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.
Do More Faster
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?
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.
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.
Compare Multiple Modeling Techniques
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.
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.
Connect to the Richness of SAS®
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.
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.
- 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.
- Logistic regression (nominal and ordinal)
- 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.
- 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.
- Principal component analysis (PCA)
- Variable clustering in PCA.
- Stepwise regression
Consumer and Market Research
- Uplift modeling
- Incremental, true-lift, net modeling technique.
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
JMP Pro runs on Microsoft Windows and Mac OS. It includes support for both 32- and 64-bit systems.