REMEDY OF EFFECTS OF MULTICOLLINEARITY IN MULTIPLE LINEAR REGRESSION MODEL | ||||
| Fayoum Journal of Agricultural Research and Development | ||||
| Article 20, Volume 33, Issue 1, January 2019, Page 77-91 PDF (3.6 MB) | ||||
| Document Type: Research articles. | ||||
| DOI: 10.21608/fjard.2019.190382 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| M. A. Gad; H. F. M. Hussein | ||||
| Cent. Lab. for Design and Stat. Analysis Res., ARC | ||||
| Abstract | ||||
| Assuming that there is no complete linear relationship between the independent variables in the multiple linear regression models leads to the multicollinearity problem. Some undesirable effects, according to this problem, resulting imprecise estimators of the equation, and lower ability of the statistical model to predict the dependent variable in the future. The study aims at investigating the nature and causes of the multicollinearity problem, methods of detection and remedy of it using the Ridge regression method as an effective way of mitigating the effects of multicollinearity. The study found that the main effect of the multicollinearity problem is that it may change the signs, significance and estimators of the explanatory variables. Thus increasing the value of the coefficient of multiple correlation and the coefficient of determination at a value greater than their true value. Consequently the low confidence in the results obtained from the statistical analysis and the low predictability of the relationship between independent and dependent variables. The study recommended detecting multicollinearity and identify its causes, and remedy of it using Ridge regression approach. | ||||
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