September 26, 2014

PhD gender gap

From Scientific American and Periscopic, an interactive display of international gender differences in PhDs awarded in various fields.

phd-gap

September 25, 2014

Asthma and job security

The Herald’s story is basically fine

People concerned that they may lose their jobs are more likely to develop asthma than those in secure employment, a new study suggests.

Those who had “high job insecurity” had a 60 per cent increased risk of developing asthma when compared to those who reported no or low fears about their employment, they found.

though it would be nice to have the absolute risks (1.3% vs 2.1% over two years) , and the story is really short on identifying information about the researchers, only giving the countries they work in (the paper is here).

The main reason to mention it is to link to the NHS “Behind the Headlines” site, which writes about stories like this one in the British Media (the Independent, in this case).

Also, the journal should be complimented for having the press release linked from the same web page as the abstract and research paper. It would be even better, as Ben Goldacre has suggested, to have authors listed for the press release, but this is at least a step in the direction of accountability.

September 24, 2014

That’s just a guess

oranges

While it’s nowhere near as annoying as Phoenix Organics “Don’t drink science“, Charlie’s could do better than ‘just a guess’ as to whether there are a million oranges in this truck

If there are ten oranges in a litre of juice, there are ten thousand in a cubic metre of juice, so a million oranges would make 100 cubic metres of juice. The little juice bottles probably don’t pack that efficiently, so you’d need more than 100 cubic metres of truck.

So, how big is a truck?  A standard twenty-foot container is 6.1m long, 2.44m wide, and 2.59m high, with a volume of 38.5 cubic metres.  That truck doesn’t look three times as big as a twenty-foot container to me.

There could be a hundred thousand oranges in that truck. I don’t think a million is feasible.

NRL Predictions for the Preliminary Finals

Team Ratings for the Preliminary Finals

The basic method is described on my Department home page. I have made some changes to the methodology this year, including shrinking the ratings between seasons.

Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

Current Rating Rating at Season Start Difference
Rabbitohs 11.42 5.82 5.60
Roosters 9.66 12.35 -2.70
Cowboys 9.46 6.01 3.40
Storm 4.47 7.64 -3.20
Panthers 4.45 -2.48 6.90
Broncos 3.86 -4.69 8.50
Warriors 2.82 -0.72 3.50
Sea Eagles 2.78 9.10 -6.30
Bulldogs 0.42 2.46 -2.00
Knights -0.28 5.23 -5.50
Dragons -2.10 -7.57 5.50
Raiders -7.64 -8.99 1.40
Eels -8.12 -18.45 10.30
Titans -8.40 1.45 -9.90
Sharks -10.92 2.32 -13.20
Wests Tigers -13.68 -11.26 -2.40

 

Performance So Far

So far there have been 198 matches played, 116 of which were correctly predicted, a success rate of 58.6%.

Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Roosters vs. Cowboys Sep 19 31 – 30 5.70 TRUE
2 Sea Eagles vs. Bulldogs Sep 20 17 – 18 3.20 FALSE

 

Predictions for the Preliminary Finals

Here are the predictions for the Preliminary Finals. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

Game Date Winner Prediction
1 Rabbitohs vs. Roosters Sep 26 Rabbitohs 6.30
2 Panthers vs. Bulldogs Sep 27 Panthers 4.00

 

Currie Cup Predictions for Round 8

Team Ratings for Round 8

The basic method is described on my Department home page. I have made some changes to the methodology this year, including shrinking the ratings between seasons.

Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

Current Rating Rating at Season Start Difference
Western Province 6.47 3.43 3.00
Sharks 2.98 5.09 -2.10
Lions 2.44 0.07 2.40
Cheetahs -0.88 0.33 -1.20
Blue Bulls -2.90 -0.74 -2.20
Pumas -5.72 -10.00 4.30
Griquas -7.69 -7.49 -0.20
Kings -14.04 -10.00 -4.00

 

Performance So Far

So far there have been 28 matches played, 21 of which were correctly predicted, a success rate of 75%.

Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Lions vs. Pumas Sep 19 29 – 15 13.00 TRUE
2 Western Province vs. Griquas Sep 20 36 – 12 18.40 TRUE
3 Blue Bulls vs. Sharks Sep 20 15 – 26 0.60 FALSE
4 Kings vs. Cheetahs Sep 20 22 – 37 -7.10 TRUE

 

Predictions for Round 8

Here are the predictions for Round 8. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

Game Date Winner Prediction
1 Cheetahs vs. Blue Bulls Sep 26 Cheetahs 7.00
2 Griquas vs. Lions Sep 27 Lions -5.10
3 Pumas vs. Western Province Sep 27 Western Province -7.20
4 Sharks vs. Kings Sep 27 Sharks 22.00

 

Revised ITM Cup Predictions for Round 7

Reviewing the ratings I noticed I have been giving Wellington a ridiculously high rating given their disastrous performance this year. I discovered a problem with my code which meant I have not been updating ratings properly. I have some different code for the ITM Cup because of the strange nature of the fixtures where teams can play more than one game a week.

Team Ratings for Round 7

Here are the team ratings prior to Round 7, along with the ratings at the start of the season. I have created a brief description of the method I use for predicting rugby games. Go to my Department home page to see this.

Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

Current Rating Rating at Season Start Difference
Canterbury 21.23 18.09 3.10
Tasman 10.08 5.78 4.30
Auckland 5.32 4.92 0.40
Hawke’s Bay 4.02 2.75 1.30
Taranaki 2.96 -3.89 6.90
Wellington 1.25 10.16 -8.90
Counties Manukau 0.04 2.40 -2.40
Otago -1.62 -1.45 -0.20
Waikato -3.89 -1.20 -2.70
Northland -5.24 -8.22 3.00
Manawatu -7.06 -10.32 3.30
Southland -9.05 -5.85 -3.20
Bay of Plenty -9.73 -5.47 -4.30
North Harbour -10.37 -9.77 -0.60

 

Performance So Far

So far there have been 46 matches played, 30 of which were correctly predicted, a success rate of 65.2%.

Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Southland vs. Tasman Sep 17 14 – 38 -13.30 TRUE
2 Northland vs. Taranaki Sep 18 20 – 31 -1.40 TRUE
3 Counties Manukau vs. Canterbury Sep 19 20 – 28 -13.50 TRUE
4 Hawke’s Bay vs. Bay of Plenty Sep 20 36 – 17 10.20 TRUE
5 Auckland vs. North Harbour Sep 20 32 – 7 18.50 TRUE
6 Manawatu vs. Southland Sep 20 41 – 20 -0.20 FALSE
7 Otago vs. Waikato Sep 21 38 – 7 1.60 TRUE
8 Wellington vs. Tasman Sep 21 20 – 42 0.10 FALSE

 

Predictions for Round 7

Here are the predictions for Round 7. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

Game Date Winner Prediction
1 North Harbour vs. Canterbury Sep 24 Canterbury -27.60
2 Bay of Plenty vs. Northland Sep 25 Northland -0.50
3 Taranaki vs. Auckland Sep 26 Taranaki 1.60
4 Waikato vs. Manawatu Sep 27 Waikato 7.20
5 Counties Manukau vs. Wellington Sep 27 Counties Manukau 2.80
6 North Harbour vs. Hawke’s Bay Sep 27 Hawke’s Bay -10.40
7 Tasman vs. Otago Sep 28 Tasman 15.70
8 Canterbury vs. Southland Sep 28 Canterbury 34.30

 

September 23, 2014

I’m not even sure where to begin on this highly important topic

New Zealand’s favourite biscuit.

I just clicked on a link on the homepage of the NZ Herald which says “NZ@Noon: NZ’s favourite biscuit revealed” which took me to an article with a snippet saying:

Bay of Plenty voters have taken to the polls. Find out which biscuit triumphed in the annual nationwide biscuit election.

This lead to another article with the headline: “Mallowpuffs voted Bay’s best biscuit” which includes the following (emphasis mine):

Bay of Plenty voters have taken to the polls and voted Mallowpuffs Original their favourite biscuit in an annual nationwide biscuit election.

Around the country, close to 5,000 votes were cast by biscuit-lovers who also voted Mallowpuffs Original as the national favourite, ahead of 57 other contenders.

Kiwi women were once again more passionate about pledging their support, contributing 94 percent of the votes nationwide.

The 2014 Bikkielections poll was conducted via an application on Griffin’s Facebook page from September 9 to 21 following weeks of campaigning via billboards, radio promotions, polling booths and street sampling. The poll has a margin of error of plus or minus zero percent.

That’s a first, right?

