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Poisson regression is often used to model count data however one of the main assumptions is that mean and the variance of the counts (dependent variable) are equal. Often the variance is greater than the mean, leading to overdispersion. This special topics workshop will cover how to identify overdispersion and present different methods to account for it in count models.

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