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MackAir is a rapidly growing airline promising people environmentally sensitive flights. One of MackAirs top competitive priorities is on-time arrivals. Each week for the past 30 weeks, Quality VP Jay Bondi has checked a random sample of 100 flight arrivals for on-time arrival and tracked the number of flights that did not meet MackAirs definition of on time. This information is given in the tab Late Flights.

a. Calculate the overall percentage of late flights and 99% (or 3-Sigma) upper and lower control limits of a p-chart for this situation.

b. Assume that the airline industrys 99% upper and lower control limits for the percentage of flights that are not on time are 10% and 4%, respectively. How do these compare with the MackAirs control limits in (a)? Comment.

c. Plot the percentage of late flights in each sample. Is the process that produces delayed flights at MackAir under control?

d. What can VP Bondi report about the quality of service? Summarize in 4-5 lines

1 Number of late flights out of 100 randomly selected flights 2 \begin{tabular}{|c|c|c|} \hline 3 & Sample(Week) & LateFlights \\ \hline 4 & 1 & 10 \\ \hline 5 & 2 & 12 \\ \hline 6 & 3 & 11 \\ \hline 7 & 4 & 12 \\ \hline 8 & 5 & 11 \\ \hline 9 & 6 & 8 \\ \hline 10 & 7 & 8 \\ \hline 11 & 8 & 14 \\ \hline 12 & 9 & 11 \\ \hline 13 & 10 & 13 \\ \hline 14 & 11 & 14 \\ \hline 15 & 12 & 12 \\ \hline 16 & 13 & 9 \\ \hline 17 & 14 & 9 \\ \hline 18 & 15 & 8 \\ \hline 19 & 16 & 14 \\ \hline 20 & 17 & 11 \\ \hline 21 & 18 & 9 \\ \hline 22 & 19 & 8 \\ \hline 23 & 20 & 14 \\ \hline 24 & 21 & 12 \\ \hline 25 & 22 & 8 \\ \hline 26 & 23 & 11 \\ \hline 27 & 24 & 11 \\ \hline 28 & 25 & 13 \\ \hline 29 & 26 & 10 \\ \hline 30 & 27 & 11 \\ \hline 31 & 28 & 8 \\ \hline 32 & 29 & 11 \\ \hline 33 & 30 & 9 \\ \hline \end{tabular}