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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Université d'État de l'Arizona
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Meilleurs avis pour 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.
DoE is an essential but forgotten initial step in the experimental work! This course gives a very good start and breaking the ice for higher quality of experimental work.
À propos du Spécialisation Plan d'expériences
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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