um overbooking factor also strongly depends on the last mile utilization and the relation of optimum OB _factor with the last mile utilizations differs for different number of Node Bs: (1) under lower last mile utilization, the optimum OB _factor is more sensitive to the configured number of Node Bs, i.e. the improvement of overbooking is more significant with the increased number of Node Bs; (2) under higher last mile utilization, the optimum OB _factor converges to 1 regardless of number of Node Bs. When the last mile utilization reaches 60%, there is almost no overbooking possible in this example. This is because that with an increased offered load per Node B, the statistical multiplexing gets more difficult and it slowly reaches a maximum value. The right figure also shows the same conclusion, it can be obviously seen that the lower the last mile utilization, the higher is the optimized OB _factor value. The overbooking is improved with higher number of Node Bs, and when the number of Node Bs is greater than 16 the increment of the optimum OB factor gets much more slowly converging to a maximum value, which should be lower than 1/last mile utilization. Appendix 16 NB 50 NB last mile utilization Number of NodeBs Figure A.36: Impact of number of Node Bs A.20 Statistical Evaluation: Confidence Interval The Confidence interval (CI) is a statistical range with a specified probability that a given parameter lies within the range. In statistics, confidence intervals are the most prevalent form of interval estimation. Confidence intervals contain two parts: An interval within which the population parameter is estimated to fall (estimate + margin of error). A confidence level which states the probability that the method used to calculate the interval will contain the population parameter. There are two methods to evaluate CI: normal distribution is used for large sample size reps; and Student's t-distribution (or simply the t-distribution) is used to small sample size (e.g. the number of samples are less than 30). The