69 Journal of Basic Physical Research Vol. 9., No. 2, July 2019 BLOCKING PROBABILITY FOR CUSTOMERS’ FLOW IN BANKING SECTOR (A CASE STUDY OF FOUR BANKS IN ANAMBRA STATE) Etaga, H.O., Ajao, K., Awopeju, A., Etaga, M.C 1 Department of Statistics, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria 2 Department of Educational Foundations, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria Email: ho.etaga@unizik.edu.ng ABSTRACT Flow of people can be affected by geographical location. In location of industry, these two are taken into consideration to reduce loss and maximize profit. In banking industry, flow of customers in a banking sector can be affected by location. This research is set to investigate possible variation in queuing model of banks with respect to geographical location. For this study, four banks were randomly selected. Primary data were used for the study. The results of the analysis shows that the queue models depend on location of the bank and banks cited in similar location have similar models. In like manner, blocking probability was investigated using Hayward Approximation Estimate, Jagarman Estimate and Recursion Estimate. Blocking Probability computed revealed that irrespective of location, Recursion Estimate is lower than any other method used which implies that the method is highly sensitive to the detection of blocking probability. Keyword: Blocking Probability, Queuing, Estimates, Heyward Approximation. Recursion, Introduction. Queuing theory is the mathematical study of waiting lines of customers in a service system such as fuel stations, supermarket check-out counters, post offices, cafeteria, and banking halls. In queuing theory, a model is constructed so that important queuing characteristics of the service systems can be obtained as a measure of the service performance of the systems. Examples of such characteristics are queue lengths (number of customers waiting to be served), the waiting times involved, etc. Arrivals at a service system may be drawn from a finite or an infinite population. The distinction is important because the analyses are based on different premises and require different equation for their solution. A finite population refers to the limited size customer pool that will use the service and, at time s, form a line. The reason this finite classification is important is because when a customer leaves its position as a member of the population of users, the size of the user group is therefore reduced by one, which reduces the probability of the next occurrence. Conversely, when a customer is serviced and returns to the user group, the population increases and the probability of a user requiring service also increases. These finite classes of problems require a separate set of formulas from that of the infinite population case. An infinite population is one large enough in relation to the service system so that the changes in the population size caused by subtraction or addition to the population do not significantly affect the system probabilities. In banking system, the arrival process consists of the arrival rate of customers per unit time and the probability distribution of inter-arrival times between successive customer arrivals. The service process consists of the service discipline, number of servers S =1, 2 …n, the service