Deck 3: Statistical Process Control

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Question
After special cause variation is detected, the focus changes to identifying the root cause of the variation and eliminating it.
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Question
Unique or special cause variation reflects the random variation associated with the output of a process.
Question
Process control charts are rarely useful for monitoring and controlling the output of service processes.
Question
A variable measure classifies while an attribute measure quantifies.
Question
One goal of statistical process control is to prevent a process from producing items that have to be scrapped or reworked.
Question
When a control chart detects no special cause (nonrandom) variation in a process, the upper and lower control limits are the same value.
Question
Employee training in statistical process control is a fundamental principle in total quality management programs.
Question
A process that is determined to be in control contains no variation.
Question
With a c-chart, the sample size is small and may contain only one item.
Question
Statistical process control is only effective for service processes.
Question
It is sometimes not necessary to determine new control limits after special cause (nonrandom) variation has been identified if the source has been eliminated without changing the process.
Question
Statistical process control is based on a philosophy of inspection as opposed to prevention.
Question
Statistical process control is a subset of statistical quality control.
Question
Statistical process control involves monitoring and controlling a process to prevent poor quality.
Question
Statistical process control is a tool used to monitor and improve quality.
Question
One reason some companies fail in their attempt to apply statistical process control is lack of training.
Question
Control limits are based on the special cause (nonrandom) variation inherent in a process.
Question
Process control is achieved by taking periodic samples from a process and plotting the sample points on a chart to determine if the process is within control limits.
Question
Common cause (random) variation provides evidence that the process is not in control.
Question
Two types of variation associated with the output of a process are common (random) cause (random) and special cause (nonrandom).
Question
An x-bar and R-chart constructed to monitor and control a process use the same raw data.
Question
Variable control charts are used for quantitative measures such as weight or time.
Question
Attribute control charts are used to monitor descriptive characteristics of the output of a process rather than measurable characteristics.
Question
The range measures the variation within samples versus the variation between samples.
Question
The range is the difference between the smallest and largest values in a sample.
Question
When a control chart detects no special cause (nonrandom) variation in a process, the upper and lower control limits are the same value.
Question
Variable control charts are used to monitor measurable characteristics of a process's outputs rather than descriptive characteristics.
Question
One advantage of using a pattern test is that special cause variations may be identified before any points are plotted outside the control limits.
Question
A p-chart is used to monitor the proportion defective in the output of a process.
Question
Control charts are never implemented until special cause variation has been detected in a process.
Question
It is possible to have low variation within samples while at the same time having high variation between sample means.
Question
The formula used to determine the upper and lower control limits are based on product specification limits.
Question
When calculating control limits for a process, the number of standard deviations (Z value) is typically six.
Question
Process control is achieved by taking periodic samples from a process and plotting the sample points on a chart to determine if the process is within control limits.
Question
A quantitative variable measure classifies while an qualitative variable (attribute) measure esq.uantifies.
Question
The individual that detects special cause variation in a process is not allowed to diagnose the root cause and correct it.
Question
It is sometimes not necessary to determine new control limits after special cause (nonrandom) variation has been identified if the source has been eliminated without changing the process.
Question
A pattern test can identify an out-of-control process even if all sample points are within control limits.
Question
When special cause variation is detected it is normally eliminated by increasing the number of standard deviations (Z value) used to calculate the control limits.
Question
A control chart is in control when the plot of the sample points exhibits a pattern.
Question
Control charts visually show when a process is not within statistical control limits.
Question
The popularity of Excel and other data analysis software has been a major factor in the increased use of statistical process control.
Question
When constructing a control chart for the first time, all points should be within the control limits indicating the process is in control.
Question
Process control charts are often used at a critical point after which it is difficult to correct or rework the process output.
Question
Control charts visually show when a process is not within statistical control limits.
Question
Defect and defective mean the same thing for attribute (qualitative) control charts.
Question
Special cause variation can be identified using statistical process control.
Question
If the points plotted on a control chart display a pattern, it is called a run.
Question
A process capability ratio greater than one shows that a process is capable of producing output within its specification limits.
Question
For a given process, the process capability ratio is not related to its specification limits.
Question
All processes contain a certain amount of variation in their output.
Question
When constructing a control chart for the first time, all points should be within the control limits indicating the process is in control.
