In an earlier post, I introduced the Probability and Multiple Choice Profiler, two new tools in the Choice Platform that help visualize comparisons between competing products and predict market share for proposed new products. This post covers step-by-step instructions for how to open and use the profilers in JMP 12.
Tag: Modeling
In my previous blog post, I created a 150-run space filling design to collect travel times over various departure times in the morning and evening. I wanted to see if I use this designed experiment to learn something useful about my commute. Google Maps gives a range of times for
In JMP Pro 11, we introduced the Fit Mixed platform for fitting models with a variety of covariance structures and random effects. With JMP Pro 12, we have improved on this platform, with noticeable changes coming to models with spatial covariance structures. These changes are detailed below. Enhanced speed Fitting
Scientists and engineers often need to find the best settings or operating conditions for their processes or products to maximise yield or performance. I will show you how the optimisation capabilities in JMP can help you work out the best settings to use. Somewhat surprisingly, the particular settings that are
The JMP Profiler is a powerful tool for visualizing your model. With one click, you can see what the model predicts when you change a product’s features or adjust one of your assumptions. It’s also a powerful communication tool. Your audience doesn’t need a statistics background to understand the model’s
The Prediction Profiler, or simply Profiler, allows you to explore cross sections of predicted responses across multiple factors. It gives you a wealth of information about your model, and in JMP 12, you can export it to interactive HTML pages to share with others who do not have JMP. The Profiler
We have two upcoming webcasts on Building Better Models presented at times convenient for a UK audience: If you are new to JMP Pro, you will want to view the webcast on 21 October 2014. If you are already using JMP Pro, the webcast on 31 October 2014 will suit
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
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