  We describe an algorithm to quantify dependence in a multivariate data set.
The algorithm is able to identify any linear and non-linear dependence in the
data set by performing a hypothesis test for two variables being independent.
As a result we obtain a reliable measure of dependence.
  In high energy physics understanding dependencies is especially important in
multidimensional maximum likelihood analyses. We therefore describe the problem
of a multidimensional maximum likelihood analysis applied on a multivariate
data set with variables that are dependent on each other. We review common
procedures used in high energy physics and show that general dependence is not
the same as linear correlation and discuss their limitations in practical
application.
  Finally we present the tool CAT, which is able to perform all reviewed
methods in a fully automatic mode and creates an analysis report document with
numeric results and visual review.
