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Note: * significant at 1% Table 3 reports mean error, mean absolute error (MAE) and mean squared error (MSE) of predicted compared to actual utility scores by EQ-5D utility range for all models estimated in Table 2. Table 3 indicates that the estimation techniques of tobit and CLAD do not clearly improve the accuracy of the generated predictions as MAE and MSE are not reduced.
The upper and lower bounds of total PFOS and PFOA uptake from foodstuffs are estimated in Table 2[14, 15].
For this, we run the algorithm for each dataset exactly the number of times estimated in Table 4.
Even half of the citizens' incomes estimated in Table 2 would more than bridge the numerical distance between wages in different sectors.
The model estimated in Table 4, column 2 delivers a counterfactual contraction of business credit in 2009 of 3.3 %, while the observed drop was of 4.0 %.
Additionally, the approximate fuzzy solution is described in Figure 9 for different fractional order v. Ultimately, the CPU time is estimated in Table 4 using MATLAB verion 7.6 (R2008a).
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Similarly, the point estimates in Table 3 are small in magnitude and statistically insignificant.
However, the corresponding root mean squared error estimates in Table 5 disprove this suspicion.
All point estimates in Table 2 suggest a negative effect of conscription on investment in human capital.
The point estimates in Table 3 are not statistically significant, but the magnitudes are sensitive to the choice of bandwidth.
The parameter estimates in Table 3 of this hierarchical regression model show that the estimated coefficients B and b estimated in Eqs.
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