Interested in JMP Pro predictive analytics software? Here are links to items that Solution Manager Ian Cox covered in his Feb. 17 live predictive modeling webcast. To access some of the items, you will need to complete a registration form or use your SAS profile to log into the site.
In an earlier post, I mentioned a new JMP add-in that can be used to split data prior to predictive modeling. This post deals with how this works and so necessarily touches on the topic of JMP Scripting Language, or JSL for short. This is a big topic, so here
JMP Systems Engineer Sam Gardner presented a webcast on the JMP 9 Neural platform. Revamped for JMP 9, the new Neural platform enhances interactive data mining by offering a richer set of modeling options, improved speed and performance, and calculations that streamline analysis. I thought I'd share Sam's answers to
This fall, we introduced a new member of the JMP family: JMP Pro. For this first version of JMP Pro, the main intention was to start to make predictive (as opposed to exploratory) modeling more accessible to those who are drawn to the JMP style of working. Indeed, JMP Pro
Jeff Perkinson, JMP Product Manager, is putting the final touches on his live webcast for next Tuesday, when he will demonstrate the new analytic and graphical capabilities in JMP 9. That's the same day -- Oct. 12 -- that JMP 9 starts shipping. The one-hour demo will show JMP users
This is the first in a series of Q&As with the JMP development team as we approach the release of JMP 9 on Oct. 12. Unless you go to events such as the Discovery Summit where attendees get to meet JMP developers in person, you probably know little about the
Data mining is looking for patterns and relationships in (sometimes large volumes of) data. Many methods, such as recursive partitioning and neural nets, are extremely sensitive to the sample of data being mined. How do you know if you are creating a model that would be useful for predicting future
Often we assume there is a complicated relationship between explanatory variables and responses. In these cases, neural networks (neural nets) are useful and can enable us to predict responses from a flexible network of functions of input variables. Neural nets can efficiently and flexibly model different response surfaces when it