A statistical process control (SPC) tool that consists of two charts:
  • The top chart (I) plots the individual measurements of a variable over time.
  • The bottom chart (MR) plots a moving range of the data. It is used for processes with continuous data.

The I-MR chart is an industry standard for monitoring and controlling process outputs over time. In manufacturing, the I-MR chart is generally used for low-volume production and destructive or expensive testing. Many situations exist in transactional or business processes in which the I-MR chart can be used (for example, sales and inventory data). Generally, if rational subgroups can be obtained, you should use the Xbar-R or Xbar-S chart; otherwise, use the I-MR chart.

Answers the questions:
  • How much common-cause variation does the process exhibit?
  • Is the process stable over time?
  • Did special causes exist during the timeframe of the plotted data?
  • Does evidence suggest something has changed or the process is performing differently than expected?
  • Does the mean of the process output change at different levels of a process input?
  • Does the variation of the process output change at different levels of a process input?
  • Do the dynamic patterns of the process output change at different levels of a process input?
When to Use Purpose
Pre-project Assist in project selection by identifying outputs that exhibit high common-cause variation, frequent special causes, unstable variation, or other symptoms that point to the need for improvement.
Start of project Verify process stability when performing a baseline capability analysis.
Mid-project Investigate effects of input variables on the process output over time.
Mid-project Verify process stability when performing confirmation runs after implementing improvements.
End of project Verify process stability after implementing controls to obtain a final assessment of process capability.
End of project Graphically compare the pre-project process dynamic behavior to the post-improvement dynamic behavior.
Post-project Control inputs to the improved process after the project is complete.
Post-project Monitor output of the improved process after the project is complete.


Continuous Y, no rational subgroups


  1. Verify the measurement system for the Y data is adequate.
  2. Establish a data collection strategy to determine the best time interval for collecting data.
  3. Enter the collected data into a single column in the Minitab worksheet. Minitab can also directly import data from databases, text files, Excel, and so on.
  4. Optionally, Minitab can evaluate eight rules to determine if special causes are present.
  5. Optionally, you can identify meaningful process stages and input conditions in the chart by entering a categorical variable into an additional column. Stages have different center lines and control limits which help you make comparisons across stages. For example, you can examine changes in the process mean and variation before, during, and after the implementation of a new procedure on one I-MR chart.


  • Look at the moving range chart (MR) first to determine whether the variation is reasonably in control. If so, the limits for the I chart are valid and the I chart can then be evaluated. If the variance is not in control, the process is unstable and the information on the mean of the process is not trustworthy.
  • The variation (and thus the limits on the I chart) is based on a moving range which implies that any two consecutive measurements are more similar (part of an assumed rational subgroup) than measurements further apart. If this is not the case, the interpretation of the I-MR chart is highly suspect.
  • You can use a categorical variable with the I-MR chart to show the effects of different input conditions, which Minitab refers to as stages. For example, if you want to examine hourly yield per FTE of a forms processing operation (Y) to see if differences exist between three operating groups, you can use operating groups as the stage variable and see whether any changes in the mean, variation, or within-shift patterns exist between the three groups.
  • If the sample size within each stage is not at least 30 observations, you may want to consider using a time series plot instead of the I-MR chart. The I-MR chart requires a sufficient number of observations in each stage to reliably estimate the mean and variation of the stage. The time series plot merely plots data values, so no requirement exists for sufficient sample sizes.
  • If you have discrete numeric data from which you can obtain every equally spaced value and you have measured at least 10 possible values, you can evaluate these data as if they are continuous.

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