In simulation, time averages are important for estimating equilibrium parameters. In particular, we would like to have the variance, bias and mean-square error for time averages. First, we will discuss various factors and their effect on the bias, the variance and the mean-square error. We will use the Markovian Event System to model various systems, including M/M/1 queues, M/E_k/1 queues, M/M/c queues, sequential queues, inventory systems and queueing networks. We use a numerical method for the computation of the variance, the bias and the mean-square error of the time average. The effectiveness of the method is tested by experimenting with models of various stochastic systems. The contribution of this thesis is to use numerical and graphical interpretations to study the general characteristics of the measures. The important characteristics included in our study are decomposability and periodicity.

In simulation, time averages are important for estimating equilibrium parameters. In particular, we would like to have the variance, bias and mean-square error for time averages. First, we will discuss various factors and their effect on the bias, the variance and the mean-square error. We will use the Markovian Event System to model various systems, including M/M/1 queues, M/E_k/1 queues, M/M/c queues, sequential queues, inventory systems and queueing networks. We use a numerical method for the computation of the variance, the bias and the mean-square error of the time average. The effectiveness of the method is tested by experimenting with models of various stochastic systems. The contribution of this thesis is to use numerical and graphical interpretations to study the general characteristics of the measures. The important characteristics included in our study are decomposability and periodicity.