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.
JMP Systems Engineer, Health and Life Sciences (Pharma)