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In addition, both models were statistically compared to two correlations developed by Vogel and also Daubert and Danner.
Comparing RSM and ANN, it was found that ANN provided higher prediction ability while both models were statistically adequate (with R2>0.90).
Both models were statistically significant (p < 0.001).
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Both models are statistically significant (p < 0.01) based on F-test.
We found that both of the nonlinear models were statistically adequate and they provided complete insights into the complex nonlinear input output relationships.
Both physician and nurse employment models were statistically stable without outliers and with a normal distribution of residuals (Table 3).
Both the linear and logarithmic models were statistically significant (p < 0.05 and p < 0.001, resp ., but the latter demonstrated greater R values at the range of 0.59 0.82.
Both the crude (p =0.0015) and adjusted (p =0.0039) Cox models were statistically significant.
When the coefficients of the ARMA models were statistically significant, the models proved to be both visually and statistically an improvement over the polynomial trend.
These results indicated that the regression models were statistically significant.
All models were statistically evaluated using strict criteria.
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