Sentence examples for multinomial regression modelling from inspiring English sources

Multinomial regression modelling will model these categories on type of ulcer (predominantly neuropathic or ischaemic), grade of ulcer, previous antibiotic therapy, antimicrobial dressing, wound duration and centre to determine whether agreement is influenced by any of the specified covariates.

Multinomial regression modelling will model these categories on type of ulcer (predominantly neuropathic or ischaemic), grade of ulcer, current antibiotic therapy, antimicrobial dressing, wound duration and centre to determine whether agreement is influenced by any of the specified covariates.

To address this issue, we have developed a novel test of association within a multinomial regression modeling framework, allowing for heterogeneity of genetic effects between sub-phenotypes.

Moreover, the statistical approaches have generally included Poisson and Negative Binomial regression models, whilst Negative Multinomial regression model has been used to a lesser extent.

Table 1 Multinomial regression models predicting the exclusion from home ownership (tenants versus homeowners) Variable Categories Model 1 Model 2 Model 3 Model 4 Gender Man (ref).

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

The Pearson χ2 values were insignificant for both the models indicating that the estimated multinomial regression models fit data reasonably well.

This means that it is improper to use multinomial regression model which is used for describing the latter behavior to estimate the former case.

A multinomial regression model was further used to examine the extent to which individual job seekers' qualifications were matched up with their use of job search channels.

The Poisson, Negative Binomial and Negative Multinomial regression models, applied separately to tangents and curves, were used to model the frequency of accident occurrence.

Multinomial regression models and 99% confidence intervals were used to examine associations between sense of community belonging and sedentary behavior, adjusting for sociodemographic variables and perceived health.