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Obtaining least squares solutions

  • G. Barrie Wetherill
  • P. Duncombe
  • M. Kenward
  • J. Köllerström
  • S. R. Paul
  • B. J. Vowden
Chapter
Part of the Monographs on Statistics and Applied Probability book series (MSAP)

Abstract

Our summary of least squares given in Sections 1.3–1.5 shows that the core problem is one of solving the set of linear equations (1.3). In some accounts of regression, therefore, we are led directly to this algebraic problem.

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References

  1. Chambers, J. M. (1971) Regression updating. J. Amer. Statist. Assoc., 66, 744–748.CrossRefGoogle Scholar
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Further reading

  1. Clarke, M. R. B. (1981) Algorithm AS163. A Gauss algorithm for moving from one linear model to another without going back to the data. Appl. Statist., 30, 198–203.CrossRefGoogle Scholar
  2. Clarke, M. R. B. (1982) The Gauss-Jordan SWEEP operator with detection of collinearity. Appl. Statist., 31, 166–168.CrossRefGoogle Scholar

Copyright information

© G. Barrie Wetherill 1986

Authors and Affiliations

  • G. Barrie Wetherill
    • 1
  • P. Duncombe
    • 2
  • M. Kenward
    • 3
  • J. Köllerström
    • 3
  • S. R. Paul
    • 4
  • B. J. Vowden
    • 3
  1. 1.Department of StatisticsThe University of Newcastle upon TyneUK
  2. 2.Applied Statistics Research UnitUniversity of Kent at CanterburyUK
  3. 3.Mathematical InstituteUniversity of Kent at CanterburyUK
  4. 4.Department of Mathematics and StatisticsUniversity of WindsorCanada

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