470-4403/03 – Statistics III (STA3)

Gurantor departmentDepartment of Applied MathematicsCredits6
Subject guarantorprof. Ing. Radim Briš, CSc.Subject version guarantorprof. Ing. Radim Briš, CSc.
Study levelundergraduate or graduateRequirementChoice-compulsory type B
Study languageCzech
Year of introduction2019/2020Year of cancellation
Intended for the facultiesFEIIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
BRI10 prof. Ing. Radim Briš, CSc.
KRA0220 Ing. Jan Kracík, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Credit and Examination 2+2
Combined Credit and Examination 10+10

Subject aims expressed by acquired skills and competences

Gemeral linear statistical models - introduction.

Teaching methods

Project work


Purpose: to introduce the general theory of linear statistical models; present standard methods of parameter estimation, testing, model building, and evaluation; to develop skills in implementing and interpreting standard statistical software for data analysis.

Compulsory literature:

Box G.E.P., Hunter W.G., Hunter J.S.; Statistics for Experimenters; J. Wiley & Sons, 1978. Searle L.R.; Linear Statistical Models; Second Edition, J. Wiley & Sons, 1986 Fahrmeir L., Tutz G., Multivariate Statistical Modelling Based on Generalized Linear Models, Springer 2001, ISBN 0-38795187-3.

Recommended literature:

Searle L.R.; Linear Statistical Models; Second Edition, J. Wiley & Sons, 1986 McCullagh P., and Nelder J.A., Generalized Linear Models, Chapmann & Hall/CRC 1989, ISBN 0-412-31760-5.

Way of continuous check of knowledge in the course of semester

2 tests, max 20 points.


Další požadavky na studenta

Semestral project under leadership of responsible teachers.


Subject has no prerequisities.


Subject has no co-requisities.

Subject syllabus:

Topics: Multivariate normal distribution. Full rank linear model. Model building. Box-Cox transformation. Generalized linear models. Logistic regression. Bayes methods. Bayes decision making. Analysis of covariance.

Conditions for subject completion

Full-time form (validity from: 2019/2020 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Credit and Examination Credit and Examination 100 (100) 51
        Credit Credit 40  20
        Examination Examination 60  15
Mandatory attendence parzicipation: Participation at all exercises is obligatory, 2 apologies are accepted. Participation at lectures is recommended, knowledge of lecture materials is a prerequisite for participation at the exercises.

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.FormStudy language Tut. centreYearWSType of duty
2019/2020 (N0541A170007) Computational and Applied Mathematics (S01) Applied Mathematics P Czech Ostrava Choice-compulsory type B study plan
2019/2020 (N0541A170007) Computational and Applied Mathematics (S01) Applied Mathematics K Czech Ostrava Choice-compulsory type B study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner