Due to some correlation of the variables especially between household size and the dummy for children, we tested the model in several specifications.
Moreover, the number of ever-smokers was relatively small affecting the statistical power to detect the effect of interactions between the analysed variables, especially in women.
In comparison to the more commonly used logistic regression approach, this method of analysis provides a less biased and more conservative estimate of magnitude of the association between variables, especially when the prevalence of the outcome is higher than 10%.
The results indicated a high localized correlation between the streamflow and other variables, especially the upstream.
Indeed, the common finding in studies using microcirculatory monitoring is the absence of a clear association between microcirculatory and macrohemodynamic variables, especially in conditions of sepsis [ 1].
Part of this stems from idiosyncrasies related to the construction and implementation of ALMO, while other factors include a fundamental shift in the relationship between housing price appreciation and key explanatory variables – especially over the past 10 15 years, which shows a heightened role of momentum in explaining changes in housing prices.
After inclusion of individual explanatory variables, especially BMI, the between-center variance in bias was reduced by 78% (from 0.0032 to 0.0007) in women (p < 0.001).
More specifically, it permitted the examination of direct and indirect relationships between multiple independent variables with one or more dependent variables, especially in the instance when there is a locally endogenous variable, i.e., a dependent variable in one equation that becomes an independent variable in another equation (Hair et al. 1998).
The numbers were smaller for specific variables, especially blood tests.
Overstory variables, especially stand basal area, explained the variation in the mean response of shrub variables.
