Course detail

Analysis of Engineering Experiment

FSI-TAIAcad. year: 2025/2026

The course is aimed at the selected parts of mathematical statistics for stochastic modeling of the engineering experiments: regression models, regression diagnostics, multivariate methodsand design iof experiment. Computations are carried out using the software Minitab.

Language of instruction

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

Ing. Pavel Hrabec, Ph.D.

Department

Institute of Mathematics (ÚM)

Entry knowledge

Descriptive statistics, probability, random variable, random vector, random sample, parameters estimation, hypotheses testing, and regression analysis.

Rules for evaluation and completion of the course

Course-unit credit requirements: active participation in seminars.
Exam: Presenting a assigned project.

 

Attendance at seminars is controlled and the teacher decides on the compensation for absences.

Aims

The course objective is to make students majoring in Mathematical Engineering and Physical Engineering acquainted with important selected methods of mathematical statistics used for a technical problems solution.

 

Students acquire needed knowledge from the mathematical statistics, which will enable them to evaluate and develop stochastic and interval models of technical phenomena and processes based on these methods and realize them on PC.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Anděl, J.: Základy matematické statistiky. Praha: Matfyzpress, 2011. (CS)
Hebák, P., Hustopecký, J., Jarošová, E., Pecáková, I.: Vícerozměrné statistické metody 1, 2, 3, Praha: INFORMATORIUM, 2004. (CS)
Montgomery, D. C., Renger, G.: Applied Statistics and Probability for Engineers. New York: John Wiley & Sons, 2010. (EN)
Agresti, A. (c2013). Categorical data analysis (3rd ed). Wiley-Interscience. (EN)
Montgomery, D. C. (c2013). Design and analysis of experiments (8th ed). Wiley. (EN)
Ryan, T. P.: Modern Regression Methods. New York : John Wiley, 2004. (EN)

Recommended reading

Davison, A. C., Hinkley, D. V.: Bootstrap Methods and their Applications. Cambridge: Cambridge University Press, 2006. (EN)
Klir, G. J., Yuan, B.: Fuzzy Sets and Fuzzy Logic. New Jersey: Prentice Hall 1995. (EN)
Moor, R. E., Kearfott, R. B., Clood, M. J.: Introduction to Interval Analysis. Philadelphia: SIAM 2009. (EN)

Classification of course in study plans

  • Programme N-FIN-P Master's 1 year of study, summer semester, compulsory
  • Programme N-MAI-P Master's 2 year of study, summer semester, compulsory
  • Programme N-PMO-P Master's 1 year of study, summer semester, compulsory-optional

Type of course unit

 

Lecture

26 hod., compulsory

Teacher / Lecturer

Ing. Pavel Hrabec, Ph.D.

Syllabus

  1. Principal components
  2. Factor analysis.
  3. Cluster analysis.
  4. ANOVA.
  5. Linear regression.
  6. Identification of regression model, regularized regression.
  7. Factorial design of experiment.
  8. Central point, blocks, replications and randomization in DoE.
  9. Fractional factorial DoE.
  10. Response surface DoE.
  11. Mixture DoE.
  12. Logistic regression.
  13. Nonparametric hypotheses testing.

Computer-assisted exercise

13 hod., compulsory

Teacher / Lecturer

Ing. Pavel Hrabec, Ph.D.

Syllabus

  1. Principal components
  2. Factor analysis.
  3. Cluster analysis.
  4. ANOVA.
  5. Linear regression.
  6. Identification of regression model, regularized regression.
  7. Factorial design of experiment.
  8. Central point, blocks, replications and randomization in DoE.
  9. Fractional factorial DoE.
  10. Response surface DoE.
  11. Mixture DoE.
  12. Logistic regression.
  13. Nonparametric hypotheses testing.