This is a basic course in designing experiments and analyzing the resulting data. The course objective is to learn how to plan, design and conduct experiments efficiently and effectively, and analyze the resulting data to obtain objective conclusions. Both design and statistical analysis issues are discussed. Opportunities to use the principles taught in the course arise in all aspects of today’s industrial and business environment. Applications from various fields will be illustrated throughout the course. Computer software packages (JMP, Design-Expert, Minitab) will be used to implement the methods presented and will be illustrated extensively.
Об этом курсе
Университет штата Аризона
Arizona State University has developed a new model for the American Research University, creating an institution that is committed to excellence, access and impact. ASU measures itself by those it includes, not by those it excludes. ASU pursues research that contributes to the public good, and ASU assumes major responsibility for the economic, social and cultural vitality of the communities that surround it.
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Лучшие отзывы о курсе EXPERIMENTAL DESIGN BASICS
I have used Dr. Montgomery's book off and on since the early 1990s! It is an enjoyment to watch his lectures. The only caveat is that it is a short course, which should have been obvious to me.
The peer review assignment is daunting as you are completely at the mercy of your classmates who might leave proper feedback on what you actually did wrong. Apart from that, great class.
If you are an engineer in Pharma, medical device or automobile looking for a basic course on design of experiments. this is a perfect course designed for you.
It was a great experience while doing experimental design basics. I learned a lot about the Strategy of experimentation and basic statistical concepts.
Специализация Планирование экспериментов: общие сведения
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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