Overdispersion is not specific to counting data. In fact, any parametric model including the normal distribution that can't explain the variance in the data set is over-dispersion or under-dispersion.

To address the overdispersion, you may want to re-estimate the model with the new data set. You may also:

• Ignore it because overdispersion isn't a problem if you don't care the standard errors
• Try NB-P model by William Greene (2008). An extension of NB for dispersion.

Overdispersion is not specific to counting data. In fact, any parametric model including the normal distribution that can't explain the variance in the data set is over-dispersion or under-dispersion.

To address the overdispersion, you may want to re-estimate the model with the new data set. You may also:

• Ignore it because overdispersion isn't a problem if you don't care about the standard errors
• Try NB-P model by William Greene (2008). An extension of NB for dispersion.

Overdispersion is not specific to counting data. In fact, any parametric model including the normal distribution that can't explain the variance in the data set is over-dispersion or under-dispersion.

Overdispersion is not specific to counting data. In fact, any parametric model including the normal distribution that can't explain the variance in the data set is over-dispersion or under-dispersion.

To address the overdispersion, you may want to re-estimate the model with the new data set. You may also:

• Ignore it because overdispersion isn't a problem if you don't care the standard errors
• Try NB-P model by William Greene (2008). An extension of NB for dispersion.