Why is it important to use adjusted r2 rather than r2 (the coefficient of determination) when comparing the fit of different regression models with the same dependent variable ?
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R² or the coefficient of determination represents the proportion of variance of depend variable that has been explained by the independent variables in the model.
If R²=90% , this means that 90% of the increase in depend variable is due to increase in independent variable.
One limitation of R² is that it increases by adding independent variables to the model which is misleading since some added variables might be useless. To overcome this limitation we use Adjusted R² .
Adjusted R² is modified version of the R² and takes into account the number independent variables in the model.
Adjusted R² will only increase if the added independent variable is useful for improving the model otherwise decrease.
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