Posts filed under General (1200)

July 27, 2017

Will we ever use this in real life?

From deep in the archives at Language Log

The Pirahã language and culture seem to lack not only the words but also the concepts for numbers, using instead less precise terms like “small size”, “large size” and “collection”. And the Pirahã people themselves seem to be suprisingly uninterested in learning about numbers, and even actively resistant to doing so, despite the fact that in their frequent dealings with traders they have a practical need to evaluate and compare numerical expressions. A similar situation seems to obtain among some other groups in Amazonia, and a lack of indigenous words for numbers has been reported elsewhere in the world.

Many people find this hard to believe. These are simple and natural concepts, of great practical importance: how could rational people resist learning to understand and use them? I don’t know the answer. But I do know that we can investigate a strictly comparable case, equally puzzling to me, right here in the U.S. of A.

From context, you can probably guess where he’s heading

July 25, 2017

Tell them to buy an ad

From the editing blog “Heads Up”

… you don’t need a course in statistics to ask what a writer means by “incident count,” “city” and “occurrence percentage,” not to mention why and how the means are weighted, or even why users of an insurance comparison website would be a good representation of a city where a huge proportion of drivers are uninsured. 

And

This isn’t “fake news” in the 2016 sense; it’s the old-school kind that has always gotten past enough gatekeepers to do its work. The traditional response is “tell them to buy an ad.”

Briefly

  • “Algorithms can dictate whether you get a mortgage or how much you pay for insurance. But sometimes they’re wrong – and sometimes they are designed to deceive” Cathy O’Neil, for Observer.
  • A talk about human factors research and what it says about data visualisation
  • “Point your phone at any mushroom and take a pic, our tech will instantly identify any mushrooms while giving you an article you can read or listen to.” This app seems to be intended as educational ‘augmented reality’, but one reason people want to identify mushrooms is to decide whether it’s safe to eat them. That’s not possible from just a photo, and the costs of some of the possible classification errors are very, very high.
  • A new trend in graphics: ‘joyplots’, named for the famous cover art of a Joy Division album. Here’s a history of the album cover, from Jen Christiansen. And now some examples:

 

Super 18 Predictions for the Semi-finals

Team Ratings for the Semi-finals

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
Hurricanes 17.61 13.22 4.40
Crusaders 14.49 8.75 5.70
Lions 13.59 7.64 6.00
Highlanders 10.62 9.17 1.50
Chiefs 9.98 9.75 0.20
Brumbies 1.81 3.83 -2.00
Stormers 1.38 1.51 -0.10
Sharks 0.72 0.42 0.30
Blues -0.22 -1.07 0.90
Waratahs -3.81 5.81 -9.60
Bulls -4.96 0.29 -5.20
Jaguares -5.03 -4.36 -0.70
Force -6.97 -9.45 2.50
Cheetahs -9.63 -7.36 -2.30
Reds -9.92 -10.28 0.40
Kings -12.08 -19.02 6.90
Rebels -15.29 -8.17 -7.10
Sunwolves -19.38 -17.76 -1.60

 

Performance So Far

So far there have been 139 matches played, 105 of which were correctly predicted, a success rate of 75.5%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Brumbies vs. Hurricanes Jul 21 16 – 35 -10.80 TRUE
2 Crusaders vs. Highlanders Jul 22 17 – 0 6.10 TRUE
3 Lions vs. Sharks Jul 22 23 – 21 18.30 TRUE
4 Stormers vs. Chiefs Jul 22 11 – 17 -4.40 TRUE

 

Predictions for the Semi-finals

Here are the predictions for the Semi-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 Crusaders vs. Chiefs Jul 29 Crusaders 8.00
2 Lions vs. Hurricanes Jul 29 Hurricanes -0.00

 

NRL Predictions for Round 21

Team Ratings for Round 21

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 7.79 8.49 -0.70
Cowboys 6.37 6.90 -0.50
Broncos 5.02 4.36 0.70
Sharks 3.29 5.84 -2.60
Panthers 2.96 6.08 -3.10
Raiders 1.82 9.94 -8.10
Roosters 1.68 -1.17 2.80
Sea Eagles 0.45 -2.98 3.40
Dragons -0.21 -7.74 7.50
Eels -0.58 -0.81 0.20
Titans -1.59 -0.98 -0.60
Rabbitohs -1.76 -1.82 0.10
Warriors -2.34 -6.02 3.70
Bulldogs -5.80 -1.34 -4.50
Wests Tigers -6.61 -3.89 -2.70
Knights -12.54 -16.94 4.40

 

