The Moving Range Chart is a control chart of moving ranges. It is used to track the process variation and detect signs of special causes with subgroup size is equals to 1.
1. Choose ProcessMA > Control Charts > Variable Charts for Individuals > Moving Range.
2. In Variable, select the column containing the data.
3. Click OK.
Optional
4. In Standard deviation, enter the population standard deviation, otherwise it will be estimated from the data.
5. In Control limit multiples, enter the position to draw the upper and lower control limits. Multiple entries must be separated by commas (,).
6. In Lower limit bound, enter the bound on the lower control limit.
7. In Upper limit bound, enter the bound on the upper control limit.
8. In Phase, select the column containing the phase codes. (Phase can useful to compare groups of data like before and after improvement)
9. In Estimate, select the method of estimating sigma.
· Average moving range - Estimate based on the average of the moving range.
· Median moving range - Estimate based on the median of the moving range
10. In Moving range length, enter the number of observations used to calculate the moving range.
Note To select a column of data into a textbox, double-click on any of the column names shown in the list on the left of the dialog box while in the textbox.
Variable: Numeric.
Standard deviation: Numeric.
Control limit multiples: Numeric.
Lower limit bound: Numeric.
Upper limit bound: Numeric.
Moving range length: Integer.
Phase: Text or Numeric; Must contain equal number of data points as the Variable.
You are the manager of a technical help division. There have been complains that response time were getting slow and erratic. You collected several measurements on response time and you want to run the I-MR Chart to evaluate the situation.
1. Open worksheet ProcessMA > Tools > Data Files > Control.xls.
2. Choose ProcessMA > Control Charts > Variable Charts for Individuals > Moving Range.
3. In Variable, select F – Response time.
4. Click OK.
Interpretation
The process is stable as the data points are randomly distributed and lie within the control limits.