Version 2 was twice the size as the original, though it was still delivered on a floppy disk. Interactive graphics and other features were added in 1991 with version 2.0.
Semiconductor manufacturers were also among JMP's early adopters. It was used mostly by scientists and engineers for design of experiments (DOE), quality and productivity support (Six Sigma), and reliability modeling. It originally stood for "John's Macintosh Project" and was first released in October 1989. JMP was developed in the mid- to late-1980s by John Sall and a team of developers to make use of the graphical user interface introduced by the Apple Macintosh. In addition, discoveries made through graphical exploration can lead to a designed experiment that can be both designed and analyzed with JMP. These explorations can also be verified by hypothesis testing, data mining, or other analytic methods.
The software is focused on exploratory visual analytics, where users investigate and explore data. JMP can be automated with its proprietary scripting language, JSL. It formerly included the Graph Builder iPad App. The software can be purchased in any of five configurations: JMP, JMP Pro, JMP Clinical, JMP Genomics and JMP Live. JMP is used in applications such as Six Sigma, quality control, and engineering, design of experiments, as well as for research in science, engineering, and social sciences.
It has since been significantly rewritten and made available also for the Windows operating system. It was launched in 1989 to take advantage of the graphical user interface introduced by the Macintosh operating systems. JMP (pronounced "jump") is a suite of computer programs for statistical analysis developed by the JMP business unit of SAS Institute. What is your favorite new statistical or SAS programming feature? Browse the What's New in SAS 9.3 document and let me know.Statistical package, visualization, multivariate analysis, genomics, biomarkers, clinical For example, the image at the end of this post shows the documentation page for PROC COPULA, and a drop-down menu that enables you to navigate within the "Details" section. The documentation for each procedure now has "tabs" that enable you to navigate directly to sections or subsections of the documentation.
Otherwise, there are new ways to search the documentation. If you know what you're looking for and which book it is in, click Find documentation for a specific product.
I've written a copula algorithm in SAS/IML, so I am looking forward to writing a "couple 'a" blog posts on comparing the two approaches. The COPULA procedure enables you to simulate data from a multivariate distribution that has a given covariance structure.