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wikiwawa

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Note that the regression is on the differences, y_t - y_t-1 not the levels, just y_t.

So the argument is more subtle, that the "change" in AirBnB share is correlated with changes in rents. So level effects such as being a nice neighborhood are accounted for.

Having said that, a large change in AirBnB share is probably still related to unobservables related to factors that would increase rent. E.g changes in hipness of a neighborhood unaccounted for by changes in demographics. Therefore the $616M figure is biased upwards, though I bet in reality still a large number.

I understand sarcasm, that's for sure.

As a matter develops, parties tend to drop out if they think that their chances have declined beyond some threshold. But if they continue, or more likely, their legal representation suggests they continue, it implies that they think they have a good chance of success.

Of course, you can get irrational litigants but most of the time, if it goes to judgement, both parties think they have a high chance of success. Otherwise they would have bailed.

A large part of statistics is devoted to making valid inference from noisy samples, largely by making sensible assumptions. Even in your example, it is common practice to assess a surgeon on outcomes of surgery, even though a lot of a surgeon's work does not lead to surgery.

We aren't claiming to assess the wide practice of law, just litigation. We do Litigation Analytics.