Response Surfaces, Mixtures, and Model Building
This course is part of Design of 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.
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What you'll learn
Build and analyze regression models from experimental data
Implement response surface methodology for system optimization
Design and analyze mixture experiments with constraints
Apply robust parameter design techniques
Optimize processes using computer experiments
Skills you'll gain
This course includes:
7.3 Hours PreRecorded video
8 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This advanced course covers response surface methodology (RSM), mixture experiments, and robust parameter design. Students learn to optimize experimental processes using regression models, steepest ascent methods, and second-order designs. The curriculum includes computer experiments, mixture constraints, and process robustness studies. Practical applications include chemical process optimization, paint formulation, and semiconductor manufacturing.
Unit 1: Additional Design and Analysis Topics for Factorial and Fractional Factorial Designs
Module 1 · 4 Hours to complete
Unit 2: Regression Models
Module 2 · 2 Hours to complete
Unit 3: Response Surface Methods and Designs
Module 3 · 4 Hours to complete
Unit 4: Robust Parameter Design and Process Robustness Studies
Module 4 · 1 Hours to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Design of Experiments
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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