Detecting overdispersion in count data: a zero-inflated poisson regression analysis
This study focusing on analysing count data of butterflies communities in Jasin, Melaka. In analysing count dependent variable, the Poisson regression model has been known as a benchmark model for regression analysis. Continuing from the previous literature that used Poisson regression analysis, thi...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2017
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| Online Access: | http://eprints.uthm.edu.my/5164/ http://eprints.uthm.edu.my/5164/1/AJ%202017%20%28294%29%20Detecting%20overdispersion%20in%20count%20data.pdf |
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| author | Muhamad Jamil, Siti Afiqah Abdullah, M. Asrul Affendi Kek, Sie Long Nor, Maria Elena Mohamed, Maryati Ismail, Norradihah |
| author_facet | Muhamad Jamil, Siti Afiqah Abdullah, M. Asrul Affendi Kek, Sie Long Nor, Maria Elena Mohamed, Maryati Ismail, Norradihah |
| author_sort | Muhamad Jamil, Siti Afiqah |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | This study focusing on analysing count data of butterflies communities in Jasin, Melaka. In analysing count dependent variable, the Poisson regression model has been known as a benchmark model for regression analysis. Continuing from the previous literature that used Poisson regression analysis, this study comprising the used of zero-inflated Poisson (ZIP) regression analysis to gain acute precision on analysing the count data of butterfly communities in Jasin, Melaka. On the other hands, Poisson regression should be abandoned in the favour of count data models, which are capable of taking into account the extra zeros explicitly. By far, one of the most popular models include ZIP regression model. The data of butterfly communities which had been called as the number of subjects in this study had been taken in Jasin, Melaka and consisted of 131 number of subjects visits Jasin, Melaka. Since the researchers are considering the number of subjects, this data set consists of five families of butterfly and represent the five variables involve in the analysis which are the types of subjects. Besides, the analysis of ZIP used the SAS procedure of overdispersion in analysing zeros value and the main purpose of continuing the previous study is to compare which models would be better than when exists zero values for the observation of the count data. The analysis used AIC, BIC and Voung test of 5% level significance in order to achieve the objectives. The finding indicates that there is a presence of over-dispersion in analysing zero value. The ZIP regression model is better than Poisson regression model when zero values exist. |
| first_indexed | 2025-11-15T20:10:58Z |
| format | Article |
| id | uthm-5164 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:10:58Z |
| publishDate | 2017 |
| publisher | IOP Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-51642022-01-06T03:39:48Z http://eprints.uthm.edu.my/5164/ Detecting overdispersion in count data: a zero-inflated poisson regression analysis Muhamad Jamil, Siti Afiqah Abdullah, M. Asrul Affendi Kek, Sie Long Nor, Maria Elena Mohamed, Maryati Ismail, Norradihah HA Statistics This study focusing on analysing count data of butterflies communities in Jasin, Melaka. In analysing count dependent variable, the Poisson regression model has been known as a benchmark model for regression analysis. Continuing from the previous literature that used Poisson regression analysis, this study comprising the used of zero-inflated Poisson (ZIP) regression analysis to gain acute precision on analysing the count data of butterfly communities in Jasin, Melaka. On the other hands, Poisson regression should be abandoned in the favour of count data models, which are capable of taking into account the extra zeros explicitly. By far, one of the most popular models include ZIP regression model. The data of butterfly communities which had been called as the number of subjects in this study had been taken in Jasin, Melaka and consisted of 131 number of subjects visits Jasin, Melaka. Since the researchers are considering the number of subjects, this data set consists of five families of butterfly and represent the five variables involve in the analysis which are the types of subjects. Besides, the analysis of ZIP used the SAS procedure of overdispersion in analysing zeros value and the main purpose of continuing the previous study is to compare which models would be better than when exists zero values for the observation of the count data. The analysis used AIC, BIC and Voung test of 5% level significance in order to achieve the objectives. The finding indicates that there is a presence of over-dispersion in analysing zero value. The ZIP regression model is better than Poisson regression model when zero values exist. IOP Publishing 2017 Article PeerReviewed text en http://eprints.uthm.edu.my/5164/1/AJ%202017%20%28294%29%20Detecting%20overdispersion%20in%20count%20data.pdf Muhamad Jamil, Siti Afiqah and Abdullah, M. Asrul Affendi and Kek, Sie Long and Nor, Maria Elena and Mohamed, Maryati and Ismail, Norradihah (2017) Detecting overdispersion in count data: a zero-inflated poisson regression analysis. Journal of Physics: Conference Series, 890 (012170). pp. 1-8. ISSN 1742-6588 http://dx.doi.org/10.1088/1742-6596/890/1/012170 |
| spellingShingle | HA Statistics Muhamad Jamil, Siti Afiqah Abdullah, M. Asrul Affendi Kek, Sie Long Nor, Maria Elena Mohamed, Maryati Ismail, Norradihah Detecting overdispersion in count data: a zero-inflated poisson regression analysis |
| title | Detecting overdispersion in count data: a zero-inflated poisson regression analysis |
| title_full | Detecting overdispersion in count data: a zero-inflated poisson regression analysis |
| title_fullStr | Detecting overdispersion in count data: a zero-inflated poisson regression analysis |
| title_full_unstemmed | Detecting overdispersion in count data: a zero-inflated poisson regression analysis |
| title_short | Detecting overdispersion in count data: a zero-inflated poisson regression analysis |
| title_sort | detecting overdispersion in count data: a zero-inflated poisson regression analysis |
| topic | HA Statistics |
| url | http://eprints.uthm.edu.my/5164/ http://eprints.uthm.edu.my/5164/ http://eprints.uthm.edu.my/5164/1/AJ%202017%20%28294%29%20Detecting%20overdispersion%20in%20count%20data.pdf |