Sentence examples for binomial regression model and from inspiring English sources

Exact(6)

The negative binomial regression model and multinomial logit model were used to analyze trip generation and travel mode, respectively.

The year effect on severe crashes was also analyzed by the Random Effects Binomial regression model and the Negative Multinomial regression model.

To further quantify the glass-ceiling effect while controlling for other factors that may potentially explain our results (e.g., profession and age), we use a negative binomial regression model and explore the effect of gender on the number of language editions including a person.

We developed a negative binomial regression model and added two variables of vaccine match/mismatch and pre/post-SARS effect for multivariate analyses with a modification of a Thompson-like model [4], because of dispersed distributions of the three influenza-associated mortality rates (variance/mean >20).

+ Note that the model for total volume was a negative binomial regression model, and the parameter presented here for it is an Incident Rate Ratio rather than an Odds Ratio.

Incidence outcomes were compared using a negative binomial regression model, and prevalence outcomes were compared using generalized estimating equations with adjustment for repeated measures in the same study participant.

Similar(54)

Regression analysis (such as linear regression models, negative binomial regression models and Poisson regression models) has been the most popular technique in crash analysis because the connection between accidents and factors affecting them can be evidently identified.

Poisson regression models, negative binomial regression models and Pareto distribution were compared for fit using total episodes of clinical malaria per child for the period of follow-up as the outcome measure.

We analyzed inequalities in health care using negative binomial regression models and top-down cost estimates.

To address this issue we have compared the results of logistic and negative binomial regression models and found similar results.

We checked for overdispersion by fitting negative binomial regression models and assessing the significance of the extra variation parameter.

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