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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 prints CUSUM tables for controlling a process mean plots exponentially weighted moving-average control charts plots p or np charts for binomial testing for defective items 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 calculates capability statistics 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 generates Box-Behnken designs generates central composite designs selects from a set of standard designs including factorials with interactions confounded with blocks generates fractional factorial designs generates designs to estimate main effects of two-level factors (Plackett-Burman designs) performs analysis of variance of a balanced or unbalanced design with up to two treatment factors analyses y-variates by analysis of variance according to the model defined by earlier `BLOCKSTRUCTURE`, `COVARIATE`, and `TREATMENTSTRUCTURE` statements plots one- or two-way tables of means from `ANOVA` plots residuals from an `ANOVA` analysis performs pairwise multiple comparison tests for `ANOVA` means performs analysis of variance for unbalanced designs plots tables of means from `AUNBALANCED` fits a linear, generalized linear, generalized additive, or generalized nonlinear model fits a standard nonlinear regression model fits a nonlinear regression model or optimizes a function forms design keys for balanced designs with several error terms, allowing for confounded and aliased treatments fits an unbalanced linear mixed model and estimates variance components fits a quadratic surface and estimates its stationary point 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