September 22, 2014

Blame it all on mum

Says the Herald (reprinting the Daily Mail)

If you have always found doing sums a struggle, you might just be able to blame your mother.

Because research has linked a woman’s hormone levels in pregnancy with her child’s maths skills at age five.

Boys and girls whose mothers were very low in the hormone thyroxine were almost twice as likely to do badly in arithmetic tests, it found.

The hormone in question is thyroxine, produced by the thyroid, and the basic issue is that iodine deficiency is getting more common again. In Australia and New Zealand, iodine has been added to bread since September 2009 to address this problem. In Australia, the fortification of bread has been fairly successful; there doesn’t seem to be data for New Zealand, but there’s no reason to expect it to be different. So the story  may not be applicable to New Zealanders.

Also, as with the cannabis paper a couple of weeks ago, the “twice as likely” is simply wrong.  Doing badly in arithmetic was defined as being in the bottom 50%, and it’s not plausible that low-thyroxine kids are twice as likely to be in the bottom half.  In fact, it’s the odds ratio for being in the lower 50% of students in maths that was 1.79.  Since the overall odds of being in the bottom half is 1:1, if you multiply by 1.79 you get 1.79:1, which is a probability of 64% of being in the bottom half.

A difference between 50% and 64% is not “almost twice as likely”, and “blame” is a completely inappropriate term — this is new research, so even if it’s true (it could be) and relevant to New Zealand (it probably isn’t) it would not be something for which ‘blame’ would be appropriate. There’s entirely too much blaming mothers already.

So, we had an election

Turnout of enrolled voters was up 3 percentage points over 2011, but enrollment was down, so as a fraction of the eligible population, turnout was only up half a percentage point.

From the Herald’s interactive, the remarkably boring trends through the count

There are a few electorates that are, arguably, still uncertain, but by 9pm the main real uncertainty at the nationwide level was whether Hone Harawira would win Te Tai Tokerau, and that wasn’t going to affect who was in government.  By 10pm it was pretty clear Harawira was out (though he hadn’t conceded) and that Internet Mana had been, in his opponent’s memorable phrase, “all steam and no hangi.”

Jonathan Marshall (@jmarshallnz) has posted swings in each electorate, for the party vote and electorate vote. He also has an interactive Sainte-Laguë seat allocation calculator and has published the data (complete apart from special votes) in a convenient form for y’all to play with.

David Heffernan (@kiwipollguy) collected a bunch of poll, poll average, and pundit predictions, and writes about them here. The basic summary is that they weren’t very good, though there weren’t any totally loony ones, as there were for the last US Presidential election. Our pundits seem to be moderately well calibrated to reality, but there’s a lot of uncertainty in the system and the improvement from averaging seems pretty small.  The only systematic bias is that the Greens did a bit worse than expected.

Based on his criterion, which is squared prediction error scaled basically by party vote, two single polls — 3 News/Reid at the high end and Herald Digipoll at the low end — spanned almost the entire range of prediction error.

The variation between predictions isn’t actually much bigger than you’d expect by chance. The prediction errors have the mean you’d expect from a random sample of about 400 people, and apart from two outliers they have the right spread as well. On the graph, the red curve is a chi-squared distribution with 9 degrees of freedom, and the black curve is the distribution of the 23 estimates. The outliers are Wikipedia and the last 3 News/Reid Research poll.

elections-dist

About half the predictions were qualitatively wrong: they had National needing New Zealand First or the Conservatives for a majority. The Conservatives were clearly treated unfairly by the MMP threshold. If someone is going to be, I’m glad it’s them, but a party with more votes than the Māori Party, Internet Mana, ACT, United Future, and Legalise Cannabis put together should have a chance to prove their unsuitability in Parliament.

 

Stat of the Week Competition: September 20 – 26 2014

Each week, we would like to invite readers of Stats Chat to submit nominations for our Stat of the Week competition and be in with the chance to win an iTunes voucher.

Here’s how it works:

  • Anyone may add a comment on this post to nominate their Stat of the Week candidate before midday Friday September 26 2014.
  • Statistics can be bad, exemplary or fascinating.
  • The statistic must be in the NZ media during the period of September 20 – 26 2014 inclusive.
  • Quote the statistic, when and where it was published and tell us why it should be our Stat of the Week.

Next Monday at midday we’ll announce the winner of this week’s Stat of the Week competition, and start a new one.

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