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Lecturer(s)
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Homolka Lubor, 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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- 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. |
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Recommended literature
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FELLER. An Introduction to Probability Theory and Its Applications, Volume II.. New York: Wiley, 1971.
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FREUND, J. E., WALPOLE, R. E. Mathematical Statistics.. Englewood Cliffs: Prantice-Hall, 1987. ISBN 0135621178.
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JAMES, G., WITTEN, D., HASTIE, T., TIBSHIRANI, R. An introduction to statistical learning: with applications in R. New York: Springer, 2013. ISBN 978-1-4614-7137-0.
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KUHN, M., JOHNSON, K. Applied predictive modeling. New York: Springer, 2013. ISBN 978-1-4614-6848-6.
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MONTGOMERY, D. C. Introduction to Statistical Quality Control. vyd. 6.. John Wiley & Sons, Inc,, 2009. ISBN 978-0470169926.
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PECK, R., OLSEN, CH., DEVORE, J., L. Introduction to Statistics and Data Analysis, Enhanced Review Edition (4th Edition). Duxbury Press, 2011. ISBN 0840054904.
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PESTMAN, W. R. Mathematical Statistics: An Introduction. New York: Walter de Gruyter, 1998.
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ROSS, S. M. Introductory Statistics. 3rd ed.. Academic Press,, 2010. ISBN 0123743885.
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ROSS, S. M. Introductory Statistics. 3rd ed.. Academic Press, 2010. ISBN 0123743885.
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