Course detail

Analysis of Engineering Experiment

FSI-TAIAcad. year: 2009/2010

If the important variables for a process are known or sought but the process model is unknown, an empirical approach to model building is required. The development of empirical models represents a continuous process that involves postulation of a model, experimentation to collect empirical data, "fitting" of the model, i.e. estimation of the model coefficients, and evaluation of results. The strategy of empirical model building is described in the course.

Language of instruction

Czech

Number of ECTS credits

4

Mode of study

Not applicable.

Guarantor

doc. RNDr. Zdeněk Karpíšek, CSc.

Department

Institute of Mathematics (ÚM)

Learning outcomes of the course unit

Empiric model, fitting, residuum, adequate model

Prerequisites

Populations, samples, binomial and Poisson distributions, distributions of averages, distributions of a continuous probability, testing of hypothesis

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations.

Assesment methods and criteria linked to learning outcomes

Course-unit credit is awarded on condition of having work out a semester work.Oral exam.

Course curriculum

Not applicable.

Work placements

Not applicable.

Aims

If the important variables for a process are known or sought but the process model is unknown, an empirical approach to model building is required. The development of empirical models represents a continuous process that involves postulation of a model, experimentation to collect empirical data, "fitting" of the model, i.e. estimation of the model coefficients, and evaluation of results. The strategy of empirical model building is described in the course.

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

Missed lessons may be compensated for via a written test.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Montgomery, D. C., Renger, G.: Applied Statistics and Probability for Engineers. New York: John Wiley & Sons, 2010. (EN)
Anděl, J.: Základy matematické statistiky. Praha: Matfyzpress, 2011. (CS)

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme M2A-P Master's

    branch M-FIN , 1. year of study, summer semester, compulsory
    branch M-PMO , 1. year of study, summer semester, compulsory
    branch M-MAI , 2. year of study, summer semester, compulsory

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

doc. RNDr. Zdeněk Karpíšek, CSc.

Syllabus

1. Linear models. Linearization of the nonlinear model.
2. Linear models with one independent variable. Least squares estimation.
3. Analysis of variance. Variances of parameters.
4. Variances of predicted values.
5. ANOVA about the adequate model.
6. Confidence intervals for parameters.
7. Locus of confidence limits.
8. Locus of tolerance limits.
9. Confidence region.
10.Linear models with several independent variables.
11.Reziduals.

Computer-assisted exercise

13 hours, compulsory

Teacher / Lecturer

doc. RNDr. Zdeněk Karpíšek, CSc.

Syllabus

1. Linear models. Linearization of the nonlinear model.
2. Linear models with one independent variable. Least squares estimation.
3. Analysis of variance. Variances of parameters.
4. Variances of predicted values.
5. ANOVA about the adequate model.
6. Confidence intervals for parameters.
7. Locus of confidence limits.
8. Locus of tolerance limits.
9. Confidence region.
10.Linear models with several independent variables.
11.Reziduals.