| A design
is selected based on the experimental objective and the number of factors
Types of designs are listed here according to the experimental objective
they meet
|
Choosing an experimental design
depends on the objectives of the experiment and the number of factors to
be investigated.
Experimental Design Objectives
Comparative objective: If you have 1 or several factors under
investigation, but the primary goal of your experiment is to make a conclusion
about 1 a-priori important factor, (in the presence of, and/or in spite
of the existence of the other factors), and the question of interest is
whether or not that factor is "significant", (or whether or not there
is a significant change in the response for different levels of that factor),
then you have a comparative problem and you need a comparative
design solution.
-
Screening objective: The primary purpose
of the experiment is to select or screen out the few important main
effects from the many lesser important ones. These screening designs
are also termed main effects designs.
-
Response Surface (method) objective:
The experiment is designed to allow us to estimate interaction and even
quadratic effects, and therefore give us an idea of the (local) shape of
the response surface we are investigating. For this reason they are termed response
surface method (RSM) designs. RSM designs are used to:
-
Find improved or optimal process settings
-
Troubleshoot process problems and weak points.
-
Make a product or process more robust against external and non-controllable
influences. "Robust" means relatively insensitive to these influences.
-
Optimizing responses when factors are proportions of a mixture objective:
If you have factors that are proportions of a mixture and you want to know
what the "best" proportions of the factors are so as to maximize (or minimize)
a response, then you need a mixture design.
-
Optimal fitting of a regression model objective: If you want to
model a response as a mathematical function (either known or empirical)
of a few continuous factors and you desire "good" model parameter estimates
(i.e. unbiased and minimum variance), then you need a regression design.
Mixture designs are discussed briefly in section
5 (Advanced Topics) and regression designs for a single factor are
discussed in chapter 4. Selection
of designs for the remaining 3 objectives is summarized in the following
table. |
Summary table for choosing an experimental
design
Save some runs for center points and "redos" that might be needed |
Choice of a design from within these various types depends on the amount
of resource available and the degree of control over making wrong decisions
(Type I and Type II errors
for testing hypotheses) that the experimenter desires.
It is a good idea to choose a design that requires somewhat fewer runs
than the budget permits, so that center point
runs can be added to check for curvature in a 2-level screening design
and and backup resources are available to redo runs that have processing
mishaps. |
