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.
Random Models, Nested and Split-plot Designs
Random Models, Nested and Split-plot Designs
This course is part of Design of Experiments Specialization
Instructor: Douglas C. Montgomery
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4.6
(33 reviews)
Intermediate level
Some related experience required
9 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace
What you'll 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
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Assessments
6 assignments
Taught in English
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This course is part of the Design of Experiments Specialization
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There are 3 modules in this course
What's included
6 videos5 readings2 assignments1 app item1 discussion prompt
What's included
6 videos1 reading2 assignments
What's included
7 videos1 reading2 assignments1 peer review
Instructor
Instructor ratings
4.7 (8 ratings)Offered by
Recommended if you're interested in Probability and Statistics
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4.6
33 reviews
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AA
5
Reviewed on Sep 25, 2020
THIS FULL COURSE WAS EXCELLENT. IT WILL HELP IN MY PROJECT. THANK YO DOCTOR MONTGOMERY SIR.
DR
5
Reviewed on Jul 25, 2020
Very exhaustive information about random models and nested and split-plot designs. Thank you to Professor Douglas C. Montgomery and Coursera Team.
ML
5
Reviewed on Nov 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.
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