Balanced Factorial Experiments

Balanced Arrays and Fractional Factorial Designs

Model-averaged confidence intervals for factorial experiments

We consider the coverage rate of model-averaged confidence intervals for the treatment means in a factorial experiment, whenwe use a normal linearmodel in the analysis.Modelaveraging provides a useful compromise between using the full model (containing all main effects and interactions) and a ‘‘best model’’ obtained by some model-selection process. Use of the full model guarantees perfect cover...

Optimal Factorial Designs for Cdna Microarray Experiments

We consider cDNA microarray experiments when the cell populations have a factorial structure, and investigate the problem of their optimal designing under a baseline parametrization where the objects of interest differ from those under the more common orthogonal parametrization. First, analytical results are given for the 2× 2 factorial. Since practical applications often involve a more complex...

Regularities in data from factorial experiments

This article documents a meta-analysis of 113 data sets from published factorial experiments. The study quantifies regularities observed among factor effects and multifactor interactions. Such regularities are known to be critical to efficient planning and analysis of experiments and to robust design of engineering systems. Three previously observed properties are analyzed: effect sparsity, hie...

Comparison of the Results of Factorial Experiments, Fractional Factorial Experiments, Regression Trees and MARS for Fuel Consumption Data

In this study effects that are significant on the fuel consumption of F-4 aircrafts are analyzed by using different statistical methods and the results of those different methods are compared. The response variable of every possible combination of factors and interactions are analyzed to provide information about main effect(s) and interaction effect(s). MARS is applied in order to find the rel...