The actual data that are plotted on the chart are the number of opportunities between defects.
![minitab control chart minitab control chart](https://i.ytimg.com/vi/_YBpa953Y0I/mqdefault.jpg)
When the calculated lower control limit is negative, the lower limit is set to 0. The calculations for the control limits almost always result in a negative lower control limit. Properties of the G Chart Like other control charts, the G chart has a center line and upper and lower control limits.
#Minitab control chart software
Benneyan cites several examples of this natural decaying pattern: the number of re-worked welds per manufactured item, the number of detected software bugs, the number of items on delivery trucks, and the number of invoices received per day.
![minitab control chart minitab control chart](https://static.wixstatic.com/media/899e45_6282ee067099428488b3f59fcdc7986e~mv2.jpg)
But the G chart is appropriate for processes in which the defect rate is very low and for processes that show a natural geometrically decaying pattern. Use Cases for the G Chart As mentioned earlier, most of the applications cited in Benneyan's paper are from healthcare settings. The key assumption used when counting the number of days is that the number of patients or procedures per day is fairly constant. What is most often done is to count the number of days between observed infections. While this is the ideal, it is also rarely done, because of complications with counting the actual number of patients through the system, or the number of procedures. Thus, in a healthcare setting where you monitor the infection rate, the ideal would be to count the number of patients or procedures until an infection is observed. In the geometric distribution, you count the number of opportunities before or until the defect occurs. The geometric distribution provides an alternative probability model. This means that it could take weeks, months, or perhaps even years to accumulate enough data to detect and respond to changes in the infection rate. As for the data requirements, if you follow the standard practice of requiring a minimum of 25 to 35 subgroups to establish control limits, and the infection rate is low (for example, < 1%), the required amount of data is at least 12,500 patients (500 patients per subgroup multiplied by 25 subgroups). If you use a P chart, the data are the number of patient days in which one or more infections occur. Thus, the data are the number of infections per patient day. For example, if you use a U chart to monitor nosocomial infections, each patient day is considered an area of opportunity in which one or more infections could occur.
![minitab control chart minitab control chart](https://lsc.studysixsigma.com/wp-content/uploads/sites/6/2016/04/230.png)
But P charts and U charts require very large quantities of data and specific definitions of the data. P charts and U charts are often used to monitor adverse events such as nosocomial infections. Nosocomial infections are infections that occur as a direct result of a patient’s treatment in a medical facility. (Health Care Management Science, Vol 4, pages 305-318, 2001, is the article used as the basis for Minitab’s G chart.) The majority of applications cited in these papers are for monitoring infection rates in healthcare, such as nosocomial infections.
![minitab control chart minitab control chart](https://support.minitab.com/en-us/minitab/19/media/generated-content/images/Xbar_chart_def.png)
Benneyan has since published several papers about the G chart and a companion chart, the h chart. The G Chart in Minitab Statistical Software Background Developed by James Benneyan in 1991, the g chart (or “G chart” in Minitab) is a control chart that is based on the geometric distribution.