JMP® Pro

Sophisticated techniques for advanced analytics

JMP Pro is the advanced analytics version of JMP that lets you use the data you have now to better anticipate the future and plan well for tomorrow.

Built with scientists and engineers in mind, JMP Pro statistical analysis software from SAS provides all the superior visual data access and manipulation, interactivity, comprehensive analyses and extensibility that are the hallmarks of JMP, plus a multitude of additional techniques. Our latest release adds advanced analytics like generalized regression, mixed-effects models, advanced consumer research analysis, reliability block diagrams and more. JMP Pro also features predictive modeling with cross-validation, model comparison and averaging features, exact tests and one-click bootstrapping.

More importantly, JMP Pro helps you construct a narrative and interactively communicate findings in ways others can readily understand and act upon.

If you already use JMP, you’ll be happy to learn that all of this statistical firepower comes in a familiar in-memory, desktop environment. And if you are new to JMP, you will find that JMP Pro lets you take data analysis to a whole new level and tackle sophisticated statistical problems more easily.

Key benefits of JMP Pro.

JMP Pro Screenshots

Key Benefits of JMP® Pro

Perform Sophisticated Analyses

JMP Pro includes a rich set of algorithms for building better models with your data. If your task is data mining, you have your choice of numerous techniques: decision-tree models (including bootstrap forests and boosted trees), neural network models, generalized (penalized) regression, cross-validated stepwise regression, partial least squares (PLS), uplift modeling and more.

If you want to model the reliability of a complex system, JMP Pro includes provisions for building reliability block diagrams so you can design a model of your system, perform what-if analyses, fix weak spots and decrease the probability of a system failure. If your task is to build and fit models with data that involves both time and space, use the mixed model capabilities in JMP Pro. If variable selection is needed in your modeling tasks, JMP Pro has many tools for that. In addition to all of these modeling techniques, JMP Pro includes other advanced analytics like exact tests and one-click bootstrapping. Having these techniques in your analytic toolbox removes roadblocks to statistical discovery and enhances your ability to uncover more clues in your data. Therefore, you make breakthroughs quickly, enabling you to become more proactive and take greater control of the future.

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Do More Faster

Boostrap Forests

The bootstrap forest technique grows dozens of decision trees using random subsets of the available data and then averages the computed influence of each factor in these trees.

Even though JMP Pro includes a full complement of advanced statistical techniques, it doesn’t mean that the software is difficult to use or that building models is time-consuming. On the contrary, JMP Pro enables you to get answers faster. Weeklong projects shrink to just hours or minutes when you can build multiple models quickly using a drag-and-drop, point-and-click workflow. Then use the variety of statistical reports and interactive graphics to determine the model that will generalize best to future data. Just like that, you’re ready to move on to the next project.

How much is your time worth?

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Build Models that Generalize Well

Anyone can do a fair job of describing last year’s performance. But without the right tools and the most modern techniques, building a model to predict what will happen with new customers, new processes or new risks becomes much more difficult.

This diagram shows how JMP Pro makes predictive analytics easy with visual and interactive tools that aid the modeling process from start to finish.

This diagram shows how JMP Pro makes predictive analytics easy with visual and interactive tools that aid the modeling process from start to finish.

For effective predictive modeling, you need sound ways to validate your model, and with a large model, you can easily get into trouble over-fitting. Models should always be cross-validated, and JMP Pro does this through methods that include data partitioning, k-fold and visual comparison tools. Dividing the data into training, validation and test data sets has long been used to avoid over-fitting, ensuring that the models you build are not reliant on the properties of the specific sample used to build them. This produces models that generalize well to tomorrow’s data, so you can make data-driven inferences about the future.

And while all of this is true, observational data can take you only so far. To truly understand cause and effect, you should consider employing design of experiments (DOE). JMP provides world-class tools for DOE in a form you can easily use.

Cross validation

Easily split your data into training, validation and test portions for honest assessment of a model’s predictive ability.

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Compare Multiple Modeling Techniques

Compare Multiple Modeling Techniques in JMP Pro

Model comparision provisions for comparing fits across multiple fit predictions.

