August 19, 2016

Has your life improved since 1966?

From Pew Research, is life better than 50 years ago for people like you?

3_1

The answers aren’t going to mean much about reality, more about the sort of people we are or want to think we are.  As Fred Clark puts it

If you ask those of us who are 18-53 years old for our opinions about what life was like before we either existed or have any memory, we’ll give you an answer. And that speculative, possibly even informed, opinion may mean something or other in the aggregate. Maybe it tells us something fuzzy about general optimism or pessimism. Or maybe something about the dismal state of history, social studies, civics and science education.

Or, for the people who do have memories of the mid-sixties…

Age 65-70: I peaked in high school. Go away, nerd, or I’ll give you a swirlie.

August 18, 2016

Post-truth data maps

The Herald has a story “New map compares breast sizes around the world”. They blame news.com.au as the immediate cause, but a very similar story at the Daily Mail actually links to where it got the map.  You might wonder how the data were collected (you might wonder why, too). The journalist did get as far as that:

The breast map doesn’t reveal how the cup sizes were measured, it’s fair to say tracking bra purchases per country would be an ideal – and maybe a little weird – approach.

Rigorously deidentified pie

footypie

Via Dale Warburton on Twitter, this graph comes from page 7 of the 2016 A-League Injury Report (PDF) produced by Professional Footballers Australia — the players’ association for the round-ball game.  It seems to be a sensible and worthwhile document, except for this pie chart. They’ve replaced the club names with letters, presumably for confidentiality reasons. Which is fine. But the numbers written on the graph bear no obvious relationship to the sizes of the pie wedges.

It’s been a bad week for this sort of thing: a TV barchart that went viral this week had the same sort of problem.

August 17, 2016

Official statistics

There has been some controversy about changes to how unemployment is computed in the Household Labour Force Survey. As StatsNZ had explained, the changes would be back-dated to March 2007, to allow for comparisons.  However, from Stuff earlier this week:

In a media release Robertson, Labour’s finance spokesman, said National was “actively massaging official unemployment statistics” by changing the measure for joblessness to exclude those using websites, such as Seek or TradeMe.

Robertson was referring to the Household Labour Force Survey, due to be released on Wednesday, which he says would “almost certainly show a decrease in unemployment” as a result of the Government “manipulating official data to suit its own needs”.

Mr Robertson has since withdrawn this claim, and is now saying

“I accept the Chief Statistician’s assurances on the reason for the change in criteria but New Zealanders need to be aware that National Ministers have a track record of misusing and misrepresenting statistics.”

That’s a reasonable position — and some of the examples have appeared on StatsChat — but I don’t think the stories in the media have made it clear how serious the original accusation was (even if perhaps unintentionally).

Official statistics such as the unemployment estimates are politically sensitive, and it’s obvious why governments would want to change them. Argentina, famously, did this to their inflation estimates. As a result, no-one believed Argentinian economic data, which gets expensive when you’re trying to borrow money. For that reason, sensible countries structure their official statistics agencies to minimise political influence, and maximise independence.  New Zealand does have a first-world official statistics system — unlike many countries with similar economic resources — and it’s a valuable asset that can’t be taken for granted.

The system is set up so the Government shouldn’t have the ability to “actively massage” official unemployment statistics for minor political gain. If they did, well, ok, it was hyperbole when I said on Twitter ‘we’d need to go through StatsNZ with fire and the sword’, but the Government Statistician wouldn’t be the only one who’d need replacing.

NRL Predictions for Round 24

Team Ratings for Round 24

The basic method is described on my Department home page.

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
Storm 8.53 4.41 4.10
Raiders 7.90 -0.55 8.50
Cowboys 7.28 10.29 -3.00
Sharks 4.36 -1.06 5.40
Panthers 2.75 -3.06 5.80
Broncos 2.55 9.81 -7.30
Bulldogs 2.28 1.50 0.80
Sea Eagles 0.30 0.36 -0.10
Roosters -0.46 11.20 -11.70
Titans -1.04 -8.39 7.30
Wests Tigers -1.18 -4.06 2.90
Warriors -2.59 -7.47 4.90
Eels -3.24 -4.62 1.40
Dragons -4.12 -0.10 -4.00
Rabbitohs -5.01 -1.20 -3.80
Knights -16.62 -5.41 -11.20

 

Performance So Far

So far there have been 168 matches played, 104 of which were correctly predicted, a success rate of 61.9%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Bulldogs vs. Sea Eagles Aug 11 20 – 16 5.20 TRUE
2 Broncos vs. Eels Aug 12 38 – 16 6.60 TRUE
3 Wests Tigers vs. Titans Aug 13 18 – 19 3.60 FALSE
4 Warriors vs. Rabbitohs Aug 13 22 – 41 10.40 FALSE
5 Dragons vs. Sharks Aug 13 32 – 18 -8.60 FALSE
6 Knights vs. Panthers Aug 14 6 – 42 -13.30 TRUE
7 Roosters vs. Cowboys Aug 14 22 – 10 -7.40 FALSE
8 Raiders vs. Storm Aug 15 22 – 8 0.50 TRUE

