In linear regression, we often get multiple R and R squared. What are the differences between them?

**Answer**

Capital R2 (as opposed to r2) should generally be the multiple R2 in a multiple regression model. In bivariate linear regression, there is no multiple R, and R2=r2. So one difference is applicability: **“multiple R” implies multiple regressors, whereas “R2” doesn’t necessarily.**

Another simple difference is interpretation. In multiple regression, the multiple R is the **coefficient of multiple correlation**, whereas its square is the **coefficient of determination**. **R can be interpreted somewhat like a bivariate correlation coefficient**, the main difference being that the multiple correlation is between the dependent variable and a linear combination of the predictors, not just any one of them, and not just the average of those bivariate correlations. **R2 can be interpreted as the percentage of variance in the dependent variable that can be explained by the predictors**; as above, this is also true if there is only one predictor.

**Attribution***Source : Link , Question Author : RockTheStar , Answer Author : Nick Stauner*