1.
Select Help > Sample Data Library and open Nonlinear Examples/Chemical Kinetics.jmp.
2.
Click the plus sign next to Model (x) in the Columns panel. The formula editor opens.
3.
TheParameters outline in the middle bottom of the formula editor shows the current values of the model parameters.The values (b1 = 1 and b2 = 1) are your initial estimates. They are used to compute the Model (x) values in the data table. For your next experiment, you want to replace these with better estimates.
4.
Click Cancel to close the formula editor window.
5.
Select Analyze > Specialized Modeling > Nonlinear.
6.
Select Velocity (y) and click Y, Response.
7.
Select Model (x) and click X, Predictor Formula.
Notice that the formula given by Model (x) appears in the Options for fitting custom formulas panel.
Figure 22.7 Nonlinear Analysis Launch Window
8.
Click OK.
10.
Figure 22.8 Nonlinear Fit Results
The Lower CL and Upper CL values for b1 and b2 define ranges of values for these parameters. Next, use these intervals to define a range for the prior values in your augmented nonlinear design.
1.
With the Chemical Kinetics.jmp data table active, select DOE > Special Purpose > Nonlinear Design.
2.
Select Velocity (y) and click Y, Response.
3.
Select Model (x) and click X, Predictor Formula.
4.
Figure 22.9 Nonlinear Design Outlines for Factors and Parameters
In the Chemical Kinetics.jmp data, the values for Concentration range from 0.417 to 6.25. Therefore, these values initially appear as the low and high values in the Factors outline. You want to change these values to encompass a broader interval.
b1: 0.568 and 3.158
bb2: 6.858 and 45.830
Figure 22.10 Updated Values for Factor and Parameters
8.
Enter 40 for the Number of Runs in the Design Generation panel.
9.
Click Make Design.
The Design outline opens, showing the Concentration and Velocity (y) values for the original 13 runs and new Concentration settings for the additional 27 runs.
10.
Click Make Table.
The new runs reflect the broader interval of Concentration values and the range of values for b1 and b2 obtained from the original experiment, which are used to define the prior distribution. Both should lead to more precise estimates of b1 and b2.

Help created on 10/11/2018