Anyway Mrs Blog pointed out that some people were made happy by raping people. I think that she was making the point that this sort of happiness was not desirable, but I am more interested in the problem of measuring such happiness.

My solution is to contact a random sample and collect where possible an anonymous blood sample – obviously it has to be very anonymous to deal with our hypothetical rapist. This can then be tested for serotonin levels to provide an accurate trend information on average happiness levels.
The detailed questions as above can then be collected separately from a different sample where they can be treated with the respect they deserve.

Robert Vale, one of the authors, has said that poultry and rabbits have a lower impact than red meat and fish when used as pet food. “When feeding a pet… the advice is to think feathers and long ears, not horns and fins,” he said.>>

OK, I suppose that the energy used to make a volkswagen is spread over the whole of the cats expected lifespan – but even so this seems a little surprising – I realise that that is the point but

Can we all join in? Please post your most amazing statistics about energy use here. Note that no proof is required.

More than two million images of child sex abuse had been circulated by 100 offenders who went on to be convicted in the last 20 months, the NSPCC says.

Ms Sutton added: “The scale of graphic child sex abuse pictures and videos over the internet is very alarming.

“The number of images seized in these cases is enough to cover the pitch at Wembley Stadium twice over – and this is just a sample.”

I wonder how they know what resolution the Wembley pitch is set to. Given all the complants about it – its probably pretty low.

They never said that to me because I did history but isn’t that roughly the speech?

Specifically the raw data was showing no trend and the adjusted data was showing an upward trend. We ODHs know that if you can’t see it in the raw data, it probably isn’t there. Now that’s not always true and sometimes the data needs to be transformed in an amazingly elegant way and suddenly it fits a new pattern. Then you might get a Nobel Prize, or maybe a word of thanks – but it’s mostly it doesn’t happen.

So I was even more interested in ‘Climategate USA’, the revelation that the number of observation stations used in producing the GISS Land-surface air temperature anomalies (Global Temperature data for the newbies) had fallen from over 6000 to about 1500 during the 1990s and onwards. If there’s one thing we ODHs know, it’s that the easiest way to find what you expect in the data is to select the observations that you include in the result.
(The second easiest way is to find something else to normalise it against!)

There are a lot of caveats and speculation after the exciting graph.

So I did a bit of work with the raw data that was made available through realclimate.org (Climate Science from climate scientists) which is the GHCN v.2 (Global Historical Climate Network: weather station records from around the world, temperature and precipitation) . I had previously checked that I got the same pattern from this data regarding the New Zealand divergence and also the Darwin station fuckup, so I am reasonable sure that I am dealing with the right sort of data.
I certainly get the right sort of numbers against the station count.

I thought that if the stations observed vary from year to year, it might be interesting to look each year in terms of ONLY stations that reported in that year and the previous year AS WELL (we sometimes call this a constant sample).

Obviously the absolute temperatures can vary very considerable, if a lot of cold stations were included in Years 1 and 2, but dropped in 3, then the Year 2 absolute would be much lower than the Year 3 absolute. However I can’t really see why the change between the two years shouldn’t be a fairly valid statistic.

I can then apply the change to a base year and compare the trend between GISS, and My Constant Station trend. My data is of course unweighted by geography and has no interpolation. So I include my All Station data (also re-aligned to starting point GIFF base temperature).

I can then take the difference between my result for the constant sample stations and my result for all stations and apply that difference to the GISS temperature data (I’ve used absolute difference rather than an index ) to show what the GISS data might have looked like on a constant sample basis. (GISS+Constant sample)

Notes and queries: Am I comparing Apples and Pears? I don’t really know – I’m using the GISS Global Land Station data so I hope not.

The series starts in 1946 as this is a bit of ground zero in the GISS data I’m using with a zero degree anomaly.

Things I haven’t take account of are probably too many to mention. The number of observations in a year from a station are taken into account in the averaging but if one year is January only and the next July only I haven’t bothered. I reckon these things tend to average out.

One interesting thing is how much more variable year to year the GISS data is than my All Station data – that’s the result of me using the data straight and not weighting the stations in any way (for instance by interpolation). I know this is land temperatures only, but logically I expect global temperatures to be pretty consistant year to year – of course possibly with a trend – because it’s the same whole Earth – is that a wrong assumption?

Of course the same analysis is possible for any country with a reasonable number of sample stations – although I haven’t done it
And by the way Jon Sadleir is a cunt.

Basically the number of weather stations used was 5000+ up to about 1990 and now its down to about 1500. Do they show a different trend? They certainly appear to.
I divided the stations into those which are live in the data at some point from 2005 onwards and the chart below shows the trend for those stations compared to all the others (from 1910 to 2009). So the ‘All Others’ line goes to 0 from 2005 onwards.

To me this really looks like the smoking gun that Climategate didn’t produce – sorry to all me ‘regular readers’ for using this site to actually produce some data rather than comment on other peoples rubbish.

http://www.guardian.co.uk/society/2009/nov/03/child-obesity-levelling-off

A separate opinion poll yesterday suggested that 50% of obese people earn less than the national average income. The Liberal Democrat shadow health secretary, Norman Lamb, said: “This report makes for particularly disturbing reading as it highlights the worrying link between poverty and obesity. Until we stop trying to dictate policy nationally and give people the freedom to tackle public health problems locally, this cycle of poverty and ill health is likely to continue.”

And I’m not even going to point out that because of high earner distribution, over 50% of everyone earn less than the national average income – so actually the fatties are doing well.

I hope that the report producers: Families Need Fathers, the Grandparents’ Association and the Family Matters Institute aren’t including dead grandparents in order to create a more exciting number. I think that would be cheating. The Daily Telegraph has the same number but mentions that the sample size is a whopping 211. Were they perhaps recruited from the ranks of Families Need Fathers, the Grandparents’ Association and the Family Matters Institute?

Crapstats Rating: 1. Honesty score: 0