• Control charts, also known as Shewhart charts or process- behavior charts, are a statistical process control tool used to determine if a manufacturing or business process is in a state of control.

• If all the points lie within the control limits, the process is said to be “in control”
• If analysis of the control chart indicates that the process is currently under control, then no corrections or changes to process control parameters are needed.
• Moreover, data from the method can be used to predict the future performance of the process.

• If the control chart indicates that the process is not in control, analysis of the chart can help determine the sources of variation, as this willresult in degradation of process performance.

• Missing data are a common occurrence and can have a significanteffect on the conclusions that can be drawn from the data.

• R Package : qcc
• R Function : groups(x) -- For Grouping the data
• R Function : qcc(x, type=, nsigmas= ,newdata= )
• x --training data
• type = “R”,” xbar” -- Type of Control Chart
• nsigmas -- Default value is 3, means ±3 standard deviation
• Newdata -- new sample data from the same process

#Control Charts
#Loading required packages
#install.packages(“qcc”)
library(“qcc”)
data(pistonrings)
attach(pistonrings)
head(pistonrings)

#Grouping the data
dia<-qcc.groups(diameter,sample)
head(dia)

#R Chart
obj<-qcc(dia[1:30,],type = “R”)
summary(obj)

#X- Bar Chart
obj<-qcc(dia[1:30,], type=”xbar”)
summary(obj)

#Changing the Values of nsigma
obj<-qcc(dia[1:30,],type=”R”,nsigmas = 1)
summary(obj)

#Testing new Data
obj<-qcc(dia[1:30,],type = “xbar”,newdata = dia[31:40,])