title: Confidence and prediction intervals for generalised linear accident models.
abstract: Generalised linear models, with "log" link and either Poisson or negative binomial errors, are commonly used for relating accident rates to explanatory variables. This paper adds to the toolkit for such models. It describes how confidence intervals (for example, for the true accident rate at given flows) and prediction intervals (for example, for the number of accidents at a new site with given flows) can be produced using spreadsheet technology.
authors.affiliation: Department of Statistics, Macquarie University, Sydney 2109, NSW, Australia. gwood@efs.mq.edu.au
authors.forename: G R
authors.initials: GR
authors.lastname: Wood
mesh_terms: D000056:Accident Prevention / Q000706:statistics & numerical data*; D000063:Accidents, Traffic / Q000517:prevention & control / Q000706:statistics & numerical data*; D016010:Binomial Distribution; D016001:Confidence Intervals; D005544:Forecasting; D006801:Humans; D016014:Linear Models; D016012:Poisson Distribution