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Uber is pretty great, and I enjoy using it, but many of these statistics are pretty weak and out of context. As others have mentioned, using "income" to describ
by rusbus 12y ago
Uber is pretty great, and I enjoy using it, but many of these statistics are pretty weak and out of context. As others have mentioned, using "income" to describe "revenue" is inaccurate. You can't compare making $90k driving an Uber to $90k at a desk job with a completely different set of expenses.
While the statistics used to determine the DUI rate in SF and Taxi crime in Chicago are beyond the levels of my knowledge and I can't verify their correctness, the calculations certainly seem a bit suspect. For example, as a pair city when doing a discontinuity test, they chose San Francisco, a city which notably, has Uber. The post doesn't mention a confidence interval or P value in a clear explicit way (outside of the table, with unexplained column headers).[1]
In the infographic, they claim these statistics as facts without mention of any underlying confidence intervals. I'm pro Uber, but this post seems to contain a lot of fuzzy math. It'd be interesting if someone with a statistics background could confirm or deny these suspicions...
[1] http://blog.uber.com/DUIratesdecline http://blog.uber.com/DUIratesdecline
- shakethemonkey 12y agoFor taxis, medallions are usually leased to drivers (few could afford one). This takes much of their revenue -- perhaps half or more surrendered to a medallion holding company just for the privilege of driving a taxi. New York City presentation suggests a driver with $90k revenue has $41k expenses. [1] [1] http://www.nyc.gov/html/tlc/downloads/pdf/presentation.pdf http://www.nyc.gov/html/tlc/downloads/pdf/presentation.pdf
- YokoZar 12y agoThey did give a P value, it's the final column labelled Pr (>|t|). The findings are significant. They also gave the standard error, which you can use to compute your own confidence interval (95% would be about 2x, or ranging from no effect to a 20% reduction)