Discussion
In a multiple linear regression setup, the adjusted coefficient of determination accounts for :
Answer: C
The adjusted coefficient of determination accounts for the number of regressors in the model.
\[ \bar{R}^{2} = 1 - (1 - R^{2})\frac{n - 1}{n - k - 1} \]
where k is the number of regressors. Ordinary \(R^{2}\) never falls when a variable is added, even a useless one; the adjustment penalises extra regressors, so \(\bar{R}^{2}\) rises only when a new variable improves the fit by more than chance would.
It does nothing special about outliers or unusually large fitted values ('valves' in the paper), which are handled by residual and influence diagnostics.
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