title: Exploratory regression analysis: a tool for selecting models and determining predictor importance.
abstract: Linear regression analysis is one of the most important tools in a researcher's toolbox for creating and testing predictive models. Although linear regression analysis indicates how strongly a set of predictor variables, taken together, will predict a relevant criterion (i.e., the multiple R), the analysis cannot indicate which predictors are the most important. Although there is no definitive or unambiguous method for establishing predictor variable importance, there are several accepted methods. This article reviews those methods for establishing predictor importance and provides a program (in Excel) for implementing them (available for direct download at http://dl.dropbox.com/u/2480715/ERA.xlsm?dl=1) . The program investigates all 2(p) - 1 submodels and produces several indices of predictor importance. This exploratory approach to linear regression, similar to other exploratory data analysis techniques, has the potential to yield both theoretical and practical benefits.
authors.affiliation: Michigan State University, East Lansing, MI, USA. michael.braun33@gmail.com
authors.forename: Michael T
authors.initials: MT
authors.lastname: Braun
authors.forename: Frederick L
authors.initials: FL
authors.lastname: Oswald
mesh_terms: D015233:Models, Statistical; D012044:Regression Analysis; D012107:Research Design; D012984:Software