Analysis of Over-Dispersed Count Data: Application to Obligate Parasite Pasteuria penetrans

dc.creator	Vagelas I.	en
dc.date.accessioned	2023-01-31T10:23:47Z
dc.date.available	2023-01-31T10:23:47Z
dc.date.issued	2022
dc.identifier	10.37394/232015.2022.18.33
dc.identifier.issn	17905079
dc.identifier.uri	http://hdl.handle.net/11615/80315
dc.description.abstract	In this article we present with STATA regression models suitable for analyzing over-dispersed count outcomes. Specifically, the Negative Binomial regression can be an appropriate choice for modeling count variables, usually for over-dispersed count outcome variables. The common problem with count data with zeroes is that the empirical data often show more zeroes than would be expected under either Poisson or the Negative Binomial model. We concluded, this publications showcases that Zero-inflated models can be used to model count data that has excessive zero counts. © 2022, World Scientific and Engineering Academy and Society. All rights reserved.	en
dc.language.iso	en	en
dc.source	WSEAS Transactions on Environment and Development	en
dc.source.uri	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133522399&doi=10.37394%2f232015.2022.18.33&partnerID=40&md5=b53dcdbbc38c70ce39d3829be7c994f2
dc.subject	World Scientific and Engineering Academy and Society	en
dc.title	Analysis of Over-Dispersed Count Data: Application to Obligate Parasite Pasteuria penetrans	en
dc.type	journalArticle	en

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