Stats Chat

This is a good illustration of why they’re meaningless…

A recurring issue with trends over time is whether they are ‘age’ trends, ‘period’ trends, or ‘cohort’ trends.  That is, when we complain about ‘kids these days’, is it ‘kids’ or ‘these days’ that’s the problem? Mark Liberman at Language Log has a nice example using analyses by Joe Fruehwald.

If you look at the frequency of “um” in speech (in this case in Philadelphia), it decreases with age at any given year

On the other hand, it increases over time for people in a given age cohort (for example, the line that stretches right across the graph is for people born in the 1950s)

It’s not that people say “um” less as they get older, it’s that people born a long time ago say “um” less than people born recently.

‘Dodgy use of data’ edition [Background: the Washington Post is the serious DC paper. The Washington Times, not so much]

• From the Washington Post  “But really, is it possible that more than 1 in 6 people in France could “back” Islamic State? When you look at the numbers closely, something doesn’t add up.”

• From journalism/editing blog HeadsUp: “Too bad a clear conscience and a pure heart can’t turn correlation into cause, no matter what your first named source says.”

• From economics blog TVHE:  “For example, [Tourism Industry Association New Zealand] claims that 15%of Upper Hutt residents’ jobs depend on the tourism industry, while only 9% of residents’ jobs in Queenstown-Lakes District depend on tourism.”

• From linguistics blog Language Log: “I’ll merely observe that the Preamble to the U.S. Constitution scores 68% on the Latinometer, well over the “probably lying” threshold. “

Team Ratings for Round 25

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 176 matches played, 99 of which were correctly predicted, a success rate of 56.2%.

Here are the predictions for last week’s games.

Predictions for Round 25

Here are the predictions for Round 25. 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 3

Here are the team ratings prior to Round 3, 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 14 matches played, 10 of which were correctly predicted, a success rate of 71.4%.

Here are the predictions for last week’s games.

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.

Team Ratings for Round 4

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 12 matches played, 11 of which were correctly predicted, a success rate of 91.7%.

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.

Infographic edition

1. Thomson Reuters illustrated the importance of fine detail in graphic in one of their ads. It looks like a Venn diagram. Oops.

Removing the transparent overlap and changing the colours makes it less Venn-ish

2. Kevin Schaul in the Washington Post came up with this neat graphical summary of state data

Because the basic outline of the US is so familiar (especially to people who live there), the huge spatial distortions aren’t actually all that disturbing.  Mark Monmonier, a geographer, seems to have been the first person to move in this direction (eg). I suggested to Kevin, on Twitter, that this technique would also allow Alaska to be moved from the tropical Pacific to its proper home in the north, and he agreed.

3. That’ll wake you up

Jawbone, who make products that tell you if you are awake and walking around, looked at the impact of this week’s Napa earthquake. The data resolution isn’t quite fine enough to see the time taken for the ground waves to propagate — compare XKCD on the Twitter event horizon

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 29 2014.
• Statistics can be bad, exemplary or fascinating.
• The statistic must be in the NZ media during the period of August 23 – 29 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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The 3% ‘margin of error’ usually quoted for poll is actually the ‘maximum margin of error’, and is an overestimate for minor parties. On the other hand, it also assumes simple random sampling and so tends to be an underestimate for major parties.

In case anyone is interested, I have done the calculations for a range of percentages (code here), both under simple random sampling and under one assumption about real sampling.

Lower and upper ‘margin of error’ limits for a sample of size 1000 and the observed percentage, under the usual assumptions of independent sampling

Lower and upper ‘margin of error’ limits for a sample of size 1000 and the observed percentage, assuming that complications in sampling inflate the variance by a factor of 2, which empirically is about right for National.

From XKCD. Both the data and the display technique are worth looking at

Presumably you could do something similar with New Zealand, which is roughly the same shape.