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  2. Mean absolute percentage error - Wikipedia

    en.wikipedia.org/wiki/Mean_absolute_percentage_error

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  3. Mean percentage error - Wikipedia

    en.wikipedia.org/wiki/Mean_percentage_error

    Percentage error; Mean absolute percentage error; Mean squared error; Mean squared prediction error; Minimum mean-square error; Squared deviations; Peak signal-to-noise ratio; Root mean square deviation; Errors and residuals in statistics; References. Khan, Aman U.; Hildreth, W. Bartley (2003). Case studies in public budgeting and financial ...

  4. Mean squared error - Wikipedia

    en.wikipedia.org/wiki/Mean_squared_error

    This property, undesirable in many applications, has led researchers to use alternatives such as the mean absolute error, or those based on the median. See also. Bias–variance tradeoff; Hodges' estimator; James–Stein estimator; Mean percentage error; Mean square quantization error; Mean square weighted deviation; Mean squared displacement

  5. Symmetric mean absolute percentage error - Wikipedia

    en.wikipedia.org/wiki/Symmetric_mean_absolute...

    In contrast to the mean absolute percentage error, SMAPE has both a lower bound and an upper bound. Indeed, the formula above provides a result between 0% and 200%. Indeed, the formula above provides a result between 0% and 200%.

  6. Margin of error - Wikipedia

    en.wikipedia.org/wiki/Margin_of_error

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  7. Mean absolute error - Wikipedia

    en.wikipedia.org/wiki/Mean_absolute_error

    Mean percentage error; Symmetric mean absolute percentage error; References This page was last edited on 2 April 2024, at 22:32 (UTC). ...

  8. Forecast error - Wikipedia

    en.wikipedia.org/wiki/Forecast_error

    Forecast errors can be evaluated using a variety of methods namely mean percentage error, root mean squared error, mean absolute percentage error, ...

  9. Errors and residuals - Wikipedia

    en.wikipedia.org/wiki/Errors_and_residuals

    One can then also calculate the mean square of the model by dividing the sum of squares of the model minus the degrees of freedom, which is just the number of parameters. Then the F value can be calculated by dividing the mean square of the model by the mean square of the error, and we can then determine significance (which is why you want the ...