Quality Advisor
A free online reference for statistical process control, process capability analysis, measurement systems analysis,
control chart interpretation, and other quality metrics.
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Too close to the average
Notice that nearly all the points lie close to the average. This pattern could be caused by a number of circumstances, including:
- Edited data
- Reduced variability without recalculation of control limits
When this pattern occurs, try to establish why. Is this apparent improvement genuine? Can the improvement be maintained? If the improvement can be maintained, then the control limits need to be recalculated. Although the data looks more stable than normal, this condition is referred to statistically as “unstable”.
See also:
>> Any nonrandom pattern
>> Too far from the average
>> Cycles
>> Trends
>> Clusters
>> Sawtooth
>> 2 of 3 points beyond 2 sigma
>> 4 of 5 points beyond 1 sigma
Quality Advisor
A free online reference for statistical process control, process capability analysis, measurement systems analysis,
control chart interpretation, and other quality metrics.
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Any nonrandom pattern
This is the most complex test for stability. If the system is in control, one could imagine tilting the chart on one end and letting all the points slip down to form a normal curve. Roughly half the points would fall above and half below the centerline. Dividing the distance between the centerline and the control limits into three equal divisions up and three down, one could expect to find about two thirds of the total points in the middle two regions, and no repeatable patterns in the data.
Patterns in data are not random, and are, therefore, cause for investigation. To apply these tests, look for patterns in the plot. The following are examples of typical patterns:
>> Too close to the average
>> Too far from the average
>> Cycles
>> Trends
>> Clusters
>> Sawtooth
>> 2 of 3 points beyond 2 sigma
>> 4 of 5 points beyond 1 sigma
Quality Advisor
A free online reference for statistical process control, process capability analysis, measurement systems analysis,
control chart interpretation, and other quality metrics.
SPC DEMO
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7 points in one direction, up or down
For this test, look for groups of points moving up or down in succession. Count consecutive points, including horizontal runs within the run. This pattern is probably the result of a trend in one of the system resources. The chart below shows a group of seven points moving downward.
See also:
>> Analyze for special causes of variation
>> Any point lying outside the control limits
>> 7 or more points in a row above or below the centerline
>> Any nonrandom pattern
Quality Advisor
A free online reference for statistical process control, process capability analysis, measurement systems analysis,
control chart interpretation, and other quality metrics.
SPC DEMO
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7 points in a row above or below the centerline
To apply this test, look for groups of points above or below the average or centerline. Count consecutive points. Are there groups of seven or more? This is probably the result of a shift in one of the system resources (materials, people, methods, environment, information aids, equipment, and measurement). The following chart, which can be created using SQCpack, shows two groups, one with eight above the centerline and one with seven below.
See also:
>> Analyze for special causes of variation
>> Any point lying outside the control limits
>> 7 or more points in one direction
>> Any nonrandom pattern
Quality Advisor
A free online reference for statistical process control, process capability analysis, measurement systems analysis,
control chart interpretation, and other quality metrics.
SPC DEMO
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Any point lying outside the control limits
This is the quickest and easiest test for system stability. Look above the upper control limit and below the lower control limit to see whether any points fall in those regions of the chart. If you are looking at a chart pair (X-bar and R, X-bar and s, or X and MR), look at both charts.
Points falling outside the control limits may be the result of a special cause that was corrected quickly, either intentionally or unintentionally. It may also point to an intermittent problem. The chart below shows two points outside the control limits.
See also:
>> Analyze for special causes of variation
>> Any point lying outside the control limits
>> 7 or more points in a row above or below the center line
>> 7 or more points in one direction
>> Any nonrandom pattern
Quality Advisor
A free online reference for statistical process control, process capability analysis, measurement systems analysis,
control chart interpretation, and other quality metrics.
SPC DEMO
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Analyze for special cause variation
The key to chart interpretation is to initially ascertain the type of variation in the system—that is, whether the variation is coming from special or common causes. When the system has only common causes of variation, it is referred to as stable or in control. If, however, the system has special causes of variation, it is referred to as unstable, or out of control.
Look any of the conditions listed below, which indicate that the process is statistically unstable:
- 7 or more points in one direction
- Any nonrandom pattern
- Too close to the average
- Too far from the average
- Cycles
- Trends
- Clusters
- Sawtooth
- 2 of 3 points beyond 2 sigma
- 4 of 5 points beyond 1 sigma
When you have determined whether or not there is special cause variation, declare the system stable or unstable.
Quality Advisor
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control chart interpretation, and other quality metrics.
SPC DEMO
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Interpretation
Quality Advisor
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control chart interpretation, and other quality metrics.
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t-chart
What is it?
The t-chart creates a picture of a process over time. Each point on the chart represents an amount of time that has passed since a prior occurrence of a rare event. The time unit might be hours, days, weeks, months, etc. For example, a chart might plot the number of days between infection outbreaks at a hospital. A traditional plot of this data may contain many points at zero and an occasional point at one. A t-chart avoids two types of mistakes: over control and under control that a traditional control chart might generate.
The t-chart helps to identify the two types of variation present in a system, special and common cause, so that appropriate improvement actions can be taken. Specific formulas for t-chart control limits are used with this type of data. T-charts can be created using software programs like SQCpack.
What does it look like?
By way of contrast, consider the information that is provided with a chart of lost production due to work time accidents. The chart shows many months where there were no occurrences of lost production. Thus, this chart is not beneficial and provides little insight to know if the process changes resulted in improvement.
Now consider the same data using a t-chart. Instead of plotting the lost production each month, plot the days passed since the last time of lost production. The data is still being collected and charted, but the method used to analyze the information is different. This shows that an improvement is occurring (or has occurred), with the days between lost production increasing.
Quality Advisor
A free online reference for statistical process control, process capability analysis, measurement systems analysis,
control chart interpretation, and other quality metrics.
SPC DEMO
Don’t miss out! Book a demo of our specialized SPC software and unlock immediate improvements in your processes.
t-chart
What is it?
The t-chart creates a picture of a process over time. Each point on the chart represents an amount of time that has passed since a prior occurrence of a rare event. The time unit might be hours, days, weeks, months, etc. For example, a chart might plot the number of days between infection outbreaks at a hospital. A traditional plot of this data may contain many points at zero and an occasional point at one. A t-chart avoids two types of mistakes: over control and under control that a traditional control chart might generate.
The t-chart helps to identify the two types of variation present in a system, special and common cause, so that appropriate improvement actions can be taken. Specific formulas for t-chart control limits are used with this type of data. T-charts can be created using software programs like SQCpack.
What does it look like?
By way of contrast, consider the information that is provided with a chart of lost production due to work time accidents. The chart shows many months where there were no occurrences of lost production. Thus, this chart is not beneficial and provides little insight to know if the process changes resulted in improvement.
Now consider the same data using a t-chart. Instead of plotting the lost production each month, plot the days passed since the last time of lost production. The data is still being collected and charted, but the method used to analyze the information is different. This shows that an improvement is occurring (or has occurred), with the days between lost production increasing.