Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

DeepDyve requires Javascript to function. Please enable Javascript on your browser to continue.

Analysing Seasonal Health Data

Part of the Statistics for Biology and Health Book Series

DOI: 10.1007/978-3-642-10748-1

ISBN: 978-3-642-10747-4

Publisher: Springer Berlin Heidelberg - Springer Journals

Jan 4, 2010

Available

Introduction

Barnett, Adrian G.; Dobson, Annette J.

Pages: 1 –47

[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....
Available

Introduction to Seasonality

Barnett, Adrian G.; Dobson, Annette J.

Pages: 49 –74

[In this chapter we define what we mean by a season and show some methods for investigating and modelling a seasonal pattern.]
Available

Cosinor

Barnett, Adrian G.; Dobson, Annette J.

Pages: 75 –92

[A simple and popular method for analysing seasonality is the cosinor. Its popularity stems from its ease of application and interpretation [2, 60]. Also, it can be applied to time series with regular dates, or survey data with irregular dates.]
Available

Decomposing Time Series

Barnett, Adrian G.; Dobson, Annette J.

Pages: 93 –128

[A useful way to view time series data is as a combination of trend, season and noise. For data that are equally spaced over time (t = 1, …, n) an equation that splits the series into these three parts is 4.1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym}...
Available

Controlling for Season

Barnett, Adrian G.; Dobson, Annette J.

Pages: 129 –150

[In some circumstances seasonality is not the focus of investigation, but is important because its effects need to be controlled for. This could be because both the outcome and the exposure have an annual seasonal pattern, but we are interested in associations at a different frequency (e.g.,...
Available

Clustered Seasonal Data

Barnett, Adrian G.; Dobson, Annette J.

Pages: 151 –158

[In this section we show how to model clustered seasonal data. We look at two types of clustered data: longitudinal data and spatial data.]

results per page

Prev
1
Next

Browse All eBooks