as.data.frame.facDesign


 needs to be an object of class <code><a rd-options="" href="/link/facDesign?package=qualityTools&version=1.55" data-mini-rdoc="qualityTools::facDesign">facDesign</a></code>.


row.names


 logical value. If &#145;TRUE&#146;, setting row names and converting column names (to syntactic names: see <code><a rd-options="" href="/link/make.names?package=qualityTools&version=1.55" data-mini-rdoc="qualityTools::make.names">make.names</a></code>) is optional.
 By default <code>optional</code> is set to &#145;TRUE&#146;.


optional


 additional arguments to be passed to or from methods.


Design of Experiments

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.

as.data.frame.facDesign: Coerce to a data.frame

Description

Usage

Arguments

Value

See Also