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  • Two World-Class Speakers Confirmed for the 2019 Analytics Solutions Conference Keynotes

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    Two globally-recognized speakers are committed to keynote the 2019 Analytics Solutions Conference. This premier conference on the applications of industrial analytics, featuring DOE, MVA, and PAT, is being held in Minneapolis, MN June 18-20. The speakers are:

    Dr. Geoff Vining is an acclaimed statistics researcher, internationally known for his expertise in the use of experimental design for quality, productivity, and reliability improvement and in the application of statistical process control. Geoff will present his views on "Solving Complex Problems".

    Dr. R. Dennis Cook is known worldwide for his pioneering work on linear and nonlinear regression, experimental design and statistical graphics, but best known as the inventor of the Cook's distance statistic that plays a prominent role in model diagnostics. Dennis will provide "A Primer on Partial Least Squares Regression".

    For more information on these talks, head to the ASC 2019 Speakers Page

    Would you like to speak? Submit an abstract! All the details can be found on the 2019 Analytics Solutions Conference webpage.

  • Four Questions that Define Which DOE is Right for You

    Do you ever stare at the broad array of DOE choices and wonder where to start? Which design is going to provide you with the information needed to solve your problem? I’ve boiled this down to a few key questions. Each of them may trigger more in-depth conversation, but the answers are key to driving your design decisions.

    1. What is the purpose of your experiment? Typical purposes are screening, characterization, and optimization. The screening design will help identify main effects (it’s important to choose a design that will estimate main effects separately from two-factor interactions (2FI)). Characterization designs will estimate 2FI’s and give you the option to add center points to detect curvature. Optimization designs generally estimate non-linear, or quadratic effects. (See the blog “A Winning Strategy for Experimenters”.)

    2. Are your factors actually components in a formulation? This leads you to a mixture design. Consider this question – if you double all the components in the process, will the response be the same? If yes, then only mixture designs will properly account for the dependencies in the system. (Check out the Formulation Simplified textbook.)

    3. Do you have any Hard-to-Change factors? An example is temperature – it’s hard to randomly vary the temp setting higher and lower due to the time required to stabilize the process. If you were planning to sort your DOE runs manually to make it easier to run the experiment, then you likely have a hard-to-change factor. In this case, a split-plot design will give a more appropriate analysis.

    4. Are your factors all numeric, or all categoric, or some of each? Multilevel categoric designs work better with categoric factors that are set at more than 2 levels. A final option - Optimal designs are highly flexible and can usually meet your needs for all factor types and require only minimal runs.

    These questions, along with your budget for number of runs, will guide your decisions regarding what type of information is important to your business, and what type of factors you are using in the experiment. Conveniently, the Design Wizard in Design-Expert® software (pictured below) asks these questions, guiding you through the decision-making process, ultimately leading you to a recommended starting design.

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    Click on the options in the wizard to read more about each choice.

    Give it a whirl – Happy Experimenting!

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