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Archive of posts filed under the Statistical graphics category.

The publication of one of my pet ideas: Simulation-efficient shortest probability intervals

In a paper to appear in Statistics and Computing, Ying Liu, Tian Zheng, and I write: Bayesian highest posterior density (HPD) intervals can be estimated directly from simulations via empirical shortest intervals. Unfortunately, these can be noisy (that is, have a high Monte Carlo error). We derive an optimal weighting strategy using bootstrap and quadratic […]

Interactive demonstrations for linear and Gaussian process regressions

Here’s a cool interactive demo of linear regression where you can grab the data points, move them around, and see the fitted regression line changing. There are various such apps around, but this one is particularly clean: (I’d like to credit the creator but I can’t find any attribution at the link, except that it’s […]

Defaults, once set, are hard to change.

So. Farewell then Rainbow color scheme. You reigned in Matlab Far too long. But now that You are no longer The default, Will we miss you? We can only Visualize. E. T. Thribb (17 1/2) Here’s the background.  Brad Stiritz writes: I know you’re a creator and big proponent of open-source tools. Given your strong interest […]

Introducing shinyStan

As a project for Andrew’s Statistical Communication and Graphics graduate course at Columbia, a few of us (Michael Andreae, Yuanjun Gao, Dongying Song, and I) had the goal of giving RStan’s print and plot functions a makeover. We ended up getting a bit carried away and instead we designed a graphical user interface for interactively exploring virtually […]

The axes are labeled but I don’t know what the dots represent.

John Sukup writes: I came across a chart recently posted by Boston Consulting Group on LinkedIn and wondered what your take on it was. To me, it seems to fall into the “suspicious” category but thought you may have a different opinion. I replied that this one baffles me cos I don’t know what the […]

Another example of why centering predictors can be good idea

Andrew Dolman writes: Just in case you need another example of why it is important to consider what the intercepts in a model represent, here is a short comment I [Dolman] just got published correcting a misinterpretation of a simple linear model, that would not have happened if they had centered their predictor around a […]

Six quick tips to improve your regression modeling

It’s Appendix A of ARM: A.1. Fit many models Think of a series of models, starting with the too-simple and continuing through to the hopelessly messy. Generally it’s a good idea to start simple. Or start complex if you’d like, but prepare to quickly drop things out and move to the simpler model to help […]

I like the clever way they tell the story. It’s a straightforward series of graphs but the reader has to figure out where to click and what to do, which makes the experience feel more like a voyage of discovery.

Jonathan Falk asks what I think of this animated slideshow by Matthew Klein on “How Americans Die”: Please click on the above to see the actual slideshow, as this static image does not do it justice. What do I think? Here was my reaction: It is good, but I was thrown off by the very […]

50 shades of gray goes pie-chart

Rogier Kievit sends in this under the heading, “Worst graph of the year . . . horribly unclear . . . Even the report doesn’t have a legend!”: My reply: It’s horrible but I still think the black-and-white Stroop test remains the worst visual display of all time: What’s particularly amusing about the Stroop image […]

I love it when I can respond to a question with a single link

Shira writes: This came up from trying to help a colleague of mine at Human Rights Watch. He has several completely observed variables X, and a variable with 29% missing, Y. He wants a histogram (and other descriptive statistics) of a “filled in” Y. He can regress Y on X, and impute missing Y’s from […]