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For each outcome, two sequential models were fit; a model including individual level covariates and a model including county level covariates.
Observational measures assess 3 layers of the social ecological model including individual, family and organizational inputs.
The results of fitting the model including individual- and community-level variables, are also displayed in Table 2.
Simple linear relationship was found between tempR and tempT in a linear regression model including individual pig as random effects.
Then we used the likelihood ratio test to examine whether the model including individual fruits had better fit than that including total fruit consumption only.
In order to assess the contributions of both individual and facility factors to women's rating of quality we estimated three separate 2-level linear random intercept models: a null model without covariates, a model including individual-factors (and a district fixed effect), and a final model including individual, facility, and district covariates.
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A model including individual-level predictors (gender, age, material situation, education, and preferred social distance) provided a relatively good fit to the data, but adding country-level predictors (Human Development Index, population density, and average temperature) did not improve model parameters.
The model includes individual fixed effects (conditional logit model) and, in practice, compares alternative destinations for a given individual.
For both groups, we estimate two models: the first model (basic model) includes individual demographic variables and the subject of study.
Input data to the Maestra model included individual tree x and y coordinates, and individual tree characteristics including crown diameter, LCL and L A. These were estimated for each tree or were predicted using site- and species-specific allometric equations described in the studies cited in Table 1.
The candidate predictors of the model included individual characteristics, professional work, expertise, and training.
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