Literature DB >> 23594471

Introduction to statistical modelling: linear regression.

Mark Lunt1.   

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.
© 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.

Entities:  

Keywords:  goodness of fit; linear regression; linear regression diagnostics; linearity; normality; predicted value; regression coefficient; residual

Mesh:

Year:  2013        PMID: 23594471     DOI: 10.1093/rheumatology/ket146

Source DB:  PubMed          Journal:  Rheumatology (Oxford)        ISSN: 1462-0324            Impact factor:   7.580


  3 in total

1.  A Genome-Wide Association Study and Machine-Learning Algorithm Analysis on the Prediction of Facial Phenotypes by Genotypes in Korean Women.

Authors:  Hye-Young Yoo; Ki-Chan Lee; Ji-Eun Woo; Sung-Ha Park; Sunghoon Lee; Joungsu Joo; Jin-Sik Bae; Hyuk-Jung Kwon; Byoung-Jun Park
Journal:  Clin Cosmet Investig Dermatol       Date:  2022-03-11

2.  Machine Learning Improvements to Human Motion Tracking with IMUs.

Authors:  Pedro Manuel Santos Ribeiro; Ana Clara Matos; Pedro Henrique Santos; Jaime S Cardoso
Journal:  Sensors (Basel)       Date:  2020-11-09       Impact factor: 3.576

3.  The impact of timetable on student's absences and performance.

Authors:  Souad Larabi-Marie-Sainte; Roohi Jan; Ali Al-Matouq; Sara Alabduhadi
Journal:  PLoS One       Date:  2021-06-25       Impact factor: 3.240

  3 in total

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