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.
This course is part of the Design of Experiments Specialization
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Design of Experiments SpecializationArizona State UniversityAbout this Course
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Course 3 of 4 in the
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Intermediate Level
Approx. 13 hours to complete
English
Subtitles: English
What you will learn
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
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 3 of 4 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 13 hours to complete
English
Subtitles: English
Offered by
Arizona State University
Arizona State University has developed a new model for the American Research University, creating an institution that is committed to excellence, access and impact. ASU measures itself by those it includes, not by those it excludes. ASU pursues research that contributes to the public good, and ASU assumes major responsibility for the economic, social and cultural vitality of the communities that surround it.
Syllabus - What you will learn from this course
5 hours to complete
Unit 1: Additional Design and Analysis Topics for Factorial and Fractional Factorial Designs
5 hours to complete
12 videos (Total 126 min), 4 readings, 3 quizzes
12 videos
Course Introduction 4m
More About Factorial and Fractional Factorial Designs15m
The 3^3 Design11m
The 3^k Factorial Design13m
Confounding17m
Fractional Replication of the 3^k Factorial Design11m
Factorials with Mixed Levels6m
Nonregular Fractional Factorial Designs14m
Use of an Optimal Design Tool17m
Syrup Loss Example4m
Unusual Blocking Example4m
4 readings
Course Description10m
Course Textbook and Resources10m
Best Practices in Online Learning (or How to Succeed in This Class)10m
Unit 1: Introduction10m
2 practice exercises
Unit 1: Concept Questions30m
Exercise 130m
2 hours to complete
Unit 2: Regression Models
2 hours to complete
7 videos (Total 98 min), 1 reading, 2 quizzes
7 videos
Properties of the Estimators10m
Regression Analysis of a 2^3 Factorial Design14m
Hypothesis Testing in Multiple Regression21m
Confidence Intervals in Multiple Regression18m
Regression Model Diagnostics12m
Viscosity Example4m
1 reading
Unit 2: Introduction10m
2 practice exercises
Unit 2: Concept Questions
Exercise 230m
4 hours to complete
Unit 3: Response Surface Methods and Designs
4 hours to complete
14 videos (Total 178 min), 1 reading, 2 quizzes
14 videos
The Method of Steepest Ascent15m
Second-Order Models in RSM11m
Ridge Systems11m
Multiple Responses14m
Experimental Designs for Fitting Response Surfaces18m
Blocking in a Second-Order Design15m
The Adhesive Pull-Off Force Experiment9m
General Structure of a Definitive Screening Design with m Factors9m
Experiments with Computer Models16m
Mixture Experiments15m
Constraints10m
Chemical Process Example13m
Paint Formulation Example7m
1 reading
Unit 3: Introduction10m
2 practice exercises
Unit 3: Concept Questions30m
Exercise 330m
2 hours to complete
Unit 4: Robust Parameter Design and Process Robustness Studies
2 hours to complete
4 videos (Total 39 min), 1 reading, 2 quizzes
4 videos
Analysis of the Crossed Array Design5m
Combined Array Designs and the Response Model Approach13m
Semiconductor Manufacturing Example7m
1 reading
Unit 4: Introduction10m
2 practice exercises
Unit 4: Concept Questions30m
Exercise 430m
About the Design of Experiments Specialization
Learn modern experimental strategy, including factorial and fractional factorial experimental designs, designs for screening many factors, designs for optimization experiments, and designs for complex experiments such as those with hard-to-change factors and unusual responses. There is thorough coverage of modern data analysis techniques for experimental design, including software. Applications include electronics and semiconductors, automotive and aerospace, chemical and process industries, pharmaceutical and bio-pharm, medical devices, and many others.
You can see an overview of the specialization from Dr. Montgomery here.
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