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Using XLSTAT for experiment planning and design of experiments

Updated

Summary

XLSTAT provides Design of Experiments (DOE) tools for planning, analysing and optimising experiments in Microsoft Excel. It supports common experimental methods such as screening, factorial, response surface, mixture and Taguchi designs.

For users who previously used Stat-Ease 360 or Design-Expert, evaluate XLSTAT first for new DOE requirements. It is not intended to be an exact feature-for-feature replacement, so specialised experimental designs may require additional assessment.

Benefits

  • Plan experiments before collecting data.
  • Reduce the number of experimental runs needed to study multiple factors.
  • Identify important factors and interactions.
  • Optimise process conditions and formulations.
  • Analyse experimental results using regression and analysis of variance (ANOVA).
  • Keep experimental plans, data and statistical analysis within Excel.
  • Use the same software for DOE and broader statistical analysis.

Quick resolution

Choose the XLSTAT DOE method that matches the experimental objective:

  • Screening designs: identify which factors have the greatest effect on a response.
  • Factorial designs: investigate factor effects and interactions.
  • Response surface designs: model and optimise quantitative process variables.
  • Mixture designs: optimise formulations where component proportions must add to a fixed total.
  • Taguchi designs: investigate robustness and the effect of variation.
  • DOE analysis: analyse responses, build statistical models and identify suitable operating conditions.

A typical workflow is:

  1. Define the objective and responses to be measured.
  2. Identify the experimental factors.
  3. Define factor levels, ranges or formulation constraints.
  4. Select the appropriate DOE method in XLSTAT.
  5. Generate the experimental plan.
  6. Conduct the experimental runs.
  7. Enter the results in Excel.
  8. Analyse the results in XLSTAT.
  9. Review factor effects, interactions and model performance.
  10. Identify suitable settings and perform confirmation experiments where required.

Important notes

XLSTAT should be the starting point for standard DOE requirements, including screening, response surface, mixture and Taguchi experiments.

Additional assessment may be needed for:

  • Highly constrained experimental regions.
  • Specialist optimal-design methods.
  • Complex combined mixture and process-variable designs.
  • Advanced sequential design or design augmentation.
  • Unusual combinations of categorical and quantitative variables.
  • Specialised multi-response optimisation.
  • Validated procedures that depend on another application's specific outputs.

Previous use of Stat-Ease 360 or Design-Expert does not automatically mean you need the same software for future experiments. Compare the required statistical methodology with the DOE functionality available in XLSTAT.

You do not need to convert historical projects. You can easily export your Stat-Ease 360 or Design-Expert data sets to Excel and continue working on them in XLSTAT.