Subsidization between channels is actually a lot more complicated than people think. Assume that channel A is a popular station, and channel B is unpopular. Maybe 500,000 people watch channel A during the month and 10,000 watch channel B. Further, let's say that the cable company pays $20/month/subscriber for channel A and $5/month/subscriber for channel B. If you only watch channel B, then it seems like you are getting shafted, since it seems like you are paying $25 to only get $5 worth of benefit.

But the problem is that the denominators typically used to present cost/subscriber does not take into account whether the subscriber watches the channel or not. So in the above case, if there are 1,000,000 total subscribers to the cable system, then channel A charges $20,000,000/month and channel B charges $5,000,000/month. If the channels were a la carte, channel A would charge $40/month to the 500,000 subscribers that want it in order to make $20,000,000 per month (40 x 500,000 = $20,000,000), and channel B would have to charge $200/month to each of its 10,000 subscribers.

So are the channel B watchers paying too much or too little when bundled?

Despite R's weaknesses as a programming language, R has such a large number of well-documented, well-tested, statistical functions with a wide array of arguments to vary that it is very difficult for another language to match. For example, maybe you want to build an arima time series model. OK, not too tough to find a library in Python or C++ that does that. Now what if you want to add an exogenous variable to the arima model? Maybe a seasonal component? Next maybe you want to automatically pick the best model according to AIC? Oops, make that BIC. Looking at it again maybe a Vector Autoregressive model is best. Or a VECM?

While I'm sure there are excellent implementations of all of these wrinkles in other languages, with R, I have great confidence that the functions that I want and need now and in the future are going to be there and are going to be implemented correctly, and kudos to the R team for giving us that kind of confidence.

R does have a lot of problems, among the worst is loop performance. It really forces you to vectorize everything, which leads to less maintainable code, and is generally a coding technique that new hires coming from other languages will face a steep learning curve with. What I have found useful is to use R as a data exploration and model parameterization tool, but once the model is ready to be put into production, you can use the parameters calculated by R in an implementation in the language of your choice, e.g., C++.

I guess this is a long winded way of saying that as with so many questions of "which language is best," the real question is "which question is best for you and your application?" R is usually the best language only for people who are regularly using a such a wide variety of statistical analyses that you won't find a large part of what you need in the libraries of other languages. For me, I couldn't imagine working without it.

From the dictionary:
Origin of CREDIT
Middle French, from Old Italian credito, from Latin creditum something entrusted to another, loan, from neuter of creditus, past participle of credere to believe, entrust

While your justifiable vigilance is a valid point in favor of Bitcoins, I am not sure about the long-term prospects for a currency whose main selling point is that you don't need to trust anyone or anything in order to use it. The ultracautious attitude displayed by Bitcoin proponents does not seem compatible with the granting of credit, which, (in its modern form) has been at the foundation of commerce since Renaissance Italy.

As long as Bitcoin users and miners are predominantly less willing to trust, and Bitcoin intermediaries like Mt Gox generally prove themselves untrustworthy, Bitcoin will be unable to be a successful currency. That is, Bitcoin will not be a robust medium of exchange that is able to support growth in the economy, because credit granted and received in Bitcoins will be so limited. (Although it could continue to be a commodity, whose main use is not to be used in direct exchange for goods and services).

A good classifier will take into account interactions as well, so if 80% of the people who like both Top Gear and Glee are heterosexual, then they can still get some predictive power. Further, if 90% of people from your city are heterosexual, they could take that into account as well. And that doesn't consider the internal stuff that Facebook knows (and can sell to advertisers), such as the demographic profile of the people near your IP address.

It's not just averages of society, it is individual profiling. At it's most benign, it will target ads that it thinks your demographic profile will be most responsive too, but at it's worst, it could certainly be (mis-)used for individual persecution.

This. But it is an argument between someone with the car data recorder versus someone who buys ink by the barrel, so get your popcorn, pull up a seat, and enjoy the show!

He has numbers, but where did they come from? Does anybody looking at the story from this distance have any way to verify that the data in the blog post is unadulterated, accurate data from the trip in question? Just because someone uses a bunch of numbers in their argument they shouldn't automatically win the day. Look, I highly doubt he's making up the data but the question of whether he is shouldn't be dismissed out of hand.

A quote from Matt Yglesias: "Amazon, as best I can tell, is a charitable organization being run by elements of the investment community for the benefit of consumers."