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Estimated Variance of Prediction Error
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\(s^{2}_{f} = SEE^{2}\times [1 + \frac{1}{n} + \frac{(X_f - \overline{X})^{2}}{(n-1)\times s^{2}_{x}}]=\\=SEE^{2}\times [1 + \frac{1}{n} + \frac{(X_f - \overline{X})^{2}}{\Sigma^{n}_{i=1} (X_{i} - \bar{Y}_{i})^{2}}]\)

  • \(s^{2}_{f}\) - estimated variance of the prediction error
  • \(SEE^{2}\) - squared standard error of estimate
  • \(n\) - number of observations
  • \(X_i\) - i-th observation of the independent variable
  • \(X_f\) - forecasted value of the independent variable
  • \(\overline{X}\) - mean of the independent variable
  • \(s^{2}_{X}\) - variance of the independent variable