Using the Poisson and Negative Binomial Regression Modeling of Zooplankton Aquatic Insect Count Data

Abstract

The aim of this study was to use for Poisson and negative binomial regressions in the modelling of zooplankton aquatic insect counts. Poisson regression is frequently used to analyze for dependent variable based on count data. In data sets, generally overdispersion is seen. In such cases, applying Poisson regression causes biased parameter estimations and standart errors. When there is overdispersion in data set, it is better to use negative binomial regression model. In negative binomial regression model, parameter estimations are obtained by considering the effect that stems from overdispersion. The overdispersion and zero-inflated parameter levels range was obtained to be quite high. All of the dependent variables were statistically significant on zooplankton aquatic insect counts (p<0.01) in the negative binomial regression. In the case of station of Haraba was taken as the reference level, most zooplankton aquatic insect counts was at the Yolcati station (7.972 times more), while at least zooplankton aquatic insect counts was at the Çarpanak station (99.59% less) (p<0.01). In the case of month of september was taken as the reference level, zooplankton density in August was found to be higher compared to other months (p<0.01). Because of the overdispersion had a significant effect, negative binomial regression was better results than the Poisson regression. © 2017, Centenary University. All rights reserved.