Question
Process control charts are often used at a critical point after which it is difficult to correct or rework the process output.
Question
The process capability index indicates how much a process mean differs from the target specification value.
Question
A sequence of sample points that display a pattern is known as a run.
Question
The smaller the historical proportion defective reported for a process, the larger the sample size required to detect special cause (nonrandom) variation with a p-chart.
Question
Tolerances reflect the amount of common cause variation allowed in a process.
Question
All processes contain a certain amount of variation in their output.
Question
Tolerances or specification limits are allowable variation prescribed in a product design.
Question
Statistical process control can prevent poor quality before it occurs if a pattern is evident in the plotted points.
Question
It is possible to have low variation within samples while at the same time having high variation between sample means.
Question
The range measures the variation within samples versus the variation between samples.
Question
With a c-chart, the sample size is small and often may only contains only one item.
Question
A control chart is in control when the plot of the sample points exhibits a pattern.
Question
Variable (quantitative) control charts are used to monitor measurable characteristics of a process's outputs rather than descriptive characteristics.
Question
Variable control charts are used for quantitative measures such as weight or time.
Question
Statistical process control can prevent poor quality before it occurs if a pattern is evident in the plotted points.
Question
The process centerline for both xbar and R charts are both the same value because they are based on the same raw data.
Question
If the points plotted on a control chart display a pattern, it is called a run.
Question
An R-chart monitors the robustness of a process.
Question
A sequence of sample points that display a pattern is known as a run.
Question
One advantage of using a pattern test is that special cause variations may be identified before any points are plotted outside the control limits.
Question
A c-chart monitors the number of defects in small samples.
Question
An x-bar and R-chart constructed to monitor and control a process use the same raw data.
Question
If a pattern is evident in the points plotted on a control chart, the points are always considered evidence that the process is in control.
Question
A pattern test can identify an out-of-control process even if all sample points are within control limits.
Question
A p-chart is used to monitor the proportion defective in the output of a process.
Question
The R-chart is used for monitoring and controlling variation within samples.
Question
The range is the difference between the smallest and largest values in a sample.
Question
Attribute (qualitative) control charts are used to monitor descriptive characteristics of the output of a process rather than measurable characteristics.
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Deck 3: Statistical Process Control
1
After special cause variation is detected, the focus changes to identifying the root cause of the variation and eliminating it.
True
2
Unique or special cause variation reflects the random variation associated with the output of a process.
False
3
Process control charts are rarely useful for monitoring and controlling the output of service processes.
False
4
A variable measure classifies while an attribute measure quantifies.
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5
One goal of statistical process control is to prevent a process from producing items that have to be scrapped or reworked.
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6
When a control chart detects no special cause (nonrandom) variation in a process, the upper and lower control limits are the same value.
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7
Employee training in statistical process control is a fundamental principle in total quality management programs.
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8
A process that is determined to be in control contains no variation.
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9
With a c-chart, the sample size is small and may contain only one item.
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10
Statistical process control is only effective for service processes.
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11
It is sometimes not necessary to determine new control limits after special cause (nonrandom) variation has been identified if the source has been eliminated without changing the process.
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12
Statistical process control is based on a philosophy of inspection as opposed to prevention.
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13
Statistical process control is a subset of statistical quality control.
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14
Statistical process control involves monitoring and controlling a process to prevent poor quality.
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15
Statistical process control is a tool used to monitor and improve quality.
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16
One reason some companies fail in their attempt to apply statistical process control is lack of training.
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17
Control limits are based on the special cause (nonrandom) variation inherent in a process.
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18
Process control is achieved by taking periodic samples from a process and plotting the sample points on a chart to determine if the process is within control limits.
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19
Common cause (random) variation provides evidence that the process is not in control.
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20
Two types of variation associated with the output of a process are common (random) cause (random) and special cause (nonrandom).
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21
An x-bar and R-chart constructed to monitor and control a process use the same raw data.
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22
Variable control charts are used for quantitative measures such as weight or time.
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23
Attribute control charts are used to monitor descriptive characteristics of the output of a process rather than measurable characteristics.
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24
The range measures the variation within samples versus the variation between samples.
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25
The range is the difference between the smallest and largest values in a sample.
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26
When a control chart detects no special cause (nonrandom) variation in a process, the upper and lower control limits are the same value.
