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This model enables us to explore the association between social metabolism (HANPP), landscape structure (composition and spatial configuration) and biodiversity (species richness) by using Negative Binomial Regression (NBR), Exploratory Factor Analysis (EFA) and Structural Equation Modelling (SEM).
†Corrected for overdispersion by using negative binomial distribution.
Data were analysed by using negative binomial regression modelling.
†Weighted rate ratios were tested for statistical significance by using negative binomial regression analyses.
For the practice pattern outcomes, we modeled the annual cesarean rate by using negative binomial regression with delivery volume as the exposure.
Both unadjusted and adjusted RRs, calculated by using negative binomial modeling, were significantly greater than unity for all three diseases (Table 3).
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The rates of the place and activity-specific falls and injurious falls were computed and compared by sex using negative binomial regression models that account for over dispersion.
Additionally, age-adjusted incidence rate ratios by income were computed using negative binomial regression for count outcomes with overdispersion.
Temporal trends in numbers of pregnancy hypertension and pre-eclampsia events by study area were modelled using negative binomial regression.
This paper investigates the above issues by means of several SPFs developed using negative binomial regression models for two-lane rural highways in Spain.
The middle plot of Figure 1 displays data simulated with a mean given by fitting Eq. (1), but using Negative Binomial variation in order to have approximately the same variance-to-mean ratio as in the real data.
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