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Linear models

  • Peter Dalgaard
Chapter
Part of the Statistics and Computing book series (SCO)

Abstract

Many data sets are inherently too complex to be handled adequately by standard procedures and thus require the formulation of ad hoc models. The class of linear models provides a flexible framework into which many — although not all — of these cases can be fitted.

Keywords

Cell Diameter Standardize Residual Data Frame Tanner Stage ANOVA Table 
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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Bibliography

  1. Altman, D. G. (1991), Practical Statistics for Medical Research, Chapman & Hall, London.Google Scholar
  2. Johnson, R. A. (1994), Miller & Freund’s Probability & Statistics for Engineers, 5th ed., Prentice-Hall, Englewood Cliffs, NJ.Google Scholar
  3. Venables, W. N. and Ripley, B. D. (2002), Modern Applied Statistics with S, 4th ed., Springer-Verlag, New York.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.Department of BiostatisticsUniversity of CopenhagenCopenhagenDenmark

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