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Well-designed experiments are a powerful tool for developing and validating cause and effect relationships between factors when evaluating and improving product and process performance. Deliberately changing the input variables to a system allows for observation and identification of the reasons for the change that may be observed in the output responses. Design of experiments can identify important interactions that are usually overlooked when experimenters vary only one factor at a time (OFAT experimentation). Unfortunately, OFATS are still widely used in many experimental settings.
Design of Experiments can be used in a variety of experimental situations. This program is suitable for participants from a broad range of industries, including electronics and semiconductor, automotive, aerospace, chemical and process, pharmaceutical, medical device, and biotechnology. There are also many business and commercial applications of designed experiments, including marketing, market research, and e-commerce. Program participants will learn how to run effective and strong experiments using modern statistical software.
We are proud to offer the Design of Experiments specialization through the Coursera platform. The course is instructed by Dr. Doug Montgomery, a Regents Professor of industrial engineering and statistics in the Ira A. Fulton Schools of Engineering at ASU, and an expert in experimental design. Dr. Montgomery has taught academic courses on experimental design for over 40 years, and his Design of Experiments textbook, in its 10th edition and utilized in the specialization, is the most widely used textbook on the subject in the world. He has also led numerous engagements with Design of Experiments, teaching the course and consulting for more than 250 companies, including Motorola, Intel, Boeing and IBM. Drawing from these commercial experiences, Montgomery provides participants with an accurate understanding of modern approaches to using Design of Experiments.
The specialization is offered in a four-course format, with each course comprising three-to-four units and, in most courses, an applied project to demonstrate the tools and concepts learned. Accessible entirely online, the courses can be attempted at your own pace. We recommend completing one unit per week.
Unit 1: Getting Started and Introduction to Design and Analysis of Experiments
Unit 2: Simple Comparative Experiments
Unit 3: Experiments with a Single Factor - The Analysis of Variance
Unit 4: Randomized Blocks, Latin Squares, and Related Designs
Unit 1: Introduction to Factorial Design
Unit 2: The 2^k Factorial Design
Unit 3: Blocking and Confounding in the 2^k Factorial Design
Unit 4: Two-Level Fractional Factorial Designs
Unit 1: Additional Design and Analysis Topics for Factorial and Fractional Factorial Designs
Unit 2: Regression Models
Unit 3: Response Surface Methods and Designs
Unit 4: Robust Parameter Design and Process Robustness Studies
Unit 1: Experiments with Random Factors
Unit 2: Nested and Split-Plot Designs
Unit 3: Other Design and Analysis Topics
If you would like to take all four courses, we recommend taking them in the above order. Each subsequent course will build on materials from the previous.
Learning outcomes are organized by course. By completing all four courses, participants will:
The Design of Experiments specialization is offered 100% online and through the Coursera platform. Participants can complete any of the four courses to receive a certificate of completion, and can complete all four to receive the specialization, thus mastering experimental design.
These courses are open to any that are interested in learning about experimental design tools. Any person working in modern industry can apply the tools acquired in these courses to their current and future positions.
We recommend working knowledge of a basic statistics course. The basic fundamentals will be covered in the Experimental Design Basics course.
The textbook used throughout the specialization is Design and Analysis of Experiments, 10th Edition by Dr. Douglas C. Montgomery. Students are recommended to purchase or rent the textbook, but not required. The courses within the specialization also utilize JMP statistical software. Students have access to a free trial in the courses.