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QUESTION 6 Consider the following multiple regression model of domestic car sales (DCS) where: DCS = domestic car sales in units sold DCSP = domestic car sales price (in dollars) PR = prime rate as a percent (i.e., 10% would be entered as 10) Q2 = quarter 2 dummy variable Q3 = quarter 3 dummy variable Multiple Regression — Result Formula DCS = 3,000 - 0.09DCSP - 20.0PR + 293Q2 + 149Q3 Durbin Watson = 1.92 AIC = 492.5 MAPE = 5.30% BIC = 495.9 Adj R-Square = 75.64% SEE = 100 In the domestic car sales function, there is evidence of seasonality. How does the regression model show this evidence? With the Adj R-Square With the Durbin Watson statistic With the F-statistic With the SEE With the Q2 and Q3 variables QUESTION 7 A real estate builder wishes to determine how house size (House) is influenced by family income (Income), family size (Size), and education of the head of household (School). House size is measured in hundreds of square feet, income is measured in thousands of dollars, and education is measured in years. The builder randomly selected 50 families and ran a multiple regression. The regression statistics are below: R Square: 0.74 Adjusted R Square: 0.72 Standard Error: 5.195 Observations: 50 Coefficient Std. Error T-test P-Value Intercept -1.6335 5.8078 -0.281 0.7798 Income 0.4485 0.1137 3.9545 0.0003 Size 4.2615 0.8062 5.286 0.0001 School -0.6517 0.4319 -1.509 0.1383 Referring to the Real Estate Builder regression results, which of the independent variables in the model are statistically significant at the 95% confidence level? Income, Size Income, School Size, School Income, Size, School QUESTION 8 Which of the following is not correct about multicollinearity? It arises when we have two or more independent variables which are highly correlated. It arises when we have two or more independent variables which essentially measure the same effect on the dependent variable. It results from a correlation between error terms in successive time periods. It can result in a very good fit but poor forecasts.

          QUESTION 6
Consider the following multiple regression model of domestic car sales (DCS) where:
DCS = domestic car sales in units sold
DCSP = domestic car sales price (in dollars)
PR = prime rate as a percent (i.e., 10% would be entered as 10)
Q2 = quarter 2 dummy variable
Q3 = quarter 3 dummy variable
Multiple Regression — Result Formula
DCS = 3,000 - 0.09*DCSP - 20.0*PR + 293*Q2 + 149*Q3
Durbin Watson = 1.92
AIC = 492.5
MAPE = 5.30%
BIC = 495.9
Adj R-Square = 75.64%
SEE = 100

In the domestic car sales function, there is evidence of seasonality. How does the regression model show this evidence?
With the Adj R-Square
With the Durbin Watson statistic
With the F-statistic
With the SEE
With the Q2 and Q3 variables

QUESTION 7
A real estate builder wishes to determine how house size (House) is influenced by family income (Income), family size (Size), and education of the head of household (School). House size is measured in hundreds of square feet, income is measured in thousands of dollars, and education is measured in years. The builder randomly selected 50 families and ran a multiple regression. The regression statistics are below:
R Square: 0.74
Adjusted R Square: 0.72
Standard Error: 5.195
Observations: 50

Coefficient Std. Error T-test P-Value
Intercept -1.6335 5.8078 -0.281 0.7798
Income 0.4485 0.1137 3.9545 0.0003
Size 4.2615 0.8062 5.286 0.0001
School -0.6517 0.4319 -1.509 0.1383

Referring to the Real Estate Builder regression results, which of the independent variables in the model are statistically significant at the 95% confidence level?
Income, Size
Income, School
Size, School
Income, Size, School

QUESTION 8
Which of the following is not correct about multicollinearity?
It arises when we have two or more independent variables which are highly correlated.
It arises when we have two or more independent variables which essentially measure the same effect on the dependent variable.
It results from a correlation between error terms in successive time periods.
It can result in a very good fit but poor forecasts.

Added by Jessica G.

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