Design of experiment.

Teacher(s)

Bogaert Patrick;

Language

French
> English-friendly

Prerequisites

Concepts and tools equivalent to those taught in teaching units

LSTAT2120	Linear models
LSTAT2020	Logiciels et programmation statistique de base

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

Learning outcomes

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.

Content

The themes discussed in this course are :

• Experimental cycle and strategies
• Linear regression as a tool to analyze the results of a designed experiment  
• Simultaneous optimization of several responses
• Problem formalization and qualities of an experimental design
• Screening designs
• Factorial designs and derivatives
• Designs for the estimation of response surfaces
• Optimal designs
• Designs for mixture experiments
• Blocking.
• Designs for the estimation of variance components.  

Teaching methods

Lectures (22.5h)

• Methods presentation on the basis of real-life situations.
• Formal but intuitive discussion of theoretical concepts and formulae for most methods.
• Interpretation of software outputs and use of the JMP software in class.
• Interactive lectures: students are encouraged to participate during the course.

 Computer labs (15h)

• Case studies on JMP, methodological exercises, and JMP Output interpretation. 

Homework and projects

• The student is invited to prepare each week an exercise, a quiz or a small project in order to apply and integrate course content.  

Evaluation methods

The course exam grade is based on:

1. Mandatory completion of assignments during the quadrennium.  
2. Participation in several quizzes during the term.  
3.  A written exam on the course content ("theory" and methodological exercises).
4. For the students who follow the complete course: the realization of an applied project (by groups of 2 students) + an oral exam and discussion of the work (by group)
5. For students taking the LSTAT2320A part of the course only: a JMP test in the computer room

For students taking the full course, the written exam counts for 12/20, the work (+ oral exam) for 6/20, the quizzes and homework for 2/20.
For students taking Partim A, the written exam counts for 14/20, the JMP test for 4/20, the quizzes and homework for 2/20.
In the overall mark, points for homework, assignment and/or JMP test are only acquired if the student has obtained a mark of minimum 45% in the written exam otherwise it is the mark of the written exam which is the overall mark.

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

Online resources

See the Moodle site: : https://moodleucl.uclouvain.be/mod/page/view.php?id=537330

Bibliography

• Box G. et Draper N. et H. Smith [1987], Empirical Model-Building and Response Surfaces, Wiley, New York
• Khuri A. et Cornell J., [1996], Response surfaces : designs and analyses, Marcel Dekker.
• Myers R.H., Douglas C. Montgomery [2002], Response Surface Methodology: Process and Product Optimization Using Designed Experiments. Wiley
• Et beaucoup d'autres possibles...

Teaching materials

• Voir le site Moodle

Faculty or entity

LSBA