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Which is more likely quasi Poisson or Gaussian?
The answer is likely to be quasipoisson. This will depend a bit on how much data you have. Is it only slightly more than the number of parameters (12)? Assuming you have at least, say, 24 counts: When you model data with a poisson distribution, you are saying that the variance of that data is equal to its mean.
Can a quasi-Poisson model be used for underdispersed count data?
The dispersiontest () function under the AER library also indicates significant underdispersion. I know I can use quasi-poisson model under the overdispersion condition. When I searched it, I found some documents stated that Quasipoison could also be used underdispersed data, while others did not suggest to use it.
When do you model data with a Poisson distribution?
When you model data with a poisson distribution, you are saying that the variance of that data is equal to its mean. In other words, if you predict a count of 10000, then the variance of that count is 10000 (std.dev 100). In real life, that isn’t always true.
Why is Poisson regression used for count data?
My understanding is primarily because counts are always positive and discrete, the Poisson can summarize such data with one parameter. The main catch being that the variance equals the mean. Thanks for contributing an answer to Cross Validated!
Is the variance of a Poisson distribution equal to its mean?
Assuming you have at least, say, 24 counts: When you model data with a poisson distribution, you are saying that the variance of that data is equal to its mean. In other words, if you predict a count of 10000, then the variance of that count is 10000 (std.dev 100). In real life, that isn’t always true.
Why does a quasi Poisson model ignore the data?
Your (non-quasi-)poisson model ignores that fact. It is taking the predictive variance to always be equal to the predictive mean even when the data indicates otherwise. This is why it thinks the parameters are insignificant, because it is highly overstating the predictive variance.
What do you need to know about Poisson regression?
Next we will see more on Poisson regression… Poisson regression is a type of a GLM model where the random component is specified by the Poisson distribution of the response variable which is a count. Before we look at the Poisson regression model, let’s quickly review the Poisson distribution.