Sentence examples for binomial regression models indicated from inspiring English sources
Results of the univariable multilevel negative binomial regression models indicated that year, season, and month were significantly associated with the rate of CDI cases (Table 4).
Results from negative binomial regression modeling indicate that the expected time-loss due to head injuries within the injured cohort was lower than the expected time-loss due to all other injuries (Time-loss Ratio = 0.76, 95% CI = [0.71, 0.83]).
Binomial logistic regression models indicated that early access to substance use services matters (within two months of the temporary custody hearing) but only when parents were connected with a recovery coach.
The omnibus binomial logistic regression model indicated a significant model overall for each of the disorders (see Tables 3, 4) as well as a reasonable proportion of explained variance (Nagelkerke R varied from 0.12 to 0.18).
A better fit using the negative binomial regression model would indicate the presence of over-dispersion.
Results from logistic and negative binomial regression models, using repeated data measures, indicated that JBTC participants, relative to baseline and a sample of comparison youth, were significantly less likely to be arrested and had significantly fewer arrests in the six to twelve months after entering the program.
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.
The crude incidence-rate ratio (IRR) from the negative binomial regression model (not shown) indicates that those with elevated depressive symptoms had 48% more injuries in the past six months (IRR 1.48, 95% CI: 132, 165).
