5 credits
22.5 h + 7.5 h
Q2
Teacher(s)
Bogaert Patrick; Govaerts Bernadette;
Language
French
Main themes
- Experimental cycle and strategies
- Linear regression as a tool to analyse the results of a designed experiment
- Problem formalisation and qualities of an experimental design
- Factorial designs and derivatives
- Designs for the estimation of response surfaces
- Optimal designs
- Experimental design as viewed by Taguchi
- Designs for mixture experiments
- Simultaneous optimisation of several responses
- Simplex and EVOP methodology to optimise one response
Aims
At the end of this learning unit, the student is able to : | |
1 | At the end of the course, the student will be awared of the interest of using a methodology to design experiments that provides a maximum information at the lower cost. He will gain knowledge on different possible classes of experimental designs and on the statistical methods available to analyse experiment results. |
The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
Content
The themes discussed in this course are :
- Experimental cycle and strategies
- Linear regression as a tool to analyse the results of a designed experiment
- Problem formalisation and qualities of an experimental design
- Factorial designs and derivatives
- Designs for the estimation of response surfaces
- Optimal designs
- Experimental design as viewed by Taguchi
- Designs for mixture experiments
- Simultaneous optimisation of several responses
- Simplex and EVOP methodology to optimise one response
Each course subject is presented on a case study.
Other information
Prerequistes
Basis courses in statistics. Course in linear models.
Evaluation:
For all: written test on the course content and practical work.
For those who follow the partim B: elaboration of a personal applied (in groups of 1 or 2) with oral discussion of work.
Reference :
Box G. et Draper N. et H. Smith [1987], Empirical Model-Building and Response Surfaces, Wiley, New York
Khuri A. et Cornell J., [1987], Response surfaces : designs and analyses, Marcel Dekker.
Myers R.H., Douglas C. Montgomery [1995], Response Surface Methodology: Process and Product Optimization Using Designed Experiments. Wiley
Faculty or entity
LSBA
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Agricultural Bioengineering
Master [120] in Biomedical Engineering
Master [120] in Statistics: General
Master [120] in Chemistry and Bioindustries
Master [120] in Statistics: Biostatistics
Master [120] in Environmental Bioengineering
Master [120] in data Science: Statistic
Minor in Statistics and data sciences
Additionnal module in Statistics and data science