I assume you mean 3.3 kW, but I would be interested to see a source stating that this is enough to maintain a 20 degree (Celsius) difference between inside and outside for any "reasonably insulated" house.

There's no need to be offensive. It was you who said "10kW/h for 10 hours". You're using kW as a measure of energy, and kW/h as a measure of power. This is incorrect, as I've pointed out. "kW/h" would be a unit at the rate at which power changes.

Smart enough to know the difference between kW, kWh and kW/h. Here's a reference which may help you understand.

Firstly, according to your figures above (where you say 100 kWh is equivalent to driving 180 miles), 60 miles is equivalent to 33 kWh. This corresponds to 10 hours at 3.3 kW. 3.3kW is not enough to keep a house warm in subzero temperatures unless it has no windows.

Secondly, 10 kW/h means '10 kilowatts per hour', which has no physical meaning whatever. A Watt is a unit of power, not energy.

Note that kW is a unit of power, kWh is a unit of energy

Your figures weaken the support for your argument (that staying at home is greener than driving to work) significantly: they suggest it's greener to drive to work as long as it's less than 90 miles away from your home.

I disagree with your calculation, but the point remains broadly the same. Here's an alternative calculation. According to Wikipedia, 1 US gallon of gasoline is equivalent to 33 kWh, so 100kWh is equivalent to 3 gallons US. At 45 mpg US you can go 135 miles, not 180.

You can look at this the other way round. Driving 60 miles at 45mpg US is equivalent to 44kWh of energy usage. For it to be worth staying at home for 10 hours, you would have be able to keep your home warm with less than 4.4 kW. That's roughly the power output of two old-fashioned electric bar heaters. Do you get freezing temperatures where you live?

It's difficult to be more precise because the figures depend on lots of things - how cold is the place you live, how efficient is your car, how you define "equivalence" between gasoline and other energy forms - but you've illustrated the following point very well: most people wrongly assume that the energy required to heat their home is negligible relative to the cost of driving anywhere.

"Kw/h" is not a unit of either energy or power. However, I can confirm that 10kW is approximately the power needed to keep my house comfortable in the winter. I know this because I know the ratings of the radiators in the house at 60 celcius, and I also know that they need to be kept at close to 60 celcius more or less constantly.

Perhaps some numbers would be helpful here.

We have a small semi-detached house with cavity wall insulation, loft insulation, and double glazing. We still need over 10kW to heat in the cold months. If we're out of the house for 10 hours, we save roughly 100 kW hours (*). How far will your car go on that amount of energy?

(*) Of course, it's not that simple because we have to use extra heat to bring the house back to normal temperature when we get home. A more accurate analysis would compare the temperature-time graphs for the two scenarios and use Newton's law of cooling. Nevertheless, the above figures are roughly correct.

No necessarily. If you don't work at home, you also don't have to keep your home warm (or cool) enough to be comfortable during the day. The office, on the other hand, will be kept at a reasonable temperature whether you're there or not.

My wife's work is about 30 miles away, but she works from home most days. We calculated that, on the coldest winter days, the carbon cost of driving to work was about the same as the extra heating that would be needed if she stayed at home. If you have a shorter commute, or have a greener method of transport than driving a car, it's quite likely that it's greener to work in the office than to work at home.

You imply that the French, and indeed the rest of the world, eat significant amount of processed food. It's difficult to get hard data on this, but my impression (from having lived there) is that processed food is a much smaller part of their diet than in the US. This article says that Americans eat rather more processed food than other countries, but it's difficult to compare because "baked goods" and "ready-to-eat" in the US and in France are rather different.

On the other hand, "most of the world" is certainly not eating significant amounts of industrial food - in China and India it's almost unheard of.

Completely untrue. When the avionics 'crash', the flight system progresses through 'alternate' to 'direct' law where the pilot has direct control of the plane.

Have a look at the statistics (pages maintained by a pro-Boeing pilot, by the way) and you'll see (i) for all your hysterical fear of Airbus aircraft, the fly-by-wire Airbus aircraft (i.e. all except A300 and A310) are just as safe as their Boeing counterparts (ii) there are no examples of an Airbus crash caused by the computer overriding the will of the pilot.

I see your point, but if x1 and (x1+x2)/2 make very different predictions then you don't expect them both to be equally good at describing a data set generated by x1 - unless (i) your data are less informative than your prior, or (ii) unless your predictions are in a regime where the parameters are identifiable but the training data are in a regime where they are not. I admit that this might often be the case when trying to modelling real systems...

Sure, the posterior mean or maximum might be very different from the true parameter value, but the the true value should sit somewhere in the full posterior distribution. If parameters have non-identifiability issues then the posterior should be very flat, but if you base your predictions on the posterior then this will show up in the distribution of your predictions. I would have thought this would only lead to a bias if your prior were TOO informative, wouldn't it?

The phenomenon this guy has observed is nothing to do with chaos theory, as several posters think, but rather to do with error propagation and model uncertainty. This is an issue whether the model is chaotic or not. His mistake is to think that calibration has to choose a single set of parameters, and then one has to make a single prediction from the model. Statistical methods can take into account many sources of uncertainty, including the range of parameters that could have produced the original data and intrinsic stochasticity in the model. The best way to do this is using Bayesian techniques.

You're still limited by how realistic your model is, and this is likely to be the real problem with economic models. However, Carter's argument (that it's fundamentally impossible to fit a model to itself and then make consistent predictions) is wrong.

The publishers are lying here to protect their cash cow. What maintains quality is the peer review system (which the journals do not pay for). The transfer of copyright to the publisher allows them to hold Universities to ransom - universities cannot function without access to the literature (present and past), and the costs of online access to journals have been spiraling over the past few years at a time when the publishers' actual costs are going down. After all, they don't pay for the research to be carried out, nor do they pay the academic editors or the reviewers, nor do they even need to typeset the document now that everyone submits a machine-readable copy.

On the contrary, it can easily be defined.

No numbers actually `exist' - they're all an abstraction, even the integers.

Your objection assumes there is an end to the series. There isn't an end to the series, so your objection is moot.

The real question is: what do we mean by 0.999...? If it's a number with an infinite number of digits, then the arguments based on algebra are correct and 1=0.999...

This is only an efficient way of doing this if each `thing I want to work on' maps onto a single application, or a single window.

What if you're working on a number of projects, each of which requires the use of a spreadsheet, a graphics program, a web browser, and some software for writing or presenting? If you assign each project to a separate virtual desktop then switching between these projects is a one-click operation. Without virtual desktops, you spend a lot of time hunting through windows.