The ordinary least squares solution is generally applied to multiple regression problems in social sciences. When the intercorrelations among predictor variables are close to one, the estimates of regression coefficients obtained from ordinary least squares are very unstable. This situation is often referred to as near multicollinearity. When there is a problem of near mulricollinearity, the ridge regression provides an alternative to the ordinary least squares method. The ridge estimates are biased but more stable from sample to sample. The purpose of this article is to describe the method of ridge regression in a simple form and to provide examples of its application.
