sum ceb detail First, we estimate a Poisson regressic without any independent variables, so to be able to fit a univariate poisson distribution with a mean equal to that of Percentiles our count dependent variable, CEB, when the Poisson regression model has no independent variables, the estimated model is reduced to u=exp(a The "CEB"variable is a count variable ranging from 0 to 9, with a mean of 1.855, a poisson ceb, nolog standard deviation of 1.125, and a variance of 1266. the mean and the variance are not the same, as they are in a univariate Poisson distribution, but they are close. Unlike the case with many count variables, there is no Log likelihaad--4293,3231 overdispersion in the CEB" variable. We will use Stata's prcounts" command to graph the distribution of "CEB"in a graph along with a mi mir om in nm son a univariate Poisson distribution that has a mean of 1.855. We can then see how closely the data are poisson distributed11 21 sum ceb, detail 22 • The “CEB” variable is a count variable, ranging from 0 to 9, with a mean of 1.855, a standard deviation of 1.125, and a variance of 1.266. The mean and the variance are not the same, as they are in a univariate Poisson distribution, but they are close. Unlike the case with many count variables, there is no overdispersion in the “CEB” variable. We will use Stata’s “prcounts” command to graph the distribution of “CEB” in a graph along with a univariate Poisson distribution that has a mean of 1.855. We can then see how closely the data are Poisson distributed. 12 23 • First, we estimate a Poisson regression without any independent variables, so to be able to fit a univariate Poisson distribution with a mean equal to that of our count dependent variable, CEB, namely 1.855. • when the Poisson regression model has no independent variables, the estimated model is reduced to: 24 poisson ceb, nolog