Or, another approach is to use Fit Model, use the Response Surface macro so that JMP sets the response surface attribute. Then you will get a response surface output that solves for the critical values.
I had JMP create this script with some data that I made up.
Fit Model(
Y( :contrast_100cm ),
Effects( :Offset_Z & RS, :Offset_Z * :Offset_Z ),
Personality( "Standard Least Squares" ),
Emphasis( "Effect Leverage" ),
Run(
:contrast_100cm << {Summary of Fit( 0 ), Analysis of Variance( 0 ),
Parameter Estimates( 0 ), Lack of Fit( 0 ), Scaled Estimates( 0 ),
Plot Actual by Predicted( 0 ), Plot Residual by Predicted( 0 ),
Plot Studentized Residuals( 0 ), Plot Effect Leverage( 0 ),
Plot Residual by Normal Quantiles( 0 ), Box Cox Y Transformation( 0 )}
),
SendToReport(
Dispatch(
{"Response contrast_100cm", "Whole Model"},
"Summary of Fit",
OutlineBox,
{Close( 1 )}
),
Dispatch(
{"Response contrast_100cm", "Whole Model"},
"Analysis of Variance",
OutlineBox,
{Close( 1 )}
),
Dispatch(
{"Response contrast_100cm", "Whole Model"},
"Parameter Estimates",
OutlineBox,
{Close( 1 )}
),
Dispatch(
{"Response contrast_100cm"},
"Response Surface",
OutlineBox,
{Close( 0 )}
)
)
)
Here is the part of the output that finds the critical value.
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