Main Article Content
Weighted distributions provide a unified approach to model formulation and data interpretation issues. In this paper, we present the weighted Weibull-G ( WW-G) as a novel family of weighted distributions that may be used to solve problems in a variety of fields, including reliability, meta analysis, biomedicine, ecology, and others. Some statistical features that hold out of any baseline model are explained using general formulations. Four useful models are offered for the new family. Diverse density function shapes, such as symmetric, uni-modal, right skewed, U-shaped, or J-shaped, are represented, as well as different hazard rate shapes. The maximum likelihood estimators for family's parameters are derived. Monte Carlo simulations are used to examine the behavior of the estimators for one specific model, which is the WW-exponential. Finally, real data depicting the proportion of primary energy consumption produced from renewables in 75 country is used to demonstrate the flexibility of one model. Another real data analysis of global Carbon Dioxide (CO2) emissions per person in 2020 is taken into account in 211 country. The results of applications reveal that the weighted Weibull exponential distribution can, in reality, better match the data when compared to other competing distributions.