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.
Este curso faz parte do Programa de cursos integrados Criação de experimentos
Sem necessidade de cartão de crédito – comece agora mesmo!
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Universidade do Estado do Arizona
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.
- 5 stars77,36%
- 4 stars15,78%
- 3 stars3,68%
- 2 stars1,05%
- 1 star2,10%
Principais avaliações do EXPERIMENTAL DESIGN BASICS
It was a great experience while doing experimental design basics. I learned a lot about the Strategy of experimentation and basic statistical concepts.
This was an excellent course that gave me knowledge in experimental design with which it is possible to apply to any industry or field.
pretty good course , it would have been great to have pdf or brief with the material, the book is great too , class notes would have been great.
Very excellent presentation. I very much enjoyed doing the course.
Sobre Programa de cursos integrados Criação de experimentos
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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