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Multivariate logistic regression is used to see the net effects of each explanatory variable over the outcome variable.
Unadjusted results consider the association between the outcome and each explanatory variable individually.
Each explanatory variable was considered as categorical with Time centered on the year 2007 and Haapiti 6 m used as an arbitrary Location and Depth reference.
Across all selected models, we examined the significance of each explanatory variable through two-tailed t-tests for evaluating its role in determining the response variable52.
The coefficients describe the influence of each explanatory variable on each of the four energy choices (dependent variables).
Also, Fig. 3 shows the sensitivity of each explanatory variable in the selected model.
In multiple regressions, the partial regression coefficient (byi) denotes the regression coefficient of each explanatory variable on the dependent variable, while removing the effect of all other explanatory variables as though they were kept constant.
However, multiparameter analysis requires that each explanatory variable is independent.
Models consisting of each explanatory variable were also examined.
A separate analysis was undertaken for each explanatory variable.
Each explanatory variable was examined separately for both age groups.
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