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Least Squares

  • Shelby J. Haberman
Part of the Springer Series in Statistics book series (SSS)

Abstract

The method of least squares has been employed at least since Legendre (1805) to treat problems in which a real variable is approximated by using a predictor selected from a linear subspace. In Section 5.1, square-integrable functions are defined and used to define variances, standard deviations, covariances, and coefficients of variation. In Section 5.2, mean-squared error and least-squares predictors are defined. In Section 5.3, simple linear regression is considered. In Section 5.4, multiple linear regression is considered. In Section 5.5, least-squares problems are considered for infinite-dimensional subspaces.

Keywords

Real Function State Population Linear Subspace Simple Linear Regression Real Matrix 
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 1996

Authors and Affiliations

  • Shelby J. Haberman
    • 1
  1. 1.Department of StatisticsNorthwestern UniversityEvanstonUSA

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