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Aug 27, 2009 7:31 AM
(1112 views)

My Test:

I have "Load Failures (ex 46.2, 89.2, 10.2, 40) " as my dependent and I have material "thickness, porosity, stiffness value, density" as my independent variable. I want to model and see if I could design a material for a specific load failure based on my independent variables.

1. Can JMP 7 and 8 run "multivariate logistic regression" analysis?

2. How do I run this in JMP? Can someone please take me through the procedure. This is such a powerful software and has a lot of choices and variables that could dramatically change the results.

This is what I have tried so far and I am confused if what I ran might be multivariate logistic regression:

logistic regression : Fit Y by X on each independent parameter.

Fit Model : Fit Y with multiple X's...this is where I am confused.

Fit Y by X and Fit model if I choose my Y variable to be nominal instead of ordinal I get an error that tells me the program cannot converge "step halving limit" etc.

So I am forced to choose "ordinal" for logistic regression and I am not very familiar with the difference this will have on my results.

Also, when I add my independent variables I could: A. Just press add B. Cross or C. Nest.

This will also change my results dramatically.

If this is the correct way then how to I go about taking 3 or 4 independent parameters and testing them (together) against my load failure?

What is the proper procedure for running multivariate logistic regression using JMP. As I am a newbie to this software I would appreciate it if you could take me through it step by step.

Thanks very much,

Jerry

2 REPLIES

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Aug 31, 2009 8:31 PM
(1041 views)

However, you have run into some sloppy semantics in the journals. In the statistician's lexicon, "multivariate" is used for multiple dependent variables whereas "multivariable" is used for a single dependent variable. Either can have multiple independent variables. What you describe in your first paragraph looks like multivariable logistic regression. JMP can certainly do that for a binary dependent variable. Check these notes for a further discussion

http://www.uncp.edu/home/frederick/DSC510/JMPlogreg.htm

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Oct 14, 2009 12:59 PM
(1041 views)

Hope this helps...