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Association of variables with NIV failure was studied through logistic regression models.
In contrast, levels of understanding of evolution increased with increasing understanding of science (i.e., a positive association of variables).
Levels of understanding of science and evolution by the general faculty, educators of prospective teachers, and students decreased with increasing religiosity (i.e., a negative association of variables).
Two reasons could be attributed to the limited significant association of variables in the models: a small sample size and the high correlation between the variables.
Logistic regression analysis was applied to determine the association of variables of interest (age group, sex, and area of residence at the time of hospitalization as the covariate) with frequency of fall injury hospitalizations.
A multiple logistic regression analysis was run to study the independent association of variables with IPIs.
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Model C (figure 1C) tested associations of variables that were hypothesised to be associated with utilisation of specialised MHS.
Models A and B (figure 1A, B) tested associations of variables that were hypothesised to be associated with receiving proactive early outreach.
Random effects longitudinal regressions were used to investigate patterns and associations of variables over time.
Associations of variables with NYHA stage were tested using Spearman's rank test.
Stepwise logistic regression analysis was performed to identify associations of variables with treatment responses.
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