One of New England Air's top competitive priorities is on-time arrivals. Quality VP Clair Bond decided to personally monitor New England Air's performance. Each week for the past 30 weeks, Bond checked a random sample of 100 flight arrivals for on-time performance. The table that follows contains the number of flights that did not meet New England Air's definition of "on time":
|
Sample (week)
|
Late Flights
|
Sample (week)
|
Late Flights
|
|
1
|
2
|
16
|
2
|
|
2
|
4
|
17
|
3
|
|
3
|
10
|
18
|
7
|
|
4
|
4
|
19
|
3
|
|
5
|
1
|
20
|
2
|
|
6
|
1
|
21
|
3
|
|
7
|
13
|
22
|
7
|
|
8
|
9
|
23
|
4
|
|
9
|
11
|
24
|
3
|
|
10
|
0
|
25
|
2
|
|
11
|
3
|
26
|
2
|
|
12
|
4
|
27
|
0
|
|
13
|
2
|
28
|
1
|
|
14
|
2
|
29
|
3
|
|
15
|
8
|
30
|
4
|
a) Using a 95% confidence level, plot the overall percentage of late flights p and the upper and lower control limits on a control chart.
b) Assume that the airline industry's upper and lower control limits for flights that are not on time are .1000 and .0400, respectively. Draw them on your control chart.
c) Plot the percentage of late flights in each sample. Do all samples fall within New England Air's control limits? When one falls outside the control limits, what should be done?
d) What can Clair Bond report about the quality of service?