Stats Chat

First, from Mother Jones magazine, via Twitter

The impact of the carbon tax looks impressive, but this is a bar chart — it starts at zero and they’ve only shown the top fifth of it.

They do link to the data, the quarterly Greenhouse Gas Inventory update.  In that report, Figure 8 is

The dotted line is the same data as the bar chart, except that the dotted line has data for every quarter and the bar chart has data only for the July-September quarter each year. And  the line chart has a wider range on the vertical axis — it doesn’t go down to zero, but it isn’t a bar chart, so it doesn’t have to. The other point about the line chart is that there’s a solid line there as well. The solid line is adjusted for seasonal variation and weather. If you wanted to know about real changes in how Australians are using energy, that’s the line you’d use.

Second, a beautiful map of CO₂ emissions from fossil fuel combustion, from the Washington Post via Flowing Data

The ‘vertical’ scale here is a colour scale; what’s misleading is that it’s a logarithmic scale. The map makes it look as if a large fraction of CO₂ emission comes from transporting stuff through empty areas, but the pale beige indicates emissions thousands of times lower than in the urban/suburban areas. Red ink isn’t anywhere close to being proportional to CO₂.

So, I was on a plane from Sydney yesterday that was old enough they told us to switch off our books half an hour before landing. As a result, I actually looked at the Auckland information on the flight map channel (photo taken after we were allowed technology, naturally):

It’s interesting to see where these numbers come from, given all the different ways these things can be defined. Two of these numbers are inconsistent with each other and somewhat obsolete, and the third isn’t even wrong.

According to the Google, the population number 1,377,200 is the June 2011 estimate of the urban population of the Auckland metropolitan area.  Ok, that’s a bit old but so was the plane. Slightly more strange is that StatsNZ thinks the urban Auckland population at 30 June 2011 was 1,351,200, but that’s probably a matter of projections being made in advance and then adjusted as more information comes in. The current (June 2014) estimate is 1,413,500.

So if the population is urban Auckland, what’s the area? With a bit of searching, you can find it’s the area of the old Auckland City, the central Auckland isthmus plus various islands. Auckland City had a population of 450,000 when it was absorbed into the Supercity in 2010. The population and area numbers are for very different entities, and the population number, although old, dates from after the area number became completely obsolete.

The area that goes with the 1,377,200 number is 1,102.9km², the size of the Statistical Urban Area. You could reasonably want the urbanised area (483km²) or the Metropolitan Urban Limits (560km²) as better summaries of the size of Auckland, but they don’t match the quoted population.

That leaves elevation. The picture next to the statistics shows that 78m is not a completely satisfactory characterisation of the elevation of Auckland. The blue stuff with boats floating on it is at sea level (up to tidal variation). Here’s a map (from FloodMap.net) of Auckland elevation; the change from pink to red is at 75m.

Overall, population and area, which could have multiple satisfactory definitions, are defined incompatibly with each other.  Elevation doesn’t really have a satisfactory definition, but isn’t 78m.

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 April 10 2015.
• Statistics can be bad, exemplary or fascinating.
• The statistic must be in the NZ media during the period of April 4 – 10 2015 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 5

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.

Performance So Far

So far there have been 32 matches played, 19 of which were correctly predicted, a success rate of 59.4%.

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.

Team Ratings for Round 8

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.

Performance So Far

So far there have been 47 matches played, 31 of which were correctly predicted, a success rate of 66%.

Here are the predictions for last week’s games.

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.

The Herald has pictures of the most beautiful faces in the world

and NPR reports on a computer algorithm that can tell if you sound trustworthy or calming or engaging.

The Herald story at least admits these faces are only world-famous in New Zealand (or, rather, the UK)

“It’s important to note that these are the idealised faces according to those living in the UK, so a study in Asia or Africa for example would no doubt have different results.”

The NPR story instead doubles down by saying

But algorithms have stamina, and they do not factor in things like age, race, gender or sexual orientation.

