The influence measures are calculated from a binary logistic regression model for one vs. others (e.g., TE group vs. non-TE group).
Four binary logistic regression models were estimated, one for each of the travel modes used (e.g. public transport, active transport, less sustainable transport such as the car/taxi, and other), to differentiate between the commuting behaviour of people living in the five types of neighbourhoods.
To test the robustness of the model, the original sample (n = 290) was randomly divided into seven sub-samples (~41 cases each), and then subjected seven times to stepwise binary logistic regression each time excluding one of the subsamples.
The confounders were also subjected to the same procedure and examined as predictors of benefit recipiency one by one using binary logistic regression.
We developed one binary logistic regression model [ 56] with the 5% binary variable (denoted by I05) as the response variable, and another with the 20% binary variable (denoted by I20) as the response variable.
Table 8 shows block-wise binary logistic regression models of active acceptability, with one block including already recognized explanatory factors of acceptability and one block including attitudinal acceptability as a single explanatory factor.
Binary logistic regression analysis revealed average length of one cycle of menstruation (COR = 0.20 0.070-0.569) and academic performance impairment (AOR = 0.345 0.183-0.653) were significantly associated with the diagnosis of PMS and use of PMS treatments respectively.
Variables with a 2-tailed P value <0.25 were included in a one-step binary logistic regression analysis to determine the independently-associated factors with laboratory-confirmed cases.
As previously noted, we first estimate the propensity score with the help of a binary logistic regression in which the dependent variable equals one for customers with social media interactions and zero otherwise.
Following Garnefeld et al. (2013), we first estimate the propensity score with the help of a binary logistic regression in which the dependent variable equals one for customers with social media interactions and zero otherwise.
