Posts filed under General (3156)

I’m glad to say it’s been quite a while since we’ve had this sort of rubbish from the NZ papers, but it’s still  going across the Tasman (the  Sydney Morning Herald)

If you’re considering buying a lottery ticket, you’d better make sure it’s from either Gladesville or Cabramatta, which are now officially Sydney’s luckiest suburbs when it comes to winning big. 

…

NSW Lotteries has released statistics that show the luckiest suburbs across all lotto games in NSW and the ACT, as well as other tips for amateurs hoping to ring their bosses tomorrow morning to say they wouldn’t be coming in to work. 

Of course, the ‘luckiest’ suburbs are nothing of the sort: just the ones where the most money is lost on the lotteries. Cabramatta has improved a lot in recent years, but it’s still not the sort of place you’d expect to see called ‘lucky’.

Team Ratings for the SemiFinals

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 40 matches played, 29 of which were correctly predicted, a success rate of 72.5%.

Here are the predictions for last week’s games.

Predictions for the SemiFinals

Here are the predictions for the SemiFinals. 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 the ITM Cup Finals

Here are the team ratings prior to the ITM Cup Finals, 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 70 matches played, 44 of which were correctly predicted, a success rate of 62.9%.

Here are the predictions for last week’s games.

Predictions for the ITM Cup Finals

Here are the predictions for the ITM Cup 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.

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]

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.

• ‘The Age of Megacities’: maps from ESRI showing the growth of some of the world’s biggest cities

• Kitties in the CPI: the new StatsNZ CPI basket of goods – Stuff, Herald, StatsNZ press release, Stats NZ explanation of CPI

• 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

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.

Team Ratings for Round 10

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 36 matches played, 27 of which were correctly predicted, a success rate of 75%.

Here are the predictions for last week’s games.

Predictions for Round 10

Here are the predictions for Round 10. 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 9

Here are the team ratings prior to Round 9, 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 62 matches played, 39 of which were correctly predicted, a success rate of 62.9%.

Here are the predictions for last week’s games.

Predictions for Round 9

Here are the predictions for Round 9. 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.

From Richard Vale, at University of Canterbury

What’s this? You are claiming that we can use Bayesian statistics to predict Game of Thrones?
Probably not, no.

But we can try?
Yes!

…

Further coverage, and his full article

(via Dion O’Neale)