Title: overdisp: an R package for direct detection of overdispersion in count data multiple regression analysis

Authors: Rafael De Freitas Souza; Luiz Paulo Fávero; Patrícia Belfiore; Hamilton Luiz Corrêa

Addresses: School of Economics, Business and Accounting, University of São Paulo, São Paulo, SP, Brazil ' School of Economics, Business and Accounting, University of São Paulo, São Paulo, SP, Brazil ' Engineering, Modeling and Applied Social Science Center, Federal University of ABC, São Bernardo do Campo, SP, Brazil ' School of Economics, Business and Accounting, University of São Paulo, São Paulo, SP, Brazil

Abstract: Within multiple areas, log-linear count data regression is one of the most popular techniques for predictive modelling 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. That said, the overdisp() command is a contribution to researchers, providing a fast and secure solution for the detection of overdispersion in count data. Real and simulated data were used to test the proposed solution, which proved to be computationally efficient, with no difference in the detection of overdispersion compared to the test postulated by the cited authors.

Keywords: overdispersion; detection of overdispersion; count data; multiple regression analysis; non-negative discrete quantitative dependent variable; Poisson model; negative binomial model; R package.

DOI: 10.1504/IJBIDM.2022.122157

International Journal of Business Intelligence and Data Mining, 2022 Vol.20 No.3, pp.327 - 344

Received: 28 Jul 2020
Accepted: 25 Sep 2020
Published online: 11 Apr 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article