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Jun 23, 2011

Which DOE Course?

SAS Education offers two courses of study for the design of experiments (DOE). The first one, Design and Analysis of Experiments, dates back to JMP 3. It began as a one-day course and changed to a more complete two-day course when JMP 4 shipped. This course is designed to help users apply traditional design methds such as full and fractional factorial designs and response surface designs like Box-Behnken and central composite designs. It concludes with a brief introduction to custom design. We continue to improve and enhance this course, as with all of our training. You can expect a new edition for JMP 7 soon. You can learn more about the first course here: Design and Analysis of Experiments.

The second course, Modern Design of Experiments, began with JMP 6. It is about custom design; none of the traditional methods are covered. Instead, this two-day course presents principles of design that cover most common situations and exercise most of the features of custom design. A new edition of this course will also be available for JMP 7 soon. You can learn more about the second course here: Modern Design of Experiments.

Offering two courses about DOE presents a dilemma for our users: which one should I take? Both courses are advanced* and both are aimed at JMP users who are new to DOE. We promote the second course, though, to all of our students because custom design is the most flexible and powerful way to design experiments with JMP. It makes designs when the traditional methods can't, and it reproduces traditional designs when appropriate. Custom design is the first choice for JMP DOE in every situation. We developed this course so that users could benefit the most from custom design. We continue to provide the first course as a service to our users who specifically require help with traditional designs.

If you have already taken the first DOE course, then you might consider attending the second course to enhance your ability to use custom designs.

*Both of these courses require that students previously attend a class for the Statistical Data Exploration and the ANOVA and Regression courses, or have the equivalent knowledge and experience.

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