Design of Experiments

Design of Experiments

  • Instructor: Shuxia (Susan) Lu
  • Open year round
  • Delivery: Self-paced online, pre-recorded video lectures in addition to self-assessment quizzes (not graded) and final exam (graded).
  • Credentials: The students who successfully complete the course by passing the final exam will receive the Design of Experiments digital badge. A printable 绿帽社 certificate will also be available for successful participants.
  • Who can take this course: This course is open to all engineers, professionals, faculty and students.

ABOUT THE COURSE

Design of Experiments (DOE) is an analytical technique that deals with the planning, conducting, analyzing and interpreting controlled tests to evaluate the factors that control the primary functioning of a process or system.

DOE is a powerful data collection and analysis tool that can be used in a variety of experimental situations to gain important insights about a process or system under investigation. DOE helps identify the factors, or variables, that have the greatest effect on the systems performance, also known as the system鈥檚 response variables.

DOE helps to identify the appropriate factor settings that result in the optimal systems response. In addition to identifying main effects, DOE efficiently and effectively identifies interactions between and amongst variables. Interactions can have a significant effect on process and system performance, and understanding them is a unique benefit of DOE versus other methods of experimentation such as the one-factor-at-a-time (OFAT) approach.   

LEARNING OUTCOMES

At the completion of this course, the learner will be able to:

  • Define Design of Experiments (DOE) and its benefits
  • Describe an input variable (factor), settings (levels) and output variables (response) in a design of experiments
  • Explain the difference between full and fractional factorial designs
  • Describe key concepts of randomization, aliasing, blocking and replications
  • Analyze the outputs of a DOE using an analysis of variance (ANOVA) table
  • Describe the steps in performing an effective DOE

ABOUT THE INSTRUCTOR

Professor Shuxia (Susan) Lu is a professor in the Systems Science and Industrial Engineering Department at 绿帽社 (SUNY). Her research interests are reliability, statistical process control, information technology and computer-integrated manufacturing.

COURSE FEES

  • $250: Standard/industry rate (group rates available, see below)
  • $150: BU and SUNY faculty/staff and alumni graduated Dec 2020 or prior.
  • $105: Non-BU and non-SUNY students (must give evidence of matriculation at University/College, please email wtsnindy@binghamton.edu)
  • $95: BU and SUNY Students and recent BU alumni graduated May 2021 or after/High School students
  • $35: retake fee Students (requires proof of previous registration)
  • $50: Retake fee Non-Students (requires proof of previous registration)

Industry group rate: 2-4 people from the same organization: $225 per person. Contact wtsnindy@binghamton.edu for promo code to use when you register.

PAYMENTS

Payment is made at the time of registration. For questions, contact Watson Industrial Outreach at wtsnindy@binghamton.edu.

CANCELLATIONS AND REFUNDS

Please note our cancellation and refund policy: All cancellations must be received in writing (email) to the Office of Industrial Outreach. All refunds will be assessed a 10% administrative fee. No refunds for cancellations or non-attendance will be given after you have started the course.  Submit your cancellation request to EMAIL: wtsnindy@binghamton.edu.

If the course is canceled, enrollees will be advised and receive a full refund.