A course is the basic teaching unit, it's design as a medium for a student to acquire comprehensive knowledge and skills indispensable in the given field. A course guarantor is responsible for the factual content of the course.
For each course, there is a department responsible for the course organisation. A person responsible for timetabling for a given department sets a time schedule of teaching and for each class, s/he assigns an instructor and/or an examiner.
Expected time consumption of the course is expressed by a course attribute extent of teaching. For example, extent = 2 +2 indicates two teaching hours of lectures and two teaching hours of seminar (lab) per week.
At the end of each semester, the course instructor has to evaluate the extent to which a student has acquired the expected knowledge and skills. The type of this evaluation is indicated by the attribute completion. So, a course can be completed by just an assessment ('pouze zápočet'), by a graded assessment ('klasifikovaný zápočet'), or by just an examination ('pouze zkouška') or by an assessment and examination ('zápočet a zkouška') .
The difficulty of a given course is evaluated by the amount of ECTS credits.
The course is in session (cf. teaching is going on) during a semester. Each course is offered either in the winter ('zimní') or summer ('letní') semester of an academic year. Exceptionally, a course might be offered in both semesters.
The subject matter of a course is described in various texts.
BI-PRS.21 Practical Statistics Extent of teaching: 1P+2C Instructor: Dedecius K., Novák P. Completion: KZ Department: 18105 Credits: 5 Semester: L Annotation:
The students will be introduced to methods of applied statistics. They will learn how to work with various types of data, perform analyses, and choose models fitting the data. The course will encompass regression and correlation analysis, analysis of variance and non-parametric methods. Students will learn to use the statistical software R and will apply the studied methods on data from real problems.
Lecture syllabus:
1. Introduction to statistical analysis and the R language ecosystem. 2. Basic descriptive statistics, visualization of data - tables and plots. 3. Statistical tests, comparison of multiple data sets. 4. Non-parametric methods. 5. Regression analysis, estimation, evaluation of results. 6. Regression analysis with categorical variables. 7. Advanced regression models, parameter estimation, evaluation. 8. Basic methods of outlier detection. 9. Model selection, selection criteriaa. 10. Analysis of variance. 11. Multiple comparisons. 12. Analysis of categorical data. 13. R and LaTeX. Seminar syllabus:
1. Introduction to statistical analysis and the R language ecosystem. 2. Basic descriptive statistics, visualization of data - tables and plots. 3. Statistical tests, comparison of multiple data sets. 4. Non-parametric methods. 5. Regression analysis, estimation, evaluation of results. 6. Regression analysis with categorical variables. 7. Advanced regression models, parameter estimation, evaluation. 8. Basic methods of outlier detection. 9. Model selection, selection criteriaa. 10. Analysis of variance. 11. Multiple comparisons. 12. Analysis of categorical data. 13. R and LaTeX. Literature:
1. Ahn H. : Probability and Statistics for Science and Engineering with Examples in R. Cognella, 2017. ISBN 978-1516513987. 2. Bruce P., Bruce A. : Practical Statistics for Data Scientists: 50 Essential Concepts. O?Reilly Media, 2017. ISBN 978-1491952962. 3. Venables W. N., Smith D. M. : An Introduction to R. R Foundation for Statistical Computing, 2009. ISBN 978-0954612085. 4. Chambers J. M. : Software for Data Analysis: Programming with R. Springer, 2008. ISBN 978-0-387-75935-7. 5. Anděl J. : Základy matematické statistiky. Matfyzpress, 2011. ISBN 978-80-7378-162-0. Requirements:
Basics of probability and statistics, mathematical analysis and linear algebra
Chybí webová stránka. The course is also part of the following Study plans:
Page updated 19. 4. 2024, semester: L/2020-1, L/2021-2, Z/2023-4, Z/2024-5, Z/2019-20, Z/2022-3, L/2019-20, L/2022-3, Z/2020-1, Z/2021-2, L/2023-4, Send comments to the content presented here to Administrator of study plans Design and implementation: J. Novák, I. Halaška