
Minitab Statistical Software
The global standard for Six Sigma and quality analytics powered by automated machine learning.

The gold standard in visual data discovery and interactive statistical modeling for R&D and manufacturing.

JMP, a subsidiary of SAS Institute, is a high-performance statistical analysis suite designed specifically for scientists, engineers, and researchers. Unlike code-heavy environments, JMP emphasizes 'visual data discovery,' utilizing an in-memory architecture that allows users to interactively explore data and instantly see the statistical results through dynamic graphics. As of 2026, JMP's market position is fortified by its specialized 'Industrial AI' capabilities, blending traditional Design of Experiments (DOE) with modern machine learning algorithms. Its technical architecture supports deep integration with Python, R, and SAS, enabling hybrid workflows where users leverage JMP's superior GUI for exploration while executing complex scripts in the background. The software is a staple in semiconductor manufacturing, pharmaceutical R&D, and aerospace engineering due to its robust handling of high-dimensional data and its 'Profiler' tool, which provides a multi-variate interactive simulation of model outputs. JMP remains a critical tool for organizations requiring high-reliability analytics where the interpretability of models is as vital as predictive accuracy.
JMP, a subsidiary of SAS Institute, is a high-performance statistical analysis suite designed specifically for scientists, engineers, and researchers.
Explore all tools that specialize in predictive modeling. This domain focus ensures JMP delivers optimized results for this specific requirement.
Explore all tools that specialize in visualize data. This domain focus ensures JMP delivers optimized results for this specific requirement.
Explore all tools that specialize in build predictive models. This domain focus ensures JMP delivers optimized results for this specific requirement.
An interactive environment that allows users to manipulate input factors in real-time to observe the effect on the predicted response using cross-sections of the model space.
A proprietary object-oriented language that allows for complete automation of data processing, analysis, and custom GUI creation.
Includes Definitive Screening Designs (DSDs) and Custom Designers that optimize for specific user constraints and cost functions.
A dynamic drag-and-drop canvas for data visualization that automatically suggests appropriate chart types based on variable metadata.
Directly embed Python scripts within JMP, allowing for seamless data transfer between JMP Data Tables and Pandas DataFrames.
Uses Latent Class Analysis and Term Frequency-Inverse Document Frequency (TF-IDF) to convert unstructured text into numeric data for modeling.
A visual interface for specifying and estimating complex causal relationships among latent variables.
Download and install the JMP executable from the official SAS/JMP portal.
Activate the license via the Serial Number or Site ID provided in the fulfillment email.
Configure Python and R paths in the JMP Preferences to enable external script execution.
Import data through the File Open dialog or the SQL Query Builder for database connections.
Clean and prepare data using the 'Columns' menu and 'Missing Value Imputation' tools.
Launch 'Graph Builder' to perform initial exploratory data analysis through drag-and-drop visuals.
Utilize 'Fit Model' to specify response variables and model effects for regression or ANOVA.
Apply the 'Prediction Profiler' to visualize the impact of individual factors on the target outcome.
Save the analysis as a JMP Script (JSL) to automate future runs with new data.
Export the final interactive report to a web-ready format for stakeholder review.
All Set
Ready to go
Verified feedback from other users.
"Users praise the 'Graph Builder' and 'Profiler' for transforming their ability to explain complex data to management, though some find the scripting language (JSL) to have a steep learning curve."
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The global standard for Six Sigma and quality analytics powered by automated machine learning.

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