January 2005
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1,977 Reads
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4,255 Citations
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Publications (1)
Citations (1)
... In the context of multiple linear regression, it violates the assumption that predictor variables should be independent of each other. Multicollinearity can have several detrimental effects on regression model, some of which includes; unreliable coefficient estimates, difficulty in interpretation, loss of predictor significance, increased standard errors (Kutner et al., 2005), instability of coefficients (Neter, et al., 1989), misleading variable importance (Belsley et al., 1980) over fitting (Hair et al., 2010). Given these challenges, understanding how to detect, diagnose, and mitigate multicollinearity is crucial for researchers and analysts working with multiple linear regression models. ...
- Citing Book
January 2005