ANISHA PUTRI RAMADHANI, - (2023) THE MODELLING NUMBER OF DAILY DEATH COVID 19 DATA USING SOME OF TWO AND ONE PARAMTER DISTRIBUTIONS IN INDONESIA. THE MODELLING NUMBER OF DAILY DEATH COVID 19 DATA USING SOME OF TWO AND ONE PARAMTER DISTRIBUTIONS IN INDONESIA, 68. pp. 106-111. ISSN 2231-5373

Preview

Text ANISHA PUTRI RAMADHANI.pdf Download (738kB) | Preview

Abstract

Abstract - The outbreak of COVID-19 pandemic has caused many deaths. The number of deaths that occur randomly per day has caused various problems, including reduced population, increased land for graves, and increased fear of the situation that has arisen which indirectly impacts decreased performance which ultimately weakens a country's economy.However, it is the concern of governments and others' responsibilities to provide the correct statistics and figures to take any practicable necessary steps. Where the statistical literature supposes that a model governs every real phenomenon, once we know the model, we can evaluate the dilemma. Therefore, in this article, we compare and analyze the frequency of a number of death from COVID-19 in Indonesia using the six probability modeling. Probability modeling will be carried out using six distributions, namely Weibull, Gamma, Log Normal, Amarendra, Rani and Akash will be used in this study. The maximum likelihood method will be used to get the estimated parameter from the models used in this study. The distribution will be selected based on some methods of Good of Fit Test namely graphical (pdf plot and cumulative distribution function (CDF) plot) and numerical (Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), and Log Likelihood (Log-L)). In most cases, graphical methods give the same results but their AIC and BIC results are different. The most suitable result is selected as the distribution with the lowest AIC and BIC values. In general, the Weibull distribution has been chosen as the best model. Keywords – Covid 19, Weibull distribution, Gamma distribution, Amarendra distribution, Log normal distribution, Rani distribution, Akash Distribution

Actions (login required)

View Item