Design of Experiments

From race cars to retail Web pages, almost anything can be improved by running designed experiments in JMP. What was thought to be impossible all of a sudden becomes real. And success becomes the rule rather than the exception when experiments are powered by JMP.

The software’s Custom Designer lets you create designs that can’t be matched by any compilation of pre-formulated designs offered by other software vendors. JMP offers novices and DOE experts alike the power to design experiments that meet the most complex conditions.

Built into JMP, Custom Designer lets you control the design process, meet complex experimental design requirements, and address optimal split plot design where one or more factors are held constant within a group but vary between groups. JMP also provides nonlinear design capabilities to generate and augment optimal experimental designs for fitting models that are nonlinear in the parameters. Custom Designer permits even the novice user to produce reasonable designs with only two days of training and no prior experimental design knowledge.

JMP Custom Designer meets your toughest challenges.

• Are your experimental challenges more complex than the basic textbook examples?
• Do you have a large number or combination of continuous, categorical, blocking or mixture factors?
• Do you ever need to specify a number of runs equal or greater than the minimum possible?
• Do you wish you could include covariate factors with values known in advance of the design but not controllable?
• Can you run your experiment in a convenient order by doing groups of runs where some factor or factors do not change within the group?
• Do you want to apply prior knowledge of your systems to rule out certain factor combinations in three different ways?
• Do you wish you could augment prior data with new data to resolve any remaining questions?
• Can you supply linear inequality constraints on a cubic region, specify disallowed combinations, or design within a sphere rather than a cube?
• Are you able to specify any lower order polynomial model as the model you want to be able to fit?

More on DOE

The JMP DOE Difference

JMP® Design of Experiments (DOE) Points of Interest
(PDF, 970K)

White Paper: Interactive Data Mining and DOE: the JMP Partition and Custom Design Platforms
(PDF, 600K)
Related Data (Zip File, 21K)

Featured Book

Introduction to Design of Experiments with JMP Examples, Third Edition
By: Jacques Goupy and Lee Creighton
Publisher: SAS Press

Download the first two chapters
of this book for free.
PDF file, SAS.com profile required.

Illustrated with numerous real-world examples borrowed from a broad base of industries, Introduction to Design of Experiments with JMP Examples, Third Edition, employs a gentle, progressive approach to using designed experiments. The book covers basic ideas, terminology, and the application of techniques necessary to conduct a study using the design of experiments (DOE) framework.

Find out more and order

DOE Training from JMP Partners

Modern Design and Analysis of Experiments is a unique, national award-winning six and one half day program in which teams develop solutions to real company problems through designed experimentation.  The program is conducted by partners of the North Haven Group. Participants will use JMP 7.0 for experimental design and analysis.

All training is being held in Boston, MA on the following dates:  November 6 and 7, December 11 and 12, January 15 and 16, and March 13, 2009 (1/2 day).

Course Description

Registration Information

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