2016
DOI: 10.1063/1.4965123
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Modelling count data: An application to a breast cancer data in Malaysia

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“…52 The negative binomial regression model fits the data better and accounted for overdispersion better than the Poisson regression model, which assumed the mean and variance were the same. 53 In addition, the residual deviance by the degree of freedom or the quotient is greater than one. Trend Analysis was done using the Cochran-Armitage trend test.…”
Section: Methodsmentioning
confidence: 99%
“…52 The negative binomial regression model fits the data better and accounted for overdispersion better than the Poisson regression model, which assumed the mean and variance were the same. 53 In addition, the residual deviance by the degree of freedom or the quotient is greater than one. Trend Analysis was done using the Cochran-Armitage trend test.…”
Section: Methodsmentioning
confidence: 99%
“…In the case of Poisson overdispersion, it is recommended that one uses more flexible model such as the negative binomial (Cameron and Trivedi, 1998). Many researchers have taken this approach in analyzing similarly structured data (Anderson et al, 2014;Mutyaba et al, 2015;Abdullah et al, 2016). In this study, both Poisson and negative binomial distributions were considered for each cancer outcome.…”
Section: Methodsmentioning
confidence: 99%