5.
Process Improvement
5.3.
Choosing an experimental design
5.3.3.

How do you select an experimental design?


A design is selected based on the experimental objective and the
number of factors

The choice of an experimental design depends on the objectives of
the experiment and the number of factors to be investigated.


Experimental Design Objectives

Types of designs are listed here according to the experimental
objective they meet

Types of designs are listed here according to the experimental
objective they meet.
 Comparative objective: If you have one or several factors
under investigation, but the primary goal of your experiment is
to make a conclusion about one apriori 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", (i.e., 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 less 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 noncontrollable 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 and regression designs

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 for comparative,
screening, and response surface designs


Resources and degree of control over wrong decisions

Choice of a design from within these various types depends on the amount
of resources available and the degree of control over making wrong
decisions (Type I and Type
II errors for testing hypotheses) that the experimenter desires.

Save some runs for center points and "redos" that might be needed

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 2level screening design
and backup resources are available to redo runs that have processing
mishaps.
