JMP® Statistical Discovery Software
- Overview
- Data Preparation
- Data visualization
- Quality & Reliability
- Modeling
- Design Of Experiments
- Extensibility
Dynamic data visualization and deep analytics on the desktop
For more than 20 years, JMP statistical discovery software from SAS has been the tool of choice for scientists, engineers, social scientists, market research analysts and other data explorers in almost every industry and government sector.
JMP links powerful statistics with interactive graphics, in memory and on the desktop. It demystifies data, producing visual representations that reveal context and insight impossible to see in a table of numbers.
With JMP, you can be more efficient, tackle difficult statistical problems, communicate findings and bring your data analysis to a whole new level.
JMP offers a seamless interface to the unparalleled richness of SAS. You can also work with your other favorite tools, including Microsoft Excel and R, when you make JMP your analytic hub.
Efficiently import and process data
The most time-consuming part of any analytics process is importing and reshaping data, which can come from a variety of sources. JMP excels at preprocessing, even with large data, and can import data from popular file formats and locations. JMP makes it easy to reshape, recode and regroup data to prepare it for tackling specific analytic questions.
Make compelling data visualizations
Spreadsheets don't easily reveal trends in large data sets. JMP provides rich data visualization tools, making statistical discovery easier and more efficient. In nearly every platform, JMP provides diverse graphical output that lets you convey findings to decision makers with clear, concise and compelling visualizations.
Learn more about data visualization with JMP
Predict product reliability
Preventing failure and improving warranty performance are two of the most important reasons for using proven techniques to fully understand the performance of your products over time. JMP pinpoints defects in materials or processes and finds design vulnerabilities so you can understand how best to correct them.
Learn more about quality improvement with JMP
Learn more about reliability analysis with JMP
Demos
Find the best fit
Statistical models clarify central trends and patterns so you can learn about your business, your competition and your customers. That knowledge empowers you to take the best course of action and more easily grow your business.
Building useful models is part science and part art, and JMP includes an array of statistical platforms that help you build robust models of your data. With methods for revealing relationships among variables in a process, JMP allows you to not only make predictions but also to identify settings for factors that yield optimal performance. JMP includes a variety of different linear and nonlinear ways to fit models, and these diverse fitting tools help you make data-driven decisions.
Use leading-edge tools for design of experiments
Most organizations rely on A/B testing for experimental design, a method for improvement that requires testing one situation against another with many factors in flux. This is a very slow way to learn about your business.
Design of experiments (DOE) offers a practical approach for exploring multifactor opportunity spaces that exist in almost all real-world situations. Using multifactor experiments, you can tease out the effect of an individual factor and hence learn more quickly at minimum cost. JMP offers leading-edge capabilities for optimal design of experiments. JMP also offers analysis in a form you can easily use and includes a rich set of modeling methods.
Provide extensibility through scripting
Buying software that cannot grow past your initial needs leads to early obsolescence and expensive replacements. JMP includes a diverse set of basic and advanced tools that let you customize the software to address unique problems that change as your organization evolves.
Learn more about analytical application development with JMP
Demos: New JMP Features
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