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Quadratic response surface models were obtained with the models significance and significant effect of the process factors at 95% confidence interval were examined by various statistical analyses.
Covariance analysis (ANCOVA) showed that in the final multivariate models, significance values were attenuated and were not statistically significant (P for trend = 0.05 in males, P for trend = 0.93 in females).
Fig. 10 US states categorized by (a) correlations-estimated models' significance and (b) AIDS rates.
Regardless of the models' significance, the extreme low values of the correlation coefficient of determination (R 2) forced us to reject the model when predicting PM2.5 concentration in summer, autumn, and winter.
To reduce the number of predictor variables for consideration in the multivariable models, significance for the univariable screening of variables was set at P < 0.1 [ 23].
In these models significance of the tail and greater covert colour terms was assessed by comparing the fit of models with and without these terms using likelihood ratio tests.
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The test of overall model significance showed that the model is significant for each dependent variable (PH and MH) in the mining and the agricultural community.
The effects of the interaction terms between roost use and geographic distribution on migratory behavior were non-significant and thus not entered in the final model (significance level for all interactions ≫0.05).
ns – the variable is not significant; numbers are Wald statistics for the variables in the model; significance is shown in parentheses.
Model parameters and model significance were determined at p < 0.05.
Likelihood ratios were used to determine the overall model significance of final regression models.
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