Extracurricular activities (ECA) are part of students' everyday life; they play important roles in student's lives. Few studies have addressed the question of how student engagements to ECA affect student's grade point average (GPA). This research was conducted to know whether the students' grade point average in King Abdulaziz University, Faisaliah campus is affected by their participation in the ECA. This study also studied the students' satisfaction on ECA. The study sample includes 239 students chosen via simple random sampling method. The study used inferential statistics to analyze this study design. To achieve the purpose of this study, a questionnaire (comprising 19 questions) was designed. The results showed that participation in ECA affects the students' GPA in a positive way. The study found that those who participated in ECA have higher GPA than those who did not; the study also found that the time spent participating in ECA did not affect the time students usually spend on studying (the result showed there wasn't any relationship between them). Furthermore, the study showed that students, based on faculty, are generally satisfied with the available extracurricular activities in the campus.

The exponentiated gamma (EG) distribution and Fisher information matrices for complete, Type I, and Type II censored observations are obtained. Asymptotic variances of the different estimators are derived. Also, we consider different estimators and compare their performance through Monte Carlo simulations.

In medical studies or in reliability analysis, it is quite common that the failure of any individual or any item may be attributable to more than one cause. So in this paper, we consider the competing risks model with very general censoring scheme, namely progressive first-failure censored scheme under the Gompertz life time distribution. The results in each of first-failure censoring, progressive Type II censoring, Type II censoring and complete sample are a special cases. We provide different methods for the analysis of the model under the assumption of independent causes of failure and Gompertz distribution lifetimes. The maximum likelihood estimators (MLE’s) of the different parameters as well as approximate confidence intervals are presented. Bayesian estimation using MCMC method under the joint prior density as a product of a conditional gamma density and inverted gamma density for unknown Gompertz parameters are presented. The analysis of a real data set to assess the performance of all these estimators, confidence intervals are developed using asymptotic distributions and Bayesian credible intervals for the parameters. The different methods are compared through a simulation study.

In this article, a new three parameters lifetime model called the Topp-Leone Generalized Inverted Exponential (TLGIE) Distribution is introduced. Various properties of the model are derived, including moments, quantile function, survival function, hazard rate function, mean deviation and mode. The method of maximum likelihood is used to estimate the unknown parameters. The properties of the maximum likelihood estimators using Fisher information matrix are studied. Three real data sets are applied for illustrative purpose of this study.

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