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Design of experiment: Analysis by model visualization

Last week, I described model visualization – which is simply applying data visualization to models – and explained why I find it useful.

Designed experiments, especially small DOEs, are a perfect place to practice model visualization. Another term for this could be “analysis by Graph Builder.”

I am not suggesting that you don’t build numerical models and look at parameter estimates, p-values and residuals. However, I am saying, “Plot the data.” I often will plot the data, build the model, revisit the plots and revisit the models. That is, graphics are part of my model-building process.

For example, I was sent the results of a four-factor fractional factorial design by a client. In Graph Builder, I plotted the four main effects and found that not only did factor X_4 have a strong effect, but I could also see something fun at the high level of  X_4. The two groupings of data immediately suggest an interaction.

With the interactivity of JMP, you can highlight the data and quickly figure out that the groupings at the high level of X_4 are most likely due to the level of X_2.

So it took four factors, eight runs, data collection and measurements, followed by a quick analysis in Graph Builder to show that the outcome appears to be driven by X_4, with the impact of X_4 dependent on X_2.

This Graph Builder analysis was all that was needed for this small designed experiment. To me, this is the beauty of DOE – and model visualization.

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Melissa wrote:

Thanks for sharing this example. Can't disagree: plotting the data is the first crucial thing to do before any modeling.

I tend to disagree when you said : so it only took 4 factors, eight runs... For me one crucial step that I guess is implicit here is the brainstorming on the Factors (which + levels...), the outcome of interest... These discussions are for me the most important before building any DOE and it's an interesting stage for us!!

Best