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Archive of posts filed under the Statistical graphics category.

mysterious shiny things

(Disclaimer: I’m new to Shiny, and blog posts, but I know something about geography.)  In the Shiny gallery, take a look at 2001 versus 2002. Something funny happens to Switzerland (and other European countries), in terms of the legend, it moves from Europe to the Middle East. Also, the legend color scheme switches.     […]

One of the worst infographics ever, but people don’t care?

This post is by Phil Price. Perhaps prompted by the ALS Ice Bucket Challenge, this infographic has been making the rounds: I think this is one of the worst I have ever seen. I don’t know where it came from, so I can’t give credit/blame where it’s due. Let’s put aside the numbers themselves – […]

My courses this fall at Columbia

Stat 6103, Bayesian Data Analysis, TuTh 1-2:30 in room 428 Pupin Hall: We’ll be going through the book, section by section. Follow the link to see slides and lecture notes from when I taught this course a couple years ago. This course has a serious workload: each week we have three homework problems, one theoretical, […]

NFL players keep getting bigger and bigger

Aleks points us to this beautiful dynamic graph by Noah Veltman showing the heights and weights of NFL players over time. The color is pretty but I think I’d prefer something simpler, just one dot per player (with some jittering to handle the discrete reporting of heights and weights). In any case, it’s a great […]

Stan World Cup update

The other day I fit a simple model to estimate team abilities from World Cup outcomes. I fit the model to the signed square roots of the score differentials, using the square root on the theory that when the game is less close, it becomes more variable. 0. Background As you might recall, the estimated […]

Stan goes to the World Cup

I thought it would be fun to fit a simple model in Stan to estimate the abilities of the teams in the World Cup, then I could post everything here on the blog, the whole story of the analysis from beginning to end, showing the results of spending a couple hours on a data analysis. […]

Visualizing sampling error and dynamic graphics

Robert Grant writes: What do you think of this visualisation from the NYT [in an article by Neil Irwin and Kevin Quealy but I’m not sure if they’re the designers of the visualization]? I’m pretty impressed as a method of showing sampling error to a general audience! I agree. P.S. In related news, Antony Unwin […]

Avoiding false parallelism in a graph

“False parallelism”—feel free to come up with a better term here—is when a graph has repeating elements that do not correspond to repeating structure in the underlying topic being graphed. An example appears in the above graphs from Dan Kahan. The content of the graphs is fine (and, more generally, I think he’s making an […]

What’s the algorithm, Kenneth?

I can’t figure out what’s the deal with the bars for Corners. The bar labeled “7” is much less than 7 times the bar labeled “1.” At first I was guessing that maybe they’re not counting the numbered part in the bar width (which would be a pretty weird choice) but that wouldn’t work for […]

Average predictive comparisons in R: David Chudzicki writes a package!

Here it is: An R Package for Understanding Arbitrary Complex Models As complex models become widely used, it’s more important than ever to have ways of understanding them. Even when a model is built primarily for prediction (rather than primarily as an aid to understanding), we still need to know what it’s telling us. For […]