Course detail

Analysis of Engineering Experiment

FSI-TAI Acad. year: 2025/2026 Summer semester

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.

Supervisor

Ing. Pavel Hrabec, Ph.D.

Department

Institute of Mathematics

Learning outcomes of the course unit

Prerequisites

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

Planned learning activities and teaching methods

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.

Language of instruction

Czech

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.

Specification of controlled education, way of implementation and compensation for absences

The study programmes with the given course

Programme N-MAI-P: Mathematical Engineering, Master's
branch ---: no specialisation, 5 credits, compulsory

Programme N-PMO-P: Precise Mechanics and Optics, Master's
branch ---: no specialisation, 5 credits, compulsory-optional

Programme N-FIN-P: Physical Engineering and Nanotechnology, Master's
branch ---: no specialisation, 5 credits, compulsory

Type of course unit

 

Lecture

26 hours, compulsory

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 hours, compulsory

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.