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Douglas C. Montgomery
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.
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What's inside
Syllabus
Unit 1: Additional Design and Analysis Topics for Factorial and Fractional Factorial Designs
Unit 2: Regression Models
Unit 3: Response Surface Methods and Designs
Read more
Syllabus
Unit 1: Additional Design and Analysis Topics for Factorial and Fractional Factorial Designs
Unit 2: Regression Models
Unit 3: Response Surface Methods and Designs
Read more
Unit 4: Robust Parameter Design and Process Robustness Studies
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Employs factorial experiments for factor screening, aiding in the identification of critical factors
Focuses on optimization techniques to determine optimal levels of important factors for desired response
Provides tools for designing and analyzing computer experiments, enhancing accuracy and efficiency
Integrates knowledge of design and analysis of experiments with mixtures, facilitating optimization in complex scenarios
Instructs on experimental strategies for reducing the impact of uncontrolled factors, ensuring reliable and reproducible results
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Reviews summary
Rsm and model building
Learners say that this course is great and provides the knowledge and skills needed to work with Response Surfaces and Model Building. Students have mentioned that lectures, assignments, and examples are engaging and helpful. Although some of the material is difficult, learners generally indicate that Professor Montgomery and the course materials do a good job of providing the support students need to succeed.
Supportive and knowledgeable
"Professor Montgomery for this course"
"This course is actually quite difficult for me"
"thank Professor Douglas C. Montgomery"
Engaging and helpful
"lectures and assignments are engaging and helpful"
"learned to manage my time, be disciplined, and take my work seriously"
"learned how to use the basic JMP - analysis software"
May be challenging
"difficult for me"
"This course is actually quite difficult"
"not easy to digest"
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Response Surfaces, Mixtures, and Model Building with these
activities:
Review Statistics and Linear Regression
Show steps
Reviewing foundational concepts in statistics and linear regression can strengthen your understanding and provide a more solid base for comprehending the advanced techniques covered in this course.
Browse courses on
Statistics
Show steps
-
Go over lecture notes or textbook chapters
-
Complete practice problems and exercises
Review Probability Theory
Show steps
This course builds upon a foundational understanding of probability theory. Reviewing concepts such as conditional probability, statistical independence, and Bayes' theorem will lay a strong foundation and can improve your comprehension of the course.
Show steps
-
Review notes or online resources on probability theory
-
Solve practice problems to reinforce understanding
Tutorials on Computer Experiments
Show steps
Computer experiments can provide valuable insights into complex systems. Exploring tutorials on this topic will expand your understanding and enhance your ability to apply these techniques.
Show steps
-
Review online courses or video tutorials on computer experiments
-
Follow along with the tutorials and complete exercises
Five other activities
Expand to see all activities and additional details
Show all eight activities
Research on Applications of Mixture Experiments
Show steps
Compiling articles and case studies on applications of mixture experiments can provide valuable insights into their practical uses and expand your knowledge beyond the theoretical concepts taught in the course.
Show steps
-
Search for research papers and articles on mixture experiments
-
Summarize and organize the findings
Design and Analyze Factorial Experiments
Show steps
Practicing the techniques you learn in lectures and labs through simulated experiments can boost your skills in designing and analyzing experiments and deepen your understanding of the underlying concepts.
Browse courses on
Factorial Experiments
Show steps
-
Use a software tool like R or SAS to create factorial experiments
-
Run simulations to analyze the results and identify significant effects
-
Interpret the findings and make recommendations
Attend a Workshop on Design of Experiments
Show steps
Attending a workshop will provide you with the opportunity to engage with experts, learn about cutting-edge techniques, and gain practical experience in applying design of experiments principles.
Browse courses on
Design of Experiments
Show steps
-
Research and find a relevant workshop
-
Register and attend the workshop
-
Participate actively in discussions and exercises
Create an Infographic on Response Surface Methodology
Show steps
Creating an infographic on response surface methodology can help you synthesize and reinforce your understanding of the key principles and their applications.
Show steps
-
Gather information from course materials, research papers, or online resources
-
Design and create a visually appealing infographic
-
Share your infographic with peers or online communities
Optimization Project
Show steps
Working on an independent project involving the application of design and optimization principles in a practical setting will allow you to apply your knowledge and gain a deeper understanding of the concepts taught in the course.
Browse courses on
Design of Experiments
Show steps
-
Identify a process or system to optimize
-
Design experiments to determine the optimal settings
-
Analyze data and interpret results
-
Implement recommendations and evaluate outcomes
Review Statistics and Linear Regression
Show steps
Reviewing foundational concepts in statistics and linear regression can strengthen your understanding and provide a more solid base for comprehending the advanced techniques covered in this course.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Go over lecture notes or textbook chapters
- Complete practice problems and exercises
Review Probability Theory
Show steps
This course builds upon a foundational understanding of probability theory. Reviewing concepts such as conditional probability, statistical independence, and Bayes' theorem will lay a strong foundation and can improve your comprehension of the course.
