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Lecturer(s)
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Bartoš Petr, doc. RNDr. Ph.D.
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Blažek Josef, doc. RNDr. CSc.
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Course content
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Random quantity and its basic characteristics. Ki-quadrant, t-tests, single-factor and multi-factor variance analysis, dependency detection by correlation and regression. Pivottable. Interpretation of results. Statistical data processing by computer - Microsoft Excel, Statistica, MATLAB and its toolboxes and more. Monte Carlo method and its use to solve tasks. Specific examples. Visualization of scalar data. Visualization of vector fields in 2D and 3D. In view of the wide dissemination of issues, the content of the course after consultation with the student will be targeted with regard to the issue of the dissertation.
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Work with text (with textbook, with book), Written action (comprehensive tests, clauses), Individual preparation for exam, Work with multi-media resources (texts, internet, IT technologies), Individual tutoring
- Preparation for exam
- 80 hours per semester
- Preparation for classes
- 50 hours per semester
- Semestral paper
- 70 hours per semester
- Class attendance
- 50 hours per semester
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Learning outcomes
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The aim of the course is to acquire knowledge and skills that will allow the student to statistically evaluate and visualize experimental data on his own. For this purpose, the student is able to use the available computing resources. The student has a sufficient theoretical basis that allows him/her to interpret the obtained data correctly.
Students will gain advanced and specific knowledge in the field of statistical evaluation and data visualization.
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Prerequisites
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Advanced knowledge in the field of statistical evaluation and data visualization.
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Assessment methods and criteria
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Combined exam, Seminar work
Active participation in consultations and workshops. Elaboration of seminar work.
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Recommended literature
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Internetové stránky dodavatelů software, návody k softwarovým balíkům.
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Budíková, M., Mikoláš, Š. Osecký, P. Teorie pravděpodobnosti a matematická statistika. Sbírka příkladů. MU Brno, 2004. ISBN 80-210-3313-4.
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Harvey, G. Introduction to Computer Simulation Methods. Addison-Wesley, USA, 2006. ISBN 0-8053-7758-1.
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Mead, Curnow, R.N., Hasted, A.M., Curnow, R.M. Curnow: Statistical Methods in Agriculture and Experimental Biology. Third Edition, Chapman and Hall, 2002. ISBN 1584881879.
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Nezbeda, I., Kotrla, M., Kolafa, J. Úvod do počítačových simulací - Metody Monte Carlo. Karolinum Praha, 2003.
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Zvára, K., Štěpán, J. Pravděpodobnost a matematická statistika, Matfyzpress. Praha, 2001.
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