MB143 Design and analysis of statistical experiments

Faculty of Informatics
Spring 2023
Extent and Intensity
2/2/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Taught in person.
doc. Mgr. David Kraus, Ph.D. (lecturer)
Bc. Jana Beregházyová (seminar tutor)
Mgr. Karolína Hrabcová (seminar tutor)
Mgr. Ondřej Pokora, Ph.D. (seminar tutor)
RNDr. Bc. Iveta Selingerová, Ph.D. (seminar tutor)
Bc. Markéta Trembaczová (seminar tutor)
Mgr. Andrea Kraus, M.Sc., Ph.D. (assistant)
Guaranteed by
prof. RNDr. Jan Slovák, DrSc.
Department of Computer Science - Faculty of Informatics
Supplier department: Department of Mathematics and Statistics - Departments - Faculty of Science
Fri 17. 2. to Fri 12. 5. Fri 10:00–11:50 D3
  • Timetable of Seminar Groups:
MB143/01: Wed 15. 2. to Wed 10. 5. Wed 14:00–15:50 B011, O. Pokora
MB143/02: Tue 14. 2. to Tue 9. 5. Tue 8:00–9:50 B204, I. Selingerová
MB143/03: Wed 15. 2. to Wed 10. 5. Wed 12:00–13:50 B204, K. Hrabcová
MB143/04: Wed 15. 2. to Wed 10. 5. Wed 8:00–9:50 B204, J. Beregházyová
MB143/05: Thu 16. 2. to Thu 11. 5. Thu 12:00–13:50 B204, M. Trembaczová
Prerequisites (in Czech)
MB141 Linear alg. and discrete math || MB142 Applied math analysis || MB101 Mathematics I || MB201 Linear models B || MB102 Calculus || MB202 Calculus B || MB151 Linear models || MB152 Calculus
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
there are 37 fields of study the course is directly associated with, display
Course objectives
The course presents principles and methods of statistical analysis, and explains what types of data are suitable for answering questions of interest.
Learning outcomes
After the course the students:
- are able to formulate questions of interest in terms of statistical inference (parameter estimation or hypothesis test within a suitable model);
- are able to choose a suitable model for basic types of data, choose a suitable method of inference to answer most common questions, implement the method in the statistical software R, and correctly interpret the results;
- are able to judge which questions and with what accuracy/certainty can be answered based on available data, or suggest what data should be collected in order to answer given questions with a desired level of accuracy/certainty.
  • Basic principles of Probability.
  • Random variables, their characteristics and mutual relationships.
  • Properties of functions of random variables.
  • Data as realisations of random variables.
  • Descriptive statistics and the choice of a suitable model.
  • Point and interval estimation: the framework and most common methods.
  • Hypotheses testing: the framework and most common methods.
  • Linear regression, Analysis of variance, Analysis of covariance.
  • Methods of data collection, their purpose, scope and limitations.
  • Design of experiment.
    recommended literature
  • CASELLA, George and Roger L. BERGER. Statistical inference. 2nd ed. Pacific Grove, Calif.: Duxbury, 2002. xxviii, 66. ISBN 0534243126. info
  • MILLIKEN, George A. and Dallas E. JOHNSON. Analysis of messy data. Second edition. Boca Raton: CRC Press, 2009. xiii, 674. ISBN 9781584883340. info
  • ZVÁRA, Karel and Josef ŠTĚPÁN. Pravděpodobnost a matematická statistika [Zvára, 2001]. 2. vyd. Praha: Matfyzpress, 2001. 230 s. ISBN 80-85863-76-6. info
  • ANDĚL, J. Základy matematické statistiky. Praha: MFF UK, 2005. info
  • ANDĚL, Jiří. Statistické metody. 1. vyd. Praha: Matfyzpress, 1993. 246 s. info
  • FORBELSKÁ, Marie and Jan KOLÁČEK. Pravděpodobnost a statistika I. 1. vyd. Brno: Masarykova univerzita, 2013. Elportál. ISBN 978-80-210-6710-3. url info
  • FORBELSKÁ, Marie and Jan KOLÁČEK. Pravděpodobnost a statistika II. 1. vyd. Brno: Masarykova univerzita, 2013. Elportál. ISBN 978-80-210-6711-0. url info
Teaching methods
Lectures: 2 hours a week; focused on explanation of terms, principles and methods.
Exercise sessions: 2 hours a week; focused on deeper understanding of the principles and methods, on their application to data using the statistical software R, and on interpretation of the obtained results.
Assessment methods
During the semester: two homework assignments worth 40 points in total. After the semester: a written exam worth 60 points. The final grade depends on the sum S of the points for assignments and for the written exam. The minimum required to pass is 51 points. Score-to-grade conversion: A for S in [91,100], B for S in [81,90], C for S in [71,80], D for S in [61,70], E for S in [51,60], F for S in [0,50].
Language of instruction
Follow-Up Courses
Further Comments
The course is taught annually.
Teacher's information
The course is also listed under the following terms Spring 2021, Spring 2022, Spring 2024.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/fi/spring2023/MB143