Sentence examples for multinomial regression models from inspiring English sources

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

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 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.

This research uses remote sensing analysis, land transition matrices, and multinomial regression models to examine LUCC dynamics over two ten-year intervals (1990 2000; 2000–2010) in Guatemala.

Multinomial regression models were used to examine the association between presence of supports with walking frequency (frequently, sometimes, rarely (referent)) and the role usefulness had on this association.

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.

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

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.

Logistic regression is a multinomial regression model and it is used for prediction by fitting data to a logistic curve.