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Multicollinearity

  • Ashish Sen
  • Muni Srivastava
Part of the Springer Texts in Statistics book series (STS)

Abstract

Until this point, the only difficulties with least squares estimation that we have considered have been associated with violations of Gauss-Markov conditions. These conditions only assure us that least squares estimates will be ‘best’ for a given set of independent variables; i.e., for a given X matrix. Unfortunately, the quality of estimates, as measured by their variances, can be seriously and adversely affected if the independent variables are closely related to each other. This situation, which (with a slight abuse of language) is called multicollinearity, is the subject of this chapter and is also the underlying factor that motivates the methods treated in Chapters 11 and 12.

Keywords

Condition Number Variance Inflation Factor Small Eigenvalue Variance Proportion Gross National Product 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1990

Authors and Affiliations

  • Ashish Sen
    • 1
  • Muni Srivastava
    • 2
  1. 1.College of Architecture, Art, and Urban Planning, School of Urban Planning and PolicyThe University of IllinoisChicagoUSA
  2. 2.Department of StatisticsUniversity of TorontoTorontoCanada

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