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The following estimated regression equation was developed for a model involving two independent variables. y = 40.7 + 8.63x1 + 2.71x2 After x2 was dropped from the model, the least squares method was used to obtain an estimated regression equation

The following estimated regression equation was developed for a model involving two independent variables.
y = 40.7 + 8.63x1 + 2.71x2
After x2 was dropped from the model, the least squares method was used to obtain an estimated regression equation involving only x1 as an independent variable.
y = 42.0 + 9.01x1
a. Give an interpretation of the coefficient of x1 in both models.
b. Could multicollinearity explain why the coefficient of x1 differs in the two models? If so, how?

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a In the two independent variable case the coefficient of x 1 represents the expect…View the full answer


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Essentials Of Statistics For Business And Economics

Essentials Of Statistics For Business And Economics

ISBN: 9781305081598

7th Edition

Authors: David Anderson, Thomas Williams, Dennis Sweeney, Jeffrey Cam

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Posted Date: February 16, 2015 02:55:22
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