Let's say I test how variable
Y depends on variable
X under different experimental conditions and obtain the following graph:
The dash lines in the graph above represent linear regression for each data series (experimental setup) and the numbers in the legend denote the Pearson correlation of each data series.
I would like to calculate the "average correlation" (or "mean correlation") between
Y. May I simply average the
r values? What about the "average determination criterion", $R^2$? Should I calculate the average
r and than take the square of that value or should I compute the average of individual $R^2$'s?