Performance So Far

So far there have been 144 matches played, 88 of which were correctly predicted, a success rate of 61.1%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Broncos vs. Bulldogs Jul 20 42 – 12 11.40 TRUE
2 Roosters vs. Knights Jul 21 28 – 4 16.50 TRUE
3 Sharks vs. Rabbitohs Jul 21 26 – 12 7.50 TRUE
4 Panthers vs. Titans Jul 22 24 – 16 8.10 TRUE
5 Raiders vs. Storm Jul 22 14 – 20 -1.70 TRUE
6 Cowboys vs. Warriors Jul 22 24 – 12 12.90 TRUE
7 Dragons vs. Sea Eagles Jul 23 52 – 22 -2.00 FALSE
8 Wests Tigers vs. Eels Jul 23 16 – 17 -2.90 TRUE

 

Predictions for Round 21

Here are the predictions for Round 21. 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 Warriors vs. Sharks Jul 23 Sharks -1.60
2 Knights vs. Dragons Jul 23 Dragons -8.80
3 Rabbitohs vs. Raiders Jul 23 Raiders -0.10
4 Roosters vs. Cowboys Jul 23 Cowboys -1.20
5 Storm vs. Sea Eagles Jul 23 Storm 10.80
6 Panthers vs. Bulldogs Jul 23 Panthers 12.30
7 Eels vs. Broncos Jul 23 Broncos -2.10
8 Titans vs. Wests Tigers Jul 23 Titans 8.50

 

Currie Cup Predictions for Round 2

Team Ratings for Round 2

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 6.74 7.41 -0.70
Cheetahs 5.16 4.33 0.80
Western Province 3.30 3.30 0.00
Blue Bulls 2.05 2.32 -0.30
Sharks 1.32 2.15 -0.80
Pumas -9.97 -10.63 0.70
Griquas -11.34 -11.62 0.30

 

Performance So Far

So far there have been 3 matches played, 2 of which were correctly predicted, a success rate of 66.7%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Cheetahs vs. Sharks Jul 21 47 – 12 6.70 TRUE
2 Griquas vs. Blue Bulls Jul 22 45 – 51 -9.40 TRUE
3 Pumas vs. Lions Jul 23 43 – 36 -13.50 FALSE

 

Predictions for Round 2

Here are the predictions for Round 2. 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 Lions vs. Griquas Jul 28 Lions 22.60
2 Sharks vs. Pumas Jul 29 Sharks 15.80
3 Cheetahs vs. Western Province Jul 30 Cheetahs 6.40

 

July 21, 2017

This time we might be number one

So, Radio NZ has a story based on a commentary at YaleGlobal on homelessness.

The point of the YaleGlobal piece is that homelessness is increasing as the world gets more urbanised, and that it’s really hard to measure because people define it differently and because some countries don’t want it measured accurately. Overall

Based on national reports, it’s estimated that no less than 150 million people, or about 2 percent of the world’s population, are homeless. However, about 1.6 billion, more than 20 percent of the world’s population, may lack adequate housing.

There’s obviously a lot of room for variation in definitions.

This report isn’t Yale research, really. It’s based on OECD figures, which are reported by governments: the OECD HC3-1 indicator (PDF).  The number for New Zealand is 41705, which we’ve seen last year in the NZ media. It comes from the 2013 census, and was estimated by researchers at Otago.  The NZ homelessness number is high for at least three reasons.  First, NZ uses a very broad definition of homelessness. Second, we’re pretty good at honest data collection. And, third, we’ve got a serious homelessness problem (and have had for a while).

The Government is right to say that the international figures aren’t all comparable. Some countries only count people who are sleeping rough. Others include people in shelters or emergency accomodation. We include a lot more. The Herald story from last year quotes an Otago researcher, Kate Amore

“If the homeless population were a hundred people, 70 are staying with extended family or friends in severely crowded houses, 20 are in a motel, boarding house or camping ground, and 10 are living on the street, in cars, or in other improvised dwellings.”

From that tally, a few countries don’t even count all of the 10; some don’t count all of the 20; many don’t count the 70 —  and some aren’t very good at counting.

