Empiric Models (FSI-9EMM)

Academic year 2022/2023
Supervisor: Ing. Josef Bednář, Ph.D.  
Supervising institute: ÚM all courses guaranted by this institute
Teaching language: Czech or English
Aims of the course unit:
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.
Learning outcomes and competences:
Empiric model, fitting, residuum, adequate model
Prerequisites:
Populations, samples, binomial and Poisson distributions, distributions of averages, distributions of a continuous probability, testing of hypothesis
Course contents:
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.
Teaching methods and criteria:
The course is taught through lectures explaining the basic principles and theory of the discipline.
Assesment methods and criteria linked to learning outcomes:
Oral exam
Controlled participation in lessons:
 
Type of course unit:
    Lecture  10 × 2 hrs. optionally                  
Course curriculum:
    Lecture 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.
Literature - fundamental:
1. B. Maroš: Empirické modely I, Brno, 1989
2. D. M. Himmelblau: Process Analysis by Statistical Methods. John Wiley&Sons,New York 1969
2. K. Zvára: Regresní analýza. Academia, Praha 1989
3. Vícerozměrné statistické metody: Vícerozměrné statistické metody. SNTL/ALFA, Praha 1987
Literature - recommended:
1. K. Zvára: Vícerozměrné statistické metody. SNTL/ALFA, Praha 1987
2. J. Anděl_: Matematická statistika. SNTL/ALFA, Praha 1978
3. B. Maroš: Empirické modely I. PC-DIR, Brno 1998
The study programmes with the given course:
Programme Study form Branch Spec. Final classification   Course-unit credits     Obligation     Level     Year     Semester  
D-KPI-K combined study --- no specialisation -- DrEx 0 Recommended course 3 1 W
D-KPI-P full-time study --- no specialisation -- DrEx 0 Recommended course 3 1 W
D-APM-K combined study --- -- DrEx 0 Recommended course 3 1 W
D-APM-P full-time study --- -- DrEx 0 Recommended course 3 1 W