M. del Águila, and N. Benítez-Parejo. Allergologia et immunopathologia, 39 (3):
159-73(2011)6391<m:linebreak></m:linebreak>CI: Copyright (c) 2011; JID: 0370073; 2011/02/01 received; 2011/02/01 accepted; 2011/05/06 aheadofprint; ppublish;.
DOI: 10.1016/j.aller.2011.02.001
Abstract
In biomedical research it is common to find problems in which we wish to relate a response variable to one or more variables capable of describing the behaviour of the former variable by means of mathematical models. Regression techniques are used to this effect, in which an equation is determined relating the two variables. While such equations can have different forms, linear equations are the most widely used form and are easy to interpret. The present article describes simple and multiple linear regression models, how they are calculated, and how their applicability assumptions are checked. Illustrative examples are provided, based on the use of the freely accessible R program.
%0 Journal Article
%1 Aguila2011
%A del Águila, M M Rodríguez
%A Benítez-Parejo, N
%D 2011
%J Allergologia et immunopathologia
%K BiomedicalResearch BiomedicalResearch:methods LinearModels Models MultivariateAnalysis Statistical
%N 3
%P 159-73
%R 10.1016/j.aller.2011.02.001
%T Simple linear and multivariate regression models.
%U http://www.ncbi.nlm.nih.gov/pubmed/21530056
%V 39
%X In biomedical research it is common to find problems in which we wish to relate a response variable to one or more variables capable of describing the behaviour of the former variable by means of mathematical models. Regression techniques are used to this effect, in which an equation is determined relating the two variables. While such equations can have different forms, linear equations are the most widely used form and are easy to interpret. The present article describes simple and multiple linear regression models, how they are calculated, and how their applicability assumptions are checked. Illustrative examples are provided, based on the use of the freely accessible R program.
%@ 1578-1267; 0301-0546
@article{Aguila2011,
abstract = {In biomedical research it is common to find problems in which we wish to relate a response variable to one or more variables capable of describing the behaviour of the former variable by means of mathematical models. Regression techniques are used to this effect, in which an equation is determined relating the two variables. While such equations can have different forms, linear equations are the most widely used form and are easy to interpret. The present article describes simple and multiple linear regression models, how they are calculated, and how their applicability assumptions are checked. Illustrative examples are provided, based on the use of the freely accessible R program.},
added-at = {2023-02-03T11:44:35.000+0100},
author = {del Águila, M M Rodríguez and Benítez-Parejo, N},
biburl = {https://www.bibsonomy.org/bibtex/213e8ad2b879d44d64f08a0423a539f86/jepcastel},
city = {UCG Salud Publica y Medicina Preventiva, Hospital Virgen de las Nieves, Granada, Spain.},
doi = {10.1016/j.aller.2011.02.001},
interhash = {962c536a21c12749f8da9ff791128b68},
intrahash = {13e8ad2b879d44d64f08a0423a539f86},
isbn = {1578-1267; 0301-0546},
issn = {1578-1267},
journal = {Allergologia et immunopathologia},
keywords = {BiomedicalResearch BiomedicalResearch:methods LinearModels Models MultivariateAnalysis Statistical},
note = {6391<m:linebreak></m:linebreak>CI: Copyright (c) 2011; JID: 0370073; 2011/02/01 [received]; 2011/02/01 [accepted]; 2011/05/06 [aheadofprint]; ppublish;},
number = 3,
pages = {159-73},
pmid = {21530056},
timestamp = {2023-02-03T11:44:35.000+0100},
title = {Simple linear and multivariate regression models.},
url = {http://www.ncbi.nlm.nih.gov/pubmed/21530056},
volume = 39,
year = 2011
}