Choose a factorial design

The design, or layout, provides the specifications for each experimental run. It includes the blocking scheme, randomization, replication, and factor level combinations. When doing the experiment, you measure the response or responses at the predetermined settings of the experimental conditions. Each experimental condition that is employed to obtain a response measurement is a run.

Minitab provides 2-level full and fractional factorial designs, 2-level split-plot designs, Plackett-Burman designs, and general full factorial designs with more than two levels. When choosing a design you need to:

Identify the number of factors that are of interest.
Determine the number of runs you can do.
Determine the impact that other considerations (such as cost, time, or the availability of facilities) have on your choice of a design.
Depending on your problem, there are other considerations that make a design desirable.
You may want to choose a design that lets you:
- increase the order of the design sequentially. That is, you may want to "build up" the initial design for subsequent experimentation.
- do the experiment in orthogonal blocks. Orthogonally blocked designs allow for model terms and block effects to be estimated independently and minimize the variation in the estimated coefficients.
- detect model lack-of-fit.
- estimate the effects that you believe are important by choosing a design with adequate resolution. The resolution of a design describes how the effects are confounded. For more information about resolutions, see What is the design resolution in a factorial design?.