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PUTU SUSAN PRADAWATI
Faculty of Mathematics and Natural Sciences, Udayana University
Mathematics

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PENERAPAN REGRESI BINOMIAL NEGATIF UNTUK MENGATASI OVERDISPERSI PADA REGRESI POISSON PUTU SUSAN PRADAWATI; KOMANG GDE SUKARSA; I GUSTI AYU MADE SRINADI
E-Jurnal Matematika Vol 2 No 2 (2013)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MTK.2013.v02.i02.p031

Abstract

Poisson regression was used to analyze the count data which Poisson distributed. Poisson regression analysis requires state equidispersion, in which the mean value of the response variable is equal to the value of the variance. However, there are deviations in which the value of the response variable variance is greater than the mean. This is called overdispersion. If overdispersion happens and Poisson Regression analysis is being used, then underestimated standard errors will be obtained. Negative Binomial Regression can handle overdispersion because it contains a dispersion parameter. From the simulation data which experienced overdispersion in the Poisson Regression model it was found that the Negative Binomial Regression was better than the Poisson Regression model.
Co-Authors I Gusti Ayu Made Srinadi I KOMANG GDE SUKARSA