XLSTAT-DOE: Design of experiments

View tutorialsXLSTAT-DOE is a complement to XLSTAT-Pro for those who want to design experiments in a structured way. XLSTAT-DOE contains all the classic experimental designs for screening factors such as factorial designs or Plackett-Burman designs as well as designs for optimization.

Screening designs

The family of screening designs aims for the study of the effect of two or more factors. In general factorial designs are the most efficient for this type of study. But the number of necessary tests is often to large when using factorial designs. There are other possible types of designs in order to take into account the limited number of experiments that can be carried out.

This tool integrates a large base of several hundred orthogonal design tables. Orthogonal design tables are preferred, as the ANOVA analysis will be based on a balanced design. Designs that are close to the design described by user input will be available for section without having to calculate for an optimal design. All existing orthogonal designs are available for up to 35 factors having each between 2 and 7 categories. Most common families like full factorial designs, Latin square and Placket and Burman designs are included.

If the existing orthogonal designs in the knowledge base do not satisfy your needs, it is possible to search for d-optimal designs. These designs might not be orthogonal.

Analysis of a screening design

Analysis of a screening design uses the same conceptual framework as linear regression and variance (ANOVA). The main difference comes from the nature of the underlying model. In ANOVA, explanatory variables are often called factors. 

Surface response designs

The family of surface response design is used for modeling and analysis of problems in which a response of interest is influenced by several variables and the objective is to optimize this response.

Remark: In contrast to this, screening designs aim to study the input factors, not the response value.
For example, suppose that an engineer wants to find the optimal levels of the pressure (x1) and the temperature (x2) of an industrial process to produce concrete, which should have a maximum hardness y.

Analysis of a surface response design

The analysis of a surface response design uses the same statistical and conceptual framework as linear regression. The main difference comes from the model that is used. 

Demo version

A trial version of XLSTAT-DOE is included in the main XLSTAT-Pro download.

Prices and ordering

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