I have a question on #7. I have not used the co-efficients to mean feature importance but some times get tempted to use them. How do you explain which factors are the most important factors behind some outcome to non-stat people?
But the article gives two points why you shouldn't use coefficient values to determine feature importance, which I think are only valid to some extent.
>a) changing the scale of the variable changes the absolute value of the coefficient
and
>(b) if features are multi-collinear, coefficients can shift from one feature to others.
Regarding a), well, that's what standardized coefficients are for.
b) is a bit trickier, but most regression models are based on the assumption of non-collinearity. This is of course a problem with real-world data, because you will quite often find some level of collinearity. That's when you (1) test for this issue and (2) look towards multilevel models.
In terms of explaining this to non-stats people, you might want to avoid explaining the p-values directly to them (as it's very easy for people to get confused about what p-values actually mean), so instead you might simply show them which features are "statistically significant". In other words, try to explain the results in a qualitative way rather than a strictly quantitative one.