How to implement Control Charts in R?

Description

To know about the data types in R programming.

Need for Control Charts?
  • Control charts, also known asShewhart charts or process-behavior charts, are a statistical processcontrol tool used to determine ifa manufacturing or businessprocess is in a state of control.
Interpretation of Control Charts:If it is in Control:
  • If all the points lie within the control limits,the process is said to be “in control”
  • If analysis of the control chart indicatesthat the process is currentlyunder control, then no correctionsor changes to process control parametersare needed.
  • Moreover, data from the methodcan be used to predict the futureperformance of the process.
If it is not in Control:
  • If the control chart indicates thatthe process is not in control,analysis of the chart can help determinethe sources of variation, as this willresult in degradation of processperformance.
 Need of detecting Outliers:
  • Missing data are a common occurrenceand can have a significanteffecton the conclusions that canbe drawn from the data.
 Package and Function:
  • R Package : qcc
  • R Function : groups(x) -- For Groupingthe data
  • R Function : qcc(x, type=, nsigmas=,newdata= )
  • x --trainingdata
  • type = “R”,”xbar” -- Type of ControlChart
  • nsigmas -- Default value is 3, means ±3standard deviation
  • Newdata -- new sample data from the sameprocess

#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,])

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