A simulation study consists of several steps such as data collection, coding and verification, model validation, experimental design, output data analysis, and implementation. Our research will concentrate on output data analysis. In this field, many researchers have studied how to construct confidence intervals for the mean u of a stationary stochastic process. However, the estimation of the value of a nonlinear function f(u) has not received a lot of attention in the simulation literature. Towards this goal, a batch-means-based methodology was proposed by Munoz and Glynn (1997). Their approach did not consider consistent estimators for the variance of the point estimator for f(u). This thesis, however, will consider consistent variance estimation techniques to construct confidence intervals for f(u). Specifically, we use methods based on the techniques of nonoverlapping batch means (NBM), standardized time series (STS), and a combination of both. Our approaches are tested on moving average, autoregressive, and M/M/1 queueing processes. The results show that our new confidence interval estimators (CIEs) perform as well as or better than the CIEs based on the method of Munoz and Glynn in terms of coverage, the mean of the CI half-width, and the variance of the CI half-width.
