Face-to-face or, if needed, on-line using MS Teams 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
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
Teacher's information
The lectures are usually in Czech or in English as needed, and the relevant terminology is always given with English equivalents.

The target skills of the study include the ability to use the English language passively and actively in their own expertise and also in potential areas of application of mathematics.

Assessment in all cases may be in Czech and English, at the student's choice.

The lectures will take place faece-to-face or, if needed, 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.

The course is also listed under the following terms Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2024.
  • Enrolment Statistics (Autumn 2023, recent)
  • Permalink: https://is.muni.cz/course/sci/autumn2023/M7986