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Welcome to Design-Expert® software!
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The new Design Wizard will ask a series of questions to quickly provide adesign to accomplish the goal of the experiment, while staying within budgetlimits. It is intended to insulate experimenters that are new to Design-Expertfrom the myriad of design choices, and act as an informative guide. Thisfeature can also benefit the experienced DOE practitioner.
Experienced users of the software can start by building a New Design or loadingan existing design with the Open Design button.
Searchable, context-sensitive help can be called by clicking the questionmark icon or F1 on the keyboard. It provides assistance on what to do next.
It is also connected to specific cells on reports such as the Evaluation andANOVA. Click on a number in a cell of the report and right-clickin the cell to request help to learn more about its contents.
Screen tips provide instructions for what to do or what to look for on thecurrent screen. Screen tips can be called either by pressing the light bulbicon or through the keyboard via shift+F1.
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Stat-Ease Tutorialsare available online and included on installation CDs.Use the tutorials to get more familiar with design of experiments (DOE).This includes building , analyzing, and interpreting the results of adesigned experiment.
Stat-Ease Academyoffers a dynamic selection of on-demand, online training.Some content is free to access, while some requires a small fee. Check backregularly as our offerings may change.
Stat-Ease Trainingprovides comprehensive instructor-led workshops. New DOEpractitioners are encouraged to attend the Experiment Design Made Easycourse. This course covers the basics of DOE and enables the attendees touse most of the tools available in Design-Expert.
Experimenters may wonder why they should choose one design over another. Below isa summary of the purpose for each response surface design type. Experimenterswill generally use Central Composite, Box-Behnken, or Optimal designs.
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Randomized¶
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Central Composite designs (CCD) are based on 2-level factorial designs,augmented with center and axial points to fit quadratic models. Regular CCD’s have5 levels for each factor. This can be modified by choosing an axial distance of1.0 creating a Face-Centered, Central Composite design which has only 3 levels perfactor. The center points are replicated to provide excellent predictioncapability near the center of the factor space. A central composite design is acommon augment from the two-level factorial design. Categoric factors can beadded to these designs, however, the design is duplicated for every categorictreatment combination.
Box-Behnken designs always have three levels for each factor and are purposebuilt to fit a quadratic model. The Box-Behnken design does not have runs at theextreme combinations of all the factors, but compensates by having betterprediction precision in the center of the factor space. While a run or two can bebotched in these designs the accuracy of the observations in the remaining runs iscritical to the dependability of the model. Categoric factors can be added tothese designs, however, the design is duplicated for every categoric treatmentcombination.
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Epub packager 1.4 download. One Factor designs are used when there is only one continuous factor in theexperiment. They create evenly spaced runs across the factor range. Categoricfactors can be added to these designs, however, the design is duplicated forevery categoric treatment combination.
Miscellaneous contains a collection of designs are not recommended butavailable for use. These designs include the 3-level Factorial, Hybrid, Pentagonal,and Hexagonal designs. These designs have limitations and/or inferior propertiesto Central Composite, Box-Behnken and Optimal designs.
Supersaturated¶
Supersaturated designs have fewer rows in the design than terms in the designmodel. The extra step of choosing a subset of the full model must be taken beforeanalysis. Terms in any subset model may be partially or completely aliased.
Definitive Screen (5 to 30 factors) – These screening designs allow forthree-level numeric factors and two-level categoric factors. This structureallows clean estimates of the main effects and detection of second-order effects.
Split-Plot¶
Split-plot designs allow some of the factors to be hard to change (HTC). Thisrestricts the randomization for these factors; they will be held constant for agroup of runs during the experiment. The cost for restricting the randomizationis lower precision for estimates coming from HTC factors. Only restrict therandomization if it is absolutely necessary.
Split-Plot Central Composite designs are similar to their randomized cousinsin that they are purpose built to fit and test quadratic models. The HTC factorsrequire modifications to the basic structure of a CCD, but the resulting designsprovide robust tests for the quadratic model and very good statistical properties.However, maintaining the central composite structure while restricting therandomization requires many runs. If the number of runs is too large, considersplit-plot optimal designs.
See also: Custom Designs for both randomized andsplit-plot type designs.