DOI: https://doi.org/10.14710/medstat.16.2.206-214

HANDLING OF OVERDISPERSION CASES IN MORBIDITY DATA IN SELUMA REGENCY

*Mey Yanti Sarumpaet  -  Statistics Study Program, The University of Bengkulu, Indonesia
Sigit Nugroho  -  Statistics Study Program, The University of Bengkulu, Indonesia
Ramya Rachmawati  -  Statistics Study Program, The University of Bengkulu, Indonesia
Open Access Copyright (c) 2023 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
The problem of overdispersion as a violation of the assumption of equidispersion in Poisson regression is generally caused by  sources of unobserved heterogeneity, missing observations on predictor variables, outliers in the data, errors in the specification of the bridging function, and many observed  values that are zero.  The  purpose of  this study is  to find out the right  model and the variables  that affect data that occurs overdispersion and excess zero in the case of the number of days of disruption at work, school, or other daily activities due to health complaints. The methods used were Poisson Regression, Negative  Binomial Regression, Hurdle  Poisson  Regression,  Zero  Inflated Poisson Regression,  Zero  Inflated  Negative  Binomial Regression, and Hurdle Negative Binomial Regression. The data used were morbidity taken from data on the number of days  of  disruption at  work,  school  or  other daily  activities due  to  health  complaints  in  Seluma district,  Bengkulu Province. It was found that the best model is Zero Inflated Negative  Poisson  with  the  smallest  Akaike  Information Criterion (AIC) value of 1620.609  and the variables that have  a  significant  effect on the  log model and the logit model are marital status and work variables.
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Keywords: Overdispersion, Poisson, Negative Binomial, Zero Inflated, Hurdle, Morbidity

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