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Lecturer(s)
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Ponížil Petr, prof. RNDr. Ph.D.
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Course content
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1. Normal distribution, normality testing. 2. Statistical hypothesis testing. 3. Linear regression. 4. Nonlinear regression. 5. Analysis of variance (ANOVA). 6. Nonparametric methods. 7. Experiment planning.
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Learning activities and teaching methods
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- Participation in classes
- 8 hours per semester
- Preparation for course credit
- 52 hours per semester
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| prerequisite |
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| Knowledge |
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| Basic knowledge of mathematics and statistics. |
| Basic knowledge of mathematics and statistics. |
| learning outcomes |
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| test statistical hypothesis |
| test statistical hypothesis |
| explain linear regression models |
| explain linear regression models |
| explain non-linear regression models |
| explain non-linear regression models |
| define one and two factor ANOVA |
| define one and two factor ANOVA |
| design appropriate non-parametric tests |
| design appropriate non-parametric tests |
| Skills |
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| use basic and more advanced statistical methods in the processing of experimental data |
| use basic and more advanced statistical methods in the processing of experimental data |
| test statistical hypotheses |
| test statistical hypotheses |
| calculate the parameters of the regression models and test them |
| calculate the parameters of the regression models and test them |
| analyze one and two factor ANOVA |
| analyze one and two factor ANOVA |
| test the data using non-parametric tests |
| test the data using non-parametric tests |
| teaching methods |
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| Knowledge |
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| Dialogic (Discussion, conversation, brainstorming) |
| Dialogic (Discussion, conversation, brainstorming) |
| Monologic (Exposition, lecture, briefing) |
| Monologic (Exposition, lecture, briefing) |
| Skills |
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| Practice exercises |
| Practice exercises |
| Individual work of students |
| Individual work of students |
| assessment methods |
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| Knowledge |
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| Didactic test |
| Didactic test |
| Grade (Using a grade system) |
| Grade (Using a grade system) |
| Oral examination |
| Oral examination |
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Recommended literature
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ANDĚL, J. Základy matematické statistiky. Praha: MatfyzPress, 2011. ISBN 9788073781620.
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BUDÍKOVÁ, M., KRÁLOVÁ, M., MAROŠ, B. Průvodce základními statistickými metodami. Grada, 2010. ISBN 978-80-247-3243-5.
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FREEDMAN, D., PISANI, R. Statistics, 4th ed.. W.W. Norton & Company, 2007. ISBN 978-0393929720.
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LEPŠ, J, ŠMILAUER, P. Biostatistika. EPISTEME, Praha, 2016. ISBN 978-80-7394-587-9.
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McCLAVE, J.T., SINCICH, T.T. Statistics. Cambridge: Pearson Publishing, 2012. ISBN 0321755936.
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MELOUN, M., MILITKÝ, J. Statistické zpracování experimentálních dat. Praha : Plus Publishing, 1994. ISBN 80-85297-56-6.
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NEUBAUER, J., SEDLAČÍK, M., KŘÍŽ, O. Základy statistiky. Praha, 2016. ISBN 978-80-247-5786-5.
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WITTE, R.S., WITTE, J.S. Statistics. New York, 2009. ISBN 978-0470392225.
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