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
oferecido por
Programa de cursos integrados Criação de experimentosUniversidade do Estado do ArizonaInformações sobre o curso
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Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 1 de 4 no
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 13 horas para completar
Inglês
Legendas: Árabe, Francês, Portuguese (European), Chinês (simplificado), Italiano, Vietnamita, Coreano, Alemão, Russo, Turco, Inglês, Espanhol
O que você vai aprender
By the end of this course, you will be able to:
Approach complex industrial and business research problems and address them through a rigorous, statistically sound experimental strategy
Use modern software to effectively plan experiments
Analyze the resulting data of an experiment, and communicate the results effectively to decision-makers.
Habilidades que você terá
statistical Methods for Process and Product improvementdesign of experimentsexperiment designdesigning experiments
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 1 de 4 no
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 13 horas para completar
Inglês
Legendas: Árabe, Francês, Portuguese (European), Chinês (simplificado), Italiano, Vietnamita, Coreano, Alemão, Russo, Turco, Inglês, Espanhol
oferecido por
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.
Programa - O que você aprenderá com este curso
4 horas para concluir
Unit 1: Getting Started and Introduction to Design and Analysis of Experiments
4 horas para concluir
6 vídeos (Total 54 mín.), 6 leituras, 2 testes
6 videos
Course Introduction4min
Specialization Overview2min
History of DOX17min
The Basic Principles of DOX11min
Factorial Designs with Several Factors14min
6 leituras
Course Description10min
Course Textbook and Resources10min
Best Practices in Online Learning (or How to Succeed in This Class)10min
Course Project10min
Unit 1 Introduction10min
Introduction to course project10min
1 exercício prático
Concept Questions30min
4 horas para concluir
Unit 2: Simple Comparative Experiments
4 horas para concluir
9 vídeos (Total 96 mín.), 1 leitura, 3 testes
9 videos
The Hypothesis Testing Framework14min
Pooled t-test and Two-sample t-test12min
Pooled t-test and Two-sample t-test, pt 213min
Hypothesis Testing on Variances13min
Paired t-test10min
Portland Cement Data Example11min
Florescence Data Example4min
Hardness Testing Example5min
1 leituras
Unit 2 Introduction10min
2 exercícios práticos
Concept Questions30min
Exercise 130min
3 horas para concluir
Unit 3: Experiments with a Single Factor - The Analysis of Variance
3 horas para concluir
10 vídeos (Total 84 mín.), 1 leitura, 2 testes
10 videos
Models for the Data10min
ANOVA for Plasma Etching Experiment9min
Post-ANOVA Comparison of Means9min
Sample Size Determination7min
Examples of Single-Factor Experiments9min
The Random Effects Model8min
Example of Random Factor Experiment7min
Plasma Etching Example5min
Fabric Strength Example2min
1 leituras
Unit 3 Introduction: Experiments with a Singe Factor; the Analysis of Variance10min
2 exercícios práticos
Concept Questions30min
Exercise 230min
2 horas para concluir
Unit 4: Randomized Blocks, Latin Squares, and Related Designs
2 horas para concluir
6 vídeos (Total 52 mín.), 1 leitura, 2 testes
6 videos
Extension of the ANOVA to the RCBD7min
Example10min
Residual Analysis for the Vascular Graft Example9min
The Latin Square Design9min
Vascular Graft Example6min
1 leituras
Unit 4 Introduction: Randomized Blocks, Latin Squares, and Related Designs; techniques for handling nuisance factor is experiments10min
2 exercícios práticos
Concept Questions30min
Exercise 330min
Avaliações
Principais avaliações do EXPERIMENTAL DESIGN BASICS
por HR21 de Set de 2020
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.
por SM27 de Out de 2020
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.
por RR25 de Jul de 2020
It was a great experience while doing experimental design basics. I learned a lot about the Strategy of experimentation and basic statistical concepts.
por RZ12 de Jan de 2021
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.
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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