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okay in a process on regression one of the things we have to look out for is over dispersion or under dispersion over dispersion is to recap is when the variance the conditional variance is greater than the conditional mean which violates the condition in possible regression that the mean is equal to the variance and under dispersion is opposite conditional variance is less than the conditional mean if we dont if the mean variance relationship doesnt hold I mean doesnt equal to the variance then pass on MLE its going to be consistent but standard errors are wrong as that T statistics are wrong we got around that by using Poisson qml a quasi MLE which is robust to that mean variance relationship another way to deal with them over and over mean variance relation holding is the negative binomial MLE this negative binomial ml e is a contender if you have over dispersion but not under dispersion okay so were going to look in our to the two cases were just going to run an example and e