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      Introduction to statistical modelling: linear regression.

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          Abstract

          In many studies we wish to assess how a range of variables are associated with a particular outcome and also determine the strength of such relationships so that we can begin to understand how these factors relate to each other at a population level. Ultimately, we may also be interested in predicting the outcome from a series of predictive factors available at, say, a routine clinic visit. In a recent article in Rheumatology, Desai et al. did precisely that when they studied the prediction of hip and spine BMD from hand BMD and various demographic, lifestyle, disease and therapy variables in patients with RA. This article aims to introduce the statistical methodology that can be used in such a situation and explain the meaning of some of the terms employed. It will also outline some common pitfalls encountered when performing such analyses.

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          Author and article information

          Journal
          Rheumatology (Oxford)
          Rheumatology (Oxford, England)
          Oxford University Press (OUP)
          1462-0332
          1462-0324
          Jul 2015
          : 54
          : 7
          Affiliations
          [1 ] Arthritis Research UK Epidemiology Unit, University of Manchester, Manchester, UK.
          Article
          ket146
          10.1093/rheumatology/ket146
          23594471
          7dbf9b00-4265-4e86-aa34-ee4a8bcea69f
          © The Author 2013. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
          History

          goodness of fit,linear regression,linear regression diagnostics,linearity,normality,predicted value,regression coefficient,residual

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