Statistical Software

For Chemical Engineers and Scientists

Engineers and scientists working in the chemical industry must quickly and efficiently gain understanding from their data.  JMP data analysis software from SAS empowers users to create optimal experimental designs, understand sources of process variation, perform root-cause analysis and perform advanced data mining techniques all without having to write a line of code.

 

Download Free Trial Now

Have a minute? See JMP in action.

Dow Chemical

"We use JMP to help us in planning our experiments in an efficient and effective way. It’s being used throughout the entire scientific method process. We use it to help us to develop strategic data collection plans. A lot of the data that is necessary to make those decisions doesn’t exist yet. So we use JMP to help us in planning our experiments in an efficient and effective way, all the way through the modeling of the resulting data.”

Jeff Sweeney | Dow Senior Research Statistician

JMP Capabilities for Chemical Engineers and Scientists


  • Design of Experiments

    Utilize mixture, classical or custom designs and analysis to learn better and faster, getting more information out of each experimental run.

  • Statistical Process Control and Process Capability

    Monitor the stability of your process and look for changes in variability.  Also determine how capable your process is at meeting your spec limits. 

  • Data Visualization and Cleaning

    Easily explore your data with drag-and-drop graphing, updating graphs utilizing filters and column switcher.

  • Data Mining and Predictive Modeling 

    Look through historical data to better understand key drivers of your response using statistical techniques such as regression, PLS, PCA, and decision tree analysis.

  • Process Optimization

    Find the ideal process settings in the design to get ideal results.

  • Monte Carlo Simulation

    Determine how robust your process is to variation utilizing a statistical model with simulation and finding a region with minimal sensitivity to variation.


Back to Top