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Exam 10: Quality Control

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Question 31

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The specifications for a product are 6 mm ± 0.1 mm. The process is known to operate at a mean of 6.05 with a standard deviation of 0.01 mm. What is the C_pk for this process?

A) 3.33
B) 1.67
C) 5.00
D) 2.50
E) 1.33

F) B) and D)
G) B) and E)

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Question 32

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Patterns of data on a control chart suggest that the process may have nonrandom variation.

A) True
B) False

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Question 33

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A quality analyst wants to construct a control chart for determining whether four machines, all producing the same product, are under control with regard to a particular quality attribute. Accordingly, she inspected 1,000 units of output from each machine in random samples, with the following results: $\begin{array} { l c } \text { Machine } & \text { Total Defectives } \\\hline \# 1 & 23 \\\# 2 & 15 \\\# 3 & 29 \\\# 4 & 13\end{array}$ What is the estimate of the standard deviation of the sampling distribution of sample proportions for whenever this process is under control?

A quality analyst wants to construct a sample mean chart for controlling a packaging process. He knows from past experience that whenever this process is under control, package weight is normally distributed with a mean of 20 ounces and a standard deviation of two ounces. Each day last week, he randomly selected four packages and weighed each: $\begin{array} { r } { \text { Weight (ounces) } }\\\begin{array} { l l l l l } \text { Day } \\\hline \text { Monday } & 23 & 22 & 23 & 24 \\\text { Tuesday } & 23 & 21 & 19 & 21 \\\text { Wednesday } & 20 & 19 & 20 & 21 \\\text { Thursday } & 18 & 19 & 20 & 19 \\\text { Friday } & 18 & 20 & 22 & 20\end{array}\end{array}$ If he uses upper and lower control limits of 22 and 18 ounces, what is his risk (alpha) of concluding this process is out of control when it is actually in control (Type I error) ?

The basis for a statistical process control chart is a(the) :

A c-chart is used to monitor the number of defects per unit for process output.

The optimum level of inspection minimizes the sum of inspection costs and the cost of passing defectives.

An operator collected the following time series data from a process: $\begin{array} { l l l l l l l l } 4.0 & 4.4 & 4.3 & 4.5 & 4.6 & 4.2 & 4.1 & 4.5\end{array}$ (A) Determine the number of A/B runs. (B) Determine the number of up/down runs.

The chair of the operations management department at Quality University wants to construct a p-chart for determining whether the four faculty teaching the basic P/OM course are under control with regard to the number of students who fail the course. Accordingly, he sampled 100 final grades from last year for each instructor, with the following results: $\begin{array} { l c } \text { Instructor } & \text { Number of Failures } \\\hline \text { Prof. A } & 13 \\\text { Prof. B } & 0 \\\text { Prof. C } & 11 \\\text { Prof. D } & 16\end{array}$ What are the .95 (5 percent risk of Type I error) upper and lower control limits for the p-chart?

Consider the following information: $\begin{array} { l r r r r r r } \text { Observations } & 1 & 2 & 3 & 4 & 5 & 6 \\\hline \text { Num. of defects } & 10 & 18 & 13 & 15 & 9 & 12\end{array}$ The number of runs up and down for the preceding data is:

A shift in the process mean for a measured characteristic would most likely be detected by a:

A stent for use in coronary surgery requires a special coating. Specifications for this coating call for it to be at least 0.05 millimeters but no more than 0.15 millimeters. If, when the coating process is in control, the long-run average is 0.09 millimeters, what metric would be used to assess this process's capability?

A quality analyst wants to construct a control chart for determining whether three machines, all producing the same product, are under control with regard to a particular quality variable. Accordingly, he sampled four units of output from each machine, with the following results: $\begin{array} {r } { \text { Measurements } } \\\begin{array} { l l l l l } \text { Machine } & \\\hline \# 1 & 17 & 15 & 15 & 17 \\\# 2 & 16 & 25 & 18 & 25 \\\# 3 & 23 & 24 & 23 & 22\end{array}\end{array}$ What is the sample mean for machine #1?

Approving the effort that occurs during the production process is known as acceptance sampling.

Statistical process control focuses on the acceptability of process output.

A quality analyst wants to construct a control chart for determining whether four machines, all producing the same product, are under control with regard to a particular quality attribute. Accordingly, she inspected 1,000 units of output from each machine in random samples, with the following results: $\begin{array} { l c } \text { Machine } & \text { Total Defectives } \\\hline \# 1 & 23 \\\# 2 & 15 \\\# 3 & 29 \\\# 4 & 13\end{array}$ For upper and lower control limits of .026 and .014, which machine(s) , if any, appear(s) to be out-of-control for process proportion of defectives?

A design engineer wants to construct a sample mean chart for controlling the service life of a halogen headlamp his company produces. He knows from numerous previous samples that this service life is normally distributed with a mean of 500 hours and a standard deviation of 20 hours. On three recent production batches, he tested service life on random samples of four headlamps, with these results: $\begin{array} { r } { \text { Service Life (hours) } }\\\begin{array} { l l l l l } \text { Sample } & \\\hline 1 & 495 & 500 & 505 & 500 \\2 & 525 & 515 & 505 & 515 \\3 & 470 & 480 & 460 & 470\end{array}\end{array}$ What is the standard deviation of the sampling distribution of sample means for whenever service life is in control?

A quality analyst wants to construct a control chart for determining whether four machines, all producing the same product, are under control with regard to a particular quality attribute. Accordingly, she inspected 1,000 units of output from each machine in random samples, with the following results: $\begin{array} { l c } \text { Machine } & \text { Total Defectives } \\\hline \# 1 & 23 \\\# 2 & 15 \\\# 3 & 29 \\\# 4 & 13\end{array}$ What are the control chart upper and lower control limits for an alpha risk of .05?

Acceptance sampling, when it is used, is used: (I) before production. (II) during production. (III) after production.

The output of a process may not conform to specifications even though the process may be statistically "in control."