The basic but essential role of a screening design is its ability to estimate main effects. In this post I will look at how well this is achieved using a DSD. (more…)

# Category Archives: DOE

# DSD: Number of Runs

The size of a classical screening design is influenced by design resolution and geometric symmetry, whereas the size of a definitive screening design grows in proportion with the number of factors. (more…)

# DSD: Three Level Designs

In my last post I introduced the key characteristics of definitive screening designs. In this post I will take a closer look at the first of these characteristics, namely, that the designs have three levels. (more…)

# Definitive Screening Designs

Definitive? Really? I’m going to be taking a close look at definitive screening designs and I’ll try and not get hung-up on the name: calling them DSDs should solve that problem!

DSDs represent a revolutionary approach to designing screening experiments. I want to take a look at the motivation behind these designs and explore their characteristics in relation to traditional screening designs. DSDs are highly efficient in the use of resources required to conduct an experiment, but that efficiency can sometimes come at the price of more complexity when it comes to analysing the experiment data. I want to take a look at the assumptions that underpin the designs and how those assumptions impact modelling of the experimental results. (more…)

# DOE Math

Understanding the matrix formulation of least squares regression can help you understand the essence of DOE. (more…)

# Statistical Thinking and DOE

An understanding of how variance propagates through a system can help us design experiments that maximise the precision of our results.

Traditionally scientists and engineers are taught how to conduct scientific experiments, then analyse the data in the context of their domain-specific knowledge (physics, chemistry, electronics, etc), and finally to apply statistical methods to calculate the precision of their results.

Statistical design of experiments reverses the sequence of data analysis, by first anticipating the precision of the results and how the precision is influenced by the experimental configuration. (more…)

# Notation of Model Interaction Terms

Here is a data table that I have created. It happens to contain data

that is the result of a designed experiment. I know that, but JMP doesn’t.

(more…)