factorial design

Finally, our model is based on a balanced factorial design.

This method of analysis does not take into account the factorial design.

These factors were first studied at two levels in an experiment based on a factorial design.

A 2X2 (country by group performance outcome) factorial design was used to test the hypothesized relationships.

A full factorial design may also be called a fully crossed design.

Data were submitted to analysis of variance according to a 3 × 3 factorial design.

A complete factorial design would satisfy this criterion, but the idea was to find smaller designs.

Therefore, the experiments were arranged in a factorial design for accurate analysis of possible interactive effects.

A full factorial design contains all possible combinations of low/high levels for all the factors.

In this case, fractional factorial designs may be used.

Factorial designs are therefore less attractive if a researcher wishes to consider more than two levels.

Design: A randomised controlled trial with a full factorial design.

All trials were established in a randomized factorial design.

To fully understand the design criteria, an understanding of history and fractional factorial designs is necessary.

But by using the Factorial Design technique, two (or more) factors can be allowed to change simultaneously.

Here is the summary for a 4-factor 3-level fractional factorial design in 9 blocks, that requires only 27 runs.

This 2 x 2 factorial design allowed the investigators to clarify the independent contributions of each treatment.

Twelve medium components were evaluated initially using the Plackett–Burman 2-level factorial design.

The experiment was a two-way factorial design with three levels for each of thinning and N fertilization.

The performance of a joint vapor–nuclei type aerosol generator was investigated using a 24 factorial design experiment.

Factorial designs were used to select the components of a culture medium that showed a significant effect on keratinase production.

Factorial designs involving one, two, or three factors; repeated measures designs.

In statistics, a Yates analysis is an approach to analyzing data obtained from a designed experiment, where a factorial design has been used.

A fractional factorial design contains a carefully chosen subset of these combinations.

A design with p such generators is a 1/(l) fraction of the full factorial design.