Analysis of Engineering Experiment (FSI-TAI-A)

Academic year 2025/2026
Supervisor: Ing. Pavel Hrabec, Ph.D.  
Supervising institute: ÚM all courses guaranted by this institute
Teaching language: English
Aims of the course unit:
 
Learning outcomes and competences:
 
Prerequisites:
 
Course contents:

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.
Teaching methods and criteria:
 
Assesment methods and criteria linked to learning outcomes:

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.
Controlled participation in lessons:
 
Type of course unit:
    Lecture  13 × 2 hrs. optionally                  
    Computer-assisted exercise  13 × 1 hrs. compulsory                  
Course curriculum:
    Lecture

  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 1.PC professional statistical software.
2.One-way analysis of variance.
3.Two-way analysis of variance.
4.Regression model identification. Semester work assignment.
5.Nonlinear regression analysis.
6.Regression diagnostic.
7.Nonparametric methods.
8.Correlation analysis.
9.Principle components. Factor analysis.
10.Cluster analysis.
11.Probability distributions estimation.
12.Semester works presentation I.
13.Semester works presentation II.
Literature - fundamental:
1. Ryan, T. P.: Modern Regression Methods. New York : John Wiley, 2004.
2. Montgomery, D. C. - Renger, G.: Applied Statistics and Probability for Engineers. New York: John Wiley & Sons, 2003.
3. Hahn, G. J. - Shapiro, S. S.: Statistical Models in Engineering. New York: John Wiley & Sons, 1994.
3. P. Hebák, J. Hustopecký: Vícerozměrné statistické metody, SNTL, Praha 1990
Literature - recommended:
1. Anděl, J.: Statistické metody. Praha: Matfyzpress, 2003.
2. Hebák, P. et al: Vícerozměrné statistické metody 1, 2, 3. Praha : Informatorium, 2004.
3. Meloun, M. - Militký, J.: Statistické zpracování experimentálních dat. Praha: Plus, 1994.
The study programmes with the given course:
Programme Study form Branch Spec. Final classification   Course-unit credits     Obligation     Level     Year     Semester  
N-MAI-A full-time study --- no specialisation -- Cr,Ex 5 Compulsory 2 2 S