sheefrex

Unless you illustrate the problem you wish to solve, the solution is not going to make sense.

A great example of productivity spillovers across industries

Key extract:

At the same time, consumers preempt their future inertia; 24%-36% of potential subscribers avoid subscribing when offered an auto-renewal promo. Further, offering an auto-renewal contract decreases the share of subscribers over the two years after the promo by 10%. Even though auto-renewal generates higher revenue in the medium-run due to payments from inert subscribers, auto-renewal and auto-cancel are revenue equivalent after one year, but with fewer subscribers in auto-renewal.

That doesn't surprise me. Yes, would be interesting to see those assumptions, but I guess the issue (as in most modelling of complex systems) is that as you relax the assumptions, the models become intractable.

I opened this article hoping it would mention the point about the rules concerning reduced sulfur content of the fuel used in super tanker marine shipping, and it didn't disappoint:

"A lot of people have looked at the impact of the marine shipping regulation change. If you take that and you put it into some climate model and you estimate the temperature change, right now you’d expect about 0.05 of a degree, 0.08 of a degree [of warming per year], and then building over a decade to about 0.1 degree. So that seems like it helps, but it doesn’t seem like it’s sufficient."

Key extracts:

Based on the results of their modeling approach, cycling and high intensity interval training (HIIT) produced the most consistent effects in improvement of memory, attention, executive function, information processing and other cognitive functions.

"We found that vigorous activities had the largest effects," Giesbrecht said. "Also, the effects were strongest for studies that tested cognition after exercise, as opposed to during exercise. And lastly, the effects of exercise less than 30 minutes in duration were bigger than those that went beyond 30 minutes."

"Also among their findings, the team discovered that executive functioning was the key cognitive domain impacted by vigorous exercise, such as HIIT protocols."

It's probably true that not a lot of people make constructive use of their sleep data, but people just love learning about themselves: it's kind of a form of narcissism, unless you are experimenting with methods to shift the numbers some way. For my part, I use it for a lot of statistical tests.

Sometimes I like to think of GDP as a measure of economic (productive) power more than well-being. But other indicators are more important for the long-term, such as those on demographics, education, debt levels, etc. You probably don't want to sacrifice those for a transient boost in GDP.

Key extract:

On average, sleep/wake classification accuracies were reported to be 87.2% based on 53 assessed devices. There was no significant difference in accuracies between devices using only accelerometer data (86.7%, d = 28) and devices using both PPG and accelerometer data (87.8%, d = 22), as determined by a t-test (significance threshold p < 0.05). All reported accuracies ranged from 79% to 96%, except for Kanady et al.’s study28, which reported lower values of 54% and 64%. This difference can be attributed to their 24-hour measurement, which had a higher wake-to-sleep ratio compared to overnight measurements in other studies. Therefore, these accuracies reflect the generally poor performance of sleep classifiers in detecting wake. The average accuracy for 3-stage classification (wake vs. NREM vs. REM) was 69.7% (d = 3), and for 4-stage classification (wake vs. light vs. deep vs. REM), it was 65.2% (d = 9).