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Prikvačeni tweet
Do you own a car and drive less than 10,000 miles a year? You're wasting money, according to my analysis. https://kylascanlon.com/2020/01/22/ditch-your-car-ridesharing-is-more-cost-effective/ …pic.twitter.com/gT65ndQZKi
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Here's to new datasets and new code and new models! (Also as a PSA Google just released their dataset search tool. It's decent) END
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It was an attempt to model video game preorders as a form of currency and compare potential ROIs to btc, USD, gld (I know, I know). It was meant to be a fun project but I ended up data dredging, which is never ideal. There's value in starting again and freeing up brain space.
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Today I had to throw out an article I'd been working on for a while. The data were good. But the model wasn't. I was spinning my wheels. I will release a new article soon. One that doesn't require p-hacking to model a relationship that doesn't make sense.
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Kyla Scanlon proslijedio/la je Tweet
Tech Twitter TLDR is out:
Founders Fund takes on local gov via @micsolana
Germany's startup problem via @kevinakwok
Magic of ads via @jeffchang and@mrsharma + tweets from@scanlon_kyla,@Austen,@julien,@nwilliams030,@matasar, etc.https://techtwittertldr.substack.com/p/-what-happened-on-tech-twitter-last-e95 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
I've been following Michael & Ben for years. Huge honor to be featured on their latest podcast! Give it a listen, if you haven't already!https://twitter.com/michaelbatnick/status/1222505608349388803 …
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**Disclaimer: none of this is investment advice, and I have no affiliation with any ride-sharing company
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20/ Overall, the driving experience is pretty subjective. From a quantitative viewpoint, it’s sometimes cheaper to get a ride share. Qualitatively, it all depends on what you value.
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19/ EXTREMELY IMPORTANT This data doesn’t consider the changes that are coming with California AB5, which could radically change all of the model
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18/ There are a lot of reasons that the data could be telling this story, including how I priced cars and my personal ride-sharing data. However, ride-sharing is a better option for low-mileage users as compared to driving according to this analysis.
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17/ In California, it makes zero sense to own a car, according to these numbers. (I think this is a bit extreme, but I would imagine that it is more cost effective to use rideshare in a big city vs rural America)pic.twitter.com/m0MAPoCGRv
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16/ I’ve color-coded each to show the point where you would be better off driving. For most of the models, the cross-over point is between 9k –12k miles. Hybrid, electric, sedan: car cost > rideshare cost up to 9k annual miles. SUV: car > rideshare up to 12k miles
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15/ I held seconds constant in the analysis for the first two models and included rush hour in the output. For the U.S. analysis, I set indirect costs equal to $3,000 to tease out the cost of California living.pic.twitter.com/hBpXNlsaki
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14.5/ (i know it's text output. i have no excuses)
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14/ Compare Models The R-2 doesn’t vary too much between models despite the change in variables. Unfortunately, the R-2isn’t very high :( The addition of more variables and more data would improve that. My sample size was small and I only had three true variables.pic.twitter.com/LfRlnqwc6m
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13/ Model 3: Drop seconds, interaction term between dist and rush hour IT: assumption that the effect of distance on fare is different for rush hour vs non-rush hour Drive 1 mile at 5 pm = $30 4 am = $3 Proxy for surge pricing.
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12/ Model 2: Including a Dummy Variable for Rush Hour Rush hour: bt 6am — 9am and 3pm — 7pm (1) Any time outside of that window is non-rush hour (0) I had 40 rides that were outside of rush hour and 18 that were within rush hour specifications.
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11/ multicollinearity - high correlation between two variables. we can't have our predictors predicting each other. I address this by dropping seconds and creating an instrumental variable (Model 3)
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10/ Model 1: Seconds and Distance I ran a basic linear regression in R to see the predictive power of seconds & distance in determining fare, since that is what most ride-sharing models are based on HOWEVER 91% correlation bt seconds and distance = multicollinearity problem
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9/ To illustrate the issue of extrapolation, consider what Robert Chira once said about Apple’s valuation: “If you extrapolate far enough out into the future, to sustain that growth Apple would have to sell an iPhone to every man, woman, child, animal and rock on the planet.
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8/ This is a correlation plot between my 3 main rideshare variables, fare, seconds, and distance. The goal of this analysis is to predict the fare of the rideshares. I will be extrapolating my data out into the thousands of miles.pic.twitter.com/JhH62JFsPA
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