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Limitations of Linear Regression Applied on Ecological Data

  • Alain F. Zuur
  • Elena N. Ieno
  • Neil J. Walker
  • Anatoly A. Saveliev
  • Graham M. Smith
Chapter
Part of the Statistics for Biology and Health book series (SBH)

Abstract

This chapter revises the basic concepts of linear regression, shows how to apply linear regression in R, discusses model validation, and outlines the limitations of linear regression when applied to ecological data. Later chapters present methods to overcome some of these limitations; but as always before doing any complicated statistical analyses, we begin with a detailed data exploration. The key concepts to consider at this stage are outliers, collinearity, and the type of relationships between the variables. Failure to apply this initial data exploration may result in an inappropriate analysis forcing you to reanalyse your data and rewrite your paper, thesis, or report.

Keywords

Explanatory Variable Linear Regression Model Data Exploration Nitrogen Isotope Ratio Influential Observation 
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.

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Alain F. Zuur
    • 1
  • Elena N. Ieno
    • 1
  • Neil J. Walker
    • 2
  • Anatoly A. Saveliev
    • 3
  • Graham M. Smith
    • 4
  1. 1.Highland Statistics LtdNewburghUK
  2. 2.Central Science LaboratoryGloucesterUK
  3. 3.Kazan State UniversityKazanRussia
  4. 4.Bath Spa UniversityBathUK

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