Detection of overdispersion in count data for multiple regression analysis.
Log-linear count data regression is one of the most popular techniques for predictive
modeling where there is a non-negative discrete quantitative dependent variable. In
order to ensure the inferences from the use of count data models are appropriate,
researchers may choose between the estimation of a Poisson model and a negative binomial
model, and the correct decision for prediction from a count data estimation is directly
linked to the existence of overdispersion of the dependent variable, conditional to the
explanatory variables. Based on the studies of Cameron and Trivedi (1990)