Appendix D: Coefficient of Determination (R-Squared)

The coefficient of determination ($R^2$) is used in the context of statistical models which we wish to use to predict future outcomes. The R^2 is defined as the proportion of the variability in the sample data that is accounted for by the statistical model. The $R^2$ serves as a goodness of fit measure.

For a given data set with observed values ${y_i}$ and an associate model's values $\{ \widehat{y_i} \}$, the variability of the data set is measure as the sum of squared differences.

$$R^2 =\frac{SS_{reg}}{SS_{tot}}=1-\frac{SS_{err}}{SS_{tot}}$$


$$SS_{tot} = \sum_{i=1}^N{(y_i-\overline{y})^2}$$ $$SS_{reg} = \sum_{i=1}^N{(\widehat{y_i}-\overline{y})^2}$$ $$SS_{err} = \sum_{i=1}^N{(y_i - \widehat{y_i})^2}$$

  • $\overline{y}$= the sample average of the observed values
  • $SS_{tot}$= the total sum of squares
  • $SS_{reg}$= the model (e.g. regression) sum of squares
  • $SS_{err}$= the sum of the squares of the residuals (residuals sum of squares)
  • $N$= number of observations

To factor in the number of explanatory variables in the model, the adjusted $R^2$ (or $\overline{R}^2$) is used as a modification.

$\overline{R}^2$ is defined as follow:



  • $p$ = number of explanatory variables
  • $N$ = number of explanatory variables


  1. The adjusted $R^2$ is not a test of the model in the sense of hypothesis testing, but can be used as a tool for model selection



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