STATGRAPHICS contains
extensive capabilities for the creation and analysis of
statistically designed experiments. The designs that can be
created are divided into several types:
 Screening  designs
intended to determine the most important factors affecting
a response. Most of the designs involve only 2 levels of
each factor. The factors may be quantitative or
categorical. Included are 2level factorial
designs, mixed level factorial designs, fractional
factorials, irregular fractions, and
PlackettBurman designs. For designs of less than
full resolution, the confounding pattern is displayed.
Blocking and randomization are options.

Response
Surface  designs intended to determine the
optimal settings of the experimental factors. The
designs involve at least 3 levels of the experimental
factors. Included are central composites, BoxBehnken
designs, 3level factorials, and
DraperLin designs.
 Mixture
 designs involving components of a mixture, where the
levels of the components are constrained to sum to 100%
(or some other fixed value). Upper and lower constraints
may be specified for each component. Included are
simplexlattice, simplexcentroid, and
extreme vertices designs.

Multilevel Factorial  designs involving
different numbers of levels for each experimental
factor. These designs produce a natural candidate set
for the DOptimal design creation procedure,
which will select an optimal subset of the runs.

Inner/Outer Arrays  designs consisting of both
controllable and uncontrollable (noise)
factors, intended to find combinations of the
controllable factors at which the responses are
relatively insensitive to the uncontrollable factors.
Following the methods of Genichi Taguchi, both an
inner and outer array are constructed. The factors may
be quantitative or categorical. As part of the analysis,
signaltonoise ratios may be constructed.

Single
Factor Categorical
 designs intended to compare
levels of a single nonquantitative factor. Includes
completely randomized designs, randomized block
designs, balanced incomplete block (BIB) designs,
Latin Squares, GraecoLatin Squares, and
hyperGraecoLatin Squares.

MultiFactor Categorical
 designs intended to study multiple nonquantitative
factors, with several levels of each. Analyzed using a
multifactor analysis of variance.
 Variance
Component (hierarchical)  designs intended to
study the effect of two or more nested factors on the
variability of a response. Estimates of the contribution
of each factor to the overall variability are obtained.
Major Steps in Constructing and Analyzing
an Experimental Design
Step 1: Create Design 
The experiment is created by completing a sequence of dialog
boxes. On these dialog boxes, the user specifies the
experimental factors and responses, the experimental region,
the order of randomization, and any blocking variables.
Step 2: Perform Experiment 
The selected experimental runs are then performed and the
responses entered into the experiment datasheet.
Step 3: Analyze Experiment 
The response variables are analyzed and a suitable
statistical model is developed. Usually, the principle of
parsimony is applied to remove insignificant effects
from the model.
Step 4: Augment Experiment  If
necessary, additional runs are added to the initial design.
STATGRAPHICS provides facilities for adding runs to resolve
confounding, following the path of steepest ascent,
and adding star points to convert a screening design
to a response surface design.
Step 5: Optimize Response(s) 
Settings of the experimental factors are found that achieve
the desired responses. If more than one response is
present, desirability functions may be defined to combine
the different goals.
