Consider the following estimated regression equation, with standard errors of the coefficients as indicated:
Salesi = 10.0 + 1.25 R&Di + 1.0 ADVi ? 2.0 COMPi + 8.0 CAPi
where the standard error for R&D is 0.45, the standard error for ADV is 2.2, the standard error for COMP 0.63, and the standard error for CAP is 2.5.
Sales are in millions of dollars. An analyst is given the following predictions on the independent variables: R&D = 5, ADV = 4, COMP = 10, and CAP = 40.
The predicted level of sales is closest to:
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Consider the following estimated regression equation, with standard errors of the coefficients as indicated:
Salesi = 10.0 + 1.25 R&Di + 1.0 ADVi ? 2.0 COMPi + 8.0 CAPi
where the standard error for R&D is 0.45, the standard error for ADV is 2.2, the standard error for COMP 0.63, and the standard error for CAP is 2.5.
Sales are in millions of dollars. An analyst is given the following predictions on the independent variables: R&D = 5, ADV = 4, COMP = 10, and CAP = 40.
The predicted level of sales is closest to:
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Predicted sales
= $10 + 1.25 (5) + 1.0 (4) ?2.0 (10) + 8 (40)
= 10 + 6.25 + 4 ? 20 + 320 = $320.25
Consider the following estimated regression equation, with calculated t-statistics of the estimates as indicated:
AUTOt = 10.0 + 1.25 PIt + 1.0 TEENt – 2.0 INSt
with a PI calculated t-statstic of 0.45, a TEEN calculated t-statstic of 2.2, and an INS calculated t-statstic of 0.63.
The equation was estimated over 40 companies. The predicted value of AUTO if PI is 4, TEEN is 0.30, and INS = 0.6 is closest to:
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Consider the following estimated regression equation, with calculated t-statistics of the estimates as indicated:
AUTOt = 10.0 + 1.25 PIt + 1.0 TEENt – 2.0 INSt
with a PI calculated t-statstic of 0.45, a TEEN calculated t-statstic of 2.2, and an INS calculated t-statstic of 0.63.
The equation was estimated over 40 companies. The predicted value of AUTO if PI is 4, TEEN is 0.30, and INS = 0.6 is closest to:
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Predicted AUTO
= 10 + 1.25 (4) + 1.0 (0.30) – 2.0 (0.6)
= 10 + 5 + 0.3 – 1.2
= 14.10
Wanda Brunner, CFA, is trying to calculate a 95% confidence interval for a regression equation based on the following information:
Coefficient
Standard Error
Intercept
-10.60%
1.357
DR
0.52
0.023
CS
0.32
0.025
What are the lower and upper bounds for variable DR?
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Wanda Brunner, CFA, is trying to calculate a 95% confidence interval for a regression equation based on the following information:
Coefficient
Standard Error
Intercept
-10.60%
1.357
DR
0.52
0.023
CS
0.32
0.025
What are the lower and upper bounds for variable DR?
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The critical t-value is 2.02 at the 95% confidence level (two tailed test). The estimated slope coefficient is 0.52 and the standard error is 0.023. The 95% confidence interval is 0.52 ± (2.02)(0.023) = 0.52 ± (0.046) = 0.474 to 0.566.
Wanda Brunner, CFA, is trying to calculate a 99% confidence interval for a regression equation based on the following information:
Coefficient
Standard Error
Intercept
-10.60%
1.357
DR
0.52
0.023
CS
0.32
0.025
What are the lower and upper bounds for variable CS?
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Wanda Brunner, CFA, is trying to calculate a 99% confidence interval for a regression equation based on the following information:
Coefficient
Standard Error
Intercept
-10.60%
1.357
DR
0.52
0.023
CS
0.32
0.025
What are the lower and upper bounds for variable CS?
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The critical t-value is 2.42 at the 99% confidence level (two tailed test). The estimated slope coefficient is 0.32 and the standard error is 0.025. The 99% confidence interval is 0.32 ± (2.42)(0.025) = 0.32 ± (0.061) = 0.260 to 0.381.
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