SixSigma-package: Six Sigma Tools for Quality and Process Improvement
Description
This package contains functions and utilities to perform Statistical Analyses in the Six Sigma way.
Through the DMAIC cycle (Define, Measure, Analyze, Improve, Control), you can manage several Quality Management
studies: Gage R&R, Capability Analysis, Control Charts, Loss Function Analysis, etc. Data frames used in
"Six Sigma with R" (Springer, 2012) are also included in the package.
Arguments
Details
ll{
Package: SixSigma
Type: Package
Version: 0.6.3
Date: 2012-09-05
URL: http://rwiki.sciviews.org/doku.php?id=packages:cran:sixsigma
License: GPL>=2
Depends: R(>=2.12), lattice, grid, ggplot2, nortest, reshape2, plyr, testthat
LazyLoad: yes
LazyData: yes
}
Use the package to perform Six Sigma Methodology tasks, throughout its
breakthrough strategy: Define, Measure, Analyze, Improve, Control (DMAIC)
Define: Process Map (ss.pMap), Cause and effect Diagram
(ss.ceDiag);
Measure: Gage R&R study (ss.rr); Capability Analysis (ss.study.ca);
Loss Function Analysis (ss.lfa)
Analyze: Confidence Intervals (ss.ci)
Soon: further functions
References
Allen, T. T. (2010) Introduction to Engineering Statistics and Lean
Six Sigma - Statistical Quality Control and Design of Experiments and
Systems (Second Edition ed.). London: Springer.
Box, G. (1991). Teaching engineers experimental design with
a paper helicopter. Report 76, Center for Quality and
Productivity Improvement. University of Wisconsin.
Cano, Emilio L., Moguerza, Javier M. and Redchuk, Andres. 2012.
Six Sigma with {R}. Statistical Engineering for Process
Improvement, Use R!, vol. 36. Springer, New York.
http://www.springer.com/statistics/book/978-1-4614-3651-5.
Chambers, J. M. (2008) Software for data analysis. Programming with
R New York: Springer.
Montgomery, DC (2008) Introduction to Statistical Quality Control
(Sixth Edition). New York: Wiley&Sons
Wikipedia, http://en.wikipedia.org/wiki/Six_Sigma