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Multicollinearity and Regression Analysis

Journal of physics. Conference series, 2017-12, Vol.949 (1), p.12009 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;2017. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/949/1/012009

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  • Title:
    Multicollinearity and Regression Analysis
  • Author: Daoud, Jamal I.
  • Subjects: Independent variables ; Regression analysis ; Regression models ; Standard error
  • Is Part Of: Journal of physics. Conference series, 2017-12, Vol.949 (1), p.12009
  • Description: In regression analysis it is obvious to have a correlation between the response and predictor(s), but having correlation among predictors is something undesired. The number of predictors included in the regression model depends on many factors among which, historical data, experience, etc. At the end selection of most important predictors is something objective due to the researcher. Multicollinearity is a phenomena when two or more predictors are correlated, if this happens, the standard error of the coefficients will increase [8]. Increased standard errors means that the coefficients for some or all independent variables may be found to be significantly different from In other words, by overinflating the standard errors, multicollinearity makes some variables statistically insignificant when they should be significant. In this paper we focus on the multicollinearity, reasons and consequences on the reliability of the regression model.
  • Publisher: Bristol: IOP Publishing
  • Language: English
  • Identifier: ISSN: 1742-6588
    EISSN: 1742-6596
    DOI: 10.1088/1742-6596/949/1/012009
  • Source: IOP Publishing Free Content
    IOPscience (Open Access)
    GFMER Free Medical Journals
    ProQuest Central
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