Graphical Views of Suppression and Multicollinearity in Multiple Linear Regression. Friedman, L. & Wall, M. *Am Statistician*, 59:127-136, 2005. bibtex @article{fri05gra,
title = {Graphical Views of Suppression and Multicollinearity in Multiple Linear Regression},
volume = {59},
journal = {Am Statistician},
author = {Friedman, Lynn and Wall, Melanie},
year = {2005},
keywords = {variable-selection,suppression,collinearity,enhancement,multiple-regression},
pages = {127-136},
citeulike-article-id = {13265412},
posted-at = {2014-07-14 14:09:56},
priority = {0},
annote = {"Horst (1941) ... gave the name 'suppressor variable' to an independent variable that (1) has no correlation with the outcome variable, but (2) is correlated with the other independent variable, and (3) increases the variance explained ... Darlington (1968) defined a suppressor variable as one that produces a negative 'beta weight' --- a regression coefficient for a variable in the standardized model---in the regression equation despite the fact that all correlations between the predictor and outcome variables are nonnegative.";graphical explanation of suppression}
}

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