Can someone with a stats background explain why the article claims there's a correlation? Those charts had R^2 values of .428, .344, and .403. I thought low R^2 values indicated no correlation.
I think of "correlation" as a measure of how much of the variation in one variable affects that in another. This explains why acceptable correlation levels vary between disciplines: one variable explaining 40% of the variation in another is pretty good in social sciences, where you expect all sorts of confounding variables, but quite bad in, say, (well designed) physics experiments.
Disclaimer: my statistics background consists entirely of one class (AP Statistics).
>one variable explaining 40% of the variation in another is pretty good in social sciences, where you expect all sorts of confounding variables, but quite bad in, say, (well designed) physics experiments.
I think this is part of the answer to your question, nhebb, but also I think we might have some confusion about r. In my understanding, (which is also limited to only AP Statistics) r is the value representing how well correlated the two categories are, and r squared represents how much of the correlation the categories you're using at that time accounts for. Does that sound right?
That makes sense, but I still have trouble believing the article's conclusion.
In my experience, people with advanced education work more hours. Doctors, lawyers, engineers, programmers, accountants,... the list of professions that require a lot of 40+ hour work week goes on. Most lower wage jobs, by contrast, work straight 40 hour work weeks
Also, is the data adjusted for cost of living? Why are they using average wages instead of median wages?
Overall, it just looks like a crappy study that doesn't lead to tangible insights.
