JMP 13 PRO PRO
In JMP Pro 13, you can now perform Monte Carlo simulations with just a few mouse-clicks.
![jmp 13 pro jmp 13 pro](https://imgs.developpaper.com/imgs/20190731160932.jpg)
Also, you need to find the time to write the script. This has been possible in previous versions of JMP using JSL, but requires a certain level of comfort with scripting and in particular scripting formulas and extracting information from JMP reports. For each of these generated responses, fit the model and, for each effect, check if the p-value falls below a certain threshold (say 0.05). To do so, we need to be able to generate responses according to a specified logistic regression model. But, it is more straight-forward to run a Monte Carlo simulation. We could do a literature review to see about estimating the power, and hope to find something that applies (and do so for each specific case that comes up in the future). Nevertheless, we’re still interested in the power. However, what if our response is based on pass/fail data, where we are planning to do 10 trials at each experimental run? For this response, we can fit a logistic regression model, but we cannot use the results in the Design Evaluation outline. If our response is continuous, and we are assuming a linear regression model, we can use results from the Power Analysis outline under Design Evaluation. I encourage you to read some of those previous blog posts if you’re unfamiliar with the topic. In essence, what is the probability that we can detect non-negligible effects given a specified model? Of course, there are a set of assumptions/specifications needed in order to do this, such as effect sizes, error, and significance level of tests.
![jmp 13 pro jmp 13 pro](http://volweb.utk.edu/~ccwiek/201Tutorials/CopyPaste/001.jpg)
Bradley Jones has written a number of blog posts on this very topic. When designing an experiment, a common diagnostic is the statistical power of effects.