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Multiple variable logistic and linear regression models examining the associations between key measures, adjusted for confounders identified a priori.
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To test whether or not the indicators of environmental measures (independent variables) were associated with each of the phenotypic risk indicators (dependent variables), logistic and linear regression analyses were undertaken.
A t-test and chi-square test were used and single and multiple variable logistic regression model analyses were performed to estimate the odds ratios of dying, along with 95% confidence intervals (CIs).
To adjust for potential confounding factors, multiple variable logistic regression analyses were performed and OR and Nagelkerkes R were calculated.
The Child-OIDP-SC was regressed on socio-demographics, behavioural and clinical oral health indicators using bivariate and multiple variable logistic regression analyses (Table 6).
The association between the frequency of oral impacts (OIDP total >0) and factors known to be associated with oral health; socio-demographic-, clinicandand behavioural variables were assessed using cross tabulation and multiple variable logistic regression analyses.
Mann–Whitney test, one-way analysis of variance, linear regression and single variable logistic regression.
Data analysis used single and multiple predictor variable logistic regression.
In regression analysis we will use generalised linear model (GLM) so that different dependent variables will be analysed in different type of model, i.e. linear regression for continuous variables, logistic for binary and multinomial for categorical variables.
Optimal cut-points for diagnosis and prognosis were identified and verified using bootstrapping and multi-variable logistic regression techniques.
In this study, we developed an alternative objective approach that combines well capture zones with multiple-variable logistic regression (LR) modeling and applied it to the highly-utilized Pearl Harbor and Honolulu aquifers on the island of Oahu, Hawaii.
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