In multiple regression, R2: (a) is the square of the multiple correlation coefficient; (b) would be unchanged if we exchanged the outcome (dependent) variable and one of the predictor (independent)...

In multiple regression, R2:

(a) is the square of the multiple correlation coefficient;

(b) would be unchanged if we exchanged the outcome (dependent) variable and one of the predictor (independent) variables;

(c) is called the proportion of variability explained by the regression;

(d) is the ratio of the error sum of squares to the total sum of squares;

(e) would increase if more predictor variables were added to the model.