Posts written by Thomas Lumley (1299)

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Thomas Lumley (@tslumley) is Professor of Biostatistics at the University of Auckland. His research interests include semiparametric models, survey sampling, statistical computing, foundations of statistics, and whatever methodological problems his medical collaborators come up with. He also blogs at Biased and Inefficient

October 14, 2014

Does it make any more sense this time?

From the Herald today

“The average annual weekly wage increase of $28.06 was not enough to offset a $30,000 increase in the national median house price and an increase in the average mortgage interest rate from 5.52% to 5.86%,” the survey found.

We did this one last time, in June. Today’s story is better in that it links to the Massey report. It could still do with a bit of interpretation.

Quick, without a calculator, roughly what would be a large enough weekly wage increase to offset a $30,000 increase in the national median house price?  Would we need to up the $28.06 by ten percent, or  ten dollars, or a factor of ten?

[Update: I should also note that the word "weekly" wasn't in the description of wage increases last time, so this is a definite improvement]

Ada Lovelace Day

October 14 is Ada Lovelace Day, an international celebration of the achievements of women in science, technology, engineering and maths.

New Zealand has (only) three female Professors of Statistics, the top position in our UK-style academic ranking. They work in very different areas of statistics, but with related applications to ecological and environmental monitoring, an area of particular interest in New Zealand.

Going north to south:

  • Marti Anderson is at Massey University in Albany (and was previously at the University of Auckland). Her research is in multivariate analysis — techniques for analysing ecological data on multiple species together, rather than one at a time — mostly applied to marine species
  • Shirley Pledger retired this year from Victoria University. Her research is on capture-recapture methods for counting animals. It’s often impossible to get a complete census of a species even in a limited area, but you can mark the individuals you catch, release them, and observe how often you catch them again. The simplest approaches to estimation are easy but unrealistic; she has worked on more sophisticated and sensible models.
  • Jennifer Brown is head of the Maths & Stats department at the University of Canterbury. Her main statistical research is on sampling techniques for monitoring sparse or patchy populations: either rare animals and plants, or invasive weeds. Sampling systematically or purely at random are both very wasteful; ‘adaptive’ sampling designs allow you to take advantage of finding a clump of your target species without biasing the overall results.

 

October 13, 2014

Context from everyday units

From @JohnDonoghue64 on Twitter

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From the Guardian, a few years ago

Perhaps, as with metric and imperial measurements, such comparisons should be given convenient abbreviations: SoWs (size of Wales), SoBs (size of Belgium), OSPs (Olympic swimming pools), DDBs (buses) and so on. Thus the Kruger national park in South Africa measures 1 SoW (Daily Telegraph), as do Lesotho (London Evening Standard) and Israel (Times), whereas Lake Nzerakera in Tanzania is 2 SoBs (Observer).

At times the most carefully calibrated calculations can go awry. So we learn that Helmand province in Afghanistan is “four times the size of Wales” (Daily Telegraph, 2 December 2009) only to find a few weeks later that it has apparently shrunk to “the size of Wales” (Daily Telegraph, 29 January 2010).

For the benefit of NZ readers, a badger appears to weigh about the same as three female North Island brown kiwi, two typical merino fleeces, or half a case of Malborough sav blanc. That should help you get a grasp on the size of the Lindisfarne Gospels.

Herald data blog starts

The Herald’s Data Editor, Harkanwal Singh,  announces the online site’s new ‘Data Blog’, with the first new post being a map of NZ internet affordability created by Jonathan Brewer.

This has got to be a Good Thing for data literacy in the local media.

October 12, 2014

Unofficially over arithmetic

From the Herald (from the Washington Post), under the headline “Teens are officially over Facebook” (yes, officially)

Now, a pretty dramatic new report out from Piper Jaffray – an investment bank with a sizable research arm – rules that the kids are over Facebook once and for all, having fled Mark Zuckerberg’s parent-flooded shores for the more forgiving embraces of Twitter and Instagram.

This is based on a survey by Piper Jaffray, of 7200 people aged 13-19, (in the US, though the Herald doesn’t say that).

It looks as though US teens are leaving Facebook, but they sure aren’t flocking to Twitter, or, really, to Instagram. If you go to a story that gives the numbers, you see that reported Facebook use has fallen 27 percentage points. Instagram has risen only 7 percentage points, and Twitter has fallen by 4.

 fb1

So, where are they going? They aren’t giving up on social media entirely — although “None” category wasn’t asked the first time around, it’s only 8 percent in the second survey.  It’s possible that teens are cutting down on the number of social media networks they use, but it seems more likely that the question was badly designed. Even I can think of at least one major site that isn’t on the list, Snapchat, which globalwebindex thinks is used by 42% of US internet-connected 16-19 year olds.

