Course detail
Analysis of Engineering Experiment
FSI-TAI Acad. year: 2024/2025 Summer semester
The course is concerned with the selected parts of mathematical statistics for stochastic modeling of the engineering experiments: regression models, regression diagnostics, multivariate methods, probability distributions estimation, interval statistical analysis, and fuzzy statistics. Computations are carried out using the software as follows: Statistica, Minitab, and Excel..
Language of instruction
Czech
Number of ECTS credits
5
Supervisor
Department
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, mastering the subject matter, and delivery of semester assignment. Examination (written form): a practical part (5 tasks), a theoretical part (5 tasks); ECTS evaluation used.
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.
The study programmes with the given course
Programme N-PMO-P: Precise Mechanics and Optics, Master's, compulsory-optional
Programme N-MAI-P: Mathematical Engineering, Master's, compulsory
Programme N-FIN-P: Physical Engineering and Nanotechnology, Master's, compulsory
Programme C-AKR-P: , Lifelong learning
specialization CLS: , elective
Type of course unit
Lecture
26 hours, optionally
Syllabus
- Stochastic modeling of the engineering problems.
- Regression model identification.
- Linear regression models and diagnostic.
- Nonlinear regression analysis.
- Correlation analysis.
- Principle components and factor analysis.
- Cluster analysis.
- Bootstrap estimates.
- Continuous probability distributions estimation.
- Discrete probability distributions estimation.
- Interval analysis.
- Interval statistical models.
- Fuzzy statistics.
Computer-assisted exercise
13 hours, compulsory
Syllabus
- PC statistical software.
- Regression model identification. Semester work assignment.
- Linear regression models and diagnostic.
- Nonlinear regression models.
- Correlation analysis.
- Principle components and factor analysis.
- Cluster analysis.
- Bootstrap estimates.
- Continuous probability distributions estimation.
- Discrete probability distributions estimation.
- Interval analysis.
- Interval statistical models.
- Fuzzy statistics.