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Data from the cross-sectional Caerphilly Health and Social Needs Survey were analysed using multilevel modelling, with individuals nested within enumeration districts.
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The overall performance of all parameterizations let us conclude that carbon fluxes of forest ecosystems can also be modeled with individual-based models.
Multi-level regression modelling with adjustment for individual and contextual variables was used.
The contest involved three rounds of modelling, with individual and group struts down a catwalk of a Harare nightclub.
Switching from the three-level to a two-level model with individuals nested within state-years will result in biased standard error estimates if there is random variation between years and federal states (Schmidt-Catran and Fairbrother 2016).
We then refit our final models with individuals only showing biochemical evidence of T2D without actual diagnosis.
The data will be analyzed with random intercept multilevel regression models with individuals nested in countries.
We used a random-intercept logistic model with individuals (level 1) nested in areas (level 2).
Regressions were specified as two-level random-intercept models with individuals nested inside of practices.
The main analyses were performed in a three-level model with individuals nested within years within municipality of residence.
We also ran the model with individuals that differed in their initial tendency to approach the predator.
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