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The first model (univariate model) examines the independent effects of barriers to health care on child survival.
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To the "best" (highest CV R) of these three models (later referred to as the "referent" model), we then added GIS covariates one at a time in separate models ("univariate" models).
Also, further description of the mortality risks among the children by ethnic affiliations is provided by Figure 2. Results from the univariate hazard model (Model 1; Table 5) indicate a significant relationship between the risk of under-five mortality and ethnic affiliation.
The best logistic regression model was the univariate model based on the UFOV3 neuropsychological test because of the lowest BIC, AIC and even Deviance.
For each variable, we assessed the difference in Akaike's information criterion (AIC) between the null model and each univariate model.
Three calibration models were examined: a univariate model with a sum of six monitorized ions and a three-way PARAFAC-based model (the analyte scores were standardized on the basis of the scores of the internal standard).
Finally, using customizing univariate model and setting the interaction model, the assumption of homogeneity of regression slopes was also examined.
Comparing these three models with the baseline univariate model discussed previously (ARIMA 2,1,2)), we found that including the environmental input series improve the AIC by 18%, the fit RMSE by 9% and the prediction RMSE by 16% from the baseline model.
Our studies showed that including the climatic variables as input series result in models with better performance than the univariate model where the influenza cases depend only on its past values and error signal.
Model 1: univariate model.
Crude model: univariate regression model, n = 406.
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CEO of Professional Science Editing for Scientists @ prosciediting.com