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We used a negative binomial regression to observe (1) the association of month of year with suicides, adjusting for different sunlight exposures, and (2) the association of sunlight exposure with suicides, adjusting for month of year.
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Results do not change when we use a negative binomial regression to accommodate the highly skewed dependent variable.
We used binomial regression to calculate RRs adjusted for possible confounders.
After testing different models, we used negative binomial regression to adjust rural and urban cases.
We used logistic binomial regression to estimate relative risks (RR) and control potential confounders.
Logistic regression was used, instead of binomial regression, to avoid convergence issues when controlling for many confounding variables.
We used logistic regression and zero truncated negative binomial regression to model the zero and count processes, respectively.
We used log binomial regressions to control for any potential confounders.
The new methodological approach was based on patent citation analysis and negative binomial regressions conducted to: 1) observe the dynamics and evolution of interdisciplinary technologies, and 2) explore how interdisciplinary knowledge influences technology developments.
The likelihood ratio test of alpha = 0 was used to compare the negative binomial regression model to Poisson regression, while the Vuong test was used to compare the zero-inflated negative binomial to the standard negative binomial model [ 29].
Figure 4 Covariates and dispersion for each factor of the Negative Binomial regression applied to the 1-minute snapshots.
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