Adjusted R Squared for Multiple Linear Regression Calculator

The Adjusted R Squared coefficient is a correction to the common R-Squared coefficient (also know as coefficient of determination), which is particularly useful in the case of multiple regression with many predictors, because in that case, the estimated explained variation is overstated/overestimated by R-Squared. The Adjusted R Squared coefficient is computed using the following formula:

\[\text{Adj. } R^2 = \displaystyle 1 - \frac{(1-R^2)(n-1)}{n-k-1}\]

where \(n\) is the sample size, \(k\) is the number of predictors (excluding the constant).

This solver is for a multiple linear regression. If you want to compute the Adjusted R Squared coefficient for a simple regression model, please use this adjusted R-Squared calculator for simple regression models calculator instead. Or, if you already know the value of the coefficient of determination \(R^2\), the use this R Squared to Adjusted R Squared calculator. Also, if you need to estimate the regression model, use this multiple linear regression calculator.

What is a Good Adjusted r-Squared for a Linear Regression Model?

The closer to 1, the better. In real life, getting an adjusted R-Squared coefficient very close to 1 is not that easy, as it would imply having some kind of a "perfect model", which is rare to find in real life.