This course is part of the Design of Experiments Specialization

4.6
stars
29 ratings

Douglas C. Montgomery

2,610 already enrolled

Offered By

Design of Experiments SpecializationArizona State University

About this Course

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Many experiments involve factors whose levels are chosen at random. A well-know situation is the study of measurement systems to determine their capability.  This course presents the design and analysis of these types of experiments, including modern methods for estimating the components of variability in these systems. The course also covers experiments with nested factors, and experiments with hard-to-change factors that require split-plot designs. We also provide an overview of designs for experiments with response distributions from nonnormal response distributions and experiments with covariates.

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Earn a Certificate upon completion

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Course 4 of 4 in the

Design of Experiments Specialization

Intermediate Level

Approx. 9 hours to complete

English

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What you will learn

Design and analyze experiments where some of the factors are random
Design and analyze experiments where there are nested factors or hard-to-change factors
Analyze experiments with covariates
Design and analyze experiments with nonnormal response distributions

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 4 of 4 in the

Design of Experiments Specialization

Intermediate Level

Approx. 9 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Instructor rating

4.83/5 (7 Ratings)

Douglas C. Montgomery

Regents’ Professor of Engineering, ASU Foundation Professor of Engineering

School of Computing, Informatics, and Decision Systems Engineering

16,386 Learners

4 Courses

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.

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Syllabus - What you will learn from this course

Week1

Week 1

4 hours to complete

Unit 1: Experiments with Random Factors

4 hours to complete

6 videos (Total 44 min), 5 readings, 3 quizzes

6 videos

Instructor Welcome3mCourse Introduction5mEstimating the Variance Components12mMaximum Likelihood Approach7mThe Two-Factor Mixed Model11mMeasurement System Study Example4m

5 readings

Course Description10mCourse Textbook and Resources10mBest Practices in Online Learning (or How to Succeed in This Class)10mProject Report10mUnit 1: Introduction10m

2 practice exercises

Unit 1: Concept Questions30mExercise 130m

Week2

Week 2

2 hours to complete

Unit 2: Nested and Split-Plot Designs

2 hours to complete

6 videos (Total 64 min), 1 reading, 2 quizzes

Week3

Week 3

3 hours to complete

Unit 3: Other Design and Analysis Topics

3 hours to complete

7 videos (Total 71 min), 1 reading, 3 quizzes

Reviews

4.6

6 reviews

5 stars
75.86%
4 stars
10.34%
3 stars
13.79%

TOP REVIEWS FROM RANDOM MODELS, NESTED AND SPLIT-PLOT DESIGNS

by DRJul 25, 2020

Very exhaustive information about random models and nested and split-plot designs. Thank you to Professor Douglas C. Montgomery and Coursera Team.

by MLNov 25, 2021

Comprehensive and practical course in the Design of Experiments specialization. Helps reinforce the need for a physical experiment to align with constraints on randomization.

by AASep 25, 2020

THIS FULL COURSE WAS EXCELLENT. IT WILL HELP IN MY PROJECT. THANK YO DOCTOR MONTGOMERY SIR.

View all reviews

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.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
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The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

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Random Models, Nested and Split-plot Designs

About this Course

What you will learn