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The logistic regression model was used to determine binary outcomes.
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A binary logistic regression model was used to determine the association of selected socio-demographic and clinical factors with higher internalized stigma experience using the mean internalized stigma score as a cut off.
Binary logistic regression modelling was used to determine variables predictive for focal therapy suitability.
Binary logistic regression models were used to determine: (1) the probability that the health problems would be identified and (2) the influence of letter-length and demographic information.
Logistic regression was used to determine the association between binary outcomes and other variables.
Differential scanning calorimetry (DSC) was used to determine the thermophysical properties of the binary system.
Binary logistic regression analysis was used to determine the five best combination models for early detection of late-onset preeclampsia.
Binary logistic regression was used to determine factors that influence households' participation in fodder production.
Binary logistic regression was used to determine the effects of demographics on the outcome variables.
Stepwise binary logistic regression was used to determine risk factors for treatment failure.
Binary logistic regression was used to determine factors associated with appropriate HMM.
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