We often square coefficients like the R coefficient in simple/multiple linear regression or standardized factor loadings to get a percentage of variance accounted for by predictor variables.
Can the same principle be applied to a Pearson correlation coefficient? Wikipedia suggests that
When an intercept is included, then r2 is simply the square of the sample correlation coefficient (i.e., r) between the observed outcomes and the observed predictor values
But in a simple Pearson correlation coefficient, intercepts are not included. So is squaring still appropriate to determine the percent of shared variance between variables?