Stats Chat

• Interesting maps: a Moral Topography of Portland “The [1913] report found, specified per type of dwelling, only 22 of 80 apartments, merely 5 out of 59 hotels and no more than 71 out of 408 rooming and lodging houses to be ‘moral’.”  If you cynically expected that “immoral” didn’t refer to racial discrimination, rent-gouging, unhygienic conditions, or lack of fire escapes, you were right. (via consumerist.com)

• An interactive display of US lifetime earnings for various groups by education and gender. The underlying data are good, but there’s inevitably an assumption that the correlations with education are broadly a result of education (including social status and networking effects) rather than selection for existing differences.

• The Royal Statistical Society is launching a pro bono statistics scheme, in collaboration with the existing Statistics Without Borders and Datakind UK

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 12 2014.
• Statistics can be bad, exemplary or fascinating.
• The statistic must be in the NZ media during the period of September 6 – 12 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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Team Ratings for Round 26

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.

Performance So Far

So far there have been 184 matches played, 107 of which were correctly predicted, a success rate of 58.2%.

Here are the predictions for last week’s games.

Predictions for Round 26

Here are the predictions for Round 26. 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.

Team Ratings for Round 4

Here are the team ratings prior to Round 4, 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.

Performance So Far

So far there have been 22 matches played, 13 of which were correctly predicted, a success rate of 59.1%.

Here are the predictions for last week’s games.

Predictions for Round 4

Here are the predictions for Round 4. 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.

Team Ratings for Round 5

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.

Performance So Far

So far there have been 16 matches played, 14 of which were correctly predicted, a success rate of 87.5%.

Here are the predictions for last week’s games.

Predictions for Round 5

Here are the predictions for Round 5. 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.

In this week’s Slate Money podcast (starting at about 2:50), there’s an example of a probability puzzle that mathematically trained people tend to get wrong.  In summary, the story is

You’re at a theatre watching a magician. The magician hands a pack of cards to one member of the audience  and asks him to check that it is an ordinary pack, and to shuffle it. He asks another member of the audience to name a card. She says “Ace of Hearts”.  The magician covers his eyes, reaches out to the pack of cards, fumbles around a bit, and pulls out a card. What’s the probability that it is the Ace of Hearts?

It’s very tempting to say 1 in 52, because the framing of the puzzle prompts you to think in terms of equal-probability sampling.  Of course, as Felix Salmon points out, this is the only definitively wrong answer. The guy’s a magician. Why would he be doing this if the probability was going to be 1 in 52?

With an ordinary well-shuffled pack of cards and random selection we do know the probability: if you like the frequency interpretation of probability it’s an unknown number quite close to 1 in 52, if you like the subjective interpretation it should be a distribution of numbers quite close to 1 in 52.

With a magic trick we’d expect the probability (in the frequency sense) to be close to either zero or one, depending on the trick, but we don’t know.  Under the subjective interpretation of probability then you do know what the probability is for you, but you’ve got no real reason to expect it to be similar for other people.

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 5 2014.
• Statistics can be bad, exemplary or fascinating.
• The statistic must be in the NZ media during the period of August 30 – September 5 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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You have probably seen the graphic from vox.com

There are several things wrong with it. From a graphics point of view it doesn’t make any of the relevant comparisons easy. The diameter of the circle is proportional to the deaths or money, exaggerating the differences. And the donation data are basically wrong — the original story tries to make it clear that these are particular events, not all donations for a disease, but it’s the graph that is quoted.

For example, the graph lists $54 million for heart disease, based on the ‘Jump Rope for Heart’ fundraiser. According to Forbes magazine’s list of top charities, the American Heart Association actually received $511 million in private donations in the year to June 2012, almost ten times as much.  Almost as much again came in grants for heart disease research from the National Institutes of Health.

There’s another graph I’ve seen on Twitter, which shows what could have been done to make the comparisons clearer:

It’s limited, because it only shows government funding, not private charity, but it shows the relationship between funding and the aggregate loss of health and life for a wide range of diseases.

There are a few outliers, and some of them are for interesting reasons. Tuberculosis is not currently a major health problem in the US, but it is in other countries, and there’s a real risk that it could spread to the US.  AIDS is highly funded partly because of successful lobbying, partly because it — like TB — is a foreign-aid issue, and partly because it has been scientifically rewarding and interesting. COPD and lung cancer are going to become much less common in the future, as the victims of the century-long smoking epidemic die off.

Depression and injuries, though?

Update: here’s how distorted the areas are: the purple number is about 4.2 times the blue number

To complement the Herald’s flying Air New Zealand vs driving costs for various NZ cities, I thought I’d work out similar comparisons for the Pacific Northwest, where I used to live.  It’s a reasonable comparison — both have relatively sparsely spaced cities, though the roads are better there.

I used Alaska Airlines for the flying costs; they are the main local airline in the region. The costs are the cheapest flight on a random weekday in September — there will be some days and seasons when it’s cheaper or more expensive.  The driving cost  is based on the actual driving distance, not the straight-line distance, and uses the cost per mile specified for business tax deductions.

The results aren’t that different from NZ, except that the impact of competition is clearer: the Seattle–Calgary flight is much less expensive that you’d predict from the others, probably because there lots of one-stop alternatives via Vancouver.

On the positive side: there’s a conference of science advisers and people who know about the field here in Auckland at the moment. There’s a blog, and there will soon be videos of the presentations.

On the negative side: Statistics Canada continues to provide an example of how a world-class official statistics agency can go downhill with budget cuts and government neglect.  The latest story is the report on how the Labour Force Survey (which is how unemployment is estimated) was off by 42000 in July. There’s a shorter writeup in Maclean’s magazine, and their archive of stories on StatsCan is depressing reading.