MB143 Design and Analysis of Statistical Experiments

Faculty of Informatics
Spring 2026
Extent and Intensity
2/2/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
In-person direct teaching
Teacher(s)
Mgr. Andrea Kraus, M.Sc., Ph.D. (lecturer)
doc. Mgr. David Kraus, Ph.D. (lecturer)
Mgr. Andrea Kraus, M.Sc., Ph.D. (seminar tutor)
RNDr. Veronika Eclerová, Ph.D. (seminar tutor)
Bc. Aneta Minibergerová (seminar tutor)
Bc. Kateřina Válková (seminar tutor)
Guaranteed by
doc. Mgr. David Kraus, Ph.D.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Thu 19. 2. to Thu 14. 5. Thu 8:00–9:50 KOM 200
  • Timetable of Seminar Groups:
MB143/01: Mon 16. 2. to Mon 11. 5. Mon 8:00–9:50 A320, A. Kraus
MB143/02: Mon 16. 2. to Mon 11. 5. Mon 10:00–11:50 A320, A. Kraus
MB143/03: Wed 18. 2. to Wed 13. 5. Wed 8:00–9:50 A320, A. Kraus
MB143/04: Wed 18. 2. to Wed 13. 5. Wed 14:00–15:50 B204, K. Válková
MB143/05: Wed 18. 2. to Wed 13. 5. Wed 16:00–17:50 B204, K. Válková
MB143/06: Fri 20. 2. to Fri 15. 5. Fri 8:00–9:50 A320; and Mon 18. 5. 8:00–9:50 A320, Tue 19. 5. 8:00–9:50 A320, A. Minibergerová
MB143/07: Fri 20. 2. to Fri 15. 5. Fri 10:00–11:50 A320; and Mon 18. 5. 10:00–11:50 A320, Tue 19. 5. 10:00–11:50 A320, A. Minibergerová
MB143/08: Wed 18. 2. to Wed 13. 5. Wed 18:00–19:50 A215, V. Eclerová
Prerequisites
( MB141 Linear Alg. and Discrete Math || MB142 Applied Math Analysis || MB151 Linear Models || MB152 Calculus ) && ! MB153 Statistics I && !NOW( MB153 Statistics I )
MB143 is a lightweight version of MB153, so it can be replaced by completing the full course.
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 38 fields of study the course is directly associated with, display
Abstract
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.
Key topics
  • 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.
  • Sample size calculation.
  • Overview of more advanced statistical methods (Linear regression, Analysis of variance, Analysis of covariance, Logistic regression).
  • Overview of more advanced topics in experimental design (Methods of data collection, their purpose, scope and limitations).
Study resources and literature
    recommended literature
  • CASELLA, George and Roger L. BERGER. Statistical inference. 2nd ed. Pacific Grove, Calif.: Duxbury, 2002, xxviii, 66. ISBN 8131503941. 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
Approaches, practices, and methods used in teaching
Lectures: 2 hours a week; focused on explanation of terms, principles and methods, and on interpretation of results.
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.
Method of verifying learning outcomes and course completion requirements
During the semester: two homework assignments, each worth 10 points, and one test during the exercise sessions, worth 20 points. Students who earn less than 20 points during the semester are given the grade X. After the semester: a written exam worth 60 points. The final grade depends on the sum S of the points gained during the semester and for the written exam. The minimum required to pass is 50 points. Score-to-grade conversion: A for S in [90, 100], B for S in [80, 89], C for S in [70, 79], D for S in [60, 69], E for S in [50, 59], F for S in [20, 49].
Language of instruction
Czech
Follow-Up Courses
Further Comments
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.
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