Design of Experiments Certification

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Upcoming Course

October 10, 2018 to February 3, 2019

Upcoming Dates

New sessions coming in Fall 2018.

Program Fee

$1,495

Contact Information

For more information on professional programs or certfications contact:

Professional & Executive Education
exec-fseonline@asu.edu
(480) 727-4189

or fill out our "Request for Information" form at the bottom of the page. 

Program Description

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

  • Basic design of experiments and analysis of the resulting data
  • Design and statistical analysis issues
  • Analysis of variance, blocking and nuisance variables
  • Factorial designs
  • Fractional factorial designs
  • Response surface methods and designs
  • Experiments with random factors
  • Nested and split-plot designs
  • Applications of designed experiments from various fields of engineering, science and business, including chemical, mechanical, electrical, materials science and industrial
  • Statistics software packages for guidance and support in designing experiments and analyzing data

Learning Outcomes

  • Ability to plan and execute experiments
  • Ability to collect data and analyze and interpret these data to provide the knowledge required for business success
  • Knowledge of a wide range of modern experimental tools that enable practitioners to customize their experiment to meet practical resource constraints

Earning a Certificate

To earn the Design of Experiments Certificate participants are required to complete the online quizzes with a cumulative score of at least 70%, and execute an applied project. The project consists of planning, designing, conducting and analyzing an experiment, using appropriate DOX principles. Two project updates are required, along with a final project report due 60 days after the end of instruction.

A project report template is provided as well as personalized coaching by an experienced course facilitator.

Note: It is recommended that the project be based on your workplace, however if unemployed or unable to identify a workplace-based project alternatives exist such as completing a project on a non-profit organization or other association you are/would like to be affiliated with. Additionally many have found success in offering a target company, such as one you hope to be employed by in the future, the opportunity to have a Six Sigma project completed. Please feel free to contact us for further ideas and support if needed.

Who Should Enroll

The program is aimed at engineers, physical/chemical scientists, scientists from variety of fields, quality professionals, R&D personnel, six-sigma practitioners, consultants, and managers from these fields.

Pre-requisites

  • Previous experience and background with six sigma methods
  • Working knowledge of statistics, to include computing and interpreting sample mean and standard deviation
  • Previous exposure to the normal distribution and concepts of testing hypotheses (e.g. t-test), as well as on constructing and interpreting a confidence interval, and model-fitting using the method of least squares