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Residual Analysis

Learn how to validate your DoE models through residual analysis, including visual comparisons, R-squared calculations, and residual plots to ensure your experimental conclusions are reliable.

Understanding the ANOVA Table Output

A practical guide to reading and interpreting ANOVA tables in Design of Experiments. Learn what each column means and how to use the output for decision-making.

Testing ANOVA Assumptions

Learn to validate your ANOVA results by checking critical assumptions. Discover what can go wrong and how to spot problems before they invalidate your conclusions.

How to perform ANOVA

Learn how to properly build models in DoE using ANOVA. Discover the systematic approach to determine which parameters truly matter and build reliable predictive models.

Mathematical Models in DOE

Learn when and how to build mathematical models from your DOE results. Turn your experimental insights into predictive equations for complex optimization goals.

Create a Fractional Factorial Design in Python

Learn to create efficient fractional factorial designs using pyDOE3: reduce experiment costs while maintaining statistical power through strategic confounding and resolution optimization.

Example of a Fractional Factorial Design

A step-by-step walkthrough of a fractional factorial design: why, when, and how to use it, with a practical example and visualizations.

Introducing Fractional & Central Composite Designs

When to move past full factorial design; how to use fractional designs to screen and central composite designs to optimize.

Create a Full Factorial Design in Python

Step-by-step guide for beginners: install pyDOE3, create 2^k designs, add factor names, map to real units, randomize, and export ready-to-run experiment plans.