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ERIC Number: ED512356
Record Type: Non-Journal
Publication Date: 2004
Pages: 12
Abstractor: As Provided
Reference Count: 31
Ridge Regression as an Alternative to Ordinary Least Squares: Improving Prediction Accuracy and the Interpretation of Beta Weights. Professional File. Number 92, Summer 2004
Walker, David A.
Association for Institutional Research (NJ1)
This article looked at non-experimental data via an ordinary least squares (OLS) model and compared its results to ridge regression models in terms of cross-validation predictor weighting precision when using fixed and random predictor cases and small and large p/n ratio models. A majority of the time with two random predictor cases, ridge regression accuracy was superior to OLS in estimating beta weights. Thus, ridge regression was very useful under this condition. However, when the fixed predictor case was reviewed, OLS was much more precise at estimating predictor weights than the ridge techniques regardless of the p/n ratio. In determining the cross validation accuracy of the ridge estimated weights in respect to the OLS estimated weights, ridge models were superior for improving the accuracy of model prediction. An appendix is included. (Contains 2 tables.)
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Publication Type: Reports - Evaluative
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: Association for Institutional Research