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Analysing Seasonal Health DataIntroduction

Analysing Seasonal Health Data: Introduction [This section describes the example data sets that we will use to demonstrate methods of analysing seasonal data. The examples aim to cover a range of health outcomes and measurement scales. The diet and exercise example uses continuous body mass index data that may have a Normal distribution. The cardiovascular disease data are counts that may have a Poisson distribution. The stillbirth data are binary and will have a Binomial distribution. The cardiovascular disease, schizophrenia and flu data sets are time series, as the results are measured at successive and equally spaced times. The exercise data are from an intervention study, and the times of observations depended on when people joined the study.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Analysing Seasonal Health DataIntroduction

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Publisher
Springer Berlin Heidelberg
Copyright
© Springer-Verlag Berlin Heidelberg 2010
ISBN
978-3-642-10747-4
Pages
1 –47
DOI
10.1007/978-3-642-10748-1_1
Publisher site
See Chapter on Publisher Site

Abstract

[This section describes the example data sets that we will use to demonstrate methods of analysing seasonal data. The examples aim to cover a range of health outcomes and measurement scales. The diet and exercise example uses continuous body mass index data that may have a Normal distribution. The cardiovascular disease data are counts that may have a Poisson distribution. The stillbirth data are binary and will have a Binomial distribution. The cardiovascular disease, schizophrenia and flu data sets are time series, as the results are measured at successive and equally spaced times. The exercise data are from an intervention study, and the times of observations depended on when people joined the study.]

Published: Jan 4, 2010

Keywords: Akaike Information Criterion; Markov Chain Monte Carlo; Southern Oscillation Index; Deviance Information Criterion; Tolerance Interval

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