Hi there,
I am trying to find the best way to select the terms to include in my quadratic model. :)
First off, I am using Least Squares Method to understand how pH and other continuous (also maybe some discrete indicators to add to my model soon) are affected by three different factors (concentration A , concentration B and ratio C). So I need to find the relationship between the factors (their square and their interactions) and the outputs that I have measured (pH, rheology values etc.)
I got my data by doing an L9 Taguchi matrix design of experiments (three parameters that have three levels each) so I only have 9 experiments. I have to choose the terms for my models used for each output. (A first model for pH, another can be used for rheology measurments etc.)
With only 9 runs, I know I cannot fit the full quadratic model (10 parameters). The main effects are orthogonal, but the two-factor interactions are aliased with main effects (|r| ≈ 0.58), so I plan to drop them. That leaves main effects + quadratic terms (6 parameters). My question is really about how to select among these remaining terms: manual reduction (removing non-significant terms one by one) vs LASSO — which is more defensible for a PhD?
Thanks for any help!
Kind regards,
Anna
