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by: Nikita Hendricks

Multicollinearity 2305

Marketplace > Baylor University > Business > 2305 > Multicollinearity
Nikita Hendricks


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About this Document

Prof. Turner
Class Notes
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This 2 page Class Notes was uploaded by Nikita Hendricks on Friday September 16, 2016. The Class Notes belongs to 2305 at Baylor University taught by Prof. Turner in Fall 2016. Since its upload, it has received 6 views. For similar materials see QBA in Business at Baylor University.


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Date Created: 09/16/16
Multicollinearity Multicollinearity When your (independent) variables correlate highly with each other (as they are measuring the same thing) High collinearity increases the likelihood of a type 2 error (so significant difference found when one exists) Multicollinearity Multicollinearity Reduce likelihood of Type 2 error To reduce Multicollinearity can combine highly correlated variables or reduce the number of variables check for multicollinearity What is the 5th step in the model building process? multicollinearity What is a condition that occurs when independent variables are correlated with one another? collinearity or intercorrelation What are two other names for multicollinearity? multicollinearity What exists in virtually all multiple regression models only when two or more independent variables are highly correlated When is the only time the problem of multicollinearity becomes serious? 1. sample coefficient may be far from the actual population parameter 2. T-Statistic will be small 3. interpretation of the coefficients What are the rules about correlations? variance inflator factor (VIF) Multicollinearity can be detected using what? 5.0 If any value of VIF exceeds ____ enough correlation exists between the independent variables to claim that multicollinearity is present in the regression model One or both of the correlated independent variables must be removed from the model How do you eliminate multicollinearity?


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