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Asian Journal of Probability and Statistics 4(2): 110, 2019; Article no.AJPAS.46795 ISSN: 25820230Comparing Zeroinflated Poisson, Zeroinflated Negative Binomial and Zeroinflated Geometric in Count
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What is zero-inflated negative binomial regression?
Zero-inflated negative binomial regression is a statistical method used to model count data that have an excess of zero counts. It combines a negative binomial distribution for count data and a separate process to account for the excess zeros.
Who is required to file zero-inflated negative binomial regression?
Researchers and analysts who need to analyze count data characterized by over-dispersion and an excess of zeros typically use the zero-inflated negative binomial regression model, particularly in fields like healthcare, ecology, and social sciences.
How to fill out zero-inflated negative binomial regression?
Filling out a zero-inflated negative binomial regression involves specifying the count model, determining the appropriate predictors for both the count and inflation components, and using statistical software to estimate the model parameters.
What is the purpose of zero-inflated negative binomial regression?
The purpose of zero-inflated negative binomial regression is to provide a better fit for count data that exhibit an overabundance of zero values, allowing researchers to understand the underlying mechanisms generating the data.
What information must be reported on zero-inflated negative binomial regression?
When reporting zero-inflated negative binomial regression, researchers should include model diagnostics, coefficients, standard errors, p-values, and any assumptions made during the analysis.
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