Design of Experiments (DOE)

Planning experiments with systematic data collection

Passive data collection leads to a number of problems in statistical modeling. Observed changes in a response variable may be correlated with, but not caused by, observed changes in individual factors (process variables). Simultaneous changes in multiple factors may produce interactions that are difficult to separate into individual effects. Observations may be dependent, while a model of the data considers them to be independent.

Designed experiments address these problems. In a designed experiment, the data-producing process is actively manipulated to improve the quality of information and to eliminate redundant data. A common goal of all experimental designs is to collect data as parsimoniously as possible while providing sufficient information to accurately estimate model parameters.

Functions

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ff2nTwo-level full factorial design
fullfactFull factorial design
fracfactFractional factorial design
fracfactgenFractional factorial design generators
bbdesignBox-Behnken design
ccdesignCentral composite design
candexchD-optimal design from candidate set using row exchanges
candgenCandidate set generation
cordexchCoordinate exchange
daugmentD-optimal augmentation
dcovaryD-optimal design with fixed covariates
rowexchRow exchange
rsmdemoInteractive response surface demonstration
lhsdesignLatin hypercube sample
lhsnormLatin hypercube sample from normal distribution
haltonsetHalton quasirandom point set
qrandstreamConstruct quasi-random number stream
sobolsetSobol quasirandom point set
interactionplotInteraction plot for grouped data
maineffectsplotMain effects plot for grouped data
multivarichartMultivari chart for grouped data
rsmdemoInteractive response surface demonstration
rstoolInteractive response surface modeling

Topics

Full Factorial Designs

Designs for all treatments

Fractional Factorial Designs

Designs for selected treatments

Response Surface Designs

Quadratic polynomial models

Improve an Engine Cooling Fan Using Design for Six Sigma Techniques

This example shows how to improve the performance of an engine cooling fan through a Design for Six Sigma approach using Define, Measure, Analyze, Improve, and Control (DMAIC).

D-Optimal Designs

Minimum variance parameter estimates