What statistical details do you want documented?

The documentation for JMP must meet the needs of JMP users with a diverse set of backgrounds. The needs of one group of users can differ markedly from the needs of another group. For instance, some users report that there is too much statistical jargon in the documentation, while others [...]

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Top 10 things about JMP 12

JMP 12 arrives next week, and I hope you've had a chance to read the series of posts by JMP developers about what's coming in this new version. I’ve been using JMP 12 during the entire development cycle (about 18 months now), and I am impressed how this version has [...]

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Coming in JMP Pro 12: Variograms in Fit Mixed

In JMP Pro 11, we introduced the Fit Mixed platform for fitting models with a variety of covariance structures and random effects. With JMP Pro 12, we have improved on this platform, with noticeable changes coming to models with spatial covariance structures. These changes are detailed below. Enhanced speed Fitting [...]

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Coming in JMP Pro 12: Interactive model building

The Generalized Regression platform was introduced in JMP Pro 11 for fitting penalized regression models. Our focus for JMP Pro 12 has been to make model building an easy and natural process using the Generalized Regression platform (we like to call it Genreg for short). This post will focus on the [...]

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Coming in JMP Pro 12: Four great features of Covering Arrays

Covering arrays are a powerful design tool that may be used to design test cases to efficiently test deterministic systems. For such systems, a particular input will always generate the same output and, as a result, standard statistical designs are usually inefficient. It turns out that failures in these systems [...]

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JMP Pro for linear mixed models — Part 3

This is the final post in my JMP for Linear Mixed Models series (see my earlier posts: Part 1 and Part 2). Here, I will show an example of spatial regression, followed by some tips for fitting mixed models in JMP Pro.  Example 4: Modeling geospatial data — taking spatial [...]

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JMP Pro for linear mixed models — Part 2

In an earlier blog post, I introduced the new Mixed Model capability in JMP Pro 11 and showed an example of random coefficient models. In this post, I continue my discussion of using mixed models for repeated measures and panel data. I’ll leave modeling geospatial data as well as tips [...]

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JMP Pro for linear mixed models — Part 1

JMP Pro 11 has added a new modeling personality, Mixed Model, to its Fit Model platform. What’s a mixed model? How does JMP Pro fit such a model? What are the key applications where mixed models can be applied? In this and future blog posts, I will try to dispel [...]

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Using Neural platform in JMP Pro for automated creation of validation column

JMP Pro is a great tool for quickly building multiple models with your data using a variety of techniques, namely tree-based methods (Boostrap Forest, Boosted Tree options in the Partition platform), neural networks and penalized regression (using the Generalized Regression personality in Fit Model). When building predictive models, you need [...]

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Is big data a big deal?

Maybe… but messy data is a bigger deal. Big data hit mainstream over the past year or so. I know this because the BBC has produced several programmes covering it. What I’ve heard is that there is no clear definition of what big data is and why it is important. [...]

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