Because Twitter never had outages before.

When social media gives someone salmonella, then come back and request regulation.

So you'd be totally fine with the Trump administration regulating how social media companies operate?

"Algorithmic bias" research is mostly trash. The more rigorous of it just pretends statisticians haven't been working on sample adjustment and balancing for causal inference for literally generations, and the less rigorous simply insists that we should ignore that things are not equally and identically distributed across population subgroups.

"Someone I don't like bought the company I worked for, so government should come in and tell him how to run his company"

Considering that Twitter is still working fine I'd say it's safe to assume the best and the brightest Twitter employees are still there.

With larger dots and labels (state abbreviations) this would be more interpretable.

Data Sources:

I collected membership numbers for the major subreddits for each programming language. TIOBE index is here: https://www.tiobe.com/tiobe-index/

Tools:

R, ggplot

Note I logged both scales due to the distributions.

MJ did not keep himself in shape the same way that LeBron has. So while MJ surely would be much closer, I do not believe it is likely that he would have eclipsed Kareem.

I applied your effort, OP, but without a negative second derivative this presents an unrealistic expectation of extending careers.

Corrupt government wants to control information flow and will totally not abuse it to suppress dissent.

The gang leaders declined to provide estimates of their income.

Fair point. Regions can be tricky. I used the region variable from the countrycodes::codelist.

Data: World Bank Data through WDI package

Tools: R, ggplot

Categories came from the data.

Data: World Bank Data for Life Expectancy. For Pharmacists Per Capita: https://www.theglobaleconomy.com/rankings/pharmacists_per_1000_people/

Tools: R, ggplot

Note: Relationship holds in panel model controlling for GDP Per Capita, with both fixed and random effects

Data:

https://www.pewresearch.org/religion/2018/10/29/methodology-23/

Tools: R, ggplot

Sure, go ahead.

Yeah I do agree that there is overfit both for Europe around the point that you specify as well as for the Americas. Oceania and Asia fit quite nicely.

Linear Model without region effects: R2 ~ 50%

Linear Model with region effects: ~ 63%

With random slope: ~ 70%

​

Polynomial model with random intercept: ~ 71%

Polynomial model with random intercept & random slope: ~ 74%

So...maybe worth it.

Used GAM since the relationship is nonlinear.

Data: https://en.wikipedia.org/wiki/List_of_countries_by_number_of_scientific_and_technical_journal_articles

https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(nominal)_per_capita

Tools: R, ggplot