The individuals (I) chart assesses whether the process center is in control. The I chart consists of the following:

·    Plotted points for each individual observation.

·    Center line (green), which is the estimate of the process average (average of all individual observations).

·    Control limits (red), which are set at a distance of 3 s above and below the center line and provide a visual display for the amount of variation expected in the individual sample values.

Minitab conducts up to eight tests for special causes for the I chart, which detect points beyond the control limits and specific patterns in the data. Points that fail are marked with a red symbol and the number of the failed test. Complete results are displayed in the Session window. A failed point indicates that there is a nonrandom pattern in the data that may be the result of special-cause variation. These points should be investigated.

The Moving Range (MR) chart must be in control before you can interpret the I chart. If the MR chart is not in control then the control limits for the I chart will be inaccurate and may inappropriately signal an out-of-control condition on the I chart.

For the liquid detergent data, the MR chart is in control, so the I chart can be examined. The I chart can be summarized as follows:

·    The lower and upper control limits are 5.579 and 6.390, respectively. Therefore, the individual observations are expected to fall between 5.579 and 6.390. The center line (estimate of process average) is 5.985.

·    One observation, failed Test 1 because it is more than 3 s above the center line. Test 1 is the strongest indicator of an out-of-control process

·    Two additional observations failed Test 5, which tests for a run of two out of three points, with the same sign, more than two s from the center line. Test 5 provides additional sensitivity for detecting smaller shifts in process mean.

These test results indicate that the process average is unstable and the process is out of control, possibly due to the presence of special causes. Next, you would try to identify and correct the factors contributing to this special-cause variation. Until these causes are eliminated, the process cannot achieve a state of statistical control.