Sentence examples for binomial regression models are from inspiring English sources

Negative binomial regression models are then used to define the role of risk factors and the diagnosing physician type.

Negative binomial regression models are used to test these at the 6-hour shift unit of analysis on street robberies in the Strathclyde region of Scotland.

Furthermore, it is noteworthy that the covariate effects in Poisson and negative binomial regression models are multiplicative whereas covariate effects in multiple linear regression model are additive.

Logistic and binomial regression models are mainly used for predicting the probability of an outcome at the individual level, while the Poisson approach works well for counts and rates.

Negative binomial regression models were constructed to test associations between generic E. coli (MPN/100 mL) in water from farms and variables.

Negative binomial regression models were used to assess the effect of street and street network characteristics on total crashes, severe injury crashes, and fatal crashes.

Negative binomial regression models were used to assess the statistical significance of risk factors associated with the within-farm number of Campylobacter-positive flocks (the outcome).

To examine the association between race, ethnicity sex, insurance, age, and age group with CRS, univariable and multivariable log binomial regression models were used.

Negative Binomial regression models were fitted to the incident number of infections with an offset with the annual number of ICU stays.

The randomized quantile residuals of the finally selected model (Eq. 8.51 Duchon) using 1st order derivatives in the penalty and three other negative binomial regression models were compared and validated for spatial independency.

Multivariable negative binomial regression models were used to evaluate the relationship between respiratory illnesses and variables found significant on univariate analysis while adjusting for potential confounding.