Here is a script that gives you the output you want, it just does it a different way. It uses the Fit Model Platform and loops through it several time, increasing the N.
Names Default To Here( 1 );
dt = Open( "$SAMPLE_DATA\semiconductor capability.jmp" );
// Create an output table to hold the results
dtOut = New Table( "summary",
New Column( "Instance" ),
New Column( "N" ),
New Column( "RSquare" ),
New Column( "RSM" ),
New Column( "Intercept" ),
New Column( "b1" ),
New Column( "b2" )
);
Loop Counter = 0;
// Loop across the different sizes
For( i = 5, i <= 111, i = i + 5,
Loop Counter++;
// Create a random sample data table from the original data table
dt << New Column( "random", formula( Random Uniform() ) );
dt << sort( by( dt:random ), , Order( Ascending ), replace table( 1 ) );
dt << delete columns( "Random" );
dt << select where( Row() <= i );
dtSample = dt << subset( invisible, selected rows( 1 ), selected columns( 0 ) );
// Run the regression
fm = dtSample << Fit Model(
Y( :NPN1 ),
Effects( :PNP1, :PNP2 ),
Personality( "Standard Least Squares" ),
Emphasis( "Minimal Report" ),
Run(
:NPN1 << {Summary of Fit( 1 ), Analysis of Variance( 1 ), Parameter Estimates( 1 ),
Lack of Fit( 0 ), Plot Actual by Predicted( 0 ), Plot Regression( 0 ),
Plot Residual by Predicted( 0 ), Plot Effect Leverage( 0 )}
)
);
// Capture the output data
dtOut << Add Rows( 1 );
dtOut:Instance[N Rows( dtOut )] = Loop Counter;
dtOut:N[N Rows( dtOut )] = i;
dtOut:RSquare[N Rows( dtOut )] = (Report( fm )["Summary of Fit"][1][2] << get)[1];
dtOut:RSM[N Rows( dtOut )] = (Report( fm )["Summary of Fit"][1][2] << get)[3];
dtOut:Intercept[N Rows( dtOut )] = (Report( fm )["Parameter Estimates"][1][3] << get)[1];
dtOut:b1[N Rows( dtOut )] = (Report( fm )["Parameter Estimates"][1][3] << get)[2];
dtOut:b2[N Rows( dtOut )] = (Report( fm )["Parameter Estimates"][1][3] << get)[3];
// Clean up after our selfs
fm << close window;
Close( dtSample, nosave );
);
Jim
