Predictum Blog

May 25, 2016

How JMP Pro delivers insights

Several of the clients of Predictum, our analytical solutions company, have asked us to help them to evaluate the capabilities of JMP Pro with their data and analytical challenges, and we’ve done so with excellent results.

We’ve seen that the advanced modelling capabilities in JMP Pro give insights over and above those available from conventional modelling methods, especially when the potential for overfitting and multicollinearity between variables are present. JMP Pro also offers several useful tools for formatting data in a way that makes subsequent analysis efficient. To describe the advantages present in JMP Pro, a brief discussion of these statistical and data formatting issues is needed.

Overfitting and multicollinearity are two common problems with big data sets. Overfitting occurs when a data set has fewer observations than predictors and in cases where cross-validation is not exploited. Multicollinearityoccurs when the two or more predictors are correlated with each other. Both overfitting and multicollinearity prevent adequate estimation of coefficients in linear least squares regression, which is the most common predictive modelling technique used in traditional statistics.

JMP Pro offers many tools for addressing overfitting and multicollinearity. Partial least squares (PLS) regression is an excellent predictive modelling technique that overcomes overfitting and multicollinearity, especially when the predictors are continuous. Some of the most useful features of PLS regression, including a choice of validation methods as well as provisions for imputing missing data, are available only in JMP Pro.

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