In design of experiments (DOE), the value of the study depends on the quality of the model. A poorly specified model produces unrealistic predictions, wastes experimental effort, and introduces risk into critical decisions. Over time, weak modeling practices can obscure the true design space and create quality challenges.
This session focuses on practical strategies for strengthening DOE models and avoiding common mistakes that compromise results.
Learn how to:
- Ensure modeling assumptions have been met.
- Detect and correct unrealistic or misleading model predictions.
- Properly specify fixed and random effects to reflect real-world variation.
- Incorporate non-normal and functional, curve-based responses into DOE models.
Whether new to DOE or refining existing skills, this session provides clear techniques for building models that support confident, defensible decisions.