A risk analyst uses on 1 million customer service tickets. By extracting latent topics, they discover that a specific phrase ("API timeout") correlates with high churn risk. They export the topic probabilities back into the data table and run a Logistic Regression (Pro’s Generalized Regression) with elastic net regularization to build a high-accuracy churn prediction model.
For creating complex, non-linear predictive models.
Advanced machine learning algorithms for forecasting.
While standard JMP focuses on exploratory data analysis (EDA) and foundational statistics, JMP 17 Pro is built specifically for advanced analytics and predictive modeling. The "Pro" designation introduces algorithms capable of handling complex data structures, missing values, and high-dimensional problem spaces without requiring users to write extensive code. Core Distinctions jmp 17 pro
The foundation of any analysis is data preparation. JMP 17 Pro reduces the time spent cleaning data through automated operations:
Introduced in recent versions, SEM receives a major overhaul in JMP 17 Pro. Analysts can now model complex webs of interrelated variables, including latent constructs (variables that cannot be measured directly, like customer satisfaction or employee morale). Version 17 introduces shortcuts for specifying models, faster optimization loops, and clearer path diagrams. 4. Workflow Builder: Code-Free Automation
JMP 17 Pro and the base JMP 17 release brought significant updates focused on automation, ease of use, and advanced modeling. JMP User Community A risk analyst uses on 1 million customer service tickets
JMP 17 Pro elevates its predictive modeling toolkit with significant updates to its core algorithms.
Since its inception in 1989, JMP has established itself as a premier tool for statistical discovery, favored for its dynamic linking between data tables and visualization graphs. JMP Pro represents the "professional" tier of the software, offering advanced techniques for predictive modeling, machine learning, and reliability engineering that extend beyond the standard offering.
A superior approach to standard regression that handles multi-collinearity and selects variables automatically. For creating complex, non-linear predictive models
Leveraging JMP 17 Pro for Advanced Data Analysis: A Paradigm Shift in Statistical Discovery
The interactive Profiler remains JMP’s flagship feature for understanding model behavior. In JMP 17, the Profiler has been optimized for speed. For high-dimensional models, the responsiveness of the profiler sliders has improved significantly, allowing for real-time "what-if" scenario planning without the computational lag associated with previous versions.
JMP 17 Pro includes an array of advanced machine learning algorithms:
A critical enhancement is the handling of in process capability analysis. Previously, ignoring these limits could lead to misleading results. Now, the Distribution platform in JMP 17 Pro recognizes the "Detection Limits" column property, allowing the fitter to adjust for censored data. This means that process capability reports, generated from fitted distributions like Normal, Log Normal, or Weibull, will provide significantly more accurate results when data is impacted by detection thresholds.
was designed specifically to minimize obstacles in that process, allowing users to focus more on what the data is saying and less on the mechanics of the software. Key Breakthroughs in JMP 17 Pro