Can JMP achieve a ready quantile at 115 weight?

Question

Dan_Obermiller · Answer

You could do trial-and-error, but that would be tedious. The only other way that I can see to do this is to fit a distribution to the data, and then use the Quantile Profiler with a desirability function to find the quantile level. For the situation you proposed, I fit a smooth curve to the data and the results looked like this:

Of course different distributions will give slightly different results, but it is the only way that I can see to determine the proper quantile level.

Since you had results in a script, here is the script to match my results:

Distribution(
	Continuous Distribution(
		Column( :weight ),
		Fit Smooth Curve(
			Quantile Profiler(
				1,
				Confidence Intervals( 1 ),
				Desirability Functions( 1 ),
				Smooth Curve Quantile <<
				Response Limits(
					{Lower( 38.2411401370744, 0.0183 ), Middle( 115, 1 ),
					Upper( 200, 0.0183 ), Goal( "Match Target" ), Importance( 1 )}
				),
				Term Value(
					Probability(
						0.685484294268159,
						N Levels( 200 ),
						Lock( 0 ),
						Show( 1 )
					)
				)
			)
		)
	),
	SendToReport(
		Dispatch( {"weight"}, "Quantiles", OutlineBox, {Close( 1 )} ),
		Dispatch( {"weight"}, "Summary Statistics", OutlineBox, {Close( 1 )} )
	)
);

lala · Answer

Thanks!

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
d2 = dt << Summary(
	Quantiles( 25, 5 ),
	Quantiles( 50, 5 ),
	Quantiles( 75, 5 ),
	Freq( 0 ),
	Weight( 0 ),
	Link to original data table( 0 )
);