Sentence examples for binomial regression model the from inspiring English sources

In the binomial regression model the logarithm of the annual number of cases in 1983 87 was used instead of the frequency itself or the quartiles of the numbers.

The incidence rate ratio for vaccinated versus unvaccinated populations was estimated by using data across the whole study period with a negative binomial regression model; the incidence rate ratio was 0.104 955% CI 0.052 0.207).

Similarly to the binomial regression model, the estimated incidence rates were slightly lower in the three biggest cities than in overall Upper Austria, but again the difference was statistically not significant.

In a multivariable negative binomial regression model the number of seropositive animals in a flock decreased with increasing flock size (for >400 vs. <300 animals: count ratio (CR) = 0.62; 95% CI: 0.41, 0.95; P = 0.028) and was greater on farms where stray cats had access to animals' water (CR = 1.54; 95% CI: 1.05, 2.26; P = 0.027).

*Logistic regression model for any use el for vs. no use **Negative binomial regression model for rate In the adjusted negative binomial regression model, the intensity of utilization among health services users was similar for women and men (incident rate ratio (IRR) = 1.07; 95% CI: 0.90, 1.27) (Table 4).

For both negative binomial regression models, the randomized quantile residuals lay on the bisecting line, indicating that they follow approximately a standard normal distribution, and hence they represent an almost optimal fit to the data.

We used Poisson and negative binomial regression models (the Poisson model can be considered a special case of the negative binomial model).

Thus, we conducted two separate negative binomial regression models: the first included average temperature but no atmospheric pressure, while the second included atmospheric pressure but no temperature.

This fact may be linked to the sensitivity and specificity evidenced by the binomial regression model for the two methods of classification.

All variables that remained significant in the final logistic regression model were included in the binomial regression model for the estimation of adjusted risk ratios.

The results of the negative binomial regression model demonstrate the relationship between patient health status and EHR data sufficiency.