To detect multicollinearity and quantify its severity in a regression model we use a measure called____________
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Some of the common methods used for detecting multicollinearity include:
The analysis exhibits the signs of multicollinearity — such as, estimates of the coefficients vary from model to model.The t-tests for each of the individual slopes are non-significant (P > 0.05), but the overall F-test for testing all of the slopes are simultaneously 0 is significant (P < 0.05).The correlations among pairs of predictor variables are large.
Looking at correlations only among pairs of predictors, however, is limiting. It is possible that the pairwise correlations are small, and yet a linear dependence exists among three or even more variables, for example, if X3 = 2X1 + 5X2 + error, say. That's why many regression analysts often rely on what are called variance inflation factors (VIF) to help detect multicollinearity.
The analysis exhibits the signs of multicollinearity — such as, estimates of the coefficients vary from model to model.The t-tests for each of the individual slopes are non-significant (P > 0.05), but the overall F-test for testing all of the slopes are simultaneously 0 is significant (P < 0.05).The correlations among pairs of predictor variables are large.
Looking at correlations only among pairs of predictors, however, is limiting. It is possible that the pairwise correlations are small, and yet a linear dependence exists among three or even more variables, for example, if X3 = 2X1 + 5X2 + error, say. That's why many regression analysts often rely on what are called variance inflation factors (VIF) to help detect multicollinearity.
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