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Lecturer(s)
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Durchan Milan, RNDr. CSc.
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Course content
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Lectures (1 lesson): 1. - 2. Definition of basic terms, scaling, measurement, elementary statistical processing. 3. - 4. Non-parametric testing of assignment of the theoretical distribution to the empirical distribution. 5. - 6. Theory of estimation ? point and interval estimates of theoretical parameters. 7. - 8. Parametric testing. 9. - 10. Regression and correlation analysis. 11. - 12. Interpretation of obtained results in practice ? practical examples. 13. - 14. Importance of statistics for health and other information systems. Exercise (2 lessons): 1. Elementary statistical processing ? practical solution of examples I. 2. Elementary statistical processing ? practical solution of examples II. 3. Non-parametric testing ? practical solutions to examples I. 4. Non-parametric testing ? practical solution of examples II. 5. Estimation theory ? practical solution of examples I. 6. Estimation theory ? practical solution of examples II. 7. Parametric testing ? single-choice testing ? practical solution of examples. 8. Parametric testing ? two-choice testing ? practical solution of examples. 9. Regression and correlation analysis - practical solution of examples I. 10. Regression and correlation analysis - practical solution of examples II. 11. Interpretation of obtained results in practice I. 12. Interpretation of obtained results in practice II. 13. Importance of statistics for health and other information systems. 14. Revision.
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Learning activities and teaching methods
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unspecified
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Learning outcomes
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The aim of the course is to acquaint students with basic statistical terms and methods. Students will be presented with information on data set evaluation and basic statistical tests. They will be introduced to the general applicability of statistical methods and their applications. As part of the exercises, students will learn practical procedures and algorithms. An integral part is understanding the structure of statistics as a whole as well as the necessary sub-areas.
Competence acquired: After completing the course, the student is able to use the basic methods of descriptive and mathematical statistics.
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Prerequisites
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unspecified
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Assessment methods and criteria
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unspecified
A written test. Additional requirements for the student: 85% participation in seminars (one absence possible), any further absences due to serious reasons must be documented
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Recommended literature
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ANDĚL, Jiří. Statistické metody.
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ŘEZANKOVÁ, Hana, Tomáš LÖSTER a Zdeněk ŠULC. Úvod do statistiky.
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ZÁŠKODNÝ, P., HAVRÁNKOVÁ, R., HAVRÁNEK, J., VURM, V. Základy statistiky (s aplikací na zdravotnictví).
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ZVÁROVÁ, J. Biomedicínská statistika.
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