Oops, pasted the wrong script, sorry about that, its corrected now.
The model is fitting :Data vs. :Standard
Then I saved the inverse prediction column, which predicts the standard from the :Data column.
In practice, this "transformation" will correct the bias from the non-linearity issue in the measurement system.
Something to try:
1. Use fit model for Data vs Standard, fit a 4p Robard model, save columns/save inverse prediction column
2. Use fit model for Data vs Standard, group by Teach Point, fit a 4p Robard model, save columns/save inverse prediction column
3. Analyze/Quality/Variiability. In the Dialog, :Standard goes into the Standard role, then :Data and the two columns you just made go into the Y response role, and Teach Point goes in to the X grouping role and Sample ID goes in to the Sample ID role.
//this works too
Variability Chart(
Y( :Data,:Standard Predictor,:Standard Predictor by Teach Point ),
X( :Teach Point, :Sample ID ),
Standard( :Standard )
):
Now that the Variability report is up, hold down the Ctrl key and from the red triangle menu go to Gauge Studies and pick Bias Report, and also Linearity report (click cancel for the sigma question)
The reports (if this worked) wlll show you that in the transformed (inverse prediction formula) the bias is smaller, and that there is no or very little linearity in the bias.
This was a very fun data set, thank you
JMP Systems Engineer, Health and Life Sciences (Pharma)