Hello.
I evaluate factors affecting the synthesis of lipid liquid crystalline nanoparticles with 2^4-1 fractional factorial design.
After the ANOVA, I have two possibilities. The first model with fewer terms (only significant in terms of p-value) has a lower R squared (0.7541). On the other hand, I can choose a model with two additional insignificant terms, one suggesting interaction between factors, and this model's R squared equals 0.8676.
What is more correct to do? To choose the model with only significant terms but with a lower R^2, or the model with more terms, including insignificant ones, but a higher R^2?
You might consider other measures of goodness-of-fit. Maybe adjusted r-squared, which penalizes the result for additional terms in the model. Or maybe AIC, AICc, or BIC. ... Usually, there are other considerations as to whether you want to include non-significant terms in a model. Sometimes whether you are using the model to explain or to predict. And if you have a theoretical understanding of terms should be included in the model.
Hi,
I suggest you see the links on the topic.
https://support.minitab.com/en-us/minitab/21/help-and-how-to/statistical-modeling/doe/how-to/factorial/analyze-factorial-design/interpret-the-results/key-results/
https://online.stat.psu.edu/stat503/lesson/6/6.3
https://www.sciencedirect.com/topics/engineering/fractional-factorial-design
https://www.spcforexcel.com/knowledge/experimental-design/fractional-factorials-part2
Best regards
You're missing the point here since you are doing ANOVA I assume explanation is your goal. So why do you need to consider two models at all.unless you are doing something like response surface methods. In any case.p-value selection doesn't work either. I suggest you see the two references below
Especially the one by Montgomery available for download from the z-library. Best wishes David Booth
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