SAS JMP Statistical Discovery V18

SAS JMP has released JMP Statistical Discovery Pro 18.0 is the data analysis tool of choice that scientists and engineers rely on for powerful analytic capabilities and dynamic statistical discovery.

SAS JMP is a statistical software environment that enables scientists, engineers, and business analysts to make discoveries through data exploration. One powerful method for beginning the process of discovery employs statistically designed experiments. A well-designed experiment ensures that the resulting data have large information content. Whether your results come from designed experiments or from an observational study, software provide analytical tools that put graphs up front. JMP's graphical user interface (GUI) makes these plots interactive and dynamically linked to each other and to the data. Moreover, in the design of JMP's user interface the priority was to make a smooth and natural workflow for data analysis.

JMP 18 and JMP Pro 18 released in Spring 2024, bringing a host of new features useful to academics, whether for teaching or research. This webinar provides an overview of new features before demonstrating several in detail, including an improved new user experience, better data management, reusable graph and report templates, Python integration improvements, and deep learning via the Torch library.
JMP is a business unit of SAS that produces interactive software for desktop statistical discovery. John Sall, SAS co-founder and Executive Vice President, created this dynamic software and remains its chief architect and leader of the JMP division. The SAS commitment to accuracy, value and quality is evident in every JMP software release. Training, technical support, licensing and distribution are provided to JMP users the world over through SAS. Teams specializing in JMP products are located in the United States, Europe, China and Japan.

Tackle even more complex and challenging analytic problems using the latest data science techniques in JMP Pro, including advanced predictive modeling and machine learning with extended analytic capabilities.
- New methods for Direct FPCA in FDE, including Multivariate Curve Resolution and Penalized SVD. Peak detection is also available for chromatography and spectrometry profiles.
- Enhanced GLMM is now more full-featured, supporting additional distributions and covariance structures.
- More Genomics capabilities, a new marker relatedness platform, and improvements to marker statistics, marker simulation, and multivariate embedding (UMAP/t-SNE) platforms.
- Models that include random effects can now be specified in Fit Response Screening.
- Enhancements to SEM including new model shortcuts, platform-wide bootstrapping, and robust inference.
Improved Python Integration
For data analysts, scientists, and engineers using Python, the new JMP integration simplifies the programming to keep the focus on the analysis. JMP 18 includes a single, supported version of Python, an extensive set of standard Python libraries, a more robust connection between JMP and Python data structures, and an improved Python coding experience within JMP.
- Run the integrated Python version in JMP using the new Python editor for a simple but familiar Python programming experience.
- Access to JMP data tables in memory from Python directly; easy access to Python data from JMP.
- Run JSL from Python.
- Continue using JMP with other software, such as SAS, R, and MATLAB, using openly available Python packages.