Consider the Half 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 two-way 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 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 Design Output
JMP can calculate parameter estimates, but degrees of freedom for error, standard errors, t-ratios, and p-values 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 Screening Design Report.
Half Screening Design Report
Estimates labeled Contrast. Effects whose individual p-value is less than 0.10 are highlighted.
A t-ratio is calculated using Lenth’s PSE (pseudo-standard error). The Lenth PSE is shown below the Half Normal Plot.
Both individual and simultaneous p-values are shown. Those that are less than 0.05 are shown with an asterisk.
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 two-factor interactions, are selected.