It can be quite confusing to compare 2 different implementations of R&R analyis for at least 2 reasons.
1. There are multiple ways to do the analysis which result in slightly different results. JMP can do the analysis using EMS, REML, or a Baysian method.
2. There are multiple ways to report results.
In any case, JMP is not "wrong". You just have to do an apples to apples comparison of results.
I imported the example from the MSA 4th edition on page 118 into jmp so it looks like this:
ID Appraiser Trial Part Y
1 A 1 1 0.29
1 A 1 2 -0.56
1 A 1 3 1.34
1 A 1 4 0.47
1 A 1 5 -0.8
1 A 1 6 0.02
1 A 1 7 0.59
1 A 1 8 -0.31
1 A 1 9 2.26
1 A 1 10 -1.36
2 A 2 1 0.41
etc.
Then I ran (interactively first) this script:
Variability Chart(
Y( :Y ),
X( :Appraiser, :Part ),
Model( "Crossed" ),
Max Iter( 100 ),
Conv Limit( 0.00000001 ),
Number Integration Abscissas( 128 ),
Number Function Evals( 65536 ),
Analysis Type( Use REML analysis ),
Historical Sigma( 0 ),
Std Dev Chart( 1 ),
Gauge RR( 6, 0, 0, 0 ),
Gauge RR Report( 1 )
);
Then I compared the R&R section of the report to the MSA report on page 129 and I found agreement with the "Anova" method results.
Note that I forced the REML method though EMS would, for this case, yielded the same results.
Or you can use this script to do the same thing:
EMP Measurement Systems Analysis(
Y( :Y ),
X( :Appraiser ),
Part( :Part ),
Model( "Crossed" ),
Dispersion Chart Type( Range ),
EMP Results( 1 ),
EMP Gauge RR Results( 1 ),
Average Chart( 1 ),
Dispersion Chart( 1 ),
Max Iter( 100 ),
Conv Limit( 0.00000001 ),
SendToReport(
Dispatch(
{"Average Chart"},
"1",
ScaleBox,
{Rotated Labels( "Automatic" )}
),
Dispatch( {"Range Chart"}, "1", ScaleBox, {Rotated Labels( "Automatic" )} )
)
);
Hope this helps.
Michael