There’s a sense in which this is true, but it’s not a very useful sense. If we can guess age, race, or gender from the sound of someone’s voice, and these perceptions affect whether we think the voice is engaging,calming or trustworthy, our prejudices will show up in the training data and any competent black-box algorithm will learn them.

New South Wales has just voted, and the new electorate created where I lived in Sydney 20 years ago is being won by the Greens, who got 46.4% of the primary vote and currently 59.7% on preferences. The ABC News background about the electorate says

In 2-party preferred terms this is a safe Labor seat with a margin of 13.7%, but in a two-candidate contest would be a marginal Green seat versus Labor. The estimated first preference votes based on the 2011 election are Green 35.5%, Labor 30.4%, Liberal 21.0%, Independent 9.1, the estimated Green margin after preferences being 4.4% versus Labor.

There was definitely a change since 2011 in this area, so how did the polls do? Political polling is a bit harder with preferential voting when there are only two relevant parties, but much harder when there are more than two.

Well, the reason for mentioning this is a piece in the Australian saying that the swing to the Greens caught Labor by surprise because they’d used cheap polls for electorate-specific prediction

“We just can’t poll these places accurately at low cost,” a Labor strategist said. “It’s too hard. The figures skew towards older voters on landlines and miss younger voters who travel around and use mobile phones.”

The company blamed in the story is ReachTEL. They report that they had the most accurate overall results, but their published poll from 19 March for Newtown is definitely off a bit, giving the Greens 33.3% support.

(via Peter Green on Twitter)

The vertical axis on a bar chart must start at zero. The very rare exceptions are ones that prove the rule: where ‘zero’ isn’t zero. Otherwise, the axis starts at zero or it isn’t a bar chart. The whole point of bar charts is that the length of the bar is proportional to the data value.

Line charts and scatterplots are different.  They don’t need to be tied down to zero, and the axis scales can be chosen to make the information as clear as possible. With great power comes great responsibility, as we can see from the following pair of line graphs of oil drilling in the US.

It’s pretty obvious that these come from people with different communications agendas. Or, it would be, except they are from the same story at Bloomberg.

Neither graph has an ideal aspect ratio. The flat one is too flat: you can’t see the wobbles over time in number of rigs. The tall one is too tall: the number of rigs has halved, but it looks as though it has crashed much more than that.

Bill Cleveland has a useful default rule for scaling line graphs: the median slope of the line segments should be about 45 degrees. The orange line on the tall graph isn’t far off that, but the blue line is steeper.  The 45-degree rule would give a graph like this:

In fact, there is plenty of room to start the blue axis at zero, but that’s not always the right choice.

Here, in a sadly-appropriate pairing, is the Keeling Curve, the graph of atmospheric CO₂ concentrations at Mauna Loa observatory, in a visualisation paper from Berkeley.

There’s no sense at all in having the vertical axis start at zero. Zero is just not a relevant value of atmospheric CO₂. What’s more interesting, though, is how the two scalings show different information. The upper graph is scaled so the year-to-year changes have slope centred at 45 degrees. This makes it easier to see that the CO₂ increase is accelerating. The lower graph is scaled so the month to month changes have slope centred at 45 degrees, making it easier to see the shape of the seasonal pattern.

Different vertical scaling can be used just to mislead the reader, but it can also be used to make data more readable and to communicate more effectively.

• Two data-related notes about the Northland by-election: the polls were amazingly accurate given how hard by-elections are to predict, and the Electoral Commission did a wonderful job in getting the vote counted and reported fast.

• A graphic of gender inequality around the world.  The income graph is not the usual one, because it is total income, not income per hour worked — most of the gap is in fewer hours of paid employment.

• According to the Independent “California is in one of its worst-ever droughts because people are growing too much weed”. Unsurprisingly, not what the research actually says

• The Medical Council of New Zealand has released a Discussion Paper on the value of performance and outcome data.

• From Significance magazine, a description of how the various political polling companies in the UK work

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 April 3 2015.
• Statistics can be bad, exemplary or fascinating.
• The statistic must be in the NZ media during the period of March 28 – April 3 2015 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.

(more…)