In the real world, some kinds of models fit well in certain situations but fit poorly in others. With JMP Pro, there are many ways to fit, and you need to find out which one is most appropriate in a given situation. Using model comparison in JMP Pro, you can compare all the saved prediction columns from various fits and pick the best combination of goodness of fit, parsimony and cross-validation. JMP Pro makes this comparison automatically and then compares the models in many ways, using traditional or alternative measures of fit. At the same time, you can interact with visual model profilers to see which important factors each model is picking up. Measures of fit, diagnostic plots and profilers are reported for easy comparison of models to help you determine the right path forward. You can also easily perform model averaging or use ridge regression to build ensemble models of your predictions.

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Assign Measures of Precision to Model Predictions

Bootstrapping approximates the sampling distribution of a statistic. JMP Pro is the only statistical software package that lets you bootstrap a statistic without writing a single line of code. One-click bootstrapping means you are only a click away from being able to bootstrap any quantity in a JMP report.

This technique is useful when textbook assumptions are in question or don’t exist at all. For example, try applying bootstrapping techniques to nonlinear model results that are being used to make predictions or determining coverage intervals around quantiles. Also, you can use bootstrapping as an alternative way to gauge the uncertainty in predictive models. Bootstrapping lets you assess the confidence in your estimates with fewer assumptions – and one-click bootstrapping in JMP Pro makes it easy.

Compare Multiple Modeling Techniques in JMP Pro

Bootstrap any statistic in a JMP report with a single click. This example shows bootstrapping confidence limits around a 10th percentile quantile.

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Connect to the Richness of SAS®

Connect to the richness of SAS

Use cross-validation and build multilayer neural network models with automatically generated SAS score code.

As one of the SAS offerings for predictive analytics and data mining, JMP Pro easily connects to SAS, expanding your options and giving access to the unparalleled depth of SAS Analytics and data integration. With or without an active SAS connection, JMP Pro can output SAS code to score new data quickly and easily with models built in JMP.

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Share Your Discoveries

JMP has always been about discovery and finding the best way of communicating those discoveries across your organization. JMP Pro includes all the visual and interactive features of JMP, making your data accessible in ways you might never have experienced. Through dynamically linked data, graphics and statistics, JMP Pro brings your investigation alive in a 3-D plot or an animated graph showing change over time, generating valuable new insights that inform both the model-building and explanation process.

Dow Chemical has adopted JMP Pro for its workforce because decision makers want the best tool available for exploring large data sets and efficiently extracting from them as much information as possible.

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Key Features Exclusive to JMP® Pro

JMP Pro includes all of the features in JMP, plus the additional capabilities for advanced analytics listed below.

Statistics, Predictive Modeling and Data Mining

Classification and regression trees (partition modeling)*
Bootstrap forest, a random-forest technique.
Boosted trees.
Cross-validation.
Contingency analysis
Exact measures of association.
Generalized regression
Normal, binomial, Poisson, zero inflated Poisson, negative binomial, zero inflated negative binomial, Gamma distribution.
Regularization techniques: Ridge, Lasso, adaptive Lasso, Elastic Net, adaptive Elastic Net.
Cross-validation.
Logistic regression (nominal and ordinal)
Cross-validation.
Mixed-effects models
Specify fixed, random and repeated effects.
Correlate groups of variables, set up subject and continuous effects.
Choice of repeated covariance structure.
Model comparison
Neural network modeling*
Automated handling of missing data.
Automatic selection of the number of hidden units using gradient boosting.
Fit both one- and two-layer neural networks.
Automated transformation of input variables.
Three activation functions (Hyperbolic Tangent, Linear, Gaussian).
Save randomly generated cross-validation columns.
Save transformed covariates.
Cross-validation.
One-click bootstrapping
Oneway analysis
Nonparametric exact tests.
Partial least squares (PLS) modeling
PLS models with categorical factors and interactions.
NIPALS-style missing value imputation.
Fit Response Surface model with PLS personality in Fit Model.
Select columns in Variable Importance plot and repeat analysis with just selected columns.
VarScale: center and scale individual variables included in a polynomial effect prior to applying centering and scaling options.
Cross-validation.
Principal component analysis (PCA)
Variable clustering in PCA.
Stepwise regression
Cross-validation.

Consumer and Market Research

Uplift modeling
Incremental, true-lift, net modeling technique.
Cross-validation.

Quality Engineering, Reliability and Six Sigma

Reliability block diagrams
Build models of complex system reliability.
Use basic, serial, parallel, knot, and K out of N nodes to build systems.
Build nested designs using elements from design library.

*Generates SAS code ready for use with SAS Model Manager

System Requirements

JMP Pro runs on Microsoft Windows and Mac OS. It includes support for both 32- and 64-bit systems.

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