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285-What Every Engineer Should Know About the Design and Analysis of Engineering Experiments II

By: O. Geoffrey Okogbaa, Ph.D., PE

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Course Objective

This continuing education course is written specifically for professional engineers with the objective of relating to and enhancing the practice of engineering.

Course Description

285-What Every Engineer Should Know About the Design and Analysis of Engineering Experiments II

Design of experiment is an activity that every Engineer should take very seriously. Engineers are called upon everyday to make decisions regarding programs, processes and systems that have significant implications on the safety and well being of society, be they chemical processes, the environment, infrastructure, machinery and equipment, and others. Engineers are known for sound and fact based judgment but while those qualities and characteristics are laudable, they may not be enough and may not serve them well. This is especially true when they are called upon to make decisions regarding variables and factors whose underlying distributions are stochastic and thus have uncertain and questionable predictability. Handling these situations requires an understanding of the formal schemes and structures necessary to deal with variability, bias, and randomness.

This second course, in the two-course sequence, focuses on some of the more practical issues that engineers encounter during the design and analysis of experiments. This course focuses on more robust and higher level designs such as Factorial designs, Confounding Schemes Fractional designs, Fixed and Random factors, Expected Mean Squares, Nested or Hierarchical designs, and Regression Analysis. The course also addresses, with realistic examples, some of the common problem in design of experiments, namely, missing data or missing values. It also provides practical justification for confounding, which arises due to the physical limitation as it relates to acquiring all the needed data. It addresses the issue of cost and resource utilization where fractional factorial designs are used because the cost to run full higher order designs is prohibitive. The course has a very practical bent and while there are theoretical foundations undergirding the material, the course itself utilizes basic arithmetic for computation and analysis. Some of the areas covered in the course include:

  • The Role of Experiments in the Engineering Design Process
  • Missing Values for Randomized Block and Latin Designs
  • Factorial Designs for 2f and 3f
  • Confounding Schemes for 2f and 3f
  • Fractional Factorial Designs for 2f and 3f
  • Modeling of Fixed and Random Effects and Expected Mean Square (EMS)
  • Nested/Hierarchical Designs
  • Regression Analysis
285-What Every Engineer Should Know About the Design and Analysis of Engineering Experiments II
Steven Kushnick, PE
07/20/2018
I'm sorry, but this course needs work: The author uses terms he does not define, leaving the reader bewildered and in need of third-party references. Some of the explanation of concepts has been omitted, with an example substituted for the missing instruction. Examples are handy, but absent instruction, the reader is left to struggle with how to modify the example for real-life problems (or for the test problems). I had to go online to look up information that should have been part of the course. I passed the test with good marks, but the learning was an unreasonable struggle and I feel I should have a better understanding of the material covered. I cannot recommend this course, or the prior course #283, unless you have already taken advanced statistics courses or you have SPC or similar training.
Statistics and experiment design are inseparable companion tools.
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Course Code: 100262-04
Course PDH Hours: 5
Engineering Course Approvals
  • This course is accepted for engineering continuing education credit in all states.
  • Ohio rules allow only 6-hours of unsupervised online courses such as this course per license renewal period

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