Consider the Half Reactor.jmp sample data table. The data are derived from a design discussed in Box, Hunter, and Hunter (1978). You are interested in a model with main effects and twoway interactions. This example uses a model with fifteen parameters for a design with sixteen runs.
For this example, select all continuous factors, except the response, Percent Reacted, as the screening effects, X. Select Percent Reacted as the response Y. The screening platform constructs interactions automatically. This is in contrast to Fit Model, where you manually specify the interactions that you want to include in your model.
Traditional Saturated Half Reactor.jmp Design Output shows the result of using the Fit Model platform, where a factorial to degree 2 model is specified. Since there are not enough observations to estimate an error term, it is not possible to conduct standard tests.
Traditional Saturated Half Reactor.jmp Design Output
JMP can calculate parameter estimates, but degrees of freedom for error, standard errors, tratios, and pvalues are all missing. Rather than use Fit Model, you can use the Screening platform, which specializes in getting the most information out of these situations, leading to a better model. The report from the Screening platform for the same data is shown in Half Reactor.jmp Screening Design Report.
Half Reactor.jmp Screening Design Report
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A tratio is calculated using Lenth’s PSE (pseudostandard error). The Lenth PSE is shown below the Half Normal Plot.

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Both individual and simultaneous pvalues are shown. Those that are less than 0.05 are shown with an asterisk.

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The Make Model button opens the Fit Model window and populates it with the selected effects. The Run Model button runs the model based on the selected effects.

For this example, Catalyst, Temperature, and Concentration, along with two of their twofactor interactions, are selected.