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
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
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
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
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
JMP Pro is predictive analytics software designed 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
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.
Bootstrapping is a popular resampling method for estimating the sampling distribution of a statistic. While the theory behind resampling methods dates back to Sir R.A. Fisher, bootstrap resampling was first proposed by Bradley Efron in the 1970s. Bootstrapping involves repeatedly sampling from a data set, with replacement, in order to
Over this past holiday season at a gathering, my family and I thought it might be fun to do a wine tasting. Some members wanted to just open different bottles of wines and talk about ones that we liked while catching up and having a relaxing Saturday. But I suggested
Understanding your customers has always been important, but it's even more so when marketing products and services related to winter snow sports. Winter sports can have a short season and a limited number of consumers. How do you find them, identify their needs, create new promotions and assess whether the