Wondering if anyone can help. I’m trying to compare two regression models with one predictor to see which best describes the data.
Model one is a linear model (y = ax + b) with R2 = .036, F = 3.047, p =.084
Model two is a reciprocal quadratic model (y = a(1/x)2 + b(1/x) + c) with R2 = .072, F = 3.128, p =.045
As you can see, neither fit the data that well, although model two is just about significant. As model one approached significance, and had fewer parameters, I used Akaike’s Information Criterion to compare the two models, with AIC suggesting that model one is more likely to be correct.
I’m a little confused as to how I should interpret this. Should I consider that model one is more likely to represent the data, even though it is not significant, or should I consider model two as a better fit because it has a larger R2 and is significant?
Any help is appreciated!