Investigators use screening designs when they want to identify the factors that have the most substantial effects on a response.
A screening design enables you to study a large number of factors in a fairly small experiment.
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Many standard screening designs focus on estimating main effects.
Definitive Screening Designs can avoid model ambiguity, enabling you to identify important factors more quickly and efficiently. And if a particular factor’s effect is strongly curved, a traditional screening design may miss this effect and screen out the factor.
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Definitive screening designs avoid confounding of effects and can identify factors having a nonlinear effect on the response.
Let's look at Definitive Screening in JMP.
Suppose that you need to determine which of six factors affect the yield of an extraction process.
If you have one I find it easier to open the factor data table first. That file is linked below. Remember we can save Response, Factors, and Constraints from the DOE platform's red triangle.
From the Extraction Factor Data Table > Select DOE > Definitive Screening > Definitive Screening Design.
Double-click Y under Response Name and type Yield.
From the red triangle menu, select Load Factors.
The factor names and ranges are added to the Factors outline along with the default Model Interactions
Click Continue.
The Design Options outline opens. Here you can specify a blocking structure. There is no need to block in this example, so you accept the default selection of No Blocks Required.
You can also choose to add Extra Runs, which greatly enhance your ability to detect second-order effects. A minimum of four Extra Runs is highly recommended, however for this demo I'm leaving at 0.
A Definitive Screening Design will add a Run for each factor and 1 additional Run to achieve a center point or curvature in our Analysis
For this Demo, under the Red Triangle, I'll set the Random Seed to "123" so you can reproduce these results.
Then, select Make Design.
The Definitive Screening Design window updates to show a Design outline and a Design Evaluation outline.
Open the Design Evaluation > Color Map on Correlations outline
The Color Map on Correlations assigns a color intensity scale to the absolute values of correlations among all main effects and two-factor interactions. I have more information on this below.
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Color Map OF Correlations
The solid deep blue area shows that there is no correlation between main effects or between main effects and two-factor interactions.
The lighter blue and gray areas indicate that the absolute correlations between two-factor interactions are small.
The solid red squares indicate absolute correlations of 1. These all appear on the diagonal, reflecting the expected correlation of an effect with itself.
Leave or set the Run Order to Randomize.
Select Make Data Table
This is where we would conduct our Experiment Runs and collect data. And I'll now open the linked Extraction Data and run the Definitive Screening Fit Model Script by selecting it'ds green arrow in the Tables Panel.
Select Run.
And we can review the Analysis Report and explore the factors that were screened starting at the top with the Stage 1 Main Effects.
Try a Definitive Screening Design yourself using the linked Extraction Sample Data Set.
