maybe take a look at the Passing Babcock fit, or the Orthagonal fit in the Fit Y by X platform
This isn't good data for the example but you'll get the idea of what the reports contain
These are pretty standard methods for comparing measurements where there is error in both the X and Y.
dt=Open( "$SAMPLE_DATA/Big Class.jmp" );
dt<<Bivariate(
Y( :height ),
X( :weight ),
Fit Passing Bablok( {Equality Line( 1 ), Line Color( {230, 159, 0} )} ),
Fit Orthogonal( Univariate Variances, {Line Color( {86, 180, 233} )} ),
SendToReport(
Dispatch( {}, "weight", ScaleBox,
{Min( 33.736 ), Max( 180 ), Inc( 20 ), Minor Ticks( 0 )}
),
Dispatch( {}, "height", ScaleBox,
{Min( 33.736 ), Max( 180 ), Inc( 20 ), Minor Ticks( 0 )}
)
)
);
dt<<Matched Pairs(
Y( :height, :weight ),
Reference Frame( 1 ),
Bland Altman Analysis( 1 )
);
JMP Systems Engineer, Health and Life Sciences (Pharma)