Faizal Resky Pamungkas, NIM. 17106010003 (2021) ESTIMASI PARAMETER MENGGUNAKAN GENERALIZED POISSON REGRESSION (GPR) METODE MAXIMUM LIKELIHOOD ESTIMATION (MLE). Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.
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Text (ESTIMASI PARAMETER MENGGUNAKAN GENERALIZED POISSON REGRESSION (GPR) METODE MAXIMUM LIKELIHOOD ESTIMATION (MLE))
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Text (ESTIMASI PARAMETER MENGGUNAKAN GENERALIZED POISSON REGRESSION (GPR) METODE MAXIMUM LIKELIHOOD ESTIMATION (MLE))
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Abstract
Poisson regression is a regression analysis used to model data in the form of discrete data (count data). There is an assumption in Poisson regression that the data is Poisson distributed and the variance and mean values of the Y response variables are the same which is called equidispersion. If the value of variance is greater than the mean value, then the data has an overdispersion case. Overdispersion causes parameter estimates in the data to be less or even inaccurate. Overdispersion cases can be handled using the Generalized Poisson Regression (GPR) model. The method used for parameter estimation is the Newton Raphson's Maximum Likelihood Estimation (MLE). This study explains the factors that affect the number of deaths due to Covid-19 in Indonesia in 2021. The results of the analysis show that the factors that significantly influence the number of deaths due to Covid- 19 in Indonesia are the percentage of deaths aged 35-45 years, percentage of death aged 46-60 years, percentage of death with hypertension, percentage of death with diabetes mellitus, percentage of death with heart, percentage of death with kidney, percentage of death with lung, number of health workers.
Item Type: | Thesis (Skripsi) |
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Additional Information: | Dr. Epha Diana Supandi, S.Si., M.Sc. |
Uncontrolled Keywords: | Poisson regression, Generalized Poisson Regression, Maximum Likelihood Estimation, Poisson distribution, Overdispersion |
Subjects: | Matematika |
Divisions: | Fakultas Sains dan Teknologi > Matematika (S1) |
Depositing User: | Drs. Mochammad Tantowi, M.Si. |
Date Deposited: | 18 Jan 2022 09:48 |
Last Modified: | 18 Jan 2022 09:48 |
URI: | http://digilib.uin-suka.ac.id/id/eprint/48618 |
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