# Table 8 Used prediction accuracy measures

ID Name Equations
1 Least absolute deviations (LAD) $$LAD=\sum \limits _{i=1}^{n}\left| y_i-\hat{y}_i\right|$$
2 Mean absolute error (MAE) $$MAE=\frac{1}{n}\sum \limits _{i=1}^{n}\left| y_i-\hat{y}_i\right|$$
3 Mean squared error (MSE) $$MSE=\frac{1}{n}\sum \limits _{i=1}^{n}\left( y_i-\hat{y}_i\right) ^2$$
4 Root mean squared error (RMSE) $$RMSE=\sqrt{\frac{1}{n}\sum \limits _{i=1}^{n}\left( y_i-\hat{y}_i\right) ^2}$$
5 Mean magnitude of relative error (MMRE) $$MMRE=\frac{1}{n}\sum \limits _{i=1}^{n}\frac{\left| y_i-\hat{y}_i\right| }{y_i}$$
6 Median magnitude of relative error (MdMRE) $$MdMRE=median\left( \frac{1}{n}\sum \limits _{i=1}^{n}\frac{\left| y_i-\hat{y}_i\right| }{y_i}\right)$$
7 MMRE relative to the estimate (MEMRE) $$MEMRE\,=\,\frac{1}{n}\sum \limits _{i=1}^{n}\frac{\left| y_i-\hat{y}_i\right| }{\hat{y}_i}$$
8 MdMRE relative to the estimate (MdEMRE) $$MdEMRE\,=\,median\left( \frac{1}{n}\sum \limits _{i=1}^{n}\frac{\left| y_i-\hat{y}_i\right| }{\hat{y}_i}\right)$$
9 R squared ($$R^2$$) $$R^2=1-\frac{\sum \limits _{i\,=\,1}^{n}\left( y_i-\hat{y}_i\right) ^2}{\sum \limits _{i=1}^{n}\left( y_i-\bar{y}\right) ^2}$$
10 Prediction within 25 % Pred(25) $$Pred(25)=\frac{Number\;of\;projects,\;where\;(MRE\le 0.25)}{Number\;of\;projects}$$