Course detail

Applied Statistics

FP-IBasPAcad. year: 2025/2026

The course deals with parametric and nonparametric tests, analysis of variance, categorical analysis, multivariate regression models, statistical process control methods and capability indices.

Language of instruction

English

Number of ECTS credits

6

Mode of study

Not applicable.

Guarantor

Ing. Karel Doubravský, Ph.D.

Department

Institute of Informatics (ÚI)

Offered to foreign students

Of all faculties

Entry knowledge

Fundamentals of probability theory, descriptive statistics and mathematical statistics is required.

Rules for evaluation and completion of the course

COURSE COMPLETION

The course-unit credit is awarded on the following conditions (max. 40 points):
- preparation of semester assignments (the topic of the assignments will be specified during the semester).

The exam (max. 60 points)
- has a written form with the possibility of using computer technology and consists of four computational examples and a theoretical question.

The grade, which corresponds to the total sum of points achieved (max 100 points), consists of:
- points achieved in semester assignments (max. 40 points),
- points achieved by solving examples (max. 51 points),
- points achieved by answering theoretical questions (max. 9 points).

The grade and corresponding points:
A (100-90), B (89-80), C (79-70), D (69-60), E (59-50), F (49-0).

Attendance at lectures is not mandatory but is recommended. Attendance at exercises is required and checked by the tutor. An excused absence of a student from seminars can be compensated for by submitting solution of alternate exercises.

COMPLETION OF THE COURSE FOR STUDENTS WITH INDIVIDUAL STUDY

The course-unit credit is awarded on the following conditions (max. 40 points):
- preparation of semester assignments (the topic of the assignments will be specified during the semester).

The exam (max. 60 points)
- has a written form with the possibility of using computer technology and consists of four computational examples and a theoretical question.

The grade, which corresponds to the total sum of points achieved (max 100 points), consists of:
- points achieved in semester assignments (max. 40 points),
- points achieved by solving examples (max. 51 points),
- points achieved by answering theoretical questions (max. 9 points).

The grade and corresponding points:
A (100-90), B (89-80), C (79-70), D (69-60), E (59-50), F (49-0).

Aims

The objective of the course is to learn students with basic principles of mathematical statistics, econometric models, categorical analysis, statistical process control methods and their use in management of company processes.
Students will acquire the knowledge which allows them to use statistical methods at such a theoretical and practical level which allow them to process and perform correct data evaluation and develop the awareness and abilities of students to use statistical methods to manage of company processes.

Study aids

see Course literature.
Study materials available on e-learning.

Prerequisites and corequisites

Not applicable.

Basic literature

FIELD, A.; MILES, J. and FIELD, Z. Discovering Statistics Using R. Los Angeles, Californie: SAGE Publications Ltd., 2012. ISBN 978-1-4462-0046-9. (EN)
MATHEWS, P. Design of Experiment with Minitab. Milwaukee: ASQ Quality Press, 2005. ISBN 9780873896375 (EN)
Study materials available on e-learning. (EN)

Recommended reading

BOX, G. E. P.; HUNTER, W. G. and HUNTER, J. S. Statistics for experimenters: an introduction to design, data analysis, and model building. Wiley, 1978. ISBN 978-0-471-09315-2.
KARPÍŠEK, Z. and DRDLA, M. Applied statisitcs. Brno: PC-DIR Real, 1999. ISBN 8021414936.

Classification of course in study plans

  • Programme MGR-IBM Master's 1 year of study, winter semester, compulsory

  • Programme MGR-Z Master's

    branch MGR-Z , 1 year of study, winter semester, elective

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

Ing. Karel Doubravský, Ph.D.

Syllabus

Topics of lectures are the following:
1. Parametric statistical tests: t-test, two sample t-test and F-test
2. Kolmogorov-Smirnov test, Pearson test and Shapiro-Wilk test
3. Analysis of variance (ANOVA): one factor and two factor ANOVA
4. Nonparametric statistical tests: one sample tests
5. Nonparametric statistical tests: two sample tests
6. Nonparametric ANOVA
7. Multivariate regression models
8. Multivariate regression models: classical assumptions
9. Categorical analysis
10. Statistical Process Control
11. Control charts for measurement control
12. Control charts for comparison control
13. Process Capability Index

Exercise

13 hod., compulsory

Teacher / Lecturer

Ing. Karel Doubravský, Ph.D.

Syllabus

The topics of exercises correspond to the topics of lectures.