In reliability and life-testing experiments, the researcher is often interested in the effects of extreme or varying stress factors on the lifetimes of experimental units. In this paper, a step-stress model is considered in which the life-testing experiment gets terminated either at a pre-fixed time (say, 1 m T + ) or at a random time ensuring at least a specified number of failures (Say, y out of n ). Under this model in which the data obtained are Type-II hybrid censored, the Kumaraswamy Weibull distribution is used for the underlying lifetimes. The maximum Likelihood estimators (MLEs) of the parameters assuming a cumulative exposure model are derived. The confidence intervals of the parameters are also obtained. The hazard rate and reliability functions are estimated at usual conditions of stress. Monte Carlo simulation is carried out to investigate the precision of the maximum likelihood estimates. An application using real data is used to indicate the properties of the maximum likelihood estimators.

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The current research aims to review the concept of Subjective Well-Being (SWB) and its literature and to reveal the factorial construction of the subjectivecomponent in the composite well-being index. This main objective of this paper is studying the factorial structure by using of Exploratory Factor Analysis (EFA) and determination of the relative weight of each factor from factor analysis outputs. To achieve the previous objectives we use a sample of 1500 women from Giza governorate, distributed equally in three regions: (Agouza represents an urban area, Manwatt represents a rural area, Abu Qatada represents a slum area). The results include the (EFA) of confidence in the psychometric measures and the reliability test of (Cronbach's alpha α) for the questions of the questionnaire suggested by the Organization for Economic Cooperation and Development (OECD) on the (SWB) of the sample The study reached that, the use of the (EFA) of the components of the subjective-component revealed nine factors that explain (69%) of the variation. this is a good percentage.

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