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Although we did not include political ideology in our starting model to avoid politicizing an as-yet non-partisan issue, we conducted a sensitivity analysis to see if its inclusion in model 1A greatly impacted our estimated ORs.
*Values were squared for inclusion in model, because values were not normally distributed.
Variables with a univariate significance level of at least 0.3 were chosen for potential inclusion in model building.
Insulin was the predictor most frequently selected for inclusion in Model 1 and was the second most important predictor after BMI in the same model (data not shown).
The demographic and clinical variables in column one are used in all three models, in addition PCS at inclusion is used in model one, MCS at inclusion in model two and QOLS at inclusion in model three * Age in five-year groups.
Squared terms for age, BMI, and MAP were prescreened for possible inclusion in model 2 and model 3 adjustment in models that included only linear and squared terms for these variables.
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Their inclusion in modelling this system will be computationally expensive.
For example, data are required on how much time and energy individuals have for dispersal, and how much energy is expended in different microclimates for inclusion in models on rates of population expansion [8].
We selected variables for inclusion in models using a stepwise forward selection procedure.
Such variables would be obvious candidates for inclusion in models for mapping the EQ-5D index in cardiac patients.
The z ij terms were tested for inclusion in models using a forward selection procedure for entering z ij.
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