Jmp | Version History

Features continue to expand on the automated discovery and visualization capabilities seen in New in JMP 19 JMP 18 (2024):

Released during COVID-19 remote work, JMP 16 focused on collaboration and workflow documentation. The biggest star was .

file management tool and the ability to use custom images as markers on graphs. JMP 13 (2016):

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The most recent versions have focused on making advanced analytics more accessible to a wider audience through workflow automation, deeper integration with other tools, and new product offerings.

Throughout its version history, JMP has evolved from a single product into a family of specialized tools: The standard version for interactive data analysis.

Control Chart Builder, exact statistics, and advanced reliability modeling. Features continue to expand on the automated discovery

JMP (pronounced "jump") has established itself as a premier statistical discovery software since its inception by SAS Institute. Designed to dynamically link statistics with graphics, JMP allows scientists, engineers, and data analysts to explore data visually and interactively.

These versions focused on refinement. JMP 11 brought improvements to reliability and formula editing. JMP 12 introduced the Functional Data Explorer , a sophisticated tool for analyzing data that varies over a continuum (like temperature over time), pushing JMP into advanced data science territory.

Stepwise regression, hierarchical clustering, and specialized Six Sigma quality tools like capability analysis. JMP 6 (2005) Focus Area: Automation and enterprise integration. JMP 13 (2016): This public link is valid

In 2012, JMP 10.0 was launched, bringing a new module called JMP Clinical. This module provided specialized tools for clinical trial data analysis, including features like data visualization, patient profiles, and efficacy and safety analysis.

From its 1989 debut on Mac floppy disks to its current status as a multi-platform powerhouse, JMP's history is one of continuous innovation. Its evolution reflects the broader shifts in data analysis—from GUI-based visualization to scripting, advanced modeling, big data, and now, collaboration and multi-language support. With JMP 19 at the forefront, the "John's Macintosh Project" continues to empower users to make impactful discoveries.

For engineers, scientists, and analysts who value discovering patterns visually rather than memorizing code, JMP is the gold standard. But how did we get here? This article traces the complete version history of JMP, from a Macintosh-only novelty to a cross-platform data science workhorse.