Re: Design of Experiment

[John Barrer <forcejb@YAHOO.COM>]

Tim - I was exposed to a short course on this topic
WAY back in the 70s when I was working in industry. I
was absolutely blown away. I kept wondering why it
wasn't taught in college. Basically, two-level
factorial block and fractional factorial block designs
allow the quick identification of which variables (out
of a large set) are most important. You can even ID
interactions between variables. You must know enough
about the system to choose two values for each
variable that are sufficiently different to produce a
difference. And the variables don't even have to be
numerical. In many real-world situations, it would be
impossible (from a time/resource/market timing, etc
perspective) to hold all variables constant and study
the effect of a single DV on the DV, one at a time. By
the time you got the answer, the reason for doing so
would likely no longer be relevant. It is a VERY
powerful technique. John Barrere

--- Tim Folkerts <tfolkert@FHSU.EDU> wrote:
> A friend of mine in industry introduced me to
> "Design of Experiment", and I
> was wondering if any of you had heard of and/or
> worked with this branch of
> study.  The basic aim of DOE is to obtain accurate,
> statistically
> significant answers to experimental questions from a
> minimal number of
> trials.  The principles are applied regularly in
> industrial settings (at
> least by smart companies ;-) to improve products and
> processes, especially
> where there are a large number of variables and the
> effects of each
> variable can't be easily predicted in advance.  This
> also applies in many
> biological settings.
>
> Over the past year or so, I have been picking up
> bits of DOE and find it
> enlightening.  Given how central experimentation is
> to physics, I'm amazed
> that more ideas from DOE aren't taught to
> physicists.  Guides to
> experimentation in physics tend to concentrate on
> specific techniques, but
> not on general questions like dealing with multiple
> variables or picking an
> effective set of trials to obtain the desired
> information.  Other than
> particle physicists, most experimentalist are happy
> that they don't have to
> stop to using statistics and are content to use
> inefficient (or even
> ineffective) plans to get to the answer they want.
>
> Some of the DOE techniques require statistical
> sophistication to fully
> understand, but you don't need to know all the
> details to apply the
> techniques (just like you don't need to know how a
> least squares fit is
> done to recognize its usefulness). There are many
> designs that have been
> developed for various types of questions - curve
> fitting, optimizing a
> result, optimizing consistency, or determining which
> subset of parameters
> has the greatest effect on the results.
>
> A few results that may or may not surprise you:
> *  DON'T vary one parameter at a time
> *  perform trials in random order, rather that
> stepping through in order
> *  standard deviation isn't a good measure of
> accuracy (but you probably
> knew that one)
> *  optimizing consistency is often more important
> that optimizing magnitude
>
> At one level I equate this with the current
> discussion of Geometric Algebra
> - both provide powerful improvements over current
> tools, but few people
> even recognize that improvement is warranted.
>
> Most university libraries have books on the subject.
>  A few guides are
> available on line, but I haven't seen any that
> really jump out at me.  One
> way to learn is to just download a trial version of
> DOE software (e.g.
http://www.umetrics.com/methodtech_doe.asp?section=methods
> or
> http://www.statease.com/dx6descr.html) and play
> around.  Many statndard
> statistical applications can do DOE as well (I know
> Minitab does)
>
>
> Tim Folkerts
>
> Department of Physics
> Fort Hays State University
> Hays, KS  67601
> 785-628-4501
>
> This posting is the position of the writer, not that
> of SUNY-BSC, NAU or the AAPT.


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This posting is the position of the writer, not that of SUNY-BSC, NAU or the AAPT.