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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| Format: | Proceeding Paper |
| Language: | English English English |
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Faculty of Engineering, International Islamic University Malaysia
2017
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| Online Access: | 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 |
| _version_ | 1848785340968468480 |
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| author | Daoud, Jamal Ibrahim |
| author_facet | Daoud, Jamal Ibrahim |
| author_sort | Daoud, Jamal Ibrahim |
| building | IIUM Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-14T16:51:36Z |
| format | Proceeding Paper |
| id | iium-59610 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English English |
| last_indexed | 2025-11-14T16:51:36Z |
| publishDate | 2017 |
| publisher | Faculty of Engineering, International Islamic University Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| 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 Proceeding Paper 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/ |
| 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 |
| title | Multicollinearity and regression analysis |
| title_full | Multicollinearity and regression analysis |
| title_fullStr | Multicollinearity and regression analysis |
| title_full_unstemmed | Multicollinearity and regression analysis |
| title_short | Multicollinearity and regression analysis |
| title_sort | multicollinearity and regression analysis |
| topic | HA Statistics HA154 Statistical data HA29 Theory and method of social science statistics HA38 Registration of vital events. Vital records |
| 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 |