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Lecturer(s)
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Fišerová Eva, doc. RNDr. Ph.D.
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Vencálek Ondřej, doc. Mgr. Ph.D.
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Ševčíková Paulína, Mgr.
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Pavlů Ivana, Mgr. Ph.D.
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Fačevicová Kamila, Mgr. Ph.D.
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Course content
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1. Introduction to the mathematical statistics - point estimation, interval estimation, hypothesis testing. 2. Analysis about one quantitative variable: one sample tests about a normal distribution parameters, one sample Wilcoxon test, verification of assumptions about the shape of the distribution (goodness-of-fit tests with unknown parameters). 3. Analysis about one categorical variable: estimation and hypothesis testing for the Bernoulli distribution, goodness-of-fit tests with known parameters. 4. Basic methods for evaluating the relationship between two categorical variables - contingency tables (tests of independence, tests of symmetry). 5. Basic methods for evaluating the relationship between quantitative and categorical variables - two samples parametric tests. 6. Basic methods for evaluating the relationship between quantitative and categorical variables - two samples nonparametric tests. 7. Basic methods for evaluating the relationship between quantitative and categorical variables - one-way analysis of variance. 8. Basic methods for evaluating the relationship between quantitative and categorical variables - Kruskal-Wallis test as a nonparametric alternative to one-way analysis of variance. 9. Basic methods for evaluating the relationship between two quantitative variables - linear regression: interpretation of parameters, their estimation and hypotheses testing about these parameters. 10. Advanced methods - categorical dependent variable and quantitative or categorical explanatory variables: logistic regression (dependent variable of 2 categories): interpretation of parameters (odds ratio), their estimation and hypotheses testing about these parameters. 11. Advanced methods - interactions. 12. Advanced methods - categorical dependent variable and quantitative or categorical explanatory variables: multinomial regression (dependent variable of more than two categories). 13. Advanced methods - quantitative dependent variable and quantitative or categorical explanatory variables: multiple regression, introduction to nonlinear regression. 14. Advanced methods - quantitative dependent variable: two-way analysis of variance.
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Learning activities and teaching methods
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Lecture, Monologic Lecture(Interpretation, Training), Dialogic Lecture (Discussion, Dialog, Brainstorming)
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Learning outcomes
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Understand mathematical statistics and its applications.
Comprehension Understanding of basic methods of mathematical statistics.
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Prerequisites
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Basic knowledge of probability theory.
KMA/PST
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Assessment methods and criteria
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Oral exam, Written exam
Credit: active participation in seminars, pass the written test Exam: the student has to present knowledge and understanding during the oral exam
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Recommended literature
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Anděl, J. (2018). Statistické úlohy, historky a paradoxy. Matfyzpress, Praha.
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Anděl, J. (2005). Základy matematické statistiky. Matfyzpress, Praha.
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Hron, K., Kunderová, P., Vencálek. (2018). Základy počtu pravděpodobnosti a metod matematické statistiky. Vydavatelství Univerzity Palackého, Olomouc.
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Procházka, B. (2015). Biostatistika pro lékaře. Karolinum, Praha.
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Walpole, R. E., Myers, R.H., Myers, S.L., Ye, K. (2002). Probability & statistics for engineers & scientists. Prentice Hall, Upper Saddle River.
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