Multiple regression, the linear predictor

  • Per Kragh Andersen
  • Lene Theil Skovgaard
Part of the Statistics for Biology and Health book series (SBH, volume 0)


In the previous two chapters we studied regression models where the linear predictor depended on a single explanatory variable, x. In Chapter 3, x was categorical and for a binary variable (Section 3.1) with values g0,g1 we added

$$ {\rm{bI}}({\rm{xi}} = {\rm{g1}}) $$

to the intercept a, whereas in general, for a variable with k + 1 levels (Section 3.2) we added instead the expression

$$ {\rm{b1I}}({\rm{xi}} = {\rm{ g1}}) + {\rm{ b2I}}({\rm{xi}} = {\rm{ g2}}) + {\rm{ }}\cdot\cdot\cdot{\rm{ }} + {\rm{ bkI}}({\rm{xi}} = {\rm{ gk}}), $$

with dummy variables for all categories except the reference category (xi = 0).


Explanatory Variable Linear Predictor Overweight Woman Normal Weight Woman Irish Woman 
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 New York 2010

Authors and Affiliations

  1. 1.Dept. BiostatisticsUniversity of CopenhagenCopenhagen KDenmark

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