One of the most challenging aspects of the design of experiments (DOE) is deciding which effects should be considered important to the relationship between inputs and responses. This process, referred to as model selection, ultimately determines effect prioritization and identifies those effects which will be part of the final model.
In this webcast, you'll learn several different approaches to determining the best model for your experiment including:
- Ad hoc model reduction in which the researcher makes incremental adjustments and demonstrates how to refit if needed.
- Stepwise regression and how it can be used to add efficiency to the selection process and potentially improve model fit.
- Effect screening with a Monte Carlo simulation and modern effect screening with generalized regression.