In Multi-Vari analysis, a graph is created that displays the possible sources of variation affecting process output.
True
The mean output of a
... [Show More] process is the major factor in controlling that process and ensuring that it meets process objectives.
False
Both mean and standard deviation, a measure of variation, are important. Some would argue that variation is more important than mean.
There is only one source of variation.
False
There are two sources: common and special
The normal or expected cause of variation is called Common Cause.
True
Special cause variation is always present. No process can avoid it.
False
Common cause is always present.
Barring any accidents, the length of time it takes to commute to work is subject to common cause variation.
True
It is not possible to eliminate all common cause variation in most processes.
True
There is some variation in even the most carefully designed and controlled processes.
Special cause variation is of most concern in Six-Sigma. It is unexpected and can disrupt process output.
True
Special cause variation represents the category of variation that effective Six Sigma monitoring systems are expected to detect.
True
The length of time it would take to be seen at a health care clinic, that has otherwise not expected emergencies during the day, would be subject to special cause variation.
False
Normal variation is considered common cause variation.
As long as a process is in-control and performing as expected there is not much that can be done to eliminate common cause variation short of redesigning the entire process.
True
Several people working at a call center have called to report that they are sick with the flu and have been replaced by temporary help. Call waiting time has increased significantly. This would be considered special cause variation since it is not inherent in the process and is not predictable.
True
The length of the vertical lines in a Multi-Vari chart represents the range of a specific variable over a specific period such as one week.
True
The longer is the vertical line in a Multi-Vari chart the lower is the "position" variation.
False
Multi-Vari charts also uncover why special cause variation occurs.
False
They only help expose the presence of special cause variation they cannot determine its sources.
Multi-Vari charts also display output means.
True
In a Multi-Vari chart, the range of variation is expressed along the "X" or horizontal axis.
False
The data within a vertical bar of a Multi-Vari chart is referred to as "within variation" or "position variation."
True
Multi-Vari Charts display the root cause of a problem
False
This is not the purpose of a multi-vari chart
Four identical machines in a manufacturing process are operated over three shifts. A multi vari chart would be appropriate to study the variability of each machine and then to determine which machine contributes most of the output variation
True
A multi-vari analysis is visual and uses no statistics to compare output variation.
True
An organization wishes to determine if the variation across five processes is different. A multi-vari analysis could be used.
True
A SIPOC chart can be useful in identifying input variables for use in the Multi-Vari chart
True
The Pareto Principle states that _______ percent of the problem can be traced to _______ percent of the possible causes.
80, 20
Multi-Vari charts can combine variables. One example is displaying the relationship between call duration, location and time of day on the same chart.
True
Consider a help line for those who have just purchased a virus protection program for their computer. The variation in the length of time it takes for a caller to reach a representative - under the assumption that the process is behaving as expected - would be considered a Special Cause variation.
False
Common cause variation
A manufacturer of headphones finds that an increasing number of phones have failed to pass the final quality control check before they are shipped to a distributor. This suggests Special Cause variation.
True
Common Cause variation is predictable.
True
Common cause variation represents variation from the target that can be attributed to many random causes. It is predictable because random variations will always occur. Special cause variation, on the other hand, is not predictable and can be attributed to a special cause.
Several employees at a distribution center have reported ill and have been replaced by temporary help. The number of errors have increased. This would be considered common cause variation.
False
The focus of Six-Sigma is on Common Cause variation.
False
The greater is the variation of data around the mean, the smaller will be the standard deviation.
False
The standard deviation represents the extent of variation in a distribution.
True
When the standard deviation is large, the spread of the data around a mean is larger than if the standard deviation were small.
True
Two statistics that define a normal distribution are the ________ and the ___________ .
mean, standard deviation
The mean represents the centralness of a distribution
True
Many distributions describing natural processes such as IQ (Intelligence Quotient) or height can be classified as normal distributions.
True
In a normal distribution most of the data items cluster toward the ends or tails of the distribution.
False
Normal distributions have special properties that specify the percent of observations or data that fall within a specified number of standard deviations from the mean.
True [Show Less]