Hi everyone,
I'm trying to fit a multiple regression model and have run into some
questions regarding the appropriate procedure to use. I am trying to
compare fish assemblages (species richness, total abundance, etc.) to
metrics of habitat quality. I swam transects are recorded all fish
observed, then I measured the structural complexity and live coral
cover over each transect. I am interested in weighting which of these
two metrics has the largest influence on structuring fish assemblages.
My strategy was to use a multiple linear regression. Since the data
were in two different measurement units, I scaled the variables to a
mean of 0 and std. dev. of 1. This should allow me to compare the
sizes of the beta coefficients to determine the relative (but not
absolute) importance of each habitat variable on the fish assemblage,
correct?
My model was lm(Species Richness~Complexity+Coral Cover). I had run a
full model and found no evidence of interactions, so I ran it without
the interaction present.
It turns out coral cover was not significant in any regression. I have
been told that the test I used was incorrect and that the appropriate
procedure is a stepwise regression, which would, undoubtedly, provide
me with Complexity as a significant variable and remove Coral Cover.
This seems to me to be the exact same interpretation as the above
model. So, since I'm very new to all of this, I am wondering how to
tell whether one model is 'incorrect' or 'inappropriate' given that
they yield almost identical results? What are the advantages of a
stepwise regression over a standard multiple regression like I have run?
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