One of the most famous ways to assess the sensitivity of the model is the global influence. Starting from case deletion (see, cook, 1977). The casedeletion is a common approach to study the effected of dropping the (i-th) observation from the data. First we obtain the estimates of parameters and get the log-like of these estimates. Then we estimate the parameters after cancelling one observation from the dataset. The aim is comparing between the log-likelihood in two cases. This measure can be written as: Cook measure = 2(Log-like before eliminating obs. -Log-like after eliminating obs.) To explain the effect of cancelling one observation on the estimation of parameters, we generate the same **data** with 99 n  observations. The  696586 We have noted that the cook is positive negative value, this means that the log-likelihood in small values (negative) is less than it is large values. This is happened because the values of estimates become less.