A long, long time ago, I was a new statistics PhD student attending my first conference. To say I was intimidated is putting it mildly: Top researchers in experimental design and analysis from around the globe were scheduled to be at this conference. How would I be able to talk
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In my previous blog entry, I talked about my frustrations in making good-looking hard-boiled eggs that were easy to peel. My Internet searches found a number of different techniques that cooks said were essential to success, but I wanted to know which techniques were best. So I set up a
A typical scene in my kitchen: I make a batch of hard-boiled eggs with the hope of an easy peel and a beautifully cooked center. But when I sit down to enjoy my egg, I find that, sadly, it’s not so easy to peel – or I have discoloration around the
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).
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 my favorite new features in JMP 11 design of experiments is the Fast Flexible Filling (FFF) design in the Space Filling Design platform. When the JMP 11 Previews were released, Brad Jones showed an example of using FFF designs to place air quality monitors over the state of
In celebration of the International Year of Statistics, the final statistician we celebrate is Karl Pearson. His work in the late 19th and early 20th centuries laid the structure of mathematical statistics. Born March 27, 1857, in London, England, Pearson was raised in an upper-middle class family. He studied mathematics