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Appendix B Main Menu 433
The DOE Menu
Full Factorial Design
You specify a set of continuous and categorical factors with arbitrary numbers of levels. JMP creates the
design containing all possible combinations of those factors. See the JMP Design of Experiments for
details.
Mixture Design
Lets you define a set of factors that are ingredients in a mixture. JMP creates a new window for
choosing among several classical mixture design approaches, such as simplex, extreme vertices, and
lattice. For the extreme vertices approach, you can supply a set of linear inequality constraints limiting
the geometry of the mixture factor space. See the JMP Design of Experiments for details.
B The Main Menu
Choice Design
Creates experiments with factors that are product attributes. Lets you find the combination of
attributes for a particular product or service that your customers rate highly. See the JMP Design of
Experiments for details.
Space Filling Design
Creates a design by spreading the design points out to the maximum distance possible between two
points. Prevents replicate points and spaces them uniformly. See the JMP Design of Experiments for
details.
Nonlinear Design
Lets you create an optimal design for models that are nonlinear in the parameters. See the JMP Design
of Experiments for details.
Taguchi Arrays
Guides you through the definition of signal and noise factors. The signal factors form the inner array
and the noise factors form the outer array. The inner and outer array designs are the traditional Taguchi
orthogonal arrays, such as L4, L8, L16, and so on. See the JMP Design of Experiments for details.
Augment Design
Lets you modify existing designs. You can add center points, replicate the design a specified number of
times, create a foldover design, and add runs to the design using a model with more terms than the
original design. See the JMP Design of Experiments for a discussion of each type of design, with details
and examples.
Sample Size and Power
Computes power, sample size, or the effect size you want to detect for a given alpha and error standard
deviation. You supply two of these values and the sample size and power feature computes the third. If
you supply only one of these values, the result is a plot of the other two. This feature is available for the
single-sample, two-sample, and k-sample situations. See the JMP Design of Experiments for a discussion
of prospective power analysis and examples.