Stepwise Multiple Linear Regression

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Step 1   Shows the effect of introducing the most explanatory variable into the model,

and a list of candidate variables that will be entered next, providing they fit the

selection criteria.

Step 2   Shows the effect of introducing the next most explanatory variable into the

model, and a list of candidate variables that will be entered next, providing they fit

the selection criteria.

...and so on.

In most species/environmental datasets it is unusual for more than three variables to

be included in any explanatory model.

A common problem with environmental data sets is that of 

multicollinearity

 between

explanatory variables. ECOM automatically checks for multicollinearity and, if

Copyright 2004  PISCES Conservation Ltd