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Archive of posts filed under the Statistical graphics category.

Go to PredictWise for forecast probabilities of events in the news

I like it. Clear, transparent, no mumbo jumbo about their secret sauce. But . . . what’s with the hyper-precision: C’mon. “27.4%”? Who are you kidding?? (See here for explication of this point.)

“For better or for worse, academics are fascinated by academic rankings . . .”

I was asked to comment on a forthcoming article, “Statistical Modeling of Citation Exchange Among Statistics Journals,” by Christiano Varin, Manuela Cattelan and David Firth. Here’s what I wrote: For better or for worse, academics are fascinated by academic rankings, perhaps because most of us reached our present positions through a series of tournaments, starting […]

“Another bad chart for you to criticize”

Perhaps in response to my lament, “People used to send me ugly graphs, now I get these things,” Stuart Buck sends me an email with the above comment and a link to this “Graphic of the day” produced by some uncredited designer at Thomson Reuters: From a statistical perspective, this graph is a disaster in […]

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 […]