Design of Experiments guides
Plain-language, practical articles on planning experiments and reading the results.
Design of Experiments (DoE): A Practical Guide
What Design of Experiments is, why it needs far fewer runs than changing one factor at a time, and how to plan, run and analyze your first experiment.
3 min read
Central Composite vs Box-Behnken: Which Should You Choose?
Compare central composite and Box-Behnken response-surface designs: run counts, factor levels, safety at the corners, and which fits your optimization problem.
· 3 min read
Fractional Factorials and Design Resolution
How fractional factorial designs cut run counts, what aliasing means, and how to read design resolution III, IV and V when screening many factors.
· 3 min read
Full Factorial Designs Explained
How a two-level full factorial design works, how many runs it needs, how effects and interactions are calculated, and when to move to a fraction or a response surface.
· 2 min read
R², Adjusted R² and Predicted R²: Why a High R² Can Mislead
R² only says how well a model fits the data it was built on. Learn how adjusted R² and predicted R² reveal overfitting in designed experiments.
· 3 min read
Why One-Factor-at-a-Time Experiments Fail
Changing one factor at a time seems careful, but it hides interactions and wastes runs. A worked example shows what it misses and what a factorial design finds.
· 2 min read