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Additional file 2: The results of multiple logistic regression modelling on nonimputed data set.
Table 3 shows the adjusted odds ratios from the multiple logistic regression modelling.
Simple logistic regression analyses identified items with a p value <.25 which were entered into multiple logistic regression modelling.
The result of multiple logistic regression modelling to determine the most important predictive factors for referring to naturopaths is shown in Table 4.
Variables with p value <0.25 in the univariable logistic regression were included in the selection of variables in the multiple logistic regression modelling.
Multiple logistic regression modelling showed presence of a small eye to be significantly associated with shorter height, lower body mass index, higher systolic blood pressure and lower intraocular pressure.
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Table 1 summarizes results from simple and multiple logistic regression modeling.
Statistical analysis included multiple logistic regression models.
The stated-choice experiment considered 2232 observations for estimating multiple logistic regression models.
Odds ratios (ORs) were computed by use of multiple logistic regression models.
Multiple logistic regression models were developed for each of the highway systems.
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