Multiple linear regression collinearity

Although subset selection along with multiple linear regression provides a means of reducing the number of variables studied, the method does not address the problems associated with collinearity. To achieve this, the... 

Equation [9] highlights the most frequent problem in multiple linear regression, namely, the matrix whose inverse must be calculated, (C C), may suffer from collinearity or singularity problems. The matrix may not possess... 

When multiple covariates are included in the regression model, the possibility for collinearity, which is sometimes called multicollinearity or ill-conditioning, among the predictors arises. The term collinear implies that there is correlation or linear dependencies among the independent variable. Entire books (Belsley, Kuh,... 

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