Abstract
In this study, Poisson regression, negative binomial regression, zero-inflated Poisson regression, and zero-inflated negative binomial regression were investigated to analyze dependent variable obtained based on zero-inflated counting. It was determined that overdispersion had a significant effect because there were many zero values in data set and there were great difference among the observations. Akaiki and Bayesian information criteria were used to choose the most appropriate model. In conclusion, zero-inflated negative binomial regression was chosen as the most appropriate model. It was determined that zero-inflated Poisson regression could be preferred to Poisson regression, and zero-inflated negative binomial regression could be preferred to negative binomial regression. In zero-inflated negative binomial regression, it was determined that predator acar (Zetzellia mali), temperature, and spraying in the model had significant effects (p<0.01) on all stages of the
harmful pest acar (Panonychus ulmi Koch).