qualityTools: Statistical Methods for Quality Science. This Package contains methods associated with the Define, Measure, Analyze, Improve and Control (i.e. DMAIC) cycle of the Six Sigma Quality Management methodology.It covers distribution fitting, normal and non-normal process capability indices, techniques for Measurement Systems Analysis especially gage capability indices and Gage Repeatability (i.e Gage RR) and Reproducibility studies, factorial and fractional factorial designs as well as response surface methods including the use of desirability functions. Improvement via Six Sigma is project based strategy that covers 5 phases: Define - Pareto Chart; Measure - Probability and Quantile-Quantile Plots, Process Capability Indices for various distributions and Gage RR Analyze i.e. Pareto Chart, Multi-Vari Chart, Dot Plot; Improve - Full and fractional factorial, response surface and mixture designs as well as the desirability approach for simultaneous optimization of more than one response variable. Normal, Pareto and Lenth Plot of effects as well as Interaction Plots; Control - Quality Control Charts can be found in the qcc package. The focus is on teaching the statistical methodology used in the Quality Sciences.
Keywords for this software
References in zbMATH (referenced in 5 articles )
Showing results 1 to 5 of 5.
- John Lawson, Cameron Willden: Mixture Experiments in R Using mixexp (2016) not zbMATH
- Cano, Emilio L.; Moguerza, Javier M.; Prieto Corcoba, Mariano: Quality control with R. An ISO standards approach (2015)
- Ulrike Grömping: R Package FrF2 for Creating and Analyzing Fractional Factorial 2-Level Designs (2014) not zbMATH
- Sonja Kuhnt; Nikolaus Rudak: Simultaneous Optimization of Multiple Responses with the R Package JOP (2013) not zbMATH
- Cano, Emilio L.; Moguerza, Javier M.; Redchuk, Andrés: Six Sigma with R. Statistical engineering for process improvement. (2012)