 following gives some examples of evaluating CIs for the statistical results measured from simulations in this thesis. From the observations of the estimated CIs, the reliability and stability of the simulation results can be determined. In Figure A.37 a web scenario is investigated in the ATM-based lub. In this example, the confidence intervals for the average application delay are estimated. The graph shows the average application delays and their 95% CI over different loads. For each load in the graph, 8 simulation runs were performed and each run used a different random seed. For each simulation run i, the average application delay is measured as Ti. In Figure A.37, every point of the curve represents the mean delay value T over 8 average application delays measured from the 8 different seeds simulations. It is calculated with equation (A-8) where n = 8. Since the number of samples is less than 30, the Student distribution is used in this case. (A-8) A.20 Statistical Evaluation: Confidence Interval The confidence interval for a confidence level (1-a) is calculated below: (A-9) t(a/2,n-1) is the upper critical value of the t-distribution with (n-1) degrees of freedom for the desired confidence level (1-a). Its value can be look up from the t-distribution table. S is the sample standard deviation computed with an unbiased estimator as follows: (A-10) Confidence Interval of the average application delay of elastic traffic Figure A.37: Average application delay with 95% CI: web scenario, RAB 128 kbps, no CAC, constant file size of 50 kbyte, lub link = 1 E1 line (ATM) The calculated values of 95% CI for the average application delays and their corresponding margin of error are given in Table A.10. It can be observed from Figure A.37 and Table A.10 that the confidence intervals get larger with a higher load (link utilization). However, when the load reaches 95% of the link capacity the relative margin of errors remain below 5%. It demonstrates that the measured average application delay statistical results from sim