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27
Variable control charts are used to monitor measurable characteristics of a process's outputs rather than descriptive characteristics.
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28
One advantage of using a pattern test is that special cause variations may be identified before any points are plotted outside the control limits.
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29
A p-chart is used to monitor the proportion defective in the output of a process.
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30
Control charts are never implemented until special cause variation has been detected in a process.
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31
It is possible to have low variation within samples while at the same time having high variation between sample means.
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32
The formula used to determine the upper and lower control limits are based on product specification limits.
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33
When calculating control limits for a process, the number of standard deviations (Z value) is typically six.
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34
Process control is achieved by taking periodic samples from a process and plotting the sample points on a chart to determine if the process is within control limits.
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35
A quantitative variable measure classifies while an qualitative variable (attribute) measure esq.uantifies.
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36
The individual that detects special cause variation in a process is not allowed to diagnose the root cause and correct it.
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37
It is sometimes not necessary to determine new control limits after special cause (nonrandom) variation has been identified if the source has been eliminated without changing the process.
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38
A pattern test can identify an out-of-control process even if all sample points are within control limits.
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39
When special cause variation is detected it is normally eliminated by increasing the number of standard deviations (Z value) used to calculate the control limits.
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40
A control chart is in control when the plot of the sample points exhibits a pattern.
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41
Control charts visually show when a process is not within statistical control limits.
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42
The popularity of Excel and other data analysis software has been a major factor in the increased use of statistical process control.
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43
When constructing a control chart for the first time, all points should be within the control limits indicating the process is in control.
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44
Process control charts are often used at a critical point after which it is difficult to correct or rework the process output.
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45
Control charts visually show when a process is not within statistical control limits.
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46
Defect and defective mean the same thing for attribute (qualitative) control charts.
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47
Special cause variation can be identified using statistical process control.
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48
If the points plotted on a control chart display a pattern, it is called a run.
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49
A process capability ratio greater than one shows that a process is capable of producing output within its specification limits.
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50
For a given process, the process capability ratio is not related to its specification limits.
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51
All processes contain a certain amount of variation in their output.
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52
When constructing a control chart for the first time, all points should be within the control limits indicating the process is in control.
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53
Process control charts are often used at a critical point after which it is difficult to correct or rework the process output.
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54
The process capability index indicates how much a process mean differs from the target specification value.
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55
A sequence of sample points that display a pattern is known as a run.
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56
The smaller the historical proportion defective reported for a process, the larger the sample size required to detect special cause (nonrandom) variation with a p-chart.
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57
Tolerances reflect the amount of common cause variation allowed in a process.
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58
All processes contain a certain amount of variation in their output.
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59
Tolerances or specification limits are allowable variation prescribed in a product design.
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60
Statistical process control can prevent poor quality before it occurs if a pattern is evident in the plotted points.
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61
It is possible to have low variation within samples while at the same time having high variation between sample means.
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62
The range measures the variation within samples versus the variation between samples.
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63
With a c-chart, the sample size is small and often may only contains only one item.
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64
A control chart is in control when the plot of the sample points exhibits a pattern.
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65
Variable (quantitative) control charts are used to monitor measurable characteristics of a process's outputs rather than descriptive characteristics.
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66
Variable control charts are used for quantitative measures such as weight or time.
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67
Statistical process control can prevent poor quality before it occurs if a pattern is evident in the plotted points.
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68
The process centerline for both xbar and R charts are both the same value because they are based on the same raw data.
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69
If the points plotted on a control chart display a pattern, it is called a run.
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70
An R-chart monitors the robustness of a process.
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71
A sequence of sample points that display a pattern is known as a run.
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72
One advantage of using a pattern test is that special cause variations may be identified before any points are plotted outside the control limits.
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73
A c-chart monitors the number of defects in small samples.
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74
An x-bar and R-chart constructed to monitor and control a process use the same raw data.
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75
If a pattern is evident in the points plotted on a control chart, the points are always considered evidence that the process is in control.
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76
A pattern test can identify an out-of-control process even if all sample points are within control limits.
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77
A p-chart is used to monitor the proportion defective in the output of a process.
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78
The R-chart is used for monitoring and controlling variation within samples.
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79
The range is the difference between the smallest and largest values in a sample.
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80
Attribute (qualitative) control charts are used to monitor descriptive characteristics of the output of a process rather than measurable characteristics.
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