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Lecturer(s)
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Metzker Zdenko, Ing. Ph.D.
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Kunčar Aleš, Ing.
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Homolka Lubor, Ing. Ph.D.
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Urbánek Tomáš, Ing. Ph.D.
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Course content
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- Introduction to inferential statistics - Dependency analysis - an introduction - Testing one-sample tests (proportions, means) - Testing two-sample tests (proportions, means) - Contingency and association tables - ANOVA for one and two factors - Regression analysis - Correlation analysis - Non-parametric methods
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Learning activities and teaching methods
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Lecturing
- Home preparation for classes
- 26 hours per semester
- Preparation for course credit
- 23 hours per semester
- Preparation for examination
- 24 hours per semester
- Participation in classes
- 52 hours per semester
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| learning outcomes |
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| Knowledge |
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| Defines the difference between descriptive and inferential statistics. |
| Defines the difference between descriptive and inferential statistics. |
| Formulate the procedure for evaluating statistical hypotheses |
| Formulate the procedure for evaluating statistical hypotheses |
| Choose the right statistical method for evaluating formulated statistical hypotheses |
| Choose the right statistical method for evaluating formulated statistical hypotheses |
| Verify the statistical significance of formulated statistical hypotheses |
| Verify the statistical significance of formulated statistical hypotheses |
| Skills |
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| Determines the degree of association in a contingency table |
| Determines the degree of association in a contingency table |
| Determines the significance of the interaction between categorical and metric variables (t-test, ANOVA) |
| Determines the significance of the interaction between categorical and metric variables (t-test, ANOVA) |
| Predict the dependent variable into the future in linear regression modeling |
| Predict the dependent variable into the future in linear regression modeling |
| Decides whether or not to reject the null hypothesis based on critical value, p-value and confidence interval. |
| Decides whether or not to reject the null hypothesis based on critical value, p-value and confidence interval. |
| Evaluates the result of the given statistical test both statistically and factually. |
| Evaluates the result of the given statistical test both statistically and factually. |
| teaching methods |
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| Knowledge |
|---|
| Lecturing |
| Lecturing |
| assessment methods |
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| Grade (Using a grade system) |
| Grade (Using a grade system) |
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Recommended literature
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Budíková, M. a kol. Průvodce základními statistickými metodami. Praha: Grada, 2010. ISBN 978-80-247-3243-5.
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Klímek, Petr. Aplikovaná statistika : cvičení. Vyd. 2. uprav. Zlín : Univerzita Tomáše Bati, 2004. ISBN 807318253X.
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Klímek, Petr. Aplikovaná statistika pro ekonomy. Vyd. 1. Zlín : Univerzita Tomáše Bati, 2003. ISBN 8073181487.
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Klímek, Petr. Statistické metody pro ekonomy. Vyd. 1. Zlín : Univerzita Tomáše Bati ve Zlíni, 2001. ISBN 8073180138.
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Pecáková, I. Statistika v terénních průzkumech. Praha: Professional Publishing, 2008. ISBN 978-80-86946-74-0.
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Řezanková, H. Analýza dat z dotazníkových šetření. Praha: Professional Pzblishing, 2007. ISBN 978-80-86946-49-8.
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Seger, Jan. Statistické metody v tržním hospodáoství. 1. vyd. Praha : Victoria Publishing, 1995. ISBN 8071870587.
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