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.
This course is part of the Design of Experiments Specialization
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Design of Experiments SpecializationArizona State UniversityAbout this Course
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Course 1 of 4 in the
Intermediate Level
Approx. 13 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
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Try Coursera for BusinessWhat you will learn
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.
Skills you will gain
- statistical Methods for Process and Product improvement
- design of experiments
- experiment design
- designing experiments
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 1 of 4 in the
Intermediate Level
Approx. 13 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Could your company benefit from training employees on in-demand skills?
Try Coursera for Business
Offered by
Arizona State University
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.
Syllabus - What you will learn from this course
4 hours to complete
Unit 1: Getting Started and Introduction to Design and Analysis of Experiments
4 hours to complete
6 videos (Total 54 min), 6 readings, 2 quizzes
4 hours to complete
Unit 2: Simple Comparative Experiments
4 hours to complete
9 videos (Total 96 min), 1 reading, 3 quizzes
3 hours to complete
Unit 3: Experiments with a Single Factor - The Analysis of Variance
3 hours to complete
10 videos (Total 84 min), 1 reading, 2 quizzes
2 hours to complete
Unit 4: Randomized Blocks, Latin Squares, and Related Designs
2 hours to complete
6 videos (Total 52 min), 1 reading, 2 quizzes
Reviews
- 5 stars77.31%
- 4 stars15.97%
- 3 stars3.60%
- 2 stars1.03%
- 1 star2.06%
TOP REVIEWS FROM EXPERIMENTAL DESIGN BASICS
by HRSep 21, 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.
by BADec 17, 2020
Very excellent presentation. I very much enjoyed doing the course.
by WBSep 11, 2022
Excellent course. It is designed for everyone to understand the principles of the DOE in every field you stand.
by SSDec 15, 2021
This course was very practical and I thank Professor Montgomery for her excellent teaching. I also thank the Coursera team for providing this opportunity.
About the Design of Experiments Specialization
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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