I've read what they mean on a technical level, but I'm trying to build intuition related to a specific problem.
So I have some data, a set of measurements. It's pretty curvy, looking like a cross between a norma and a gamma maybe. I do a qqplot of it with a normal distribution. The qqplot looks reasonably linear, it has a high correlation coefficient. I do a qqplot with a gamma distribution; same thing. I do a qqplot with a binomial; same thing. At this point I guess suspicious. So I do a qqplot with a uniform distribution, which is blatantly wrong if you look at it. However, I still get back a correlation coefficient that's reasonably high (>0.95).
I'm wondering why this is, and if what I did sounds similar.
Fundamentally, what good is a qqplot if this is the case? And is there some other metric besides this correlation coefficient that would be a better indicator of whether the data fits a certain distribution. What I'm thinking of in particular is something like the sum of squared errors that we use in the caes of linear regressions. Is there any way to get the square errors of the data with the distribution we're trying to fit to it? Does this make sense?