Variable reduction is a crucial step for accelerating model building without losing potential predictive power of the
data. This paper provides a SAS macro that uses Weight of Evidence and Information Value to screen continuous,
ordinal and categorical variables based on their predictive power. The results can lend useful insights to variable
reduction. The reduced list of variables enables statisticians to quickly identify the most informative predictors for
building logistic regression models.