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QUESTION 1 In the equation, y = Bo + Bzxz + Bzxz + u, ßz is aln). O a. intercept parameter O b.slope parameter O dependent variable o d. independent variable

QUESTION 1 In the equation, y = Bo + Bzxz + Bzxz + u, ßz is aln). O a. intercept parameter O b.slope parameter O dependent variable o d. independent variable

Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)
Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)
14th Edition
ISBN:9781305506381
Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris
Publisher:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris
Chapter4A: Problems In Applying The Linear Regression Model
Section: Chapter Questions
Problem 2E
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QUESTION 1 In the equation, y = 8o + Bjx1 + 8zx2 + u, 8z is a(n) O a. intercept parameter O b. slope parameter O. dependent variable O d. independent variable QUESTION 2 If an independent variable in a multiple linear regression model is an exact linear combination of other independent variables, the model suffers from the problem of O a. perfect collinearity O b.heteroskedasticty O . homoskedasticity O d. omitted variable bias QUESTION 3 Which of the following is true of R 2? O a. R- usually decreases with an increase in the number of independent variables in a regression. O b.R2 shows what percentage of the total variation in the dependent variable, Y, is explained by the explanatory variables. OC.A low R2 indicates that the Ordinary Least Squares line fits the data well. O d. R² is also called the standard error of regression. QUESTION 4 We estimate the model Wage, = -2.91+0.568educ; + 0.033 exper; +0.115 tenure; by OLS, where wage is the hourly wage of a worker measured in dollars, educ measures the number of years of schooling, exper is years of labour market experience and tenure is the number of years at current employer. Suppose the R2 of this regression is 0.307, and that the number of observations is n=205. What can you conclude? O 62.9% (=205x0.307) of the total variation in wages is explained by the proposed model. O 30.7% of the total variation in wages is explained by the proposed model. O 59.3% of the total variation in wages is explained by the proposed model. O None of the above.
Transcribed Image Text:QUESTION 1 In the equation, y = 8o + Bjx1 + 8zx2 + u, 8z is a(n) O a. intercept parameter O b. slope parameter O. dependent variable O d. independent variable QUESTION 2 If an independent variable in a multiple linear regression model is an exact linear combination of other independent variables, the model suffers from the problem of O a. perfect collinearity O b.heteroskedasticty O . homoskedasticity O d. omitted variable bias QUESTION 3 Which of the following is true of R 2? O a. R- usually decreases with an increase in the number of independent variables in a regression. O b.R2 shows what percentage of the total variation in the dependent variable, Y, is explained by the explanatory variables. OC.A low R2 indicates that the Ordinary Least Squares line fits the data well. O d. R² is also called the standard error of regression. QUESTION 4 We estimate the model Wage, = -2.91+0.568educ; + 0.033 exper; +0.115 tenure; by OLS, where wage is the hourly wage of a worker measured in dollars, educ measures the number of years of schooling, exper is years of labour market experience and tenure is the number of years at current employer. Suppose the R2 of this regression is 0.307, and that the number of observations is n=205. What can you conclude? O 62.9% (=205x0.307) of the total variation in wages is explained by the proposed model. O 30.7% of the total variation in wages is explained by the proposed model. O 59.3% of the total variation in wages is explained by the proposed model. O None of the above.
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