Design of experiment is an activity that every Engineer should take very seriously. Engineers are called upon every day 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. And while Engineers are known for sound
and fact based judgment, those laudable qualities and characteristics may not be enough and may not serve them well in certain circumstances. 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, albeit questionable, predictability. Handling these situations requires an understanding of
the formal schemes and structures necessary to deal with variability, bias, and randomness.
This is the first of a two-course sequence in this subject area. As the prerequisite to the second course, it provides the Engineer with the rudimentary, but necessary, toolkit
needed to plan, design and analyze basic engineering experiments and to make recommendations about design and operational decisions. It sets the stage for the second course, where more robust and higher
level designs are explored, including Factorial designs, Fractional designs, Nested designs, Confounding schemes and Regression Analysis. The second course also addresses a fundamental problem of design,
namely cost and resource utilization, and also the all important issue of missing values. While the two courses are not strictly about mathematics and statistics, they do utilize those subject matters to
further elucidate how to plan, design, and analyze engineering experiments. Some of the areas covered in this course include:
- The Role of Experiments in the Engineering Design Process
- The Role of Statistics and Probability in Engineering Design
- Purpose and Nature of Planned Experiments
- Important Issues in Planned Experiments
- The Effects of Changes in the Independent Variables
- The Effect of Noise in An Experiment
- Restrictions on Randomization
- Single Factor Experiments including Model Analysis
- Randomized Block Designs
- Latin and Other Designs
- Incomplete Block Designs