“Air quality data saves lives. Where air quality is reported, action is taken, and air quality improves.”

Oh I so wish 🌝

Which place is this?

Depends on what we are doing with the data. A regression on it will optimise for global fit - thereby compromising predictions/inferences over the local imbalanced parts of the data.

medium.com/@saikrishna_...

We wrote about gaps in monitoring data and its interpretation of air quality.

urbanemissions.info/wp-content/u...

Stats gaps of representativeness, selection bias etc., creep into these datasets. Media, policy making should consider them before forming opinions.

I’m assuming this is Python for Data Science - this is a good free course www.wqu.edu/adsl-apply

I wrote a book in Telugu where I addressed a few harmful beliefs (astrology, homeo, superstitions, biases) using basic stats concepts.

This blog of mine is one such attempt.

medium.com/@saikrishna_...

In a Gaussian distribution. An uninformative prior need not have any closed bounds.

So MLE would be identical to Bayesian estimate.

But give a closed bound (say mean height is uniformly distributed but within 100-200 cm) — Bayesian posterior estimates will consider this close bound — not MLE