Statistics for Applied Informatics

Course guarantor:

Lecturer:

Tutor:

Supervisor:

Faculty of Information Technology

Synopsis:

Requirements:

Semestral work + exam.

Syllabus of lectures:

1. Introduction, basic descriptive statistics

2. Theory of random sample

3. Important distributions, important statistical tests

4. Theory of statistical testing

5. Regression analysis, estimation, evaluation of results

6. Theory of linear regression models, estimation and properties

7. Analysis of variance

8. Advanced theory of estimation

9. Non-parametric methods

10. Likelihood methods

11. Generalized linear models

12. Simulation methods

13. Bootstrap

Syllabus of tutorials:

1. Introduction to statistical analysis; familiarization with the R programming environment.

2. Basic descriptive statistics; data visualization - tables and graphs.

3. Statistical tests; comparison of multiple datasets.

4. Analysis of categorical data.

5. Regression analysis: estimation and evaluation of results.

6. Regression analysis with factor (categorical) variables.

7. Detection of outliers.

8. Analysis of variance (ANOVA).

9. Model selection issues; selection criteria.

10. Verification of regression model assumptions.

11. Nonparametric methods.

12. Generalized linear models.

13. Simulation methods.

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.

Note:

Further information:

courses

No time-table has been prepared for this course

The course is a part of the following study plans:

• Mgr. programe Applied informatics (code ANIE) for the phase of study without specialization (compulsory course in the program)
• Master specialization Embedded systems (compulsory course in the program)
• Master specialization Business Informatics, 2026 (compulsory course in the program)
• Master specialization Software Engineering (compulsory course in the program)
• Master specialization Web Engineering (compulsory course in the program)
• Master specialization Visual computing and Game design (compulsory course in the program)