EAN: 9781441929181

SPRINGER SERIES IN STATISTICS REGRESSION MODE...

Many texts are excellent sources of knowledge about individual statistical tools  but the art
of data analysis is about choosing and using multiple tools. Instead of presenting isolated
techniques  this text emphasizes problem solving strategies that address the many issues
arising when developing multivariable models using real data and not standard textbook
examples. It includes imputation methods for dealing with missing data effectively  methods for
dealing with nonlinear relationships and for making the estimation of transformations a formal
part of the modeling process  methods for dealing with too many variables to analyze and not
enough observations  and powerful model validation techniques based on the bootstrap. This text
realistically deals with model uncertainty and its effects on inference to achieve safe data
mining. There are many books that are excellent sources of knowledge about individual stastical
tools (survival models  general linear models  etc.)  but the art of data analysis is about
choosing and using multiple tools. In the words of Chatfield ...students typically know the
technical details of regressin for example  but not necessarily when and how to apply it. This
argues the need for a better balance in the literature and in statistical teaching between
techniques and problem solving strategies. Whether analyzing risk factors  adjusting for biases
in observational studies  or developing predictive models  there are common problems that few
regression texts address. For example  there are missing data in the majority of datasets one
is likely to encounter (other than those used in textbooks!) but most regression texts do not
include methods for dealing with such data effectively  and texts on missing data do not cover
regression modeling.

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