Discover how Bayesian Optimization might revolutionize experimental design by saving time and resources efficiently. It might be smarter than you.

Here I show you how to create a central composite design (CCD) with Python. It’s really easy to do.

Central composite designs are used instead of multilevel full factorial designs to optimize (min./max.) a system or to estimate non-linear relationships.

Comparing fractional design results to the results from a full factorial design with twice as many runs.

Fractional design is a real timesaver. Forget about full factorial design. Get started here.

If you perform an ANOVA, you need to check if the data is normally distributed, the variances are homogeneous, and the observations are independent.

This is an example about how to perform a 2-level full factorial design with python from beginning to end.

QQ-plots are an important part of model control…

Validating your model is as important as building it… Learn how to

Learn how to perform an ANOVA with Python

The basics that you need to know about ANOVA

This is how to make complexity simple…

Learn to visualize your results with the Python library Seaborn

A guide to creating a full factorial design with python

Learn the basics in Python quickly

How to install python on your computer and execute your first python program

An example from a real experiment…

A way to reduce variability, randomness and bias in DoE

Let's discuss errors so we can prevent them.

A comparison between fractional factorial, full factorial and central composite design