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So, the same factors were included in both multiple regression models, to promote comparison between sites.
Because both multiple regression models and FIM have distinct advantages and disadvantages, as discussed below, they should be considered complementary approaches to a complex problem.
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The associations between serum levels of galectins and cytokines in cancer patients were tested using both simple and multiple regression models.
Both simple and multiple regression models were used to study the statistical associations between the explanatory and the ANC outcome variables.
Associations with other variables were tested through both bivariate correlations and multiple regression models.
Significant and consistent predictors of infant undernutrition in both logistic and linear multiple regression models were male gender, low birth weight, poor maternal nutritional status, poor household sanitary facilities and living in a rural residence.
To check the prognostic value of studied variables on follow-up BMC, we fitted multiple regression models, both without and after adjustment for baseline values of age, body mass index (BMI), erythrocyte sedimentation rate (ESR), smoking, QCT derived BMC stratum, radiologic score and duration of disease prior to baseline visit.
Potential risk factors or confounders for both outcomes were analyzed by fitting Cox multiple regression models.
The contributions using multiple regression models are higher than are the ones for single regression models.
The multiple regression models for the prediction of mechanical properties are developed.
Multiple regression models are developed to analyze these relationships at individual level.
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