savagedata

6 ms·

1.
by savagedata 3y ago
Was PayPal trying to verify your identity or that you know your recipient's identity (who's coincidentally your mother)? I'm surprised if PayPal expected you to know your recipient's birthday, but "What's your
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by savagedata 4y ago
I've found them at HEB and Randall's too!
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by savagedata 6y ago
One of my favorite data science factoids is how "regression" (to return to a former state) came to mean "prediction of a continuous variable". In the late 1800s, Sir Francis Galton noticed that extremely tall or short pa
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by savagedata 6y ago
Santa Clara County (South Bay Area) shares a dashboard with cases by age group and deaths by age group: https://www.sccgov.org/sites/phd/DiseaseInformation/novel-co...
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by savagedata 7y ago
They approximated income, education, and ethnicity at the neighborhood-level (400-600 people) based on census data. That doesn't seem granular enough to me if the poorest families in each neighborhood live closest to the major roads.
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by savagedata 7y ago
The problem is with multiple-day recovery periods after the surgery. The later in the week your surgery is scheduled, the more likely you'll overlap with the weekend when care is hypothesized to be lower quality.
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by savagedata 7y ago
> Choosing Monday for a surgery increases your chance of success 2 times The title seems poorly worded. The researchers studied mortality rate, not success of procedures. Since these were elective surgeries, I assume the success rate was
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by savagedata 7y ago
You're looking to test the difference in proportions. I entered 0.93 / 1000 / 0.91 / 650 into this online calculator [1] and got a p-value of 0.14 which means that the difference is not statistically significant. Technic
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by savagedata 7y ago
When Irma was making landfall two years ago, I did an animation demonstrating this for r/DataIsBeautiful. [1] The real hurricane path is in red with the forecast at each timestamp in black. If you look at the forecasts through time, yo
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by savagedata 7y ago
There are always potential issues when a machine learning algorithm is applied over time. Example #1: Let's say that cancer rates are increasing over time and cameras are improving over time. You might end up with a weird artifact in y
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by savagedata 7y ago
Thank you for the useful feedback! I'll have to look up GUIDE trees. > This is interesting. I hear it was sqroot(total number of predictors). I was probably looking at the randomForest R package documentation [1], which says: > m
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by savagedata 7y ago
I wrote this regression tree tutorial a few years back that might be a good complement to the tutorial above since it covers regression instead of classification and goes on to talk about bagging vs random forest, out-of-bag samples, and tu
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by savagedata 9y ago
I wonder if this study takes into account that, because some professions are skewed toward women and others are skewed toward men, on average women will interact more with women than with men on a daily basis. Instances of incivility from w
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by savagedata 9y ago
I couldn't find a more recent source (this is from 2013), but it claims that 12% of SF (San Francisco-San Mateo-Redwood City, CA) jobs are in "high tech" and 29% of San Jose-Sunnyvale-Santa Clara, CA jobs. My guess is the dif
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by savagedata 9y ago
This is my favorite statistics factoid! Regression/reversion to the mean is the idea that if you observe an extreme value and remeasure it, it will tend toward the average value on the second observation. For example, students who have