Incidentally: those little blue letters that look like they should be a link? They aren’t on the Herald site either, and on the Washington Post site they link to a message that basically says “no, not for you.”

October 10, 2014

Briefly

  • Something strange happened to this month’s unemployment data in Australia: Guardian, ABC News, interview with Rob Hyndman (who knows from time series)
  • “Ferguson’s 3,287 new registrants (in two months) is more than recorded by any township in St. Louis County in any midterm election since 2002.” Or not. A number that seems really extreme may just be wrong.
  • When there’s a lot of variation, it can be a mistake to make statements about “typical” attitudes: Andrew Gelman
October 9, 2014

…and to divide the light from the darkness

Q: There’s a story that charging your phone in your bedroom make you fat.

A: Yes, there is.

Q: Why?

A: Because it looked like a good headline.

Q: No, why does it make you fat?

A: Melatonin. The theory is that any light at night time makes your body not produce enough melatonin and that this is bad.

Q: How much more did people who charged their phones in their bedroom end up weighing?

A: There weren’t any people involved.

Q: Ok, so they had mice with cellphones in their bedrooms?

A: Rats. And not cellphones.

Q: Some other light source of a similar brightness?

A: No.

Q: What, then?

A: They put melatonin in the rats’ drinking water.

Q: So that should make them lose weight. Did it?

A: Not that they reported.

Q: Can you work with me here?

A: They measured the conversion of fat under the rats’ skin from ‘white’ to ‘brown‘, which is theoretically relevant to energy use and perhaps to diabetes and heart disease. It’s interesting research. (abstract)

Q: So it could be relevant, but doesn’t the generalisation seems a bit indirect?

A: Yes, “a bit.”

Q: Do international patterns of cellphone use match patterns of obesity?

A: Not really, but maybe in East Asia they use different chargers or something.

Q: Is the LED on a charger really enough to make a difference?

A: That’s what the story lead implies, but the second paragraph talks about research involving phone screens, laptops, artificial lighting, and street lights, so I’m guessing there’s a bit of a bait and switch going on.

Q: Couldn’t it be enough? I mean, in nature, it would be completely dark at night, like they say.

A: Only up to a point. There was another relevant story today, too.

 

October 8, 2014

Communicating the obvious (to you)

From the Herald

People’s coffee-drinking habits are linked to their genes, scientists say.

A large-scale study, which analysed 20,000 regular coffee drinkers of European and African American ancestry, identified six new genetic variants associated with habitual coffee drinking.

What the story (and the press information) doesn’t say is how small the effects are: among regular coffee drinkers, each of these variants predicted a difference in average consumption of one or two cups per month. (research paper, paywalled)

The  researchers would think it’s obvious that the effects are going to be tiny, so it makes sense that they wouldn’t point this out explicitly. The journalists and publicists wouldn’t know, but there’s no reason they would think to ask.

What are CEOs paid; what should they be paid?

From Harvard Business Review, reporting on recent research

Using data from the International Social Survey Programme (ISSP) from December 2012, in which respondents were asked to both “estimate how much a chairman of a national company (CEO), a cabinet minister in a national government, and an unskilled factory worker actually earn” and how much each person should earn, the researchers calculated the median ratios for the full sample and for 40 countries separately.

The graph:

actualestimated

 

The radial graph exaggerates the differences, but they are already huge. Respondents dramatically underestimated what CEOs are actually paid, and still thought it was too much.  Here’s a barchart of the blue and grey data (the red data seems to only be available in the graph). Ordering by ideal pay ratio (rather than alphabetically) helps with the nearly-invisible blue bars: it’s interesting that Australia has the highest ideal ratio.

ceo

The findings are a contrast to foreign aid budgets, where the desired level of expenditure is less than the estimated level, but more than the actual level.  On the other hand, it’s less clear exactly what the implications are in the CEO case.

 

October 7, 2014

Marriage equality maps

The US Supreme Court declined to review seven same-sex marriage decisions today. The StatsChat-relevant aspect is the flurry of maps this prompted:

I think the New York Times (via Twitter) is my favorite version: the square statebins use geography just as an index to make states easier to find, and (in contrast to the last statebins I linked to) they’ve moved Alaska to the right place

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