# Selecting predictor in regression: What is more important - significance of the intercept or residual standard error

I am trying to find the best predictor for Leaf Area Index (LAI, a plant growth indicator) among several spectral indices (these are calculated from reflectances measured in different spectral wave lengths). I am using exponential model like this: $$y=a \exp(b x)$$. Two of the spectral indices gave me relatively good results but I do not know which is better.

The problem is that the first spectral index has smaller residual standard error but the $$a$$ coefficient is insignificant; the second spectral index has significant $$a$$ but the residual standard error is higher. For both models $$b$$ is significant. Graphs of the two model fits are shown below. As I have some very low LAI measurements I think that it is not unusual to have “a” that is not significantly different of zero.

So my question is should I reject (in this situation) the model with the lower residual standard error just because the $$a$$ coefficient is not significant.

Why do you think it is a problem that the $$a$$ coefficient is not statistically significant? It is of course practically significant, since $$a=0$$ for this model implies $$y=0$$ (or, with an additive error term, $$y=\text{pure noise}$$). So a hypothesis test of $$H_0\colon a=0$$ is practically meaningless!