Show steps
Show steps
Show steps
- Review notes or online resources on probability theory
- Solve practice problems to reinforce understanding
Tutorials on Computer Experiments
Show steps
Computer experiments can provide valuable insights into complex systems. Exploring tutorials on this topic will expand your understanding and enhance your ability to apply these techniques.
Show steps
Show steps
Show steps
- Review online courses or video tutorials on computer experiments
- Follow along with the tutorials and complete exercises
Five other activities
Expand to see all activities and additional details
Show all eight activities
Research on Applications of Mixture Experiments
Show steps
Compiling articles and case studies on applications of mixture experiments can provide valuable insights into their practical uses and expand your knowledge beyond the theoretical concepts taught in the course.
Show steps
Show steps
Show steps
- Search for research papers and articles on mixture experiments
- Summarize and organize the findings
Design and Analyze Factorial Experiments
Show steps
Practicing the techniques you learn in lectures and labs through simulated experiments can boost your skills in designing and analyzing experiments and deepen your understanding of the underlying concepts.
Browse courses on
Factorial Experiments
Show steps
Show steps
Show steps
- Use a software tool like R or SAS to create factorial experiments
- Run simulations to analyze the results and identify significant effects
- Interpret the findings and make recommendations
Attend a Workshop on Design of Experiments
Show steps
Attending a workshop will provide you with the opportunity to engage with experts, learn about cutting-edge techniques, and gain practical experience in applying design of experiments principles.
Browse courses on
Design of Experiments
Show steps
Show steps
Show steps
- Research and find a relevant workshop
- Register and attend the workshop
- Participate actively in discussions and exercises
Create an Infographic on Response Surface Methodology
Show steps
Creating an infographic on response surface methodology can help you synthesize and reinforce your understanding of the key principles and their applications.
Show steps
Show steps
Show steps
- Gather information from course materials, research papers, or online resources
- Design and create a visually appealing infographic
- Share your infographic with peers or online communities
Optimization Project
Show steps
Working on an independent project involving the application of design and optimization principles in a practical setting will allow you to apply your knowledge and gain a deeper understanding of the concepts taught in the course.
Browse courses on
Design of Experiments
Show steps
Show steps
Show steps
- Identify a process or system to optimize
- Design experiments to determine the optimal settings
- Analyze data and interpret results
- Implement recommendations and evaluate outcomes
Career center
Learners who complete Response Surfaces, Mixtures, and Model Building will develop knowledge and skills
that may be useful to these careers:
Operations Research Analyst
Operations Research Analyst
Operations research analysts use advanced analytical techniques to help organizations make better decisions. These techniques include response surface modeling, which is a statistical method used to optimize processes and systems. This course provides a strong foundation in response surface modeling and other techniques used by operations research analysts.
Quality Engineer
Quality Engineer
Quality engineers use statistical methods to improve the quality of products and services. These methods include response surface modeling, which is a statistical method used to optimize processes and systems. This course provides a strong foundation in response surface modeling and other techniques used by quality engineers.
Industrial Engineer
Industrial Engineer
Industrial engineers use their knowledge of engineering and management to improve the efficiency of production systems. These systems include response surface modeling, which is a statistical method used to optimize processes and systems. This course provides a strong foundation in response surface modeling and other techniques used by industrial engineers.
Manufacturing Engineer
Manufacturing Engineer
Manufacturing engineers use their knowledge of engineering and management to improve the efficiency of manufacturing processes. These processes include response surface modeling, which is a statistical method used to optimize processes and systems. This course provides a strong foundation in response surface modeling and other techniques used by manufacturing engineers.
Process Engineer
Process Engineer
Process engineers use their knowledge of engineering and chemistry to improve the efficiency of chemical processes. These processes include response surface modeling, which is a statistical method used to optimize processes and systems. This course provides a strong foundation in response surface modeling and other techniques used by process engineers.
Chemical Engineer
Chemical Engineer
Chemical engineers use their knowledge of chemistry and engineering to design and operate chemical plants. These plants include response surface modeling, which is a statistical method used to optimize processes and systems. This course provides a strong foundation in response surface modeling and other techniques used by chemical engineers.
Mechanical Engineer
Mechanical Engineer
Mechanical engineers use their knowledge of engineering and physics to design and operate mechanical systems. These systems include response surface modeling, which is a statistical method used to optimize processes and systems. This course provides a strong foundation in response surface modeling and other techniques used by mechanical engineers.
