John Sall
John Sall RSS
Co-Founder and Executive Vice President, SAS

John Sall leads the JMP business unit at SAS and remains the chief architect of JMP statistical discovery software. JMP has been a part of SAS since the first version of JMP was launched in 1989, bringing interactive data visualization and analysis to the desktop.

Recent Posts

Wide data discriminant analysis

With multivariate methods, you have to do things differently when you have wide (many thousands of columns) data. The Achilles heel for the traditional approaches ... Read More

Handling outliers at scale

In an earlier blog post, we looked at cleaning up dirty data in categories. This time, we look at cleaning dirty data in the form ... Read More

Accessing data at scale from databases

Many JMP users get their data from databases. A few releases ago, we introduced an interactive wizard import dialog to make it easier to import ... Read More

Cleaning categories at scale with Recode

Data entered manually is usually not clean and consistent. Even when data is entered by multiple-choice fields rather than by text-entry fields, it might need ... Read More

Flow and Frontier in JMP 12

Long lists of improvements go into each new version of our software, and usually there are one or two themes that characterize the release. JMP ... Read More

Statistical discovery with JMP at the 25-year point

For you, today is Oct. 4. At JMP, we call it Sept. 34. We had been determined to release the first version of JMP by ... Read More

“The desktop computer is dead” and other myths

The desktop or laptop is now in decline, squeezed from one side by mobile platforms and from the other side by the cloud. As a ... Read More

Big real data is different from big simulated data: Benchmarking

To benchmark computer performance on statistical methods with big data, we can just generate random data and measure performance on that, right? Well, it could ... Read More

It’s not just what you say, but what you don’t say: Informative missing values

Sometimes emptiness is meaningful. If a loan applicant leaves his debt and salary fields empty, don’t you think that emptiness is meaningful? If a job ... Read More

Big Data always has significant differences but not always practical differences: Practical significance and equivalence

When you have millions of observations of real data and do a simple fit across two variables, if you don’t get a significant test, then ... Read More