 

Predictions for Round 24

Here are the predictions for Round 24. 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 Broncos vs. Bulldogs Aug 18 Broncos 3.30
2 Panthers vs. Wests Tigers Aug 19 Panthers 6.90
3 Knights vs. Titans Aug 20 Titans -12.60
4 Sea Eagles vs. Storm Aug 20 Storm -5.20
5 Cowboys vs. Warriors Aug 20 Cowboys 13.90
6 Raiders vs. Eels Aug 21 Raiders 14.10
7 Roosters vs. Dragons Aug 21 Roosters 6.70
8 Rabbitohs vs. Sharks Aug 22 Sharks -6.40

 

Mitre 10 Cup Predictions for Round 1

Team Ratings for Round 1

The basic method is described on my Department home page.

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 12.85 12.85 0.00
Auckland 11.34 11.34 0.00
Tasman 8.71 8.71 0.00
Taranaki 8.25 8.25 0.00
Wellington 4.32 4.32 0.00
Counties Manukau 2.45 2.45 0.00
Hawke’s Bay 1.85 1.85 0.00
Otago 0.54 0.54 0.00
Waikato -4.31 -4.31 0.00
Bay of Plenty -5.54 -5.54 0.00
Manawatu -6.71 -6.71 0.00
North Harbour -8.15 -8.15 0.00
Southland -9.71 -9.71 0.00
Northland -19.37 -19.37 0.00

 

Predictions for Round 1

Here are the predictions for Round 1. 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. Counties Manukau Aug 18 Counties Manukau -6.60
2 Northland vs. Manawatu Aug 19 Manawatu -8.70
3 Bay of Plenty vs. Taranaki Aug 20 Taranaki -9.80
4 Hawke’s Bay vs. Wellington Aug 20 Hawke’s Bay 1.50
5 Canterbury vs. Auckland Aug 20 Canterbury 5.50
6 Southland vs. Otago Aug 21 Otago -6.20
7 Tasman vs. Waikato Aug 21 Tasman 17.00

 

Currie Cup Predictions for Round 3

Team Ratings for Round 3

The basic method is described on my Department home page.

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
Lions 10.77 9.69 1.10
Western Province 5.35 6.46 -1.10
Blue Bulls 1.61 1.80 -0.20
Sharks 0.70 -0.60 1.30
Cheetahs -0.82 -3.42 2.60
Cavaliers -10.28 -10.00 -0.30
Pumas -10.40 -8.62 -1.80
Griquas -13.05 -12.45 -0.60
Kings -15.32 -14.29 -1.00

 

Performance So Far

So far there have been 7 matches played, 4 of which were correctly predicted, a success rate of 57%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Lions vs. Pumas Aug 12 68 – 26 22.50 TRUE
2 Sharks vs. Griquas Aug 12 46 – 24 16.10 TRUE
3 Kings vs. Cavaliers Aug 13 10 – 28 0.50 FALSE
4 Cheetahs vs. Blue Bulls Aug 13 43 – 20 -1.50 FALSE

 

Predictions for Round 3

Here are the predictions for Round 3. 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 Western Province vs. Cheetahs Aug 19 Western Province 9.70
2 Griquas vs. Lions Aug 19 Lions -20.30
3 Blue Bulls vs. Kings Aug 20 Blue Bulls 20.40
4 Cavaliers vs. Sharks Aug 20 Sharks -7.50

 

August 15, 2016

New Zealand at the top of the (per-capita) table

On a medals-per-capita basis, New Zealand now ranks at the top of the table with two gold medals and six  silver at the Rio Olympics, Statistics NZ said today.

With eight medals overall at the half way stage at Rio, New Zealand is the highest performing country, with the equivalent of 1.77 medals for every one million people.

Slovenia is second on 1.45 medals for every one million people. Hungary and Denmark are third and fourth respectively, with Fiji coming in fifth based on its one gold for the men’s rugby sevens win.

Capture

However, on a per-capita basis for gold medals alone, Fiji tops the table, with its one gold for a population of just under 900,000. On that basis, New Zealand’s two gold medals leave it in sixth place, with a population of more than 4.5 million.

During the weekend, Mahe Drysdale’s single sculls gold medal was the high point for the New Zealand team.

On Saturday, New Zealand won two silver medals, for shot-putter Valerie Adams and at the rowing where Genevieve Behrent and Rebecca Scown also picked up a medal in the pair.

See the SNZ data here: http://www.stats.govt.nz/browse_for_stats/population/estimates_and_projections/olympics-2016.aspx#tables

Graph of the week

From a real estate agent who will remain nameless

IMAG0280

Another example of the rule ‘if you have to write out all the numbers, the graph isn’t doing its work.”

Stat of the Week Competition: August 13 – 19 2016

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 August 19 2016.
  • Statistics can be bad, exemplary or fascinating.
  • The statistic must be in the NZ media during the period of August 13 – 19 2016 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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