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Multiple Regression—More Than One Predictor

  • Richard M. Heiberger
  • Burt Holland
Part of the Springer Texts in Statistics book series (STS)

Abstract

In Chapter 8 we introduce the algebra and geometry behind the fitting of a linear model relating a response variable to one or more explanatory (predictor) variables using the criterion of least squares. In this chapter we consider in more detail situations where there are two or more predictors.

Keywords

Variance Inflation Factor Prediction Interval Candidate Predictor Variable Plot Significant Regression Coefficient 
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 2004

Authors and Affiliations

  • Richard M. Heiberger
    • 1
  • Burt Holland
    • 1
  1. 1.Department of StatisticsTemple UniversityPhiladelphiaUSA

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