Today’s Oxford English Dictionary “Word of the Day” is quod erat demonstrandum. (I’m not sure if this link will stop working at the end of the day.) Apparently it took some time to find the right translation of Euclid’s Greek ὅπερ ἔδει δεῖξαι. Perhaps my art department, which knows ancient Greek, has something to say.
Via Emanuel Kowalski.
From quora, which planet spends the greatest share of time closest to Earth? (Hint: it’s not Pluto.)
From the math graduate students at Carnegie Mellon, slides for the convergence of a random walk on slides to a presentation and video of the talk based on these slides.
A few talks from the MIT Sloan sports analytics conference:
(Unfortunately these videos were filmed with a single camera on the speaker, and some of them refer to visuals that can’t be seen. They still make sense, though.
Stephanie Coontz on how averages can be misleading – in this case when averaging people’s experiences.
findthebest.com offers unbiased, data-driven comparison of lots of things. (But despite being in Santa Barbara they don’t seem to have enough information to find the best Santa Barbara wine! I suppose I’ll just have to go down there and drink more.)
John Baez on (the number) 42.
The always excellent DataGenetics asks how far can you overhang blocks?
Keith Devlin has some remarks (from his course “Introduction to Mathematical Thinking”) on the brilliance of calculus.
An oldie but goodie: Diaconis and Efron, 1983, “Computer Intensive Methods in Statistics” on the bootstrap and other advances in theoretical statistics made possible by the computer. I was particularly struck by a claim that a computation of a bootstrapped confidence interval for a correlation coefficient took less than a second and cost less than a dollar. That seems like a fantastic deal if the alternative is hand computation…
(also, this was written for Scientific American! They’ve gone downhill…)
Colm Mulcahy on the perennial student question, what do I have to do to get an A in this class?
A puzzle about balls from urns from MathOverflow and Gil Kalai’s blog post on the same problem.
The Eurovision final predicted with Bayesian statistics.
I’m in London, where according to the Guardian we’re supposed to get half a month’s rain today.
Sounds scary, doesn’t it? That makes it sound like it’s going to rain fifteen times what it does on a typical rainy day; of course it means that we’re supposed to get fifteen times what London gets on a typical day, including the days when it doesn’t rain.
And in fact the forecast calls for only an inch. (Is that a lot for a single event here? I don’t know – my usual sources for weather data are US-centric.) Certainly a lot, but not apocalyptic.
And since rain usually comes in storms, it stands to reason that the single biggest rain event in any given month would be a large portion of the rain for the whole month. I’d wager that in a typical month, the biggest rain event is at least a quarter of the total rain for the month.
(Incidentally, there are bookmakers everywhere here. Can I actually bet on the weather?)
Every odd integer greater than 5 is the sum of three primes, says Harald Helfgott. And there are infinitely many prime gaps less than seventy million, says Yitang Zhang. (As Dan Goldston quips in this blog post from Nature, this is within a factor of thirty-five million of the target.
