Response Surfaces, Mixtures, and Model Building
- 4.7
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Approx. 13 hours to complete
Course Summary
Learn how to build response surfaces and mixtures models in this course. Discover unorthodox techniques that will help you create better models and improve your modeling skills.Key Learning Points
- Use advanced statistical techniques to build response surfaces and mixtures models
- Learn how to optimize your models using unorthodox methods
- Improve your modeling skills with real-world examples and case studies
Job Positions & Salaries of people who have taken this course might have
- USA: $113,000
- India: ₹1,497,000
- Spain: €50,000
- USA: $113,000
- India: ₹1,497,000
- Spain: €50,000
- USA: $93,000
- India: ₹1,222,000
- Spain: €40,000
- USA: $113,000
- India: ₹1,497,000
- Spain: €50,000
- USA: $93,000
- India: ₹1,222,000
- Spain: €40,000
- USA: $70,000
- India: ₹927,000
- Spain: €31,000
Related Topics for further study
Learning Outcomes
- Understand the principles and techniques of response surfaces and mixtures models
- Learn how to apply these techniques to real-world problems
- Improve your modeling skills and optimize your models
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of statistics
- Familiarity with statistical software such as R or Python
Course Difficulty Level
IntermediateCourse Format
- Online self-paced course
- Video lectures
- Real-world examples and case studies
Similar Courses
- Advanced Regression Models
- Design of Experiments
Related Education Paths
Related Books
Description
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.
Knowledge
- Conduct experiments w/computer models and understand how least squares regression is used to build an empirical model from experimental design data
- Understand the response surface methodology strategy to conduct experiments where system optimization is the objective
- Recognize how the response surface approach can be used for experiments where the factors are the components of a mixture
- Recognize where the objective of the experiment is to minimize the variability transmitted into the response from uncontrollable factors
Outline
- Unit 1: Additional Design and Analysis Topics for Factorial and Fractional Factorial Designs
- Instructor Introduction
- Course Introduction
- 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
- Course Description
- Course Textbook and Resources
- Best Practices in Online Learning (or How to Succeed in This Class)
- Unit 1: Introduction
- Unit 1: Concept Questions
- Exercise 1
- Unit 2: Regression Models
- 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
- Unit 2: Introduction
- Exercise 2
- Unit 3: Response Surface Methods and Designs
- 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
- Unit 3: Introduction
- Unit 3: Concept Questions
- Exercise 3
- Unit 4: Robust Parameter Design and Process Robustness Studies
- Robust Design
- Analysis of the Crossed Array Design
- Combined Array Designs and the Response Model Approach
- Semiconductor Manufacturing Example
- Unit 4: Introduction
- Unit 4: Concept Questions
- Exercise 4
Summary of User Reviews
Response Surfaces & Mixtures Model Building course on Coursera has received positive reviews from users. The course covers topics related to response surfaces and mixtures model building. Users have appreciated the course for its comprehensiveness.Key Aspect Users Liked About This Course
Comprehensive course contentPros from User Reviews
- Well-structured course material
- Easy to follow explanations
- Practical examples provided
Cons from User Reviews
- Lack of interactivity in the course
- Limited scope of the course
- Some users found the course too theoretical
Douglas C. Montgomery
4.7 Raiting
Response Surfaces, Mixtures, and Model Building
- 4.7
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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....
