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Multicollinearity and regression analysis

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.

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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.

Item Type: Conference or Workshop Item (Plenary Papers)
Additional Information: 5017/59610
Uncontrolled Keywords: regression analysis, Multicollinearity
Subjects: H Social Sciences > HA Statistics
H Social Sciences > HA Statistics > HA154 Statistical data
H Social Sciences > HA Statistics > HA29 Theory and method of social science statistics
H Social Sciences > HA Statistics > HA38 Registration of vital events. Vital records
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering > Department of Science
Depositing User: Assoc.Prof.Dr Jamal Daoud
Date Deposited: 28 Nov 2017 13:47
Last Modified: 28 Feb 2018 17:04
URI: http://irep.iium.edu.my/id/eprint/59610

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