POV (partitions of variation) vs COV (coefficients of variation)Hi,
I have someone asking me if we do Partitions of Variation natively in JMP, or have something similar. I can't find much of anything online. Apparently, it's used in semiconductor "when you are trying to solve a problem, one of the steps that is important is to partition out the sources of variation." POV adjusts for handling continuous and attribute data along with balanced and unbalanced data. "It also breaks out the within and between variation, including common / noise."
Does anyone know if this is included in JMP and how it compares to the standard Varience Components in Variability/Attribute Gauge platform?
Re: POV (partitions of variation) vs COV (coefficients of variation)I bet they mean variance components, and yes, JMP computes these in several places.
Re: POV (partitions of variation) vs COV (coefficients of variation)Hey Hadley,
Is it possible they are talking about variance components analysis? If so fitting a model with random effects will generate variance components estimates
Within variation is the residual in the Variance Components Estimates table. Between is available for an interaction model. Total gives total variance. Before I was a JMP user this is how I solved MSA problems. It is handy for any circumstance where there are random effects as well. Could this be what they are asking for?
J
sorry Mark, we must have been replying at the same time
Re: POV (partitions of variation) vs COV (coefficients of variation)Hi,
Thanks for the responses. I think I've got enough to take back with me for now. Thanks again.
Best regards,
Hadley
