Chapter

Statistical models and methods

in Statistics

Published in print October 2008 | ISBN: 9780199233564
Published online September 2013 | e-ISBN: 9780191777097 | DOI: https://dx.doi.org/10.1093/actrade/9780199233564.003.0006

Series: Very Short Introductions

Statistical models and methods

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‘Statistical models and methods’ explores the construction of models. Models are a representation of the system being studied. These can be mechanistic, i.e. rooted in an underlying theory, or be empirical, having no theoretical basis. Models can be used to seek new patterns or support proposed links, and can be used to describe known values or predict unknown ones. Models should be as simple as possible. Statistical methods can describe symmetrical relationships, such as correlations, using correlation coefficients. They can also describe asymmetrical relationships using regression analyses, survival analyses, ANOVA, and supervised classification. Methods also exist for investigating latent variables and time. Statistical graphics allow us to identify patterns visually.

Keywords: Bayesian inference; correlation; discriminant analysis; experimental design; generalized linear model; hidden Markov model; latent variable; least squares; quality control; regression; time series

Chapter.  5072 words.  Illustrated.

Subjects: Probability and Statistics

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