Random Noise in the Simulator

[#1]

Hi,

I want to simulate some responses from a model I have built using the simulator in Fit Model. As I have only fixed factors, I want to generate a distribution of simulated responses by adding random noise to the response. My questions are

1. What exactly is the default random noise value for the response provided by JMP? (On one help page I saw this was the Root Mean Square Error but I'm not sure how to interpret this in practical terms.)

2. How do I capture measurement error in the response when simulating? i.e. do I replace the default random noise value with a std dev for measurement error or, do I combine the default random noise value with the measurement error value (perhaps by taking the sum of the variances for each and then square rooting?)

Thanks in advance

[#2]

And a note of caution to @Alicia in case you do not know: variances are additive, standard deviations are not. So if you have estimates of the batch and measurement variation as standard deviations, you must square them, add them, and then take the square root to get the SD for the simulation as outlined by @SDF1.

[#3]

Many thanks!

[#4]

Hello Mark,

One query: Can JMP optimize uncertain (discrete) integer values using Monte Carlo? ....that is, for each solution that JMP finds during optimization, it runs a Monte Carlo simulation and finds the combination of adjustable inputs that gives the best simulation results?

Cheers,

Marco

[#5]

What do you mean by "uncertain (discrete) integer values?" What do you mean by "each solution that JMP finds?"

I want to check my interpretation of your question. Are you asking if you can make JMP apply a simulation at each point on the path of optimization? No, JMP can't do that in the Prediction Profiler. You must write a script to implement the optimization algorithm and the simulation.

The Prediction Profiler provides simulation based on a function relating the mean response to the factors, injecting realistic noise to the inputs, and adding unconditional noise to the response.

[#6]

Thanks so much!