Data considerations for Acceptance Xbar-S Chart

To ensure that your results are valid, consider the following guidelines when you collect data, perform the analysis, and interpret your results.

The data should be continuous

Continuous data are measurements that can take on any numeric value within a range of values along a continuous scale, including fractional or decimal values. Common examples include measurements such as length, weight, and temperature.

The data should be in time order

Because control charts detect changes over time, the order of the data is important. You should enter the data in the order it was collected, with the oldest data at the top of the worksheet.

The data should be collected at appropriate time intervals

Collect data at equally spaced time intervals, such as every hour, every shift, or every day. Select a time interval that is short enough that you can identify changes to the process soon after the changes occur.

The data should be in rational subgroups

A rational subgroup is a small sample of items that are similar, that are produced in a short period of time under the same conditions (such as operator, equipment, or supplier), and that are representative of the output from a process. If the subgroups are not rational subgroups, the estimated control limits might be too narrow.

The subgroup size should be 9 or more observations

For subgroups that have 2 to 8 observations, use Acceptance Xbar-R Chart. If you do not have subgroups, use Acceptance I-MR Chart.

The data should include at least 60 total observations

If you have fewer than the recommended number of observations, then you can still use the control chart, but the results are preliminary because the control limits might not be precise. If you use the chart regularly, re-estimate the standard deviation and the control limits after you collect the recommended number of observations.

The data do not need to be normally distributed

Although most control charts for variables data are formally based on the assumption of normality, you can still obtain good results with nonnormal data if you collect data in subgroups. The required subgroup size depends on how nonnormal the data are.

The observations within each subgroup should not be correlated with each other

If consecutive observations within each subgroup are correlated, the within-subgroup standard deviation might be underestimated. As a result, the acceptance control limits might be too wide, and the chart might incorrectly show some out-of-control points as in control.