Aerospace Engineer
Aerospace Engineer
Aerospace engineers use their knowledge of engineering and physics to design and operate aircraft. These aircraft include response surface modeling, which is a statistical method used to optimize processes and systems. This course provides a strong foundation in response surface modeling and other techniques used by aerospace engineers.
Civil Engineer
Civil Engineer
Civil engineers use their knowledge of engineering and physics to design and operate civil structures. These structures include response surface modeling, which is a statistical method used to optimize processes and systems. This course provides a strong foundation in response surface modeling and other techniques used by civil engineers.
Environmental Engineer
Environmental Engineer
Environmental engineers use their knowledge of engineering and chemistry to protect the environment. These processes include response surface modeling, which is a statistical method used to optimize processes and systems. This course provides a strong foundation in response surface modeling and other techniques used by environmental engineers.
Materials Scientist
Materials Scientist
Materials scientists use their knowledge of chemistry and physics to develop new materials. These materials include response surface modeling, which is a statistical method used to optimize processes and systems. This course provides a strong foundation in response surface modeling and other techniques used by materials scientists.
Biomedical Engineer
Biomedical Engineer
Biomedical engineers use their knowledge of engineering and biology to develop new medical devices and treatments. These devices and treatments include response surface modeling, which is a statistical method used to optimize processes and systems. This course provides a strong foundation in response surface modeling and other techniques used by biomedical engineers.
Computer Engineer
Computer Engineer
Computer engineers use their knowledge of engineering and computer science to design and operate computer systems. These systems include response surface modeling, which is a statistical method used to optimize processes and systems. This course provides a strong foundation in response surface modeling and other techniques used by computer engineers.
Electrical Engineer
Electrical Engineer
Electrical engineers use their knowledge of engineering and physics to design and operate electrical systems. These systems include response surface modeling, which is a statistical method used to optimize processes and systems. This course provides a strong foundation in response surface modeling and other techniques used by electrical engineers.
Software Engineer
Software Engineer
Software engineers use their knowledge of computer science to design and develop software. This software includes response surface modeling, which is a statistical method used to optimize processes and systems. This course provides a strong foundation in response surface modeling and other techniques used by software engineers.
For more career information including salaries, visit:
OpenCourser.com/course/gdmnb1/response
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Response Surfaces, Mixtures, and Model Building.
Classic text on response surface methodology, providing a comprehensive overview of the theory and practice of this powerful technique. It valuable resource for anyone interested in using response surface methodology to optimize processes and products.
Comprehensive treatment of mixture experiments, covering a wide range of topics from basic concepts to advanced techniques. It valuable resource for anyone interested in designing, analyzing, and interpreting mixture experiments.
Comprehensive textbook on the design and analysis of experiments, covering a wide range of topics from basic concepts to advanced techniques. It valuable reference for anyone interested in learning more about experimental design.
Classic text on the design of experiments, providing a comprehensive overview of the theory and practice of this powerful technique. It valuable resource for anyone interested in learning more about the design of experiments.
Provides a comprehensive overview of statistical quality control, covering a wide range of topics from basic concepts to advanced techniques. It valuable resource for anyone interested in learning more about statistical quality control.
Provides a comprehensive overview of applied statistics and probability for engineers, covering a wide range of topics from basic concepts to advanced techniques. It valuable resource for anyone interested in learning more about applied statistics and probability.
Provides a practical guide to the use of statistical methods for quality improvement. It covers a wide range of topics, from basic concepts to advanced techniques, and valuable resource for anyone interested in using statistical methods to improve the quality of their products and processes.
Provides a comprehensive overview of statistical methods for engineers and scientists, covering a wide range of topics from basic concepts to advanced techniques. It valuable resource for anyone interested in learning more about statistical methods.
Provides a comprehensive overview of mathematical statistics, covering a wide range of topics from basic concepts to advanced techniques. It valuable resource for anyone interested in learning more about mathematical statistics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/gdmnb1/response
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Effort
4 weeks of study, 4-8 hours/week
Level
Intermediate
Via
Coursera
Institution
Arizona State University
Instructor
Douglas C. Montgomery
Language
English
Transcripts
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This course belongs to a
collection
called
Design of Experiments. It's comprised of four courses:
- Factorial and Fractional Factorial Designs
- Experimental Design Basics
- Random Models, Nested and Split-plot Designs
- Response Surfaces, Mixtures, and Model Building (viewing)
Good to know
Employs factorial experiments for factor screening, aiding in the identification of critical factors
Focuses on optimization techniques to determine optimal levels of important factors for desired response
Provides tools for designing and analyzing computer experiments, enhancing accuracy and efficiency
Integrates knowledge of design and analysis of experiments with mixtures, facilitating optimization in complex scenarios
Instructs on experimental strategies for reducing the impact of uncontrolled factors, ensuring reliable and reproducible results
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