I am doing a multiple regression
My response variable is monthly insect abundance
my predictors are 2 climate variables (monthly accumulated precipitation and
monthly temperature)
I want to verify which of these climate variables is best in predicting insect
abundance. So far it is just a simple multiple regression, but I also want to
verify if delayed climate response is better predictor of insect abundance,
meaning that I will regress insect abundance on month T vs. climate variable on
month T-1.
1) Should I do two multiple regressions, delayed and non delayed, and just
choose the one that has the lower AIC?
2) Is there a way to add all these variables in a single model. I know i would
have collinearity if have both delayed and non delayed variables in one
model, and I don't know how to deal with that .
Any feedback would very much appreciated indeed.
Thanks
Humberto Dutra
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'Discipline - Success doesn't just happen. You have to be intentional
about it, and that takes discipline.' - John Maxwell
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