Abstract
This paper suggests that log-linear analysis can be used to predict the expected number of patients in a queueing system. A log-linear Poisson regression model has been developed to analyse the time series of count data. In finding a Poisson regression model, parameters are estimated and goodness-of-fit is utilized to carefully extract the best model to fit the count data. The marginal effect is the basis function which can be used in the Poisson regression analysis. As a result, it allows us to arrive at better predictions of welfare health services and rehabilitation decision making.