Experimental Design

Experimental Design Overview
The general ideas and principles on which experimentation in industry is based, and the types of designs used will be discussed in the following links. The following links are meant to be introductory in nature. However, it is assumed that you are familiar with the basic ideas of analysis of variance and the interpretation of main effects and interactions in ANOVA. Otherwise, it is strongly recommended that you read the Introductory Overview section for ANOVA/MANOVA.
Design & Analysis of Experiments Startup Panel
Design & Analysis of Experiments with Two-Level Factors
Design of an Experiment with Two-Level Factors
Analysis of an Experiment with Two-Level Factors
Marginal Means for Experiments with Two-Level Factors
2-level Screening Designs
2^(k-p) Maximally Unconfounded and Minimum Aberration Designs
3**(k-p) and Box-Behnken Designs
Bayesian Reliability Optimization for Continuous/Binary Response Overview
The Bayesian Reliability Optimization for Continuous/Binary Response nodes address problems with current frequentist response optimization methods. The nodes implement a Bayesian reliability approach as put forth by Peterson (2004) that explicitly take into account the correlation structure of the data, the variability of the process distribution, and the model parameter uncertainty. There are two nodes available depending on the type of response variables, continuous and binary.
Central Composite, Non-Factorial, Surface Designs
Constructing D- and A-Optimal Designs
D-Optimal Split Designs Overview
The standard split plot design is characterized by two sizes of experimental units. Split plot designs began in agriculture where one factor was typically applied to one large plot of land (e.g. fertilizer). This factor was called the whole plot factor. Another factor was applied within the whole plot (e.g. seed variety). This factor was referred to as the sub plot factor. The two experimental units in this case are the whole plot and the sub plot. Since there are two sizes of experimental units, there are two sources of experimental error. This extra source of error affects the subsequent hypothesis tests that are performed.
Designs for Constrained Surfaces and Mixtures
Experimental Design Builder and Quick Tab
Latin Square Designs
Mixed 2 and 3 Level Designs
Mixture Designs and Triangular Surfaces
Six Sigma Calculator
Experimental Design Examples
Experimental Design - Box-Cox Plots for Selecting Transformations

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Experimental Design