Course detail

Empiric Models

FSI-9EMM Acad. year: 2022/2023 Winter semester

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

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

Planned learning activities and teaching methods

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

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.

The study programmes with the given course

Programme D-KPI-K: Design and Process Engineering, Doctoral, recommended course

Programme D-KPI-P: Design and Process Engineering, Doctoral, recommended course

Programme D-APM-P: Applied Mathematics, Doctoral, recommended course

Programme D-APM-K: Applied Mathematics, Doctoral, recommended course

Type of course unit

 

Lecture

20 hours, optionally

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.