What is Design of Experiments (DoE)?
DoE is a statistical tool for designing experiments that will enable enhanced decision-making about optimal formulations or production processes.
#10: Some guidelines before you start
Some good advice to consider...
#9: Example of a full factorial design in python
This is an example about how to perform a 2-level full factorial design with python. You can copy the code and use it in your projects.
#8: Central composite design (CCD) for non-linear models
A short introduction to central composite design (CCD) and why it is important.
#7: Fractional design vs. full factorial design
Explore the advantages and challenges of using fractional factorial designs over full factorial designs. Learn about aliasing and strategies for interpreting results obtained with fractional design plans.
#6: The only 3 DoE plans you need: Fractional, factorial, and CCD
Performing a full factorial design might be the most intuitive but not always the best choice. Here are two more design plans that will make your life easier.
#5 Example: Two-level full factorial design
A DoE always follows the same steps. Learn which ones at the example of a two-level full factorial design.
#4: Deep-Dive: Replication, randomization and blocking
Read about the basic concepts of replication, randomization, and blocking and why they are so important.
#3: Terminology of DoE
A dictionary for understanding key terminology that is used in Design of Experiments (DoE)
#2: What is DoE?
It's not rocket science, it's smarter! Efficiency wins 🚀✨
#1: Why DoE?
Discover how DoE turned a 19th-century fertilizer puzzle into a modern toolkit for making smart, data-driven decisions.