“At the extremes, other factors may weigh more.”
Nothing that hasn’t been said before, and in my opinion better.
I don’t particularly like your “ellipse” generalization, either, because it’s just wrong. We already know a perfect correlation would be linear. We already know a lesser correlation is “fatter”. Bringing ellipses into the issue is just an intuitive, illustrative fiction, which I really don’t appreciate very much because it’s not particularly informative and it isn’t scientifically sound at all.
Please don’t misunderstand me: I do think it is illustrative, and I do think it has its place. In the newby section maybe.
Understand, I am aware that may come across as overly harsh, but it isn’t meant that way. I’m not trying to be impolite. It’s just my opinion and I honestly don’t know a better way to express it right now without being dishonest.
What isn’t “concrete” about it? I think the whole article is an exercise in stating the obvious, to those who have had basic education in statistics. Stricter correlations tend to be more linear. A broader spectrum of data points is pretty much by definition “fatter”. I don’t see how this is actually very instructive. And to be honest, I don’t see how I could be much more specific.
You mean you’ve never had a statistics class? Honestly? I’m not trying to be snide, just asking.
Extreme data points are often called “outliers” for a reason. Since (again, almost—but not quite—by definition, it depends on circumstances) they do not generally show as strong a correlation, “other factors may weigh more”. This is a not a revelation. I don’t disagree with it, I’m simply saying it’s rather elementary logic.
Which brings us back to the main point I was making: I did not feel this was particularly instructive.
Wrong in the sense that I don’t see any actual demonstrated relationship between his ellipses and the data, except for simple, rather intuitive observation. It’s merely an illustrative tool. More specifically:
This is an incorrect statement. What he is offering is a way to describe how data at the extreme ends may vary from correlation. Not “why”. There is nothing here establishing causation.
If we are to be “less wrong”, then we should endeavor to not make confused comments like that.