I'm trying to automate linear regression with R, although I don't really have a concrete background in statistics. I was wondering:
Are there numerical techniques in determining whether the predictor values is even worth trying to fit against a response value before attempting to do this in R:
lmfit <- lm(x ~ y)
My initial guess was covariance and correlation, but again what are the accepted values? Also, what if the relationship between response and predictor isn't linear.
In most scenarios (as mentioned by commenters already) one would base predictors based on an a priori hypothesis. However; in knowledge discovery, there are times where you don't know what the hypothesis is, hence my motivation towards formulating a set of rules whereby a quick numerical parameter would allow me to decide if a certain predictor variable should be added or removed in the linear regression model.
Note that the discussion above is not data dependent, it should work with any general data that may or may not have linear relationships between variables.