DoE online generator

Analysis examples

Here you can create a DoE (Design of Experiments) online, simply select the type of design and then enter the number of factors.

You can create the following Designs:

• 2-level factorial design
• Full factorial design
• Fractional factorial design
• Plackett-Burman design
• Box-Behnken design
• Central composite design
• Mixture Design
• D-optimal design
• I-optimal design
• Custom optimal design
• Taguchi design

In the DoE generator you can enter the names of the factors and define the levels, i.e. the values that the respective factors can assume. The created design of experiments can then be exported to Excel and the experiments can be carried out.

DoE Calculator

The DoE Calculator simplifies the process of planning experiments. Select a suitable design, define the factors and levels, and export the resulting test plan.

DoE online

Design of Experiments (DoE) is a systematic method to determine the relationship between factors affecting a process and the output of that process. It is used to find cause-and-effect relationships and optimize processes in various fields such as manufacturing, engineering, and pharmaceuticals. Here you can easily create a DoE online, just select the design you want.

Why Use DoE Calculator?

• Efficiency: Reduce the number of experimental trials needed, saving time and resources.
• Accuracy: Improve the quality of data and the reliability of results.
• Optimization: Easily identify which variables have the most significant effect on your outcomes.
• User-Friendly Interface: Our intuitive design makes it easy for both beginners and experts to use.

Design of Experiments Software

numiqo is browser-based design of experiments software for planning, analyzing, and optimizing experiments. Use it to define factors and levels, choose a suitable design, add replicates, center points, or blocks where applicable, randomize the run order, and export the test plan to Excel.

How to Choose an Experimental Design

• Use a factorial design when you want to study factor effects and interactions.
• Use a screening design when many factors could influence the response.
• Use a response surface design when you want to model curvature and optimize settings.
• Use a mixture design when the factors are proportions that add up to a fixed total.
• Use a Taguchi design for an efficient, balanced study with orthogonal arrays.

After completing the runs, enter the measured response values in the analysis section. Regression results, ANOVA, effect estimates, and visualizations help you evaluate the fitted model. The statistical calculations run locally in your browser.