JMP has always been a leader in DOE, and version 17 introduced new platforms that empower both standard and Pro users.
: Automated decision-tree building in the Partition platform and cross-validated stepwise regression.
: Preprocessing spectral and raw sensor data is drastically easier with native tools like Standard Normal Variate (SNV) and Savitzky-Golay derivatives built right into the platform.
The Generalized Regression personality in the Fit Model platform remains a crown jewel of JMP Pro. In version 17, it handles complex covariance structures better, making it easier to model data with repeated measures or spatial correlations.
For researchers in the social sciences, market research, and psychology, JMP Pro 17 introduced significant enhancements to its Structural Equation Modeling (SEM) platform. These are invaluable for designing and validating surveys and questionnaires. Key updates include: jmp 17 pro
: A dedicated tool to compare multiple candidate models side-by-side using statistics like cap R squared , misclassification rates, and ROC curves. Formula Depot
For quality engineers, JMP 17 brings substantial improvements to its SPC platforms. The Control Chart Builder received several new features, including a label role, a row legend, and a button to quickly switch between chart types, such as from an XBar/R chart to an IMR chart.
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Combines multiple decision trees for superior prediction accuracy. JMP has always been a leader in DOE,
In modern engineering and scientific applications, data often arrives as continuous curves, profiles, or time-series streams rather than single data points. The Functional Data Explorer (FDE) in JMP 17 Pro converts these complex curves into functional principal components. These components can then be used directly in downstream Design of Experiments (DoE) or predictive models. Structural Equation Modeling (SEM)
When you manipulate data in JMP Pro, the software immediately updates all linked graphs, histograms, and data tables. Clicking a specific data point in a scatter plot automatically highlights that exact observation across every other open window and data table. This instant feedback loop allows analysts to spot outliers, detect patterns, and form hypotheses rapidly.
0;449;: Includes Generalized Regression enhancements like SVEM (Self-Validated Ensemble Models) for small data or mixture experiments.
This feature helps predict product lifespans under normal conditions by analyzing failure data obtained under high-stress conditions (e.g., elevated temperature or humidity). Key Enhancements in Version 17 The Generalized Regression personality in the Fit Model
For advanced users, JMP 17 Pro features deeper support for and split-plot designs. The platform allows for the optimization of multiple responses simultaneously using interactive desirability profilers, ensuring that users find the exact "sweet spot" in manufacturing processes or product formulations. Advanced Predictive Modeling and Machine Learning
Data has become the most valuable currency in the modern industrial and scientific landscape. However, raw data is useless without the tools to analyze, visualize, and interpret it. For scientists, engineers, and data analysts, SAS Institute’s JMP software has long been the gold standard for statistical discovery.
The release of version 17 brings a wealth of productivity tools and statistical methods designed to handle larger datasets and more complex problem-solving scenarios. 1. Enhanced Predictive Modeling and Machine Learning