International Journal of Tre Volume 4 Issue 2, February 2 @ IJTSRD | Unique Paper ID – IJTSRD3 Applica Distributi Ayeni Taiwo Michael, Og Department of ABSTRACT There are many events in daily life whe theory is the study of waiting lines and i procedure of queuing in daily life of hum not only in day to day life but also in sequ networks, medical field, banking sectors many statistical distributions in analyzing apply a new distribution named Exponen a data on waiting time of bank customers We compared the adequacy and perform existing statistical distributions. The res Gamma distribution is adequate and a existing distributions. KEYWORDS: Exponential-Gamma distrib Networks INRODUCTION There are many events in daily life wh formed. Queuing theory is the study of wa is very crucial in analyzing the procedur daily life of human being. Queuing theory in day to day activities but also in sequen programming, networks, medical field, etc. A queuing theory called a random s one of the issues in mathematics so th techniques in the queuing theory h importance in solving mathematical analyzing different systems [1]. Statistical distributions are very crucial in predicting real life occurrence. A distributions have been developed and t rooms for developing distributions w flexible and capable of handling real wo Lately, studies have shown that some real be analyzed adequately by existing distrib it may be discovered to follow distrib combined form of two or more random known probability distributions. In adequacies, variety in usage and perform distributions have received a numerous various researchers such as; [2],[3],[4] an this study aimed to examine the performance of the new Exponential-Gam to other exiting probability distributions u end in Scientific Research and Dev 2020 Available Online: www.ijtsrd.com e 30097 | Volume – 4 | Issue – 2 | January-Fe ation of Exponential-Gamm ion in Modeling Queuing D gunwale Olukunle Daniel, Adewusi Oluw Statistics, Ekiti State University, Ado-Ekiti, Nig ere a queue is formed. Queuing it is very crucial in analyzing the man being. Queuing theory applies uence of computer programming, s etc. Researchers have applied g a queuing data. In this study, we ntial-Gamma distribution in fitting s before service is been rendered. mance of the results with other sult shows that the Exponential- also performed better than the bution, Queuing theory, AIC, BIC, How to ci Michael | Adewusi "Applicatio Distributio Published Internation Journal of Scientific and Dev (ijtsrd), ISS 6470, Vol Issue-2, 2020, www.ijtsrd Copyright Internation Scientific Journal. Th distributed the terms Creative C Attribution (http://cre by/4.0) here a queue is aiting lines and it re of queuing in applies not only nce of computer banking sectors service theory is hat the existing have substantial problems and n describing and Although, many there are always which are more orld application. l life data cannot butions. At times, butions of some m variables with light of their mance, statistical attentions from nd [5]. Therefore adequacy and mma distribution using the data on waiting time of bank custo rendered, using the mode Akaike information criterion criterion (BIC) and the log lik METHODS The Exponential-Gamma dis [6] and its pdf is defined as 1 1 2 (; , ) ( ) x e fx α α λ αλ α + - - = Γ With the mean and variance; 1 2 α α μ = and () Vx α = The cumulative distribution f () Fx λ = The survival function for () 1 () Sx Fx = - was obtain velopment (IJTSRD) e-ISSN: 2456 – 6470 ebruary 2020 Page 839 ma Data wasesan Adeoye geria ite this paper: Ayeni Taiwo Ogunwale Olukunle Daniel | Oluwasesan Adeoye on of Exponential-Gamma on in Modeling Queuing Data" in nal Trend in Research velopment SN: 2456- lume-4 | February pp.839-842, URL: d.com/papers/ijtsrd30097.pdf © 2019 by author(s) and nal Journal of Trend in Research and Development his is an Open Access article d under s of the Commons n License (CC BY 4.0) eativecommons.org/licenses/ omers before service is being el selection criteria like the n (AIC), Bayesian information kelihood function (Ɩ). stribution was developed by 2 ,, , 0 x x λ λα > (1) ; 1 (2) ( 29 ( 29 ( 29 2 1 2 2 2 α α α αα λα λ + - + (3) function is defined as ( ,) 2 ( ) x α λγ α α Γ (4) the distribution defined by ned as; IJTSRD30097