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Description

Beta Control Charts.

Applies Beta Control Charts to defined values. The Beta Chart presents control limits based on the Beta probability distribution, making it suitable for monitoring fraction data from a Binomial distribution as a replacement for p-Charts. The Beta Chart has been applied in three real studies and compared with control limits from three different schemes. The comparative analysis showed that: (i) the Beta approximation to the Binomial distribution is more appropriate for values confined within the [0, 1] interval; and (ii) the proposed charts are more sensitive to the average run length (ARL) in both in-control and out-of-control process monitoring. Overall, the Beta Charts outperform the Shewhart control charts in monitoring fraction data. For more details, see Ângelo Márcio Oliveira Sant’Anna and Carla Schwengber ten Caten (2012) <doi:10.1016/j.eswa.2012.02.146>.

Beta Control Charts (bcc)

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Table of Contents

  1. About The Project
  2. Getting Started
  3. Usage
  4. Roadmap
  5. License
  6. Contact

About The Project

The Beta Control Charts (bcc) package provides tools to apply beta control charts to defined values. The Beta Chart presents control limits based on the Beta probability distribution and is used for monitoring fraction data from a Binomial distribution as a replacement for p-Charts. This package helps to effectively monitor variables, offering enhanced sensitivity in process control.

The Beta Chart has been applied in three real studies and compared with control limits from three different schemes. The comparative analysis showed that: (i) the Beta approximation to the Binomial distribution is more appropriate for values confined within the [0, 1] interval; and (ii) the proposed charts are more sensitive to the average run length (ARL) in both in-control and out-of-control process monitoring. Overall, the Beta Charts outperform the Shewhart control charts in monitoring fraction data.

This package not only provides a robust alternative to traditional p-Charts but also ensures more accurate and sensitive monitoring of fraction data, making it an invaluable tool for quality control and process improvement. For more details, see Ângelo Márcio Oliveira Sant’Anna and Carla Schwengber ten Caten (2012)

Built With

  • R
  • ggplot2
  • dplyr

Getting Started

Ensure you have R and devtools installed on your machine:

install.packages("devtools")

Installation

  1. Clone the repo:

    git clone https://github.com/DanieLucas28/BCCPackage.git
    
  2. Install the package:

    devtools::install("BCCPackage")
    

Usage

Here are some examples of how to use the package:

Example for Type 1 Chart with Discrete Data

library(bcc)
data <- c(0.12, 0.18, 0.14, 0.28, 0.22)
sizes <- c(101, 98, 110, 105, 95)
bcc(data, sizes, type=1, title="Beta Control Chart for Discrete Data")

Example for Type 2 Chart with Continuous Data

data <- c(0.59, 0.67, 0.61, 0.70, 0.75)
bcc(data, type=2, title="Beta Control Chart for Continuous Data")

See the open issues for a full list of proposed features (and known issues).

License

Distributed under the GPL-3 License.

Contact

Daniel Cerqueira - [email protected].

Metadata

Version

1.5

License

Unknown

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