M8986 Statistical inferences II

Faculty of Science
Spring 2016
Extent and Intensity
2/2. 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)
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
Wed 8:00–9:50 M6,01011
  • Timetable of Seminar Groups:
M8986/01: Tue 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.
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
  • 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
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Spring 2014, Spring 2015, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.
  • Enrolment Statistics (Spring 2016, recent)
  • Permalink: https://is.muni.cz/course/sci/spring2016/M8986