Factorial experiments can be used when there are more than two levels of each factor.

For the vast majority of factorial experiments, each factor has only two levels.

Factorial experiments are very efficient, since each experimental unit is used in the estimation of every main effect and interaction.

Use of factorial experiments instead of the one-factor-at-a-time method.

In the statistical analysis of the results from factorial experiments, the sparsity-of-effects principle states that a system is usually dominated by main effects and low-order interactions.

Other useful exploratory analysis tools for factorial experiments include main effects plots, interaction plots, and a normal probability plot of the estimated effects.

Shainin further developed this work into an analysis of variance, or ANOVA, permitting non-parametric analysis of Fisher's full factorial experiments.

Full Factorial and Fractional Factorial Experiments: Frequently Asked Questions (The Methodology Center, Penn State University)

The design and analysis of factorial experiments, Technical Communication no. 35 of the Commonwealth Bureau of Soils (1937) (alternatively attributed to the Imperial Bureau of Soil Science).

Software that is used for designing factorial experiments plays an important role in scientific experiments and represents a route to the implementation of design of experiments procedures that derive from statistical and combinatorial theory.

Factorial experiments can be used when there are more than two levels of each factor.

Factorial experiments can involve factors with different numbers of levels.

For the vast majority of factorial experiments, each factor has only two levels.

An orthogonal array of strength 2 can be used to design a factorial experiment.

A factorial experiment allows for estimation of experimental error in two ways.

Fractional factorial experiment designs for experiments with factors at two and three levels.

The simplest factorial experiment contains two levels for each of two factors.

One very promising development here is a new approach to designing factorial experiments based on Bayesian methods.

The notation used to denote factorial experiments conveys a lot of information.

Simple examples of experimental designs; introduction to factorial experiments.

Factorial experiments are very efficient, since each experimental unit is used in the estimation of every main effect and interaction.

The effects of aluminum on Picea rubens: factorial experiments using sand culture.

Treatment structure: factors, main effects, interactions, factorial experiments.

The matrix F obtained from the factorial experiment.

The sparsity-of-effects principle actually refers to the idea that only a few effects in a factorial experiment will be statistically significant.

Use of factorial experiments instead of the one-factor-at-a-time method.

Analysis of variance for regression (2 lectures) Linear models for factorial experiments, factors, levels.

One experiment was a factorial experiment, and the other was a more naturalistic investigation.

The analysis of factorial experiments should focus on comparisons of means of research interest specified by the investigator.

After coding all factors in a 2-level full factorial experiment, the design matrix has all orthogonal columns.

To save space, the points in a two-level factorial experiment are often abbreviated with strings of plus and minus signs.

Rather than the 32 runs that would be required for the full 2 factorial experiment, this experiment requires only eight runs.

Thus it is most likely that main (single factor) effects and two-factor interactions are the most significant responses in a factorial experiment.

Carr.) to different vegetation-control site-preparation treatment and timing combinations was examined using a factorial experiment.

The patients received various treatments according to the display of the orthogonal table L16(2)15 which corresponds to 2(3) factorial experiment design.