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. 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.
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About this Course
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
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Arizona State University
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Syllabus - What you will learn from this course
Unit 1: Additional Design and Analysis Topics for Factorial and Fractional Factorial Designs
Unit 2: Regression Models
Unit 3: Response Surface Methods and Designs
Unit 4: Robust Parameter Design and Process Robustness Studies
Reviews
TOP REVIEWS FROM RESPONSE SURFACES, MIXTURES, AND MODEL BUILDING
It was a great experience for me to do the RSM model building an online course. I learned experimental designs for fitting response surfaces.
About the Design of Experiments Specialization
Learn modern experimental strategy, including factorial and fractional factorial experimental designs, designs for screening many factors, designs for optimization experiments, and designs for complex experiments such as those with hard-to-change factors and unusual responses. There is thorough coverage of modern data analysis techniques for experimental design, including software. Applications include electronics and semiconductors, automotive and aerospace, chemical and process industries, pharmaceutical and bio-pharm, medical devices, and many others.

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