In statistics, **Control charts** are the tools in control processes to determine whether a manufacturing process or a business process is in a controlled statistical state. This chart is a graph which is used to study process changes over time. The data is plotted in a timely order. It is bound to have a central line of average, an upper line of upper control limit and a lower line of lower control limit. In addition, the data obtained from the process can also be applied in making the prediction of the future performances of the process.

When the analysis made by the control chart indicates that the process is currently under control, it reveals that the process is stable with the variations that come from sources familiar with the process. No changes or corrections are required to be made to the parameters of process control.

After the basic chart is created, one can use various menus and options to make required changes that may be in a format, type or statistics of the chart.

To create a chart it is not necessary to know the name or structure of any chart. You just need to select the columns or variables that are to be charted and drag them in respective zones. When the data column is dragged to the workplace, the user starts working on it to create an accurate chart that is based on the data type and given sample size.

The control charts of variables can be classified based on the statistics of subgroup summary plotted on the chart.

- X¯ chart
- R Chart
- S Chart

X¯ chart describes the subset of averages or means, R chart displays the subgroup ranges, and S chart shows the subgroup standard deviations. Regarding the quality that is to be measured on a continuous scale, a particular analysis makes both the process mean and its variability apparent along with a mean chart that is aligned over it’s corresponding S- or R- chart.

This chart displays a mean process based on a long-term sigma with control limits. The control limits are placed such that the distance between them and the centerline is ‘3s’. The standard deviation value ‘s’ for these charts is determined by the same method as the standard deviation for the distribution platform.

This type of data is usually continuous and based on the theoretical concept of continuous data. Count data is a different kind of data available which is also known as level counts of character data. The interest variable is a unique count here for the number of blemishes or defects per subgroups. These attribute charts are appropriately applied for such discrete count data.

The data can be combined into one measurement unit if the data you have contains repetitive measurements of the same unit process. But this is not recommended until the data contains repeating measurements of every measurement process.

Typically, pre-summarize summarizes the process columns into standard deviations or sample means based on the size of the sample.