dc.contributor.author | Williams, Gail | |
dc.contributor.author | Ware, Robert S. | |
dc.contributor.editor | S.A.R. Doi and G.M. Williams | |
dc.date.accessioned | 2017-11-08T12:00:45Z | |
dc.date.available | 2017-11-08T12:00:45Z | |
dc.date.issued | 2013 | |
dc.identifier.isbn | 9783642371318 | |
dc.identifier.doi | 10.1007/978-3-642-37131-8_10 | |
dc.identifier.uri | http://hdl.handle.net/10072/337248 | |
dc.description.abstract | This chapter introduces regression, a powerful statistical technique applied to the problem of predicting health outcomes from data collected on a set of observed variables. We usually want to identify those variables that contribute to the outcome, either by increasing or decreasing risk, and to quantify these effects. A major task within this framework is to separate out those variables that are independently the most important, after controlling for other associated variables. We do this using a statistical model. We demonstrate the use of logistic regression, a particular form of regression when the health outcome of interest is binary; for example, dead/alive, recovered/not recovered. | |
dc.description.peerreviewed | Yes | |
dc.language | English | |
dc.language.iso | eng | |
dc.publisher | Springer Berlin Heidelberg | |
dc.publisher.place | Germany | |
dc.relation.ispartofbooktitle | Methods of Clinical Epidemiology | |
dc.relation.ispartofchapter | 10 | |
dc.relation.ispartofpagefrom | 141 | |
dc.relation.ispartofpageto | 163 | |
dc.subject.fieldofresearch | Bioinformatics | |
dc.subject.fieldofresearchcode | 060102 | |
dc.title | Modelling Binary Outcomes: Logistic Regression | |
dc.type | Book chapter | |
dc.type.description | B1 - Chapters | |
dc.type.code | B - Book Chapters | |
gro.hasfulltext | No Full Text | |
gro.griffith.author | Ware, Robert | |