Chapter 10 – Quality Control
1. Approving the effort that occurs during the production process is known as acceptance
sampling.
2. Statistical Process Control is the measurement of rejects in the final product.
3. The optimum level of inspection occurs when we catch at least 98.6 percent of the defects.
Chapter 10 – Quality Control
4. The optimum level of inspection minimizes the sum of inspection costs and the cost of
passing defectives.
5. Processes that are in control eliminate variations.
6. High-cost, low-volume items often require careful inspection since we make them so
infrequently.
Chapter 10 – Quality Control
7. Low-cost, high-volume items often require more intensive inspection.
8. A lower control limit must by definition be a value less than an upper control limit.
9. Attributes need to be measured, variable data can be counted.
Chapter 10 – Quality Control
10. The amount of inspection we choose can range from no inspection at all to inspecting each
item numerous times.
11. The amount of inspection needed is governed by the costs of inspection and the expected
costs of passing defective items.
12. The purpose of statistical process control is to ensure that historical output is random.
Chapter 10 – Quality Control
13. A process that exhibits random variability would be judged to be out of control.
14. If a point on a control chart falls outside one of the control limits, this suggests that the
process output is non-random and should be investigated.
15. An x-bar control chart can only be valid if the underlying population it measures is a
normal distribution.
Chapter 10 – Quality Control
16. Concluding a process is out of control when it is not is known as a Type I error.
17. An R value of zero (on a range chart) means that the process must be in control since all
sample values are equal.
18. Range charts are used mainly with attribute data.
Chapter 10 – Quality Control
19. Range charts and p-charts are both used for variable data.
20. A p-chart is used to monitor the fraction of defectives in the output of a process.
21. A c-chart is used to monitor the total number of defectives in the output of a process.
Chapter 10 – Quality Control
22. A c-chart is used to monitor the number of defects per unit for process output.
23. Tolerances represent the control limits we use on the charts.
24. “Process capability” compares “process variability” to the “tolerances.”
Chapter 10 – Quality Control
25. Control limits used on process control charts are specifications established by design or
customers.
26. Control limits tend to be wider for more variable processes.
27. Patterns of data on a control chart suggest that the process may have non-random
variation.
Chapter 10 – Quality Control
28. The output of a process may not conform to specifications even though the process may
be statistically “in control.”
29. Run tests are useful in helping to identify nonrandom variations in a process.
30. Run tests give managers an alternative to control charts; they are quicker and cost less.
Chapter 10 – Quality Control
31. Statistical process control focuses on the acceptability of process output.
32. A run test checks a sequence of observations for randomness.
33. Even if the process is not centered, the process capability index (indicated by Cpk) is very
useful.
Chapter 10 – Quality Control
34. The process capability index (indicated by Cpk) can be used only when the process is
centered.
35. Quality control is assuring that processes are performing in an acceptable manner.
36. The primary purpose of statistical process control is to detect a defective product before it
is shipped to a customer.
Chapter 10 – Quality Control
37. The Taguchi Cost Function suggests that the capability ratio can be improved by
extending the spread between LCL and UCL.
38. The variation of a sampling distribution is tighter than the variation of the underlying
process distribution.
39. The sampling distribution can be assumed to be approximately normal even when the
underlying process distribution is not normally distributed.
Chapter 10 – Quality Control
40. Approximately 99.7% of sample means will fall within two standard deviations of the
process mean if the process is under control.
41. The best way to assure quality is to use extensive inspection and control charts.
42. Control limits are based on multiples of the process standard deviation.
Chapter 10 – Quality Control
43. Attribute data are counted, variable data are measured.
44. The number of defective parts in a sample is an example of variable data because it will
“vary” from one sample to another.
45. Larger samples will require wider x-bar control limits because there is more data.
Chapter 10 – Quality Control
46. When a process is not centered, its capability is measured in a slightly different way. The
symbol for this case is Cpk.
47. Range control charts are used to monitor process central tendency.
48. An “up and down” run test uses the median as a reference point and measures the
percentage above and below the median.
Chapter 10 – Quality Control
49. “Assignable variation” is variation due to a specific cause, such as tool wear.
50. Variation in a sample statistic collected from a process may be either random variation or
assignable variation – or both.
51. “Quality of conformance” is concerned with whether a product or service conforms to its
specifications.
Chapter 10 – Quality Control
52. The larger the process variation, the tighter the specifications should be.
53. Type I and Type II errors refer to the magnitude of variation from the standard.
54. The greater the capability ratio, the higher the rejects.
Chapter 10 – Quality Control
55. Non-random variation is likely whenever all observations are between the LCL and UCL.
56. Which of the following quality control sample statistics indicates a quality characteristic
that is an attribute?
Chapter 10 – Quality Control
57. A time-ordered plot of representative sample statistics is called a:
58. A control chart used to monitor the process mean is the: