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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2024/2025
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Welcome page > Faculty of Information Technology > Department of Applied Mathematics > Practical Statistics

Practical Statistics

The course is not on the list Without time-table
Code Completion Credits Range Language
BI-PRS.21 KZ 5 1P+2C Czech
Course guarantor:
Lecturer:
Tutor:
Supervisor:
Department of Applied Mathematics
Synopsis:

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.

Requirements:

Basics of probability and statistics, mathematical analysis and linear algebra

Syllabus of lectures:

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.

Syllabus of tutorials:

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.

Study Objective:
Study materials:

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.

Note:
Further information:
No time-table has been prepared for this course
The course is a part of the following study plans:
Data valid to 2024-10-13
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