Design of Experiments

We look at uncertainty in predictions and discuss model validation to ensure that technical assumptions that are inherent in the modeling process is satisfied.

We explore the use of contour plots and other tools to quickly find solutions to our models. We revisit the battery life DOE example, we previous discussed.

In this article we look at using the model to develop solutions. So that we learn the basics, we will first use some simple algebra to find a solution.

We focus on the development of first order models. These models are easy to develop when running screening experiments where the factors are set at 2 levels.

In time the statistically significant effects will be used to develop a predictive model. But how do we determine which effects are statistically significant?

We’re ready to learn how to analyze the data collected during the experiment. This is the most exciting part of DOE! We will cover the analysis in this article.

We discuss guidelines for conducting an experiment. Good planning of the study is critical for a successful outcome, and will make the study straight-forward.

In this article we discuss several important design of experiments (DOE) techniques to consider when conducting and analyzing an experiment.

We discuss the suggested steps for planning a Designed Experiment (DOE). After the planning is complete, we will then be ready to conduct the experiment.