Course detail

Applied Analytical Statistics

FP-BAASEAcad. year: 2026/2027

Students will acquire both practical and theoretical knowledge in the areas of random variables, mathematical statistics, categorical and correlation analysis, regression analysis, and time series analysis. They will learn to apply these methods in solving tasks related to the management of business processes, with an emphasis on the use of statistical procedures in the R programming environment and work with real data. Students will thus gain the skills necessary to understand data sources, critically evaluate them, and use statistical findings to support the development of sustainable and potentially scalable business solutions that can contribute to effective decision‑making and to the enhancement of an organization’s innovation potential.

Language of instruction

English

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

Ing. Karel Doubravský, Ph.D.

Department

Institute of Informatics (ÚI)

Entry knowledge

Recommended prerequisites for completing the course are basic mathematics (working with functions, basic algebraic operations, basic differential and integral calculus), basic probability (the concept of random events and probability).

Rules for evaluation and completion of the course

The course-unit credit is awarded on the following conditions (max. 40 points):

  • 80% attendance at exercise classes.
  • Completion of two semester assignments (more detailed information on the topics of the assignments and the method of submission will be specified at the beginning of the semester).

The exam (max. 60 points)

  • The exam is written, lasts 120 minutes, and consists of four examples and one theoretical question.
  • During the exam, students may use their own notes and materials posted on the e-learning website.

The mark, which corresponds to the total sum of points achieved (max 100 points), consists of:

  • points achieved in semestral assignments,
  • points achieved by solving examples,
  • points achieved by quality of answer to the theoretical question.

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

COMPLETION OF THE COURSE FOR STUDENTS WITH INDIVIDUAL STUDY

The course-unit credit is awarded on the following conditions (max. 40 points):

  • Completion of two semester assignments (more detailed information on the topics of the assignments and the method of submission will be specified at the beginning of the semester).

The exam (max. 60 points)

  • The exam is written, lasts 120 minutes, and consists of four examples and one theoretical question.
  • During the exam, students may use their own notes and materials posted on the e-learning website.

The mark, which corresponds to the total sum of points achieved (max 100 points), consists of:

  • points achieved in semestral assignments,
  • points achieved by solving examples,
  • points achieved by quality of answer to the theoretical question.

The grades 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 seminars is controlled.

Aims

Students will acquire fundamental knowledge of discrete and continuous random variables and their important types of distributions, processing of quantitative and qualitative data sets, point and interval estimation, the most commonly used parametric tests and goodness‑of‑fit tests, linear and nonlinear regression models, and time series analysis. They will be able to apply this knowledge using statistical software in real business environments to obtain relevant information needed to support business process management, and to develop a research‑analytical approach enabling them to identify and validate innovation opportunities leading to the design of sustainable and potentially scalable business solutions.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

FIELD, A., J. MILES and Z. FIELD. Discovering Statistics Using R. First edition. Los Angeles, Calif.: SAGE Publications Ltd., 2012. ISBN 978-1-4462-0046-9. (EN)
DEVORE, Jay L.; BERK, Kenneth N a CARLTON, Matthew A. Modern mathematical statistics with applications. Third edition. Cham: Springer, 2021. ISBN 978-3-030-55158-2. (EN)
CHATTAMVELLI, Rajan a SHANMUGAM, Ramalingam. Descriptive statistics for scientists and engineers: applications in R. Second edition. Cham: Springer, 2023. ISBN 978-3-031-32329-4. (EN)
Study materials available in the Moodle E-learning system. (EN)

Recommended reading

KARPÍŠEK, Z. a M. DRDLA. Applied Statistics. Brno University of Technology, Faculty of Business and Management. Brno, 1999. ISBN 80-214-1493-6. (EN)
BETTI, Matthew. Mathematics and statistics for the quantitative sciences. Boca Raton: CRC Press, 2023. ISBN 978-1-032-20814-5. (EN)
EWENS, W. J. a BRUMBERG, Katherine. Introductory statistics for data analysis. Cham: Springer, 2023. ISBN 978-3-031-28188-4. (EN)
PIEGORSCH, Walter W.; LEVINE, Richard A; ZHANG, Hao Helen a LEE, Thomas C. M. Computational statistics in data science. Hoboken: Wiley, 2022. ISBN 978-1-119-56107-1. (EN)

Classification of course in study plans

  • Programme BAK-ESBD Bachelor's 2 year of study, summer semester, compulsory

Type of course unit

 

Lecture

13 hours, optionally

Teacher / Lecturer

Ing. Karel Doubravský, Ph.D.

Syllabus

1. Discrete and continuous random variable (basic concepts, empirical and function characteristics).
2. Important type of distributions (Binomial distribution, Poisson distribution, Gauss distribution, Exponential distribution).
3. Descriptive statistics (basic concepts, empirical characteristics, empirical distribution function).
4. Data sample analysis.
5. Parameters’ estimation (point and interval estimates).
6. Test of statistical hypothesis (basic concepts and procedure).
7. Basic one-sample parametric tests.

8. Basic two-sample parametric tests.
9. Goodness-of-fit tests (Kolmogorov-Smirnov test, Pearson test, Shapiro-Wilk test).
10. Correlation and categorical analysis.
11. Linear regression model (basic concepts, the least square method).
12. Non-linear regression model (linearizable and non-linearizable regression models).
13. Time series analysis (basic characteristics, decomposition).

Exercise

26 hours, compulsory

Teacher / Lecturer

Ing. Karel Doubravský, Ph.D.

Syllabus

1. Discrete and continuous random variable (basic concepts, empirical and function characteristics).
2. Important type of distributions (Binomial distribution, Poisson distribution, Gauss distribution, Exponential distribution).
3. Descriptive statistics (basic concepts, empirical characteristics, empirical distribution function).
4. Data sample analysis.
5. Parameters’ estimation (point and interval estimates).
6. Test of statistical hypothesis (basic concepts and procedure).
7. Basic one-sample parametric tests.

8. Basic two-sample parametric tests.
9. Goodness-of-fit tests (Kolmogorov-Smirnov test, Pearson test, Shapiro-Wilk test).
10. Correlation and categorical analysis.
11. Linear regression model (basic concepts, the least square method).
12. Non-linear regression model (linearizable and non-linearizable regression models).
13. Time series analysis (basic characteristics, decomposition).

 

Professional Knowledge

Students acquire both theoretical and applied knowledge in the areas of random variables and mathematical statistics. They further gain knowledge of categorical and correlation analysis, regression analysis, and time series analysis. They develop an understanding of the principles of statistical modelling, data variability assessment, and the interpretation of statistical conclusions within the context of business process management.

Professional Competencies

Students become capable of applying appropriate statistical methods to solve tasks related to business processes, with an emphasis on working with real-world data. They develop the ability to critically evaluate the quality of data sources, identify suitable analytical procedures, and interpret statistical results in relation to organizational decision-making processes. They will also be able to use the R programming environment to conduct analyses and formulate data-driven conclusions.

Professional Skills

Students develop practical skills in performing statistical analyses in R, including data preparation, implementation of statistical methods, and visualization of results. They become able to translate analytical insights into recommendations that support the development of sustainable and potentially scalable business solutions. They learn to work with data in a way that effectively supports innovation and enhances the quality of decision-making within an organization.

Self-study

55 hours, optionally

Teacher / Lecturer

Ing. Karel Doubravský, Ph.D.

Individual preparation for an ending of the course

36 hours, optionally

Teacher / Lecturer

Ing. Karel Doubravský, Ph.D.