Here’s a set of charts I made based on a crude classification of definitions from the OECD HC3-1 report. The numbers on the axis are in % of the population.

homeless

Even within the top panel, NZ, the Czech Republic, and Australia have the broadest definitions. The HC3-1 report says

Australia, the Czech Republic and New Zealand report a relatively large incidence of homelessness, and this is partly explained by the fact that these countries adopt a broad definition of homelessness. In Australia people are considered as homeless if they have “no other options to acquire safe and secure housing are without shelter, in temporary accommodation, sharing accommodation with a household or living in uninhabitable housing”. In the Czech Republic the term homeless covers “persons sleeping rough (roofless), people who are not able to procure any dwelling and hence live in accommodation for the homeless, and people living in insecure accommodation and people staying in conditions which do not fulfil the minimum standards of living […]”. In New Zealand homelessness is defined as “living situations where people with no other options to acquire safe and secure housing: are without shelter, in temporary accommodation, sharing accommodation with a household or living in uninhabitable housing.”

I think the New Zealand definition is a good one for measuring the housing deprivation problem, but it’s not good for international comparisons.

On the other hand, the comparison to Australia is pretty fair, and there’s at least no evidence that anywhere else has higher rates.  To some extent we have an apples vs oranges comparison, but that doesn’t stop us concluding it’s a bad apple.

July 20, 2017

Currie 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
Lions 7.41 7.41 0.00
Cheetahs 4.33 4.33 -0.00
Western Province 3.30 3.30 0.00
Blue Bulls 2.32 2.32 -0.00
Sharks 2.15 2.15 0.00
Pumas -10.63 -10.63 -0.00
Griquas -11.62 -11.62 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 Cheetahs vs. Sharks Jul 21 Cheetahs 6.70
2 Griquas vs. Blue Bulls Jul 22 Blue Bulls -9.40
3 Pumas vs. Lions Jul 23 Lions -13.50

 

Avocado denominators

Time magazine’s website has had at least seven stories about avocado on toast since May. The most recent one says

Square, a tech company that helps businesses process credit card payments, crunched data from sellers around the U.S. and found that Americans are spending nearly $900,000 per month on crusty bread topped with mashed green fruit.

What’s impressive about that number is how unimpressive it is.  The US is a big place. Even if we only count millennials, there are 80 million of them, so we’re talking about an average of 1 cent each per month.

But it gets worse from there. The story talks about the 50-fold increase since 2014. That’s an increase in sales handled by Square, an expanding business. Neither Time nor Square seems to have made any attempt to look at sales from comparable businesses over time, which Square could have done easily.

The whole avocado-toast thing only makes sense as a synecdoche for an eating-out lifestyle, so accurate data about avocado on toast isn’t really going to be very helpful for anything important. Even so, it’s possible for innumerate presentations of inaccurate data to be less helpful.

July 19, 2017

Super 18 Predictions for the Qualifiers

Team Ratings for the Qualifiers

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
Hurricanes 17.12 13.22 3.90
Lions 14.57 7.64 6.90
Crusaders 13.83 8.75 5.10
Highlanders 11.28 9.17 2.10
Chiefs 9.88 9.75 0.10
Brumbies 2.30 3.83 -1.50
Stormers 1.48 1.51 -0.00
Blues -0.22 -1.07 0.90
Sharks -0.26 0.42 -0.70
Waratahs -3.81 5.81 -9.60
Bulls -4.96 0.29 -5.20
Jaguares -5.03 -4.36 -0.70
Force -6.97 -9.45 2.50
Cheetahs -9.63 -7.36 -2.30
Reds -9.92 -10.28 0.40
Kings -12.08 -19.02 6.90
Rebels -15.29 -8.17 -7.10
Sunwolves -19.38 -17.76 -1.60

 

Performance So Far

So far there have been 135 matches played, 101 of which were correctly predicted, a success rate of 74.8%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Highlanders vs. Reds Jul 14 40 – 17 25.50 TRUE
2 Rebels vs. Jaguares Jul 14 29 – 32 -6.70 TRUE
3 Kings vs. Cheetahs Jul 14 20 – 21 1.30 FALSE
4 Sunwolves vs. Blues Jul 15 48 – 21 -20.90 FALSE
5 Chiefs vs. Brumbies Jul 15 28 – 10 10.70 TRUE
6 Hurricanes vs. Crusaders Jul 15 31 – 22 6.50 TRUE
7 Force vs. Waratahs Jul 15 40 – 11 -3.60 FALSE
8 Sharks vs. Lions Jul 15 10 – 27 -10.60 TRUE
9 Bulls vs. Stormers Jul 15 33 – 41 -2.20 TRUE

 

Predictions for the Qualifiers

Here are the predictions for the Qualifiers. 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 Brumbies vs. Hurricanes Jul 21 Hurricanes -10.80
2 Crusaders vs. Highlanders Jul 22 Crusaders 6.10
3 Lions vs. Sharks Jul 22 Lions 18.30
4 Stormers vs. Chiefs Jul 22 Chiefs -4.40