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We applied a logistic regression methodology.
In this study, binomial logistic regression methodology has been applied.
For individual matched case-control studies, we need to use conditional logistic modeling instead of the ordinary logistic regression methodology [2].
Multivariate Logistic Regression methodology was used to identify the most important biophysical and human variables to explain the emergence of ignition points, incorporating spatial analysis from Remote Sensing and Geographical Information Systems GISS) data.
The major advantage of the logistic regression methodology is it flexibility.
Analysis of those factors associated to surgical morbidity and mortality were performed using logistic regression methodology.
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In order to identify factors associated with underweight, stunting, and wasting, we used random effects logistic regression modeling methodology using several maternal and infant covariates.
Then, ANFIS results were compared with those achieved by both ANN and Logistic Regression (LR -based methodoLR -baseding Percent Area methodologiesy and Percent Area Match location to assess model goodness of fit.
Comparison of the descriptive two-factor methodology with that of the logistic regression results demonstrates the utility of this methodology in assessing satisfaction with health care.
The nursing workload [measured using the Therapeutic Intervention Scoring System (TISS-76)] for all patients in the ICU during each day to average number of bedside nurses per shift on that day (workload/nurse) ratio, severity of illness (using Acute Physiology and Chronic Health Evaluation III) and hospital survival were analysed using net-benefit regression methodology and logistic regression.
For the logistic regression analysis, we used the methodology proposed by Cordell and Clayton [12] in which nested hypotheses involving logistic regression models of different loci are compared using likelihood ratio tests.
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