Collinearity Diagnostics Table

Multiple Regression

Collinearity Diagnostics Table

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Figure 14.6: Tables Menu

This displays the table shown in Figure 14.7.

Figure 14.7: Collinearity Diagnostics Table

When an explanatory variable is nearly a linear combination of other explanatory variables in the model, the estimates of the coefficients in the regression model are unstable and have high standard errors. This problem is called collinearity. The Collinearity Diagnostics table is calculated using the eigenstructure of the X'X matrix. See Chapter 13, "Fitting Curves," for a complete explanation.

A collinearity problem exists when a component associated with a high condition index contributes strongly to the variance of two or more variables. The highest condition number in this table is 17.0416. Belsley, Kuh, and Welsch (1980) propose that a condition index of 30 to 100 indicates moderate to strong collinearity.

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