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precision. The value <strong>of</strong> RMSE is always positive, representing zero in the ideal case.<br />

The RMSE may be computed from the following equation<br />

3.2.3. t-Statistic Method<br />

<br />

RMSE = (y − x ) /n<br />

<br />

(3.2)<br />

After an <strong>estimation</strong> <strong>of</strong> a coefficient, the t-statistic for that coefficient is the ratio<br />

<strong>of</strong> the coefficient to its st<strong>and</strong>ard error.<br />

In the literature, Stone (1993) demonstrated that MBE <strong>and</strong> RMSE separately do<br />

not represent a reliable assessment <strong>of</strong> the model’s performance <strong>and</strong> can lead to the false<br />

selection <strong>of</strong> the best model from a set <strong>of</strong> c<strong>and</strong>idates. To determine whether or not the<br />

equation estimates are statistically significant, Stone (1993) proposed t-stat as:<br />

3.2.4. Relative Percentage Error (e)<br />

(n − 1)MBE<br />

t − stat = <br />

RMSE − MBE<br />

The relative percentage error is given by;<br />

e = <br />

<br />

(3.3)<br />

100 (3.4)<br />

where; y is the kth calculated value, x is the kth measured value. The “e” value<br />

provides the percentage deviation between the calculated <strong>and</strong> measured data. The ideal<br />

value <strong>of</strong> “e” is zero.<br />

19

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