1) Scott provides his data. The r is .418, which isn't mightily impressive. However, I think you could teach your students a about influential observations/outliers in regression/correlation by asking them to return to the original data, eliminate the 9 data points that are inconsistent with the larger pattern, and reanalyze the data to see the effect on r/p. Heck, just remove one or two inconsistent data points and let your students see what that does to the data.
2) Linear relationships. Correlations. Regressions. Generate an experiment to test the assumption that beer snobs just really like getting drunk (and, hence, this relationship).
3) For more beer figures, see here.
4) Take a look at that sample size (see at top of the figure). How does this make the data more reliable?
Why, no, I'm not above pandering to undergraduates.