Here's an example using Big Class.JMP.
Fit Model(
Y( :weight ),
Effects( :height, :sex, :sex * :height ),
Personality( "Standard Least Squares" ),
Emphasis( "Effect Leverage" ),
Run(
:weight << {Summary of Fit( 1 ), Analysis of Variance( 1 ),
Parameter Estimates( 1 ), Scaled Estimates( 0 ),
Plot Actual by Predicted( 1 ), Plot Residual by Predicted( 1 ),
Plot Studentized Residuals( 0 ), Plot Effect Leverage( 1 ),
Plot Residual by Normal Quantiles( 0 ), Box Cox Y Transformation( 0 )}
)
);
Fit Model(
Y( :weight ),
Effects( :height, :sex * :height ),
Random Effects( :sex ),
NoBounds( 1 ),
Personality( "Standard Least Squares" ),
Method( "REML" ),
Emphasis( "Minimal Report" ),
Run(
:weight << {Summary of Fit( 1 ), Analysis of Variance( 0 ),
Parameter Estimates( 1 ), 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 )}
)
)
In the first model there are two terms and their interaction.
The second model has :sex added as a random effect.
A good way to see the difference in the Model's JSL it to create it without the desired feature and with the desired feature and compare the two.
