Forward Stepwise Linear Regression
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Forward stepwise regression starts with no variables in the model. It then adds the most significant explanatory variable, that is, the one with the lowest p-value, at each step, until all variables have been added. By scrutinising the overall fit of the model variables will be automatically added (and, if they do not improve the fit, removed again) until the optimum model is found.
Load both your species and your environmental datasets as described in Opening a Data Set
From the Regression menu on the Toolbar select Forward Stepwise. A list of dependent variables (usually species) is displayed.
The results report shows the sequences of the procedure as Steps:
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 detected, will take to you a screen that details the problem and allows you to remove one of the correlated variables.