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However, in the available literature this result is achieved by using a set of centralized variables that keep track of the global state of the system.
Likewise, variance inflation factor (VIF) of centralized variables (shown with suffix Cent: tamUseCent, tamPuCent, and tamPuCent, in the output, Appendix E) are below the cut point proposed by Kutner et al. [100] of β VIF < 10 and √β VIF < 2.
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This was achieved by centralizing predictor variables to a mean of zero and an SD of 0.5, which places continuous variables and binary variables on a common scale for direct comparison.
For regression analyses, all variables were centralized around the mean (continuous variables) or scored as −0.5 versus 0.5 (dichotomous variables).
The continuous variables were centralized around their mean values, and the categorical variables were fixed in the reference category.
All study variables were centralized before analysis to account for differences in scale scores.
All the continuous variables were centralized in order to avoid multicollinearity [ 31] before regression analyses were performed.
All study variables were centralized before being entered into the hierarchical linear regression model for the purpose of avoiding multicollinearity [ 40].
Centralized monitoring generally involves all measured variables in one model.
Are they centralized or de-centralized?
and diagnosis was centralized to minimize inter-observer variability.
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