Collinearity. The Problem of Large Correlations Among the Independent Variables. What is collinearity? Why is it a problem?. How do I know if I’ve got it? What can I do about it?. Skill Set. Collinearity Defined.
Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
The Problem of Large Correlations Among the Independent Variables
Why is it a problem?
How do I know if I’ve got it?
What can I do about it?Skill Set
1. Variance Inflation Factor (VIF).
Standard error of the b weight with 2 IVs:
Sampling Variance of b weight
Standard Error with k predictors:
Large values of VIF are trouble. Some say values > 10 are high.
Small values are trouble. Maybe .10?
Lambda is an eigenvalue.
Number refers to a linear combination of the predictors.
Eigenvalue refers to the variance of that combination.
Collinearity is spotted by finding 2 or more variables that have large proportions of variance (.50 or more) that correspond to large condition indices. A rule of thumb is to label as large those condition indices in the range of 30 or larger. No apparent problem here.
.868Condition Index (2)
The last condition index (15.128) is highly associated with X2 and X3. The b weights for X2 and X3 are probably not well estimated.