Modeling and design of experiments
Summary
In the academic or industrial world, to optimize a system, it is necessary to establish strategies for the experimental approach. The DOE allows you to choose the best set of measurement points to minimize the variance of the results. The concepts learned are applicable in all areas.
Content
- Fundamentals of DOE theory and data analysis
- Multilineal regression
- Greaco-Latin squares
- Placket-Burman designs
- Factorial and fractional factorial designs
- Surface response designs
- Mixture designs
Keywords
Design of experiments, ANOVA, Least square fit, Statistics, Multilineal regression, variance minimization
Learning Prerequisites
Recommended courses
Statistics, metrology
Important concepts to start the course
Basic statistical conceps such as average, variance, statistical distributions, Calculus, linear algebra matriciel, Matlab or Python fundamentals, coding fundamentals
Learning Outcomes
By the end of the course, the student must be able to:
- Propose an empirical model in function of the experimental objectives
- Analyze an experimental situation and identify the critical elements from a statistical point of view
- Establish a design of experiments in relation with the candidate models and the experimental constraints
Transversal skills
- Plan and carry out activities in a way which makes optimal use of available time and other resources.
- Use a work methodology appropriate to the task.
- Demonstrate the capacity for critical thinking
- Use both general and domain specific IT resources and tools
Teaching methods
Theoretical presentation, cases calculation and analysis
Expected student activities
- Synthesized the theoretical presentation in personal summary with concept maps
- Solve exercise problems
Assessment methods
1/3 Imposed project prepared and reported in group of 3 students
2/3 Oral exam concisting in presenting individually the project (1/3) and answering theoretical question (1/3)
Resources
Bibliography
- Box, G.E.P.; Hunter, J.S.; Hunter, W.G. Statistics for Experimenters; Wiley Series in Probability and Mathematical Statistics, John Wyleyand Son, 1978.
- Montgomery, D.C. Design and analysis of experiments, 7th edition ed.; John Wyley and Son, 2009.
- Davison A.C.; Statistical model, Cambridge University Press in June 2003.
- Ryan Th.; Modern Experimental Design, John Wyley and Son, 2007.
Ressources en bibliothèque
- Modern Experimental Design
- Statistical model
- Design and analysis of experiments
- Statistics for Experimenters, An introduction to design, data analysis and model building
Moodle Link
In the programs
- Semester: Fall
- Exam form: Oral (winter session)
- Subject examined: Modeling and design of experiments
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 1 Hour(s) per week x 14 weeks
- Project: 1 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Fall
- Exam form: Oral (winter session)
- Subject examined: Modeling and design of experiments
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 1 Hour(s) per week x 14 weeks
- Project: 1 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Fall
- Exam form: Oral (winter session)
- Subject examined: Modeling and design of experiments
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 1 Hour(s) per week x 14 weeks
- Project: 1 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Fall
- Exam form: Oral (winter session)
- Subject examined: Modeling and design of experiments
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 1 Hour(s) per week x 14 weeks
- Project: 1 Hour(s) per week x 14 weeks
- Type: optional