Tag: Modeling

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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 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

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What-if analysis with JMP (no spreadsheets) -- Part 5

Last week, I showed how the Excel Add-In for JMP can bring more value to Excel spreadsheets for what-if analysis and optimization. Today, we’ll look at how using that same data from within JMP alone is more elegant. First, let’s look at the Excel spreadsheet from last week's post (see

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Get sample chapter of Numbersense by Kaiser Fung

Regular readers of this blog know that we are big fans of Kaiser Fung, his blog Junk Charts and his books Numbers Rule Your World and Numbersense. Earlier this fall, I interviewed him about Numbersense, and we gave away copies of that book. If you didn't get a copy of

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Simple nonlinear least squares curve fitting in JMP

In his Walking Randomly blog, Mike Croucher shows how to fit a simple nonlinear curve using five different statistical programming libraries: R, MATLAB, Maple, Julia and Python/numpy. The idea is to provide concrete examples for a commonly asked modeling question that is simple to state but not so simple to

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Best teams for college bowl attendance & TV ratings

For college football, the regular season is coming to an end, and in a few days we’ll know which teams are going to which end-of-season bowl games. Though some bowl assignments are determined by formula, each bowl often has a choice of several teams to invite to their game. Some

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Why a penalty is good in generalized regression

A penalty can seriously ruin your day. Forget to pay a bill on time, and a late penalty will cost you a few more dollars. When a yellow flag hits the football field, a penalty can cost your favorite team field position, momentum and maybe even some points. But a penalty

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Bart Baesens on theory and application of analytics

Having completed co-chairing a successful Analytics 2013 conference in London last month, Bart Baesens, professor and author, offers his insights to give JMP Blog readers a sample of some of the topics he will address in this month’s installment of Analytically Speaking: Conversations with Thought Leaders webcasts. Question: You teach both university students

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