An economist is in the process of developing a model to predict the price of gold (y). She believes that the two most important variables are the price of a barrel of oil (x2) and the interest rate (x3) as well as inflation (x4). She proposes the model a multiple regression. A random sample of 20 daily observations was taken. The computer output is shown below. The fitted regression result is: ŷ = 115.6+22.3x2+14.7x3+1.36x4 Coefficient Standard Error of Coefficient Constant 115.6 X2 X3 X4 22.3 7.1 A 14.7 6.3 2.333 1.36 B 2.615 R-Square = C Source of Variation DF Sum of Mean F-value Squares Squares Regression Error Total 3 8661 2887.0 E 16 6971 D 19 15632

Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)
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Chapter3: Straight Lines And Linear Functions
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  1. What are the values in A, B, C, D, and E, write out the calculation steps?
  2. Do these results allow us at the 5% significance level to conclude that the model is useful in predicting

the price of gold? 

3. Is there sufficient evidence at the 1% significance level to conclude that the price of a barrel of oil

and the price of gold are significantly related? Interpret the coefficient b2 .

An economist is in the process of developing a model to predict the price of gold (y). She
believes that the two most important variables are the price of a barrel of oil (x2) and the
interest rate (x3) as well as inflation (x4). She proposes the model a multiple regression. A
random sample of 20 daily observations was taken. The computer output is shown below.
The fitted regression result is: ŷ = 115.6+22.3x2+14.7x3+1.36x4
Coefficient Standard Error of
Coefficient
Constant
115.6
X2
X3
X4
22.3
7.1
A
14.7
6.3
2.333
1.36
B
2.615
R-Square
=
C
Source of Variation
DF Sum of Mean
F-value
Squares
Squares
Regression
Error
Total
3
8661
2887.0
E
16
6971
D
19
15632
Transcribed Image Text:An economist is in the process of developing a model to predict the price of gold (y). She believes that the two most important variables are the price of a barrel of oil (x2) and the interest rate (x3) as well as inflation (x4). She proposes the model a multiple regression. A random sample of 20 daily observations was taken. The computer output is shown below. The fitted regression result is: ŷ = 115.6+22.3x2+14.7x3+1.36x4 Coefficient Standard Error of Coefficient Constant 115.6 X2 X3 X4 22.3 7.1 A 14.7 6.3 2.333 1.36 B 2.615 R-Square = C Source of Variation DF Sum of Mean F-value Squares Squares Regression Error Total 3 8661 2887.0 E 16 6971 D 19 15632
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