Random Models, Nested and Split-plot Designs
Master advanced experimental design techniques for random factors, nested designs, and split-plot experiments.
Master advanced experimental design techniques for random factors, nested designs, and split-plot experiments.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Design of Experiments Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.6
(30 ratings)
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What you'll learn
Design and analyze experiments with random factors
Implement nested and split-plot experimental designs
Estimate variance components in measurement systems
Handle experiments with non-normal responses
Apply covariance analysis techniques
Skills you'll gain
This course includes:
2.9 Hours PreRecorded video
6 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 3 modules in this course
This advanced course covers the design and analysis of experiments with random factors, nested designs, and split-plot arrangements. Students learn modern methods for estimating variance components in measurement systems, handling hard-to-change factors, and analyzing experiments with non-normal responses. The curriculum includes maximum likelihood approaches, generalized linear models, and analysis of covariance, with practical applications in industrial and research settings.
Unit 1: Experiments with Random Factors
Module 1 · 3 Hours to complete
Unit 2: Nested and Split-Plot Designs
Module 2 · 2 Hours to complete
Unit 3: Other Design and Analysis Topics
Module 3 · 3 Hours to complete
Fee Structure
Instructor
Pioneering Expert in Industrial Engineering and Statistics
Douglas C. Montgomery serves as the Regents' Professor of Industrial Engineering and the ASU Foundation Professor of Engineering at Arizona State University. With a distinguished academic career, he previously held prominent positions at the University of Washington and Georgia Institute of Technology. Dr. Montgomery earned his BSIE, MS, and Ph.D. degrees from Virginia Tech and has extensive industrial experience with companies such as Union Carbide Corporation and Eli Lilly. His research primarily focuses on industrial statistics, including design of experiments, quality and reliability engineering, and time series analysis. A prolific author, he has written thirteen influential books and over 275 journal articles, contributing significantly to the fields of statistical methodology and engineering practices. Dr. Montgomery's expertise has been recognized through numerous awards, including the Shewhart Medal and the George Box Medal. As a mentor, he has supervised 69 doctoral dissertations and continues to impact the next generation of engineers through his teaching and research initiatives. His commitment to advancing industrial statistics positions him as a leading figure in engineering education and practice.
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4.6 course rating
30 ratings
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