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Archive of posts filed under the Statistical graphics category.

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 […]

Can we make better graphs of global temperature history?

Chris Gittins sends along this post by Gavin Schmidt, who writes: Some editors at Wikipedia have made an attempt to produce a complete record for the Phanerozoic: But these collations are imperfect in many ways. On the last figure the time axis is a rather confusing mix of linear segments and logarithmic scaling, there is […]

Small multiples of lineplots > maps (ok, not always, but yes in this case)

Kaiser Fung shares this graph from Ritchie King: Kaiser writes: What they did right: – Did not put the data on a map – Ordered the countries by the most recent data point rather than alphabetically – Scale labels are found only on outer edge of the chart area, rather than one set per panel […]

Understanding Simpson’s paradox using a graph

Joshua Vogelstein pointed me to this post by Michael Nielsen on how to teach Simpson’s paradox. I don’t know if Nielsen (and others) are aware that people have developed some snappy graphical methods for displaying Simpson’s paradox (and, more generally, aggregation issues). We do some this in our Red State Blue State book, but before […]