New in JMP® Pro 12

As the advanced analytics version of our software, JMP Pro contains everything users know and love about JMP – and more. With the release of this latest version – JMP Pro 12 – users will enjoy new capabilities and see a leap in performance in almost all of its earlier platforms.

Learn how to get JMP Pro 12

Enhancements to Advanced Analytics

The Generalized Regression personality in Fit Model is an all-inclusive approach to doing regression. It’s your complete modeling framework from variable selection through model diagnostics to LS means comparisons, inverse prediction and profiling. If you are fitting models to designed experimental data, you can do it with greater certainty with Generalized Regression than ordinary least squares.

Generalized Regression in JMP Pro 12 allows you to select models graphically and interactively, and has an informative, new diagnostics bundle. You can tackle more problems in a day with performance that’s up to 1,000 times better. Additionally, you can model new distributions such as Cauchy, Beta, Exponential, Zero-Inflated Gamma, Beta Binomial and Zero-Inflated Beta Binomial. The feature now includes support for effect heredity, inverse prediction and forced effects.

Enhancements include:

  • Greatly improved performance on larger problems using the Mixed Model or Generalized Regression personalities of Fit Model.
  • Support for stepwise fitting and Quantile regression in Generalized Regression.
  • Additional distributions in Generalized Regression.
  • Variograms now available in the Mixed Model personality of the Fit Model platform. They serve as a diagnostic to determine which, if any, spatial correlation structure is most appropriate.
  • Discriminant platform now supports validation.
  • Addition of K-nearest neighbors (K-NN) prediction to the Partition platform.

Covering Arrays

Covering arrays are used in testing applications where factor interactions may lead to failures. Each experimental run may cost tens of thousands of dollars or more. As a result, you need to design an experiment to maximize the probability of finding defects while also minimizing cost and time. Covering arrays let you do just that. And when there are combinations of factors that create implausible conditions, you can use the interactive Disallowed Combinations wizard to automatically exclude these combinations of factor settings from the design.

One of the huge advantages of covering arrays in JMP is that JMP is a statistical analysis tool, not just a covering arrays design tool. You can do all sorts of statistical analyses in JMP. For example, there is currently no other covering arrays design tool that also fits Generalized Regression models to data you collect. This is a huge advantage of JMP Pro over other covering array design tools.

JMP Pro is not just strictly a design tool; it also allows you to import any covering array design – generated by any software – and further optimize it and analyze the results. You can design the arrays yourself without having to rely on others to build experiments for you. Test smarter with covering arrays in JMP Pro.

Reliability Block Diagram Improvements

Reliability Block Diagram gives you an interactive canvas for designing and understanding the reliability of systems of many components.

JMP Pro 12 includes enhancements that enable you to build and understand these systems more easily:

  • The option to construct series or parallel diagrams from existing blocks.
  • Overlay likelihood contour on top of the prior and post scatter plot.
  • New cumulative hazard profiler.
  • By-component overlay plots in CDF, PDF, Hazard and Cumulative Hazard.
  • Component integrated importance report.
  • System designs and library items are easier to copy and paste for system design template creation.

PLS-DA (Discriminant Analysis)

PLS discriminant analysis (PLS-DA) fits a PLS regression with a categorical response. The advantage of this classification method compared to generalized linear models or discriminant analysis is that it handles more factors than observations and missing data – which is the reality of real-world, messy data. And there are almost no assumptions that must be made about the data prior to modeling. Additionally, bootstrapping the model predictions is now supported for all PLS model fits.

Validation Column Utility

Creating a validation column was largely a manual process that required multiple clicks to divide a data set into training, validation and test sets. If you required more than a simple random sample of the data for splitting, you had to use an add-in or other techniques to generate the best splits.

Now you can split data based on the problem, using one of several included algorithms. Also, if you are in an analysis platform, clicking on the validation column role without selecting a column will prompt you to choose a validation column, or create one directly from the platform. This keeps you in the analysis flow and lets you cross-validate models easier, in fewer clicks.


Download a PDF of the new features in JMP and JMP Pro or view our online searchable documentation.

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