Aplia3 Multiple Regress × p-Cengage Lea × Chegg Study Guided S שx Aplia3 Multip

Question

Aplia3 Multiple Regress × p-Cengage Lea × Chegg Study Guided S שx Aplia3 Multiple RX Mind cng.cengage.com/static/nb/ui/index.html?nbId=623328&nbNodeld;:229149248&deploymentld;:5423002017005116793491468529&eISBN;:9781305404236#|&parentld;:229149283 Open link in new tab Open link in new window Open link in incognito window Aplia3: Multiple Regression Analysis: Estimation Due on Tomorrow 1159 PM CDT Back to Assignment Save link a... Copy link address Inspect Attempts Average: 1 8. MLR.3 No perfect collinearity Ctri Shift+ Supposs you and a reearch partnar are intarssted in studying tha detaminants of wagas. Your partner collects a ampla of 1000 obsarvations and proposes using the following multiple regression model where * wage = annual salary . educ years of formal education exper years of work experience heightinheighc, in inches . heightcm- haight, in cancimacara . observations 1.000 A-Z True or False: Based on the model specification and the number of observations, this model suffers from perfect col inearity. Falae Grade It Now Seve & Continue Consnue thout saving 10:10 PM Type here to seardh 10/2/20172

Explanation / Answer

collinearity or multicollinearity is the phenomenon where one or more explanatory variables are linearely dependent.
here heightin and heightcm is very much linearly related.
perfect multicollinearity: linear dependence between all the explanatory variables .
but here , educ and expr may not be related and they are definitely not related to heightin and heightcm, thus we cannot conclude there is perfect collinearity. hence the statement is false.
there is collinearity in the model , as heightin and heightcm are perfectly related but not perfect collinearity.

but if the model consisted of only heightin and heightcm then we could have concluded that the model has perfect collinearity.

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