Modified Cubic Transmuted Sujatha Distribution (MCT-SUJATHA)

Abstract As a result of the tremendous technological development, single and classical probability distributions have become unable to represent apparent data well, which prompted researchers to build new probability models based on these single and classical distributions, which are characterized by being more flexible in modeling and representing data. There are several methods for building probabilistic models, including the modified cubic transmuted method Therefore, the research aims to propose a new modified cubic transmuted Sujatha distribution (MCT-SUJATHA) as a member belongs to the modified cubic transmuted family (MCT-G FAMILY), using the Sujatha distribution (SD), the probability and cumulative density functions of the distribution are derived, and its structural properties represented by (The Non-Central Moment, The Central Moment, Coefficient of Skewness, Coefficient of Kurtosis, and Coefficient of Variation), in addition to estimating the distribution parameters using the Maximum Likelihood Estimation method (MLE) and the (Cran) method And compare them using simulation.