Factorial: designed experiments in your browser

What will you measure?

Response Units

Goal Maximise Minimise Hit a target Just learn

What you know of it already

Typical value Run-to-run noise Smallest change worth finding

What might change it?

Factor name	Type	Low	High	Units	Remove

What do you want to learn?

Which factors matterMain effects Which matter, and how they act togetherMain effects and two-factor interactions Where each factor's effect curvesMain effects and squares, the book's model The whole response surfaceThe full quadratic model

Designs that can learn this

Enter two factors or more to list them.

The design

Design type

Center points Axial points Resolution Runs Replicates Blocks Model to design for

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Run the experiments

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The design and its results together, so it runs as it is.

What the results say

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Model

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The design, its results and their analysis together, so it runs as it is.

The designs compared in detail

Each design scored on the book's nine measures.

How to read the table

No design is best on every row, and which row matters depends on what the experiment is for: A/p or E for precise coefficients, I or G for prediction over the region, max |r| for keeping second-order effects apart, and power for detecting an effect of a stated size. Each measure's name links to its definition, and the book's comparison of design families reads such a table row by row.

Show the Python code

The process-improve code for this comparison. Copy it and it runs as it is.