MB143 Design and analysis of statistical experiments

Faculty of Informatics
Spring 2021
Extent and Intensity
2/2/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
Mgr. Andrea Kraus, M.Sc., Ph.D. (lecturer)
prof. RNDr. Jan Slovák, DrSc. (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
Timetable
Thu 14:00–15:50 Virtuální místnost
  • Timetable of Seminar Groups:
MB143/01: Tue 16:00–17:50 Virtuální místnost, A. Kraus
MB143/02: Mon 14:00–15:50 Virtuální místnost, A. Kraus
MB143/03: Tue 14:00–15:50 Virtuální místnost, A. Kraus
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
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 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.
Syllabus
  • 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.
Literature
    recommended literature
  • 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
  • MILLIKEN, George A. and Dallas E. JOHNSON. Analysis of messy data. Second edition. Boca Raton: CRC Press, 2009, xiii, 674. ISBN 9781584883340. info
    not specified
  • 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
  • BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Teorie pravděpodobnosti a matematická statistika. Sbírka příkladů. (Probability Theory and Mathematical Statistics. Collection of Tasks.). 3rd ed. Brno: Masarykova univerzita, 2004, 127 pp. ISBN 80-210-3313-4. info
  • BUDÍKOVÁ, Marie, Maria KRÁLOVÁ and Bohumil MAROŠ. Průvodce základními statistickými metodami (Guide to basic statistical methods). vydání první. Praha: Grada Publishing, a.s., 2010, 272 pp. edice Expert. ISBN 978-80-247-3243-5. URL info
  • BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Popisná statistika (Descriptive Statistics). 3., doplněné vyd. Brno: Masarykova univerzita, 1998, 52 pp. ISBN 80-210-1831-3. info
Teaching methods
Lectures: 2 hours a week by means of videoconference via MS Teams at the time given by the timetable; focused on explanation of terms, principles and methods.
Exercise sessions: 2 hours a week by means of videoconference via MS Teams at the time given by the timetable; 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: teamwork on two projects, presentation of results during the exercise sessions.
After the semester: a written exam (online form).
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
https://is.muni.cz/auth/el/fi/jaro2021/MB143/index.qwarp
https://is.muni.cz/auth/el/fi/jaro2021/MB143/index-myLOLl.qwarp
The course is also listed under the following terms Spring 2022, Spring 2023, Spring 2024, Spring 2025.
  • Enrolment Statistics (Spring 2021, recent)
  • Permalink: https://is.muni.cz/course/fi/spring2021/MB143