Use of the Singular Value Decomposition in Regression Analysis

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Abstract

Abstract Principal component analysis, particularly in the form of singular value decomposition, is a useful technique for a number of applications, including the analysis of two-way tables, evaluation of experimental design, empirical fitting of functions, and regression. This paper is a discussion in expository form of the use of singular value decomposition in multiple linear regression, with special reference to the problems of collinearity and near collinearity.

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Cite This Study

John Mandel (Mon,) studied this question.

synapsesocial.com/papers/6a163d29ed257bd69ec516a0 https://doi.org/https://doi.org/10.1080/00031305.1982.10482771

Discussion

Journals

The American Statistician

Institutions

National Institute of Standards and Technology

Material Measurement Laboratory

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