Alias Optimal versus D-optimal designs

In my previous blog entry, I discussed the purpose of the Alias Matrix in quantifying the potential bias in estimated effects due to the alias terms. In this blog post, we look at an example that creates a D-optimal design and an Alias Optimal design with the same number of […]

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What is an Alias Matrix?

When I create a design, the first place I typically look to evaluate the design is the Color Map On Correlations. Hopefully, I see a lot of blue, implying orthogonality between different terms. The Color Map On Correlations contains both the model terms and alias terms. If you have taken […]

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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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Ruining a perfectly good family gathering with mixed models

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 […]

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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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