Teaching with JMP, part 2

After writing the post on Teaching statistics with JMP last month, I didn’t think about a follow-on post since we had so many wonderful comments. But when we heard from Roger Hoerl at Union College about the thesis his student, Keilah Creedon, wrote (using JMP for the designed experiment part), […]

Post a Comment

Mmm cookies: a tale of discrete numeric variables, disallowed combinations and alias optimality

When I was in graduate school, one of my hobbies was to bake cookies for the department. For one of the basic chocolate chip cookie recipes, it wasn’t uncommon to switch the chocolate chips with another ingredient that was on sale that week (I was a grad student, after all). […]

Post a Comment

Fun and effective: Teaching statistics with JMP

JMP has a growing fan club of people who are passionate about the software as a great teaching tool to more easily convey statistical concepts. Colleagues on our global academic team and I pooled some comments from noteworthy educators about why they like teaching with JMP.   “In the early […]

Post a Comment

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 […]

Post a Comment

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 […]

Post a Comment

Determining chemical concentration with standard addition: An application of linear regression in JMP

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 […]

Post a Comment

See optimal settings with JMP Pareto Efficient Frontier

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 […]

Post a Comment

Coming in July: Book on centralized monitoring of clinical trials

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 […]

Post a Comment

Celebrating Improbability with David Hand

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 […]

Post a Comment

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 […]

Post a Comment