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We will refer to these models as "significant models".
Days from treatment to slaughter were included in all linear regression models as significant predictor.
Against our expectations, in this study three environmental factors - Immediate family, Individual attitudes of immediate family members and Health services, systems and policies - were not selected in the corresponding models as significant independent variables.
Due to the statistical methods employed and the large population size, only covariates fitting models with p < 0.01 were retained in final models as significant influences, to reduce the risk of type-1 error.
The BMA model, which averaged over the multiple selected models, only identified the 2 risk factors common to all models as significant but included 10 risk factors in total.
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We found that PM0.3 1.0 effects on HRV reduction in multipollutant models remained as significant as those in the single-pollutant models.
As you might deduce, these models aren't as significant as a redesign.
Because of multiple testing, we only considered predictor variables in the regression model as significant if p <0.01.
Temperature and dew point showed a strong relationship with HRI and were modeled as significant predictors of HRI.
The models were also adjusted by baseline measurements (as a function of the linear regression model) as significant differences were found at baseline between schools.
To define the variables in the model, the ENTER 38 method was used and to define the model as significant was defined as P<0.20 and alpha 5%.
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