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Your question starts with a premise, namely that people actually use logistic regression for count data. I have not seen so, except when employing a hurdle model. Logistic (and probabilistic) models are designed for binary dependent variables. Because of this, the coefficients (which are oddodds ratios) can be transformed to marginal effects on probability of having a 1 in the dependent variable. I don't see how this can be meaningful for count data. I also think that you run into errors when you regress a logistic model with count data that actually has no 0 values - which is possible.

Also, Poisson regression is not the only possibility to deal with count data. There is negative binomial as well. The difference between these two: Poisson restricts the first two moments (mean and variance) to be equal, while negative binomial doesn't.

Your question starts with a premise, namely that people actually use logistic regression for count data. I have not seen so, except when employing a hurdle model. Logistic (and probabilistic) models are designed for binary dependent variables. Because of this, the coefficients (which are odd ratios) can be transformed to marginal effects on probability of having a 1 in the dependent variable. I don't see how this can be meaningful for count data. I also think that you run into errors when you regress a logistic model with count data that actually has no 0 values - which is possible.

Also, Poisson regression is not the only possibility to deal with count data. There is negative binomial as well. The difference between these two: Poisson restricts the first two moments (mean and variance) to be equal, while negative binomial doesn't.

Your question starts with a premise, namely that people actually use logistic regression for count data. I have not seen so, except when employing a hurdle model. Logistic (and probabilistic) models are designed for binary dependent variables. Because of this, the coefficients (which are odds ratios) can be transformed to marginal effects on probability of having a 1 in the dependent variable. I don't see how this can be meaningful for count data. I also think that you run into errors when you regress a logistic model with count data that actually has no 0 values - which is possible.

Also, Poisson regression is not the only possibility to deal with count data. There is negative binomial as well. The difference between these two: Poisson restricts the first two moments (mean and variance) to be equal, while negative binomial doesn't.

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Your question starts with a premise, namely that people actually use logistic regression for count data. I have not seen so, except when employing a hurdle model. Logistic (and probabilistic) models are designed for binary dependent variables. Because of this, the coefficients (which are odd ratios) can be transformed to marginal effects on probability of having a 1 in the dependent variable. I don't see how this can be meaningful for count data. I also think that you run into errors when you regress a logistic model with count data that actually has no 0 values - which is possible.

Also, Poisson regression is not the only possibility to deal with count data. There is negative binomial as well. The difference between these two: Poisson restricts the first two moments (mean and variance) to be equal, while negative binomial doesn't.