Which of the following is NOT a method to address multicollinearity in regression analysis? Removing one of the correlated predictors Regularization techniques Transforming the predictors Increasing the number of predictors
Question
Which of the following is NOT a method to address multicollinearity in regression analysis?
Removing one of the correlated predictors Regularization techniques Transforming the predictors Increasing the number of predictors
Solution
The method that is NOT used to address multicollinearity in regression analysis is "Increasing the number of predictors". This is because adding more predictors can actually exacerbate the problem of multicollinearity, especially if the new predictors are correlated with the existing ones. The other methods - removing one of the correlated predictors, using regularization techniques, and transforming the predictors - are all valid ways to address multicollinearity.
Similar Questions
Which of the following is NOT a remedy for multicollinearity?Removing one of the correlated variables.Combining correlated variables.Increasing the sample size.Applying a logarithmic transformation
4.5 – One potential issue the analyst faces when using multiple linear regression analysis isthe multicollinearity of the independent variables. Verify whether or not multicollinearity existsamong the independent variables. This is done by examining the correlation between each ofthe independent variables. Think of this as a correlation matrix (must be included in yourdocument) which can be easily performed in Excel using the “Data Analysis” tool pack. If theindependent variables are highly correlated, then the analyst is unable to isolate the effect ofeach independent variable on the dependent variable. Thus, analysis essentially becomespointless
How does multicollinearity affect the stability of coefficient estimates in regression analysis? It makes the estimates more stable It makes the estimates less stable It has no effect on the stability of estimates It depends on the sample size
Assume you have two independent variables and you found that the coefficient of determination between the two independent variables is 0.20. Based on this information, there no reason to suspect the existence of collinearity. Use the Variance Inflationary Factor (VIF) to answer the question. True False
Assume you believe the demand for a good can be determined by the model:demand i =β 0 +β 1 po i +β 2 pc i +β 3 ps i +ϵ i where demandi measures demand for the good in units, poi measures price in $ for the good, pci measures price in $ for a complement good, psi measures price in $ for a secondary complement good. A researcher gathers some results and feels they should drop variables to solve multicollinearity in order to solve issues in their model. Based only on the residuals you believe the most likely issue is:ASpecification Error from wrong functional formBNo specification error but pure heteroskedasticityCNo specification error but multicollinearityDSpecification Error from omitted variables
Upgrade your grade with Knowee
Get personalized homework help. Review tough concepts in more detail, or go deeper into your topic by exploring other relevant questions.