General Linear Model methods are the most widely used in data
analysis in applied empirical research. Still, there exists no
compact text that can be used in statistics courses and as a guide
in data analysis. This volume fills this void by introducing the
General Linear Model (GLM), whose basic concept is that an observed
variable can be explained from weighted independent variables plus
an additive error term that reflects imperfections of the model and
measurement error. It also covers multivariate regression, analysis
of variance, analysis under consideration of covariates, variable
selection methods, symmetric regression, and the recently developed
methods of recursive partitioning and direction dependence
analysis. Each method is formally derived and embedded in the GLM,
and characteristics of these methods are highlighted. Real-world
data examples illustrate the application of each of these methods,
and it is shown how results can be interpreted.
General
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