In regression analysis, what does R-squared indicate?

• A. The proportion of variance in the dependent variable explained by the model
• B. The slope of the regression line
• C. The correlation between dependent and independent variables
• D. The standard error of estimate

Answer: (A)

R-squared measures how much of the variation in the dependent variable is explained by the regression model. It represents the proportion of the total variance in the outcome that the predictors account for, with values between 0 and 1 (0% to 100%). A higher R-squared means the model captures more of what's causing differences in the outcome, but it doesn't imply causation and it doesn’t indicate the size of the relationship (that’s shown by the slope) or the precision of predictions (the standard error of estimate). In simple regression, R-squared is the square of the correlation between X and Y; in multiple regression, it extends to the variance explained by all predictors together.