rsalaza4

# Import the qcc library
library(qcc)

# Create the defects data column
defects <- as.integer(rnorm(50, 3, 1))

# Create the sample size data column
sample_size <- as.integer(rnorm(50, 20, 3))

# Create a data frame with both columns

rsalaza4

# Import the qcc library
library(qcc)

# Create the defects data column
defects <- as.integer(rnorm(50, 3, 1))

# Create the sample size data column
sample_size <- as.integer(rep(20, 50))

# Create a data frame with both columns

rsalaza4

# Import the qcc package
library(qcc)

# Create the defects data column
defects <- as.integer(rnorm(50, 3, 1))

# Create the sample size data column
sample_size <- as.integer(rep(20, 50))

# Create a data frame with both columns

rsalaza4

# Import the qcc library
library(qcc)

# Create the defects data column
defects <- as.integer(rnorm(50, 3, 1))

# Create the sample size data column
sample_size <- as.integer(rnorm(50, 20, 3))

# Create a data frame with both columns

rsalaza4

# Import the qcc library
library(qcc)

# Create a measures column
measures <- rnorm(200, 3, 1)

# Specify the sample size
sample_size <- 5

# Get the total number of samples

rsalaza4

# Create the R-chart
r_chart <- qcc(measure, type = "R", data.name = measure_unit)

# Get the summaries for R-chart
summary(r_chart)

rsalaza4

# Specify the lower control limit
LSL <- as.numeric(1)

# Specify the upper control limit
USL <- as.numeric(5)

# Specify the target
Target <- as.numeric(3)

# Plot the process capability chart

rsalaza4

# Import the qcc package
library(qcc)                  

# Create a vector with the number of defects per defect type
defects <- c(27, 389, 65, 9, 15, 30, 12, 109, 45, 321)            

# Create a vector with the names of the defects 
names(defects) <- c("Defect 1", "Defect 2", "Defect 3", "Defect 4",
                    "Defect 5", "Defect 6", "Defect 7", "Defect 8",
                    "Defect 9", "Defect 10")   

rsalaza4

# Import the SixSigma package
library(SixSigma)

# Specify the effect to be analyzed
b.effect <- "Delay"

# Create a vector with the names of the causes classification groups
b.groups <- c("Personnel", "Weather", "Suppliers", "Planning")

# Create a vector that contains the causes

rsalaza4

# Import the SixSigma package
library(SixSigma)

# Create 50 random data points
x <- rnorm(50 ,15, 5)

# Build the run chart
plot(x,                        
     type = "b",              
     pch  = 16,