The ratio of observed variation to expected variation compares the variation in your data to the variation that you would expect based on a binomial distribution. The ratio is expressed as a percentage.
Overdispersion can cause a traditional P chart to show an increased number of points outside the control limits. Underdispersion can cause a traditional P chart to show too few points outside of the control limits. The Laney P' chart adjusts for these conditions.
The ratio of observed variation to expected variation is 175.7%. This value indicates overdispersion because it is greater than the upper confidence limit of 136.6%. Overdispersion can cause points on a traditional P chart to appear to be out of control when they are not. To adjust for overdispersion, use a Laney P' chart.
The ratio of observed variation to expected variation is 46%. This value indicates underdispersion because it is less than the lower confidence limit of 60%. Underdispersion can cause the control limits on a traditional P chart to be too wide. If the control limits are too wide, you can overlook special-cause variation and mistake it for common-cause variation. To adjust for underdispersion, use a Laney P' chart.