**X chart**

The mean or average change in process over time from subgroup values. The control limits on the X-Bar brings the sample’s mean and center into consideration.

**R chart**

The range of the process over the time from subgroups values. This monitors the spread of the process over the time.

**p chart**

The p chart is for the fraction of defective items in a sample.

The fraction defective is the number of defective items in a sample divided by the total number of items in a sample. The fraction defective chart is used when the sample size varies. If we have a high percentage of good items, say 99%, the fraction defective is small, 0.01. In order to get any defectives in as sample from a high quality population, the sample size must be large. In many cases the sample size is all the daily production. In this situation the sample size will vary from day to day. The only statistical measure of quality would be the fraction rejected.

**np chart**

The np chart is for the number of defective items in a sample.

The number of defective, np, chart shows the number of defective items in samples rather than the fraction of defective items. It requires that the sample size remains constant. It has two benefits over the p chart: there is no calculation required of each sample result; it easier for some people to understand.

**C chart**

The c chart is for the number of defects in an item.

The number of defects, c, chart is based on the Poisson distribution. It is a plot of the number of defects in items. The item may be a given length of steel bar, a welded tank, a bolt of cloth and so on. For the control chart, the size of the item must be constant. If the chart is for the number of defects in a bolt of cloth, all the cloths must be of the same size.

The c chart can also be used for the number of defects in a fixed number of items. The number of defects per 10 bolts of cloth can be plotted on c charts just as well as the number of defects per single roll. The essential factor for using c charts is that each sample has the same opportunity for defects.

How to Interpret the X Bar R Control Charts

- To correctly interpret X bar R chart, always examine the R chart first.
- The X bar chart control limits are derived from the R bar (average range) values, if the values are out of control in R chart that means the X bar chart control limits are not accurate.
- If the points are out of control in R chart, then stop the process. Identify the special cause and address the issue. Remove those subgroups from the calculations.
- Once the R bar chart is in control, then review X bar chart and interpret the points against the control limits.
- All the points to be interpret against the control limits but not specification limits. As specification limits are provided by customer or management whereas control limits are derived from the average and range values of the subgroups.
- If any point out of control in X bar chat. Identify the special cause and address the issue.
- Process capability studies can be performed only after both X bar and R chart values are within the control limits. There is no meaning to perform process capability studies of an unstable process.