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Multicollinearity refers to the existence of correlation among the independent variables in a multiple regression model. Discuss how multicollinearity can impact your regression analysis. How do you indentify it? What do you do in response to identifying a multicollinearity problem?

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Impact on Regression Analysis
If your goal is simply to predict Y from a set of X variables, then multicollinearity is not a problem. The predictions of Y will still be accurate.

If your goal is to understand how the various X variables impact Y, then multicollinearity is a big problem. One problem is that the individual P values can be misleading (a P value can be high, even though the variable is important). The second problem is that the confidence intervals on the regression coefficients will be very wide. The confidence intervals may even include zero, which means you can't even be confident whether an increase in the X value is ...

Solution Summary

Help is given with a regression analysis.