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Six sigma

Genstat has wide range of facilities to support the six-sigma approach to quality improvement. It can display many different types of control chart.

    SPCCHART plots c or u charts representing numbers of defective items
    SPCUSUM prints CUSUM tables for controlling a process mean
    SPEWMA plots exponentially weighted moving-average control charts
    SPPCHART plots p or np charts for binomial testing for defective items
    SPSHEWHART plots control charts for mean and standard deviation or range

It can test for Normality, display Pareto charts and calculate capability statistics.

    NORMTEST performs tests of univariate and/or multivariate normality
    SPCAPABILITY calculates capability statistics
    TABSORT sorts tables to put margins are in ascending or descending order for display as a Pareto chart

It also provides full statistical backup for wider-ranging investigations. The list below highlights some of the commands that may be useful.

    AFRESPONSESURFACE uses the BLKL algorithm to construct response-surface designs
    AGBOXBEHNKEN generates Box-Behnken designs
    AGCENTRALCOMPOSITE generates central composite designs
    AGDESIGN selects from a set of standard designs including factorials with interactions confounded with blocks
    AGFRACTION generates fractional factorial designs
    AGMAINEFFECT generates designs to estimate main effects of two-level factors (Plackett-Burman designs)
    A2WAY performs analysis of variance of a balanced or unbalanced design with up to two treatment factors
    ANOVA analyses y-variates by analysis of variance according to the model defined by earlier BLOCKSTRUCTURE, COVARIATE, and TREATMENTSTRUCTURE statements
    AGRAPH plots one- or two-way tables of means from ANOVA
    APLOT plots residuals from an ANOVA analysis
    AMCOMPARISON performs pairwise multiple comparison tests for ANOVA means
    AUNBALANCED performs analysis of variance for unbalanced designs
    AUGRAPH plots tables of means from AUNBALANCED
    FIT fits a linear, generalized linear, generalized additive, or generalized nonlinear model
    FITCURVE fits a standard nonlinear regression model
    FITNONLINEAR fits a nonlinear regression model or optimizes a function
    FKEY forms design keys for balanced designs with several error terms, allowing for confounded and aliased treatments
    REML fits an unbalanced linear mixed model and estimates variance components
    RQUADRATIC fits a quadratic surface and estimates its stationary point
    YTRANSFORM estimates the parameter lambda from various single-parameter transformations, includling power (Box-Cox), modulus, folded power, Guerrero-Johnson, Aranda-Ordaz and power logit
Updated on June 18, 2019

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