Design of experiment.

lstat2320  2019-2020  Louvain-la-Neuve

Design of experiment.
Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
5 credits
22.5 h + 7.5 h
Q2
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 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 final evaluation is based on 
  • The participation in the homework.
  • A written exam.
  • A project.
  • An oral discussion of the project.
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: : https://moodleucl.uclouvain.be/mod/page/view.php?id=537330
Faculty or entity


Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Sigle
Credits
Prerequisites
Aims
Approfondissement en statistique et sciences des données

Minor in Statistics, Actuarial Sciences and Data Sciences

Master [120] in Statistic: Biostatistics

Master [120] in Data Science : Statistic

Certificat d'université : Statistique et sciences des données (15/30 crédits)

Master [120] in Biomedical Engineering

Master [120] in Environmental Bioengineering

Master [120] in Chemistry and Bioindustries

Master [120] in Agricultural Bioengineering

Master [120] in Statistic: General