Now available on the JMP File Exchange.
I’m super-excited about this. (more…)
In a recent post I created a table that contained two classes of data: images that represent either the handwritten digit ‘5’ or the digit ‘6’. In this post I’ll model the data using logistic regression. I will also take the opportunity to look at the role of training and test datasets, and to highlight the distinction between testing and validation.
Box-Cox transformations have always been a feature that has been tucked away under the red triangle options of Fit Model. In version 13 of JMP this functionality is brought to the foreground. It appears as default output when you choose the Effect Screening emphasis.
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JMP Software comes complete with a large number of sample data tables.
The other day I found one containing body measurement data: (more…)
I’m working on some predictive modelling projects and I need to iteratively compute R2 statistics over 100’s of variables. Each time I do the calculations I need to go and have an extended coffee break – and I’m starting to buzz with too much caffeine so I thought I would look to see whether I could make my code more efficient!
Here is a data table that I have created. It happens to contain data
that is the result of a designed experiment. I know that, but JMP doesn’t.
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The Fit Model platform within JMP is incredibly powerful but can sometimes feel a little bit overwhelming when models are simultaneously being constructed for multiple responses.