UCL is 5.17, LCL becomes 0.
All values are within the limits.
9.
100
)946(.054.
96.1054.
n
)p1(p
zp are limits Control
054.
)100(16
87
nsobservatio ofnumber total
defectives ofnumber total
p
=
−
===
Hence, UCL = 0.10
LCL = 0.01
Note that observations must be converted to fraction defective, or control limits must be
converted to number of defectives. In the latter case, the upper control limit would be 7.9
defectives and the lower control limit would be .1 defective. Even though all points are
within these limits, the process appears to be out of control because 75% of the values are
above 4%.
10. There are several slightly different ways to solve this problem. The most straightforward
seems to be the following:
1) Observe that the upper control limit is six standard deviations above the lower control
limit.
2) Compute the value of the upper control limit at the start:
3) Determine how many pieces can be produced before the upper control limit just
touches the upper tolerance, given that the upper limit increases by .004 cm. per
piece:
11. Out of the 30 observations, only one value exceeds the tolerances, or 3.3%. [This case is
essentially the one portrayed in the text in Figure 10–9A.] Thus, it seems that the
tolerances are being met: approximately 97 percent of the output will be acceptable.
12. a. = .146
n = 14
85.3
39
15.150
39 === x
x
117.85.3
14
146.
3385.3 ==
N
x
So UCL is 3.97, LCL is 3.73. Sample 29 is outside the UCL, so the
process is not in control.