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Binary logistic regression model was used for analysing data.
A binary logistic regression model was used to identify the risk factors for HAH.
To find factors affecting route diversion, researchers applied a binary logistic regression model.
A binary logistic regression model was employed to analyze the collected data.
First, the binary logistic regression model was used to select risk factors significant to hypertension.
As for detailed analysis, lane blockage probability is interpreted by using a binary logistic regression model.
A binary logistic regression model was built to explore factors that influence bicycle crashes.
The factors that influenced the adoption of improved cassava varieties were examined using the binary logistic regression model.
Binary logistic regression model was used (Hosmer et al. 2013) to determine the socio-economic factors influencing households' forest dependency.
A binary logistic regression model was used to find out the factors that influence the consumption of organic vegetables (Y3).
Moreover, the prediction accuracy of a binary logistic regression model is not sufficiently strong for banks (Chen and Huang 2002).
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