Multicollinearity and regression analysis

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 e...

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Main Author:	Daoud, Jamal Ibrahim
Format:	Conference or Workshop Item
Language:	English English English
Published:	Faculty of Engineering, International Islamic University Malaysia 2017
Subjects:	HA Statistics HA154 Statistical data HA29 Theory and method of social science statistics HA38 Registration of vital events. Vital records
Online Access:	http://irep.iium.edu.my/59610/ http://irep.iium.edu.my/59610/ http://irep.iium.edu.my/59610/1/59610_Multicollinearity%20and%20Regression%20Analysis.pdf http://irep.iium.edu.my/59610/7/59610_Daoud_2017_J_Phys_Conf_Ser.pdf http://irep.iium.edu.my/59610/13/59610_Multicollinearity%20and%20Regression%20Analysis_scopus.pdf

id	iium-59610
recordtype	eprints
spelling	iium-596102018-02-28T09:04:31Z http://irep.iium.edu.my/59610/ Multicollinearity and regression analysis Daoud, Jamal Ibrahim HA Statistics HA154 Statistical data HA29 Theory and method of social science statistics HA38 Registration of vital events. Vital records 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. Increased standard errors means that the coefficients for some or all independent variables may be found to be significantly different from 0. In other words, by overinflating the standard errors, multicollinearity makes some variables statistically insignificant when they should be significant In this paper we want to focus on the multicollinearity and reasons and consequences on the reliability of the regression model. Faculty of Engineering, International Islamic University Malaysia 2017-08 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/59610/1/59610_Multicollinearity%20and%20Regression%20Analysis.pdf application/pdf en http://irep.iium.edu.my/59610/7/59610_Daoud_2017_J_Phys_Conf_Ser.pdf application/pdf en http://irep.iium.edu.my/59610/13/59610_Multicollinearity%20and%20Regression%20Analysis_scopus.pdf Daoud, Jamal Ibrahim (2017) Multicollinearity and regression analysis. In: The 4th International Conference on Mathematical Applications in Engineering 2017, 8-9 Aug 2017, Kuala Lumpur. http://www.iium.edu.my/icmae/17/index.php/publications/
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English English
topic	HA Statistics HA154 Statistical data HA29 Theory and method of social science statistics HA38 Registration of vital events. Vital records
spellingShingle	HA Statistics HA154 Statistical data HA29 Theory and method of social science statistics HA38 Registration of vital events. Vital records Daoud, Jamal Ibrahim Multicollinearity and regression analysis
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. Increased standard errors means that the coefficients for some or all independent variables may be found to be significantly different from 0. In other words, by overinflating the standard errors, multicollinearity makes some variables statistically insignificant when they should be significant In this paper we want to focus on the multicollinearity and reasons and consequences on the reliability of the regression model.
format	Conference or Workshop Item
author	Daoud, Jamal Ibrahim
author_facet	Daoud, Jamal Ibrahim
author_sort	Daoud, Jamal Ibrahim
title	Multicollinearity and regression analysis
title_short	Multicollinearity and regression analysis
title_full	Multicollinearity and regression analysis
title_fullStr	Multicollinearity and regression analysis
title_full_unstemmed	Multicollinearity and regression analysis
title_sort	multicollinearity and regression analysis
publisher	Faculty of Engineering, International Islamic University Malaysia
publishDate	2017
url	http://irep.iium.edu.my/59610/ http://irep.iium.edu.my/59610/ http://irep.iium.edu.my/59610/1/59610_Multicollinearity%20and%20Regression%20Analysis.pdf http://irep.iium.edu.my/59610/7/59610_Daoud_2017_J_Phys_Conf_Ser.pdf http://irep.iium.edu.my/59610/13/59610_Multicollinearity%20and%20Regression%20Analysis_scopus.pdf
first_indexed	2023-09-18T21:24:27Z
last_indexed	2023-09-18T21:24:27Z
_version_	1777412098213543936

Multicollinearity and regression analysis

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