Analysis steps: graphics, theoretical model, actual model, validate
model, use model

The following are the basic steps in a DOE analysis.
 Look at the data. Examine it for outliers, typos and obvious
problems. Construct as many graphs as you can to get the big
picture.
 Response distributions
(histograms,
box plots,
etc.)
 Responses versus
time order
scatter plot (a check for possible time effects)
 Responses versus factor
levels (first look at magnitude of factor effects)
 Typical DOE plots (which assume standard models for effects
and errors)
 Sometimes the right graphs and plots of the data lead to
obvious answers for your experimental objective questions
and you can skip to step 5. In most cases, however, you
will want to continue by fitting and validating a model
that can be used to answer your questions.
 Create the theoretical model (the experiment should have been
designed with this model in mind!).
 Create a model from the data. Simplify the model, if possible,
using stepwise regression methods and/or parameter pvalue
significance information.
 Test the model assumptions using residual graphs.
 If none of the model assumptions were violated, examine
the ANOVA.
 Simplify the model further, if appropriate. If
reduction is appropriate, then return to step 3
with a new model.
 If model assumptions were violated, try to find a
cause.
 Are necessary terms missing from the model?
 Will a transformation of the response help? If a
transformation is used, return to step 3 with a
new model.
 Use the results to answer the questions in your experimental
objectives  finding important factors, finding optimum
settings, etc.

Flowchart is a guideline, not a hardand
fast rule

Note: The above flowchart and sequence of steps should not
be regarded as a "hardandfast rule" for analyzing all DOE's. Different
analysts may prefer a different sequence of steps and not all types of
experiments can be analyzed with one set procedure. There still remains
some art in both the design and the analysis of experiments, which
can only be learned from experience. In addition, the role of engineering
judgment should not be underestimated.
