Simulated car-to-car side impacts, designed for the analysis of traumatological aspects, involve two sets of variables. Predictors include exogenous biomechanical factors as well as anthropometric variables, such as age. The response is measured a scale of injuy scores and is thus multinominal.

It is the aim of a statistical analysis of such data to devise a multinominal response model that explains possible patterns of injury as a function of a suitable set of predictor variables. Several approaches for modelling such a multinominal response relationship have been proposed in the literature, among them the Logistic and the Weibull regression models. Two major questions in applying such models are as follows:​ What model is appropriate and how should different models be compared. Another concern is how the quality of a given model should be presented for varying sets of predictors.

In this paper we discuss the first question by constructing a goodness-of-fit test based on bootstrapping flexible, nonparametric alternatives to a given parametric candidate model. Secondly, we present several graphical techniques that allow relatively simple comparisons of different models.