Suppose that we have been conducting a simulation analysis for a call center system. A simulation model has been developed in order to analyze the current system and propose an alternative configuration which performs significantly better in terms of the daily average total waiting time (ATWT). After analyzing the reports obtained from the simulation model of the current system, we proposed some modifications in the system (such as hiring call agents and increasing the number of trunk lines) that might be useful to improve the performance measure of interest. We refer to the initial configuration as the Scenario 1 and the one with the proposed modifications as the Scenario 2. After performing certain steps of the simulation analysis, we need to employ an output analysis method to decide whether the improvement by Scenario 2 is statistically significant or not. 10 response values of the performance measure the values of ATWT, in minutes) were collected for each scenario by running 10 independent replications. The output data at the end of each simulation run for the ATWT are obtained as follows: Replication Number: ATWT under Scenario 1 8 1 2 3 4 5 6 7 8.55 8.0 8.32 9.02 9.85 9.9 7.58 9 10 9.1 9.41 8.73 ATWT under Scenario 2 7.84 8.3 8.22 9.25 8.35 8.7 7.64 7.7 8.1 8.79 Conduct an appropriate test to decide whether the improvement by Scenario 2 is statistically significant with respect to average total waiting time. Precisely state the hypotheses. Use the level of significance a = 0.05