### More Exploratory Data Analysis

I missed two items in my last posting on EDA. First, there's another tool I use: J. Over time, I seem to oscillate between R and J. J, at least when I don't let myself get rusty in expressing ideas in J, is a powerful and concise way of thinking. R has an enormous library collection. I won't advocate for one over the other here, but you should try both.

Second, some tend to think of EDA as model-free statistics, but that's not quite right. To get a better explanation of what that means, see Andrew Gelman's A Bayesian Formulation of Exploratory Data Analysis and Goodness-of-fit Testing.I'd go past what he wrote and note that using models in EDA extends to work in system dynamics. To make sense of a dynamic (time-varying) situation, often trying to craft a model that approximates the situation is a great way to get started in making sense of the situation.

Second, some tend to think of EDA as model-free statistics, but that's not quite right. To get a better explanation of what that means, see Andrew Gelman's A Bayesian Formulation of Exploratory Data Analysis and Goodness-of-fit Testing.I'd go past what he wrote and note that using models in EDA extends to work in system dynamics. To make sense of a dynamic (time-varying) situation, often trying to craft a model that approximates the situation is a great way to get started in making sense of the situation.

Labels: data, mathematics, simulation, statistics, system dynamics

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