JMP Software comes complete with a large number of sample data tables.
The other day I found one containing body measurement data: (more…)
JMP Software comes complete with a large number of sample data tables.
The other day I found one containing body measurement data: (more…)
In my last post I illustrated the performance boost generated by using matrix operations to conduct least squares regression calculations. Matrices by their nature require numerical data. So what about handling a categorical predictor variable? To do this it’s necessary to create dummy variables – separate variables for each unique level of the predictor variable.
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.
(more…)
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.