Sentence examples for binomial regression models the from inspiring English sources

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.

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.

*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).

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.

We used count data models such as Poisson and negative binomial regression models to compare the LOS between the HI and the MA group.

We used negative binomial regression models to examine the association between the predictors and the rate of positive and negative interactions.

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

Table 1 shows a description of the variables that were included in the (zero-truncated) negative binomial regression models calculated for the LTF applicants.