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The adjusted R2 modifies the standard R2 to account for the degrees of freedom used when estimating model parameters. Adjusted R-Squared can be determined using the sum of squares formula. The degree of freedom is the only difference between R-square and Adjusted R-square equations. An adjusted R-squared value can be derived based on the r-squared value, the number of independent variables (predictors), and the total sample size.

R2 is not comparable across models with different dependent (Y) variables.

The adjusted R2 value is expressed as:

$R^{2}\, =\, 1\, -\, [(\frac{n-1}{n-k-1})\, \times (1\, -\, R^{2})]$

where:
n = number of observations
k = number of independent variables

## Why is calculating Adjusted R-Squared important?

Adjusted R square adds further value to our analysis and assists analysts in analysing whether the newly added factor is bringing more value or not. Hence, it assists analysts in making better-informed risk decisions.

Owais Siddiqui
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