Course detail

Applied Analytical Statistics

FP-BAASEAcad. year: 2025/2026

Students will gain knowledge of random variables, mathematical statistics, categorical and correlation analysis, analysis of variance, regression analysis and time series analysis and their use in business process management. Emphasis is primarily placed on the practical part, which is aimed at familiarizing with the use of the statistical program R in the implementation of the above-mentioned methods and procedures.

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

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):

  • 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 be introduced to basic concepts of discrete and continuous random variables and their major distribution, processing data files, point and interval estimation, hypothesis testing, linear and nonlinear regression models and time series analysis. Students will be able to use appropriate methods in dealing with informatics and economic problems. After completing the course, students will be prepared to practically apply these methods in ICT and related economic subjects using statistical programs.
Students acquire basic knowledge of random variables and important types of their distribution, processing data sets of quantitative and qualitative character, point and interval estimation, the most widely used parametric tests and tests of goodness of fit, linear and nonlinear regression models and analysis of time series, and will be able to use this knowledge in real business environment so that they are able to receive relevant information needed to support the management of business activities.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

FIELD, A., J. MILES and Z. FIELD. Discovering Statistics Using R. 1 edition. Los Angeles, Calif.: SAGE Publications Ltd., 2012. ISBN 978-1-4462-0046-9. (EN)
MATHEWS, P. Design of Experiments with Minitab. Milwaukee: ASQ Quality Press, 2005. ISBN 978-08-738-9637-5. (EN)
Study materials available in the Moodle E-learning system. (EN)

Recommended reading

BOX, George E. P., William Gordon HUNTER a J. Stuart HUNTER, 1978. Statistics for experimenters: an introduction to design, data analysis, and model building. B.m.: Wiley. ISBN 978-0-471-09315-2. (EN)
KARPÍŠEK, Z. a M. DRDLA. Applied Statistics. Brno University of Technology, Faculty of Business and Management. Brno, 1999. ISBN 80-214-1493-6. (EN)
MONTGOMERY, Douglas C., 2008. Design and Analysis of Experiments. B.m.: John Wiley & Sons. ISBN 978-0-470-12866-4. (EN)

Elearning

eLearning: currently opened course

Classification of course in study plans

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

  • Programme BAK-Z Bachelor's

    branch BAK-Z , 1 year of study, summer semester, elective

Type of course unit

 

Lecture

13 hod., 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 hod., 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).

Self-study

70 hod., optionally

Teacher / Lecturer

Ing. Karel Doubravský, Ph.D.

Individual preparation for an ending of the course

47 hod., optionally

Teacher / Lecturer

Ing. Karel Doubravský, Ph.D.

Elearning

eLearning: currently opened course