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.
Ce cours fait partie de la Spécialisation Plan d'expériences
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Spécialisation Plan d'expériencesUniversité d'État de l'ArizonaÀ propos de ce cours
24 503 consultations récentes
Dates limites flexibles
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Certificat partageable
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100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 1 sur 4 dans le
Niveau intermédiaire
Approx. 13 heures pour terminer
Anglais
Sous-titres : Arabe, Français, Portugais (européen), Italien, Vietnamien, Allemand, Russe, Anglais, Espagnol
Ce que vous allez apprendre
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.
Compétences que vous acquerrez
- statistical Methods for Process and Product improvement
- design of experiments
- experiment design
- designing experiments
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 1 sur 4 dans le
Niveau intermédiaire
Approx. 13 heures pour terminer
Anglais
Sous-titres : Arabe, Français, Portugais (européen), Italien, Vietnamien, Allemand, Russe, Anglais, Espagnol
Offert par
Université d'État de l'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.
Programme de cours : ce que vous apprendrez dans ce cours
4 heures pour terminer
Unit 1: Getting Started and Introduction to Design and Analysis of Experiments
4 heures pour terminer
6 vidéos (Total 54 min), 6 lectures, 2 quiz
4 heures pour terminer
Unit 2: Simple Comparative Experiments
4 heures pour terminer
9 vidéos (Total 96 min), 1 lecture, 3 quiz
3 heures pour terminer
Unit 3: Experiments with a Single Factor - The Analysis of Variance
3 heures pour terminer
10 vidéos (Total 84 min), 1 lecture, 2 quiz
2 heures pour terminer
Unit 4: Randomized Blocks, Latin Squares, and Related Designs
2 heures pour terminer
6 vidéos (Total 52 min), 1 lecture, 2 quiz
Avis
- 5 stars78,57 %
- 4 stars15,38 %
- 3 stars2,74 %
- 2 stars1,09 %
- 1 star2,19 %
Meilleurs avis pour EXPERIMENTAL DESIGN BASICS
par NB4 janv. 2021
Good material but slow and dry. Assignments were bad and in a non-standard programming language.
par AK17 sept. 2021
Very well organized and good exposure to basic concepts! JMP trials are an add on benefit!
par KG21 mars 2021
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.
par RZ12 janv. 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.
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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