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Multicollinearity. S. K. Bhaumik. Issues for discussion. 1. Definition. 2. Consequences. 3. Tests. 4. Some Remedial Measures. Perfect multicollinearity is the violation of Assumption 6 (no explanatory variable is a perfect linear function of any other explanatory variables). Due to large standard errors, the regression coefficients may not appear significant. Consequently, essential variables may be dropped. For example, to test Multicollinearity analysis is needed to test the predictive of selected forest fragmentation causative factors before applying the model, i.e., the WOE for the hypothesis test (H0: ?j = 0 versus H1: ?j ? 0), which produces an insignificant result. Condition Number and Condition Index. Each explanatory variable The most classic symptom of multicollinearity is very high value of R2. When we perform the overall test of goodness of fit of the model, the F statistic in.