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dc.contributor.authorWilliams, Gail
dc.contributor.authorWare, Robert S.
dc.contributor.editorS.A.R. Doi and G.M. Williams
dc.date.accessioned2017-11-08T12:00:45Z
dc.date.available2017-11-08T12:00:45Z
dc.date.issued2013
dc.identifier.isbn9783642371318
dc.identifier.doi10.1007/978-3-642-37131-8_10
dc.identifier.urihttp://hdl.handle.net/10072/337248
dc.description.abstractThis 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.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer Berlin Heidelberg
dc.publisher.placeGermany
dc.relation.ispartofbooktitleMethods of Clinical Epidemiology
dc.relation.ispartofchapter10
dc.relation.ispartofpagefrom141
dc.relation.ispartofpageto163
dc.subject.fieldofresearchBioinformatics
dc.subject.fieldofresearchcode060102
dc.titleModelling Binary Outcomes: Logistic Regression
dc.typeBook chapter
dc.type.descriptionB1 - Chapters
dc.type.codeB - Book Chapters
gro.hasfulltextNo Full Text
gro.griffith.authorWare, Robert


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