PHYS-442 / 4 credits

Teacher: Fuerbringer Jean-Marie

Language: English

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

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)

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.

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

Reference week

Wednesday, 9h - 11h: Lecture BC01

Wednesday, 11h - 12h: Exercise, TP BC01

Wednesday, 14h - 15h: Project, other DIA003

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