JMP (by SAS) is a statistical discovery software designed for interactive data visualization and exploratory data analysis. Developed by SAS Institute, JMP is widely used in industries such as engineering, manufacturing, life sciences, and scientific research to analyze complex data efficiently.
Key Features of JMP:
- Interactive Data Visualization – Allows users to explore data dynamically using graphs, charts, and scatter plots.
- Statistical Analysis – Offers powerful statistical tools for regression, ANOVA, multivariate analysis, time series forecasting, and more.
- Design of Experiments (DOE) – Provides tools for optimizing experimental designs and process improvements.
- Predictive Modeling & Machine Learning – Supports decision trees, clustering, neural networks, and other ML techniques.
- Quality & Reliability Analysis – Helps in Six Sigma, SPC, and reliability analysis for process and product quality improvement.
- Data Wrangling & Cleaning – Includes tools for handling missing data, transforming variables, and reshaping datasets.
- Scripting & Automation (JSL – JMP Scripting Language) – Enables automation of repetitive tasks and creation of custom analytical workflows.
- Integration with SAS & R – Can connect with SAS software and integrate with R for advanced analytics.
JMP is popular for its intuitive, no-code/low-code approach to statistics, making it accessible to both statisticians and non-statisticians. It is commonly used in R&D, quality control, and academic research.
