Interview Question: Goodness of Fit

Sample Question #304 (statistics)
Explain the difference between R2 and adjusted-R2. When is R2 (or adjusted-R2) not useful?
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One Response to Interview Question: Goodness of Fit

  1. Brett says:

    Adjusted-R^2 takes into account the number of regressors. In a linear regression, the more regressors you add, the higher the R^2 is (since adding regressors cannot explain less of the variations in the dependent variable). Adjusted-R^2 adjusts the original R^2 by the degrees of freedom to arrive at a goodness of fit measure that’s independent of the  number of independent variables.
    R^2 is only useful when you have an unconstrained linear regression. It’s no good in all other regressions: nonlinear, constrained, censored, truncated, discrete, etc.

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