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It was shown that the maximum R2 value, a measurement of the amount of variability explained by the model, was highest when a general regression neural network (GRNN) with iterative calibration (GRNNIT) was used to train the ANN.
Correlation between the exogenous variables in the regression on the latent variables in the structural model was highest between age and time in Norway (0.40) for the total sample.
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Match of data and model was high in this pH range.
The fitness of the ANN-GA model was higher than that of the RSM model.
The predictive capability of ANN model was higher than the RSM models.
The significance of the regression model was high; with 95% confidence interval (less than 5% error).
The model of native forest conversion explained 43% of the deviance and the discrimination ability of the model was high.
The discriminatory power of the multivariate model was high.
Adherence to the Geriatric Care Model was high for most of the essential intervention components.
The positive predictive value of the main effect model was higher in women than in men.
The negative predictive value of the main effect model was higher in men than in women.
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CEO of Professional Science Editing for Scientists @ prosciediting.com