Counting web ads served to users as revenue generated by the computer's operating system is ludicrous. Oracle is trying to misrepresent the amount of money made so they can sue for damages. The numbers are BS.
White Flight is not a recent change. It's primarily associated with the post WWII movemnt of white people from cities to the suburbs. If anything its inverse white flight that is driving gentrification now.
Well, if you are trying to measure whether men founders or women founders you have funded on average make more, then you would have to include Uber. The real issue with the analysis is that results are unlikely to be statistically significant due to small samples and high variance, which means they are useless.
It's still BS. Outliers are a signal that you don't have a simple, nicely decaying distribution.
The right way to deal with outliers is to use a method that acknowledges their existence, not to ignore them. For example, if outliers destroy your OLS linear regression, it's because your error is not normal. That means you need to do Bayesian linear regression with a non-normal error term, not just throw them away.
> In many instances outliers can be just invalid measurements and you should ignore them.
signal[i] = value[i] + noise[i].
If you know that value[i] == NaN, then by all means throw out signal[i]. If value[i] != NaN, then you're better off modeling error[i], and using that model to give you information about value[i] as yummyfajitas suggests.
This is trivial to see if noise[i] == 0, but for some reason becomes progressively harder for people as noise[i] increases.
Statistics 101. When you have samples you throw away the highest and lowest member, to counteract some random occurrence. The mean net worth of the patrons in any restaurant carlos slim frequents rises substantially when he is there.
That, and the fact that outliers can often be discounted due to measurement/instrumentation error.
Moreover, the fact that Carlos entered your restaurant may be a significant event depending on the analysis that you're attempting to do. So you need to have to have a good rationale for dropping outliers, and you should probably also watch for bias when dropping outliers that don't support your hypothesis!
Yes, because that is how mean net worth is defined. I don't see specifically what that argues against, except that mean is not the best indicator to use in all situations; perhaps a different indicator is appropriate, such as the income per patron by percentile. 100th percentile will be Carlos Slim, but 99th percentile and lower will be other patrons.
If Carlos Slim actually does frequent the casino, then his attendance is an important part of understanding the situation.
Google ads linked to site that contained a malware laden download of google ads directly served an exploit? The parent is talking about the latter, which I have not heard of happening to Google. Do you have a link about that?
I do remember hearing about the ad for a third party Firefox download bundled with a malware toolbar. Which, while bad, is quite different.
> of course high income folks pay 10-20% more of their income in taxes than low income folks.
This is blatantly false. The effective tax rates for the super rich are much lower than the average person, due to the majority of their income coming from capital gains. For example, Mitt Romney paid 14% on the 13.7M he made in 2011.
No need to use such vitriolic language when I think it is clear what the parent comment was trying to say. People making 6 figures are paying anywhere from 3 to 25% more of their income than a person earning median income in the US. For reference, see the base tax rates for 2013