| dc.contributor.author | Fathurahman, M. | |
| dc.date.accessioned | 2022-12-20T00:49:20Z | |
| dc.date.available | 2022-12-20T00:49:20Z | |
| dc.date.issued | 2022-06-09 | |
| dc.identifier.issn | 2798-3455 | |
| dc.identifier.uri | http://repository.unmul.ac.id/handle/123456789/42756 | |
| dc.description.abstract | Negative Binomial Regression (NBR) is an alternative regression model to model the relationship
between the dependent variable in overdispersion count data and one or more independent variables.
Overdispersion is a problem in Poisson regression modeling. Namely, the variance of the dependent
variable is more than the mean. If there is overdispersion, then the parameter estimator of the Poisson
regression model has a standard error value that is not under-estimated. The NBR model was applied to
modeling infant mortality in East Kalimantan in 2019. Data on infant mortality in East Kalimantan in 2019
indicated overdispersion. Infant mortality is an indicator that can measure the progress of development
outcomes in the health sector in a region. In the last three years, from 2017 to 2019, infant mortality data in
East Kalimantan has increased. Therefore, it is necessary to do modeling to get the factors that cause it.
The modeling results with NBR show that the percentage of the complete neonatal visit of KN3, the
percentage of infant health services, and the percentage of visits by pregnant women K4 significantly affect
infant mortality in East Kalimantan in 2019. | en_US |
| dc.language.iso | other | en_US |
| dc.publisher | Jurusan Matematika FMIPA Universitas Mulawarman | en_US |
| dc.subject | Count Data, Poisson Regression, Overdispersion, NBR, Infant Mortality | en_US |
| dc.title | Regresi Binomial Negatif untuk Memodelkan Kematian Bayi di Kalimantan Timur | en_US |
| dc.type | Article | en_US |
