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Why do I get different standard errors when i group the data before fitting a Quasi-Poisson GLM for counts with an offset = log(population)?

I found an answer to this question here: https://doi.org/10.1515/ijb-2020-0079. Essentially, grouping the data has no effect on coefficients but changes the standard error (upwards for overdispersed ...
Colin's user avatar
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6 votes

Why do I get different standard errors when i group the data before fitting a Quasi-Poisson GLM for counts with an offset = log(population)?

I think the problem is that your ungrouped data don't fit the model. If you divided the 24795 people in the first grouped record into 45 equal chunks of 551 before recording the response you would not ...
Thomas Lumley's user avatar
7 votes
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Does this comparison of the quasipoisson model to the poisson model make sense?

The deviance is the same because the code forces it to be the same. The deviance is computed in stats::glm.fit by ...
Thomas Lumley's user avatar
0 votes

What does the dispersion parameter mean in negative binomial regression?

I find it useful to verify these things empirically via simulation. You can parametrized the negative binomial distribution via the mean number of successes at each trial and the dispersion (as ...
dariober's user avatar
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1 vote
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How to eliminate variables from regression models due to collinearity and multicollinearity (linear, Poisson, and negative binomial)

I think that I should eliminate my variables because there are some high values of correlation (0.6 to 0.8) between the two variables in my model. I have read documents that this would affect the ...
EdM's user avatar
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