The results in Table exemplify the fact that the annual regulatory programme was insufficient to be confident that the data is anything but random noise at the 1% level of significance. Indeed, the data even fails to detect anything significant at a 5% level of significance. However, by combining five years of data there is evidence of some non-random variation emerging. Further enhancement is achieved by combining the regulatory programme with the much larger operational program. Henceforth, analysis will focus on this larger combined data set. The question arises as in what respect does the observed zonal distribution differ from the predicted distribution. Since the number of samples taken in each zone is different a theoretical version was created by the Monte Carlo method. For each year the number of infringements in each zone was randomly generated from a binomial distribution given the likelihood of infringing the standard. For each repetition the zonal compliance distribution was determined. The results from 10,000 iterations were then averaged to provide a theoretical distribution which can be compared to the observed distribution (Figure ). Comparison of the observed and the theoretical distribution shows that random chance does indeed account for a large part of the observed variation but there are some notable differences. Firstly, there are a few zones with a much higher level of non-compliance than would be expected by chance. Secondly, the distribution is zero-inflated; the number of zones with zero infringements is very much higher than that expected by chance (53 zones had no infringements while simulation predicted only 22 zones). Thirdly, the core of the distribution is overdispersed. Overall, the observed distribution can be described as a zero-inflated overdispersed Poisson. In order to identify whether any zone had an unusual lead non-compliance the confidence limits for the number of infringements in each zone were calculated using the exact binomial method . Since a large number of comparisons are made a 99.9 % level of confidence was applied to identify potential anomalous data. This criterion identified six zones with much higher levels of lead non-compliance than expected ("hot spot" zones). Four of the zones were accounted for by the high proportion of properties that had lead service pipes (see section 2.6). Of the two other zones one was found not to have a full treatment solution in place by 2010. No explanation could be found in one case which had the highest level of lead non-compliance of all the zones. Removing these two outlier zones from the data set (R&O-X2) reduces the chi-squared substantially, but the chi squared is still significant (see Table ). Even when all the outliers are excluded there are still significant differences from the Poisson distribution. The core of the distribution remains overdispersed and the zero-inflated aspect is not impacted.