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Lesson 1: introduction to design of experiments, overview section .

In this course we will pretty much cover the textbook - all of the concepts and designs included. I think we will have plenty of examples to look at and experience to draw from.

Please note: the main topics listed in the syllabus follow the chapters in the book.

A word of advice regarding the analyses. The prerequisite for this course is STAT 501 - Regression Methods and STAT 502 - Analysis of Variance . However, the focus of the course is on the design and not on the analysis. Thus, one can successfully complete this course without these prerequisites, with just STAT 500 - Applied Statistics for instance, but it will require much more work, and for the analysis less appreciation of the subtleties involved. You might say it is more conceptual than it is math oriented.

Text Reference: Montgomery, D. C. (2019). Design and Analysis of Experiments , 10th Edition, John Wiley & Sons. ISBN 978-1-119-59340-9

What is the Scientific Method? Section

Do you remember learning about this back in high school or junior high even? What were those steps again?

Decide what phenomenon you wish to investigate. Specify how you can manipulate the factor and hold all other conditions fixed, to insure that these extraneous conditions aren't influencing the response you plan to measure.

Then measure your chosen response variable at several (at least two) settings of the factor under study. If changing the factor causes the phenomenon to change, then you conclude that there is indeed a cause-and-effect relationship at work.

How many factors are involved when you do an experiment? Some say two - perhaps this is a comparative experiment? Perhaps there is a treatment group and a control group? If you have a treatment group and a control group then, in this case, you probably only have one factor with two levels.

How many of you have baked a cake? What are the factors involved to ensure a successful cake? Factors might include preheating the oven, baking time, ingredients, amount of moisture, baking temperature, etc.-- what else? You probably follow a recipe so there are many additional factors that control the ingredients - i.e., a mixture. In other words, someone did the experiment in advance! What parts of the recipe did they vary to make the recipe a success? Probably many factors, temperature and moisture, various ratios of ingredients, and presence or absence of many additives. Now, should one keep all the factors involved in the experiment at a constant level and just vary one to see what would happen? This is a strategy that works but is not very efficient. This is one of the concepts that we will address in this course.

understand the issues and principles of Design of Experiments (DOE),
understand experimentation is a process,
list the guidelines for designing experiments, and
recognize the key historical figures in DOE.

Design of Experiments

Credit hours:, learning objective:, description:.

A thorough and practical course in design and analysis of experiments for experimental workers and applied statisticians. SAS statistical software is used for analysis. Taken by graduate students from many fields. F2018 STAT514 Syllabus

Topics Covered:

Prerequisites:, applied / theory:, web address:, web content:, computer requirements:, other requirements:, proed minimum requirements:.

Design of Experiments Specialization

Program fee.

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.

Program Topics

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.

Live Fireside Chats

Unique to this specialization, Dr. Montgomery hosts monthly fireside chats using Zoom where he discusses different topics in the areas and application of Design of Experiments concepts. Drawing from his expertise and vast network, Dr. Montgomery is frequently joined by a special guest and expert in the topic area being discussed. Planned for the second Wednesday of every month, these chats are open to the public for viewing. During this time, viewers can ask questions to Dr. Montgomery and his guest related to Design of Experiments’ concepts, application, and situational experiences. The previous fireside chat recordings can be found here .

If you would like information on how to join the monthly live fireside chats, please contact us at [email protected] .

Specialization Courses

Experimental Design Basics

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

Factorial and Fractional Factorial 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

Response Surfaces, Mixtures, and Model Building

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

Random Models, Nested and Split-Plot Designs

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

Learning outcomes are organized by course. By completing all four courses, participants will:

Organize a step-by-step process for designing, conducting and analyzing that experiments will lead to successful results
Collect, analyze, and interpret data to provide the knowledge required for project success
Demonstrate effective use of a wide range of modern experimental tools that enable practitioners to customize their experiment to meet practice resource constraints
Use the analysis of variance, ANOVA, to analyze data from single-factor experiments with several factor levels

Earning a Certificate

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.

Who Should Enroll

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.

Pre-requisites

We recommend working knowledge of a basic statistics course. The basic fundamentals will be covered in the Experimental Design Basics course.

Textbook and Software

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 are not required. The courses within the specialization also utilize JMP statistical software. Participants have access to a free trial in the courses.