pg_0159
5.
Process Improvement
5.4.
Analysis of DOE data
5.4.3.
How to model DOE data
DOE models
should be
consistent
with the
goal of the
experiment
In general, the trial model that will be fit to DOE data should be
consistent with the goal of the experiment and has been predetermined
by the goal of the experiment and the experimental design and data
collection methodology.
Comparative
designs
Models were given earlier for comparative designs (
completely
randomized designs
,
randomized block designs
and
Latin square
designs
).
Full
factorial
designs
For full factorial designs with k factors (2
k
runs, not counting any center
points or replication runs), the full model contains all the main effects
and all orders of interaction terms. Usually, higher-order (three or more
factors) interaction terms are included initially to construct the normal
(or half-normal) plot of effects, but later dropped when a simpler,
adequate model is fit. Depending on the software available or the
analyst's preferences, various techniques such as normal or half-normal
plots, Youden plots, p-value comparisons and stepwise regression
routines are used to reduce the model to the minimum number of needed
terms. A JMP example of model selection is shown
later in this section
and a Dataplot example is given as a
case study
.
Fractional
factorial
designs
For fractional factorial screening designs, it is necessary to know the
alias structure in order to write an appropriate starting model containing
only the interaction terms the experiment was designed to estimate
(assuming all terms confounded with these selected terms are
insignificant). This is illustrated by the JMP fractional factorial example
later in this section
. The starting model is then possibly reduced by the
same techniques described above for full factorial models.
5.4.3. How to model DOE data
http://www.itl.nist.gov/div898/handbook/pri/section4/pri43.htm (1 of 2) [5/7/2002 4:02:06 PM]
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