I want to conduct a screening experiment to identify which factors affect my response (Y) the most. From past research, I am aware that these variables have interactions, so I need to have interaction terms in my model. The factors I identified as relevant are the following:
- X1 - continuous
- X2 - continuous
- X3 - continuous
- X4 - continuous
- X5 - continuous
- X6 - categorical with 2 levels only
- X7 - categorical with 10 levels
To be specific, X7 is a type of chemical: a fatty acid. Fatty acids can be saturated or monounsaturated or polyunsaturated, longer chain or shorter chain, etc, so I selected 10 of them that spanned this range of properties. Because X7 has more than 3 levels, I am forced to use the Custom Design in JMP, and accounting for interaction terms in my model, this results in about 42 experiments. I wanted to ask if there is a more efficient way to do this? Specifically, a more efficient way to setup a DOE to study variables like X7 (that are categorical).
Thinking out loud, at first I thought that converting X7 from discrete to continuous variable (by maybe focusing on an important feature of fatty acids) could be the helpful because then I could simply pick a "high" and a "low" setting. But chemicals are multidimensional i.e. they cannot be just boiled down to one feature/number, so I do not believe this is the way forward.
So is there a more efficient way to design a study where you have a categorical variable like X7?
