as.data.frame.facDesign

needs to be an object of class <code><a href="/link/facDesign?package=qualityTools&version=1.53" rd-options="" data-mini-rdoc="qualityTools::facDesign">facDesign</a></code>.

row.names

logical value. If <squote>TRUE</squote>, setting row names and converting column names (to syntactic names: see <code><a href="/link/make.names?package=qualityTools&version=1.53" rd-options="" data-mini-rdoc="qualityTools::make.names">make.names</a></code>) is optional.By default <code>optional</code> is set to <squote>TRUE</squote>.

optional

additional arguments to be passed to or from methods.

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.

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

Description

Usage

Arguments

Value

See Also