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
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
One of the most common tasks in chemistry is to determine the concentration of a chemical in an aqueous solution (i.e., the chemical is dissolved in water, with other chemicals possibly in the solution). A common way to accomplish this task is to create a calibration curve by measuring the
Pareto Efficient Frontier (PEF) is becoming an increasingly popular tool for measuring and selecting project or design parameters that will yield the highest value at the lowest risk. PEF is being used widely in many industrial areas, such as when selecting the best exploration projects in oil and gas, finding
In the spirit of shameless self-promotion full disclosure with the goal of collecting huge royalty checks promoting the efficient review of clinical trials, I’d like to make everyone aware of the forthcoming SAS Press title Risk-Based Monitoring and Fraud Detection in Clinical Trials Using JMP and SAS. Clinical trials are
Earlier this year, we were treated to spending some time with David J. Hand, Senior Research Investigator and Emeritus Professor of Mathematics at the Imperial College of London, and Chief Scientific Advisor at Winton Capital Management. David’s most recent book, The Improbability Principle: Why Coincidences, Miracles, and Rare Events Happen
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