Response Surfaces, Mixtures, and Model Building
Factorial experiments are often used in factor screening.; that is, identify the subset of factors in a process or system that are of primary important to the response. Once the set of important factors are identified interest then usually turns to optimization; that is, what levels of the important factors produce the best values of the response. This course provides design and optimization tools to answer that questions using the response surface framework.
Monthly Subscription Starting at AED 99 + VAT
- Level Professional
- Duration 13 hours
- Course by Arizona State University
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Offered by
About
Factorial experiments are often used in factor screening.; that is, identify the subset of factors in a process or system that are of primary important to the response. Once the set of important factors are identified interest then usually turns to optimization; that is, what levels of the important factors produce the best values of the response. This course provides design and optimization tools to answer that questions using the response surface framework. Other related topics include design and analysis of computer experiments, experiments with mixtures, and experimental strategies to reduce the effect of uncontrollable factors on unwanted variability in the response.Modules
Welcome
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2
Videos
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3
Readings
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1
Discussion
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1
ExternalTool
1
External Tool
- JMP Virtual Lab
1
Discussions
- Meet the class
2
Videos
- Instructor Introduction
- Course Introduction
3
Readings
- Course Description
- Course Textbook and Resources
- Best Practices in Online Learning (or How to Succeed in This Class)
Additional Design and Analysis Topics for Factorial and Fractional Factorial Designs
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10
Videos
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1
Readings
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2
Assignment
2
Assignment
- Unit 1: Concept Questions
- Exercise 1
10
Videos
- More About Factorial and Fractional Factorial Designs
- The 3^3 Design
- The 3^k Factorial Design
- Confounding
- Fractional Replication of the 3^k Factorial Design
- Factorials with Mixed Levels
- Nonregular Fractional Factorial Designs
- Use of an Optimal Design Tool
- Syrup Loss Example
- Unusual Blocking Example
1
Readings
- Unit 1: Introduction
Regression Models
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7
Videos
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1
Readings
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2
Assignment
2
Assignment
- Unit 2: Concept Questions
- Exercise 2
7
Videos
- Linear Regression Models
- Properties of the Estimators
- Regression Analysis of a 2^3 Factorial Design
- Hypothesis Testing in Multiple Regression
- Confidence Intervals in Multiple Regression
- Regression Model Diagnostics
- Viscosity Example
1
Readings
- Unit 2: Introduction
Response Surface Methods and Designs
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14
Videos
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1
Readings
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2
Assignment
2
Assignment
- Unit 3: Concept Questions
- Exercise 3
14
Videos
- Response Surface Methodology
- The Method of Steepest Ascent
- Second-Order Models in RSM
- Ridge Systems
- Multiple Responses
- Experimental Designs for Fitting Response Surfaces
- Blocking in a Second-Order Design
- The Adhesive Pull-Off Force Experiment
- General Structure of a Definitive Screening Design with m Factors
- Experiments with Computer Models
- Mixture Experiments
- Constraints
- Chemical Process Example
- Paint Formulation Example
1
Readings
- Unit 3: Introduction
Robust Parameter Design and Process Robustness Studies
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4
Videos
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1
Readings
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2
Assignment
2
Assignment
- Unit 4: Concept Questions
- Exercise 4
4
Videos
- Robust Design
- Analysis of the Crossed Array Design
- Combined Array Designs and the Response Model Approach
- Semiconductor Manufacturing Example
1
Readings
- Unit 4: Introduction
Auto Summary
"Response Surfaces, Mixtures, and Model Building" is a professional-level course in Maths & Statistics offered by Coursera. It focuses on factorial experiments for factor screening and optimization, utilizing the response surface framework. The course also covers computer experiments, mixture experiments, and strategies to minimize the impact of uncontrollable factors. With a duration of 780 minutes, learners can subscribe through Starter or Professional plans, making it ideal for professionals seeking advanced design and optimization tools.
Douglas C. Montgomery