A company reports bi-annual (twice a year) sales data. The sales data for the last three years is shown in below Table. The residual sum of squares of the regression is RSS = a 50 b 70 c 120 d 20

ENGR.ECONOMIC ANALYSIS
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Chapter1: Making Economics Decisions
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Questions 1-30 refer to the following scenario: A company reports bi-annual (twice a year) sales data. The sales data for the last three years is shown in below Table.

The residual sum of squares of the regression is RSS =

a

50

b

70

c

120

d

20

 

The total sum of squares of the regression is TSS =

a

20

b

120

c

70

d

50

 

The R-squared of the regression is R2=

a

0.78

b

0.68

c

0.58

d

0.88

 

The mean sum of squares of the regression is Mean ESS = 

a

20

b

50

c

120

d

70

 

The mean sum of squares of the residuals is Mean RSS = 

a

12.5

b

15.0

c

7.5

d

10.0

 

You want to test whether the regression in its entirety explains something which is different from zero. For this purpose you use a

a

Chi-squared test

b

T-test

c

E-test

d

F-test

 

The value for testing the explanatory significance of the entirety of the regression is

a

7.6

b

5.6

c

6.6

d

8.6

 

The probability of obtaining your regression results from a hypothesized population where there is no relationship between the two variables is somewhere between

a

1% and 2%

b

10% and 20%

c

2% and 5%

d

5% and 10%

 

In a regression with one intercept and only one slope coefficient, which two values will be the same?

a

The p-value for the F-ratio and the p-value for the t-score of the intercept

b

The F-ratio and the t-score for theslope 

c

The p-value for the F-ratio and the p-value for the t-score of the slope coefficient 

d

The F-ratio and the t-score for the intercept

 

In the regression, how many observations have a residual of zero (are "direct hits")?

a

1

b

2

c

4

d

0

 

 

Year 1
Year 2
Year 3
Forecast
Year 4
Forecast
Year 5
Sales
25
24
23
22
21
20
19
18
17
16
15
14
13-
12
11
10
9
00
7
6
5
4
3
2
1
1
2
3
Bi-
Annual
Term
1
2
1
2
1
2
1
2
1
4
2
5
Obs Sales
("Xi") ("Yi")
1
2
3
4
5
609
7
8
10
7 8
6
3
13
9
16
13
10
10
XiYi Xi²(X-X₁²Ŷ (Y-Ŷ)² Y/Ŷ
Obs
Regression
Residual
Total
df S.S.
ANOVA
Seasonal Decomp.
Indices Forecast
Mean S.S.
Abs.
%
Errror
Significance
Note: In order to answer all questions, I recommend that
you fill out the above tables first. Please round all
answers to two digits only (e.g. 1.23).
Transcribed Image Text:Year 1 Year 2 Year 3 Forecast Year 4 Forecast Year 5 Sales 25 24 23 22 21 20 19 18 17 16 15 14 13- 12 11 10 9 00 7 6 5 4 3 2 1 1 2 3 Bi- Annual Term 1 2 1 2 1 2 1 2 1 4 2 5 Obs Sales ("Xi") ("Yi") 1 2 3 4 5 609 7 8 10 7 8 6 3 13 9 16 13 10 10 XiYi Xi²(X-X₁²Ŷ (Y-Ŷ)² Y/Ŷ Obs Regression Residual Total df S.S. ANOVA Seasonal Decomp. Indices Forecast Mean S.S. Abs. % Errror Significance Note: In order to answer all questions, I recommend that you fill out the above tables first. Please round all answers to two digits only (e.g. 1.23).
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