Factorial experiments are often used in factor screening.; that is, identify the subset of factors in a process or system that are of primary important to the response. Once the set of important factors are identified interest then usually turns to optimization; that is, what levels of the important factors produce the best values of the response. This course provides design and optimization tools to answer that questions using the response surface framework.
Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.
Other related topics include design and analysis of computer experiments, experiments with mixtures, and experimental strategies to reduce the effect of uncontrollable factors on unwanted variability in the response.
What You Will Learn
- Conduct experiments w/computer models and understand how least squares regression is used to build an empirical model from experimental design data
- Understand the response surface methodology strategy to conduct experiments where system optimization is the objective
- Recognize how the response surface approach can be used for experiments where the factors are the components of a mixture
- Recognize where the objective of the experiment is to minimize the variability transmitted into the response from uncontrollable factors
Course 3 of 4 in the Design of Experiments Specialization
Syllabus
WEEK 1: Unit 1: Additional Design and Analysis Topics for Factorial and Fractional Factorial Designs
WEEK 2: Unit 2: Regression Models
WEEK 3: Unit 3: Response Surface Methods and Designs
WEEK 4: Unit 4: Robust Parameter Design and Process Robustness Studies
