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. Why would it be a bad idea to use the linear regression line to make forecasts when looking at the data? a The linear trend line does not capture the fact that, on average, sales go up. b The linear trend line does not capture the seasonality of the data. c Both a. and b. are correct. d None of the above.   Decomposition forecasting decomposes data into which two factors? a Slope and intercept b Trend and seasonality c Past and future data d Decom and position   In decomposition forecasting, the calculated seasonal index for the first bi-annual period is a 0.71 b 1.29 c 0.89 d 1.41   In decomposition forecasting, the calculated seasonal index for the second bi-annual period is a 0.89 b 0.71 c 1.41 d 1.29   Using only the regression line, the predicted value for the first bi-annual term of forecast period year 4 (obs=7) is a 15 b 16 c 17 d 14   Using decomposition forecasting, the predicted value for the first bi-annual term of forecast period year 4 (obs=7) is a 13.49 b 21.93 c 10.65 d 27.09   Using only the regression line, the predicted value for the second bi-annual term of forecast period year 5 (obs=10) is a 24 b 22 c 23 d 21   Using decomposition forecasting, the predicted value for the second bi-annual term of forecast period year 5 (obs=10) is a 13.49 b 16.33 c 27.09 d 21.93   The Mean Absolute Percentage Error (MAPE) for all the forecasts using decomposition forecasting and using the formula  is (forecast- actual)/actual is  a 0.40 b 0.10 c 0.30 d 0.20   The Mean Absolute Percentage Error (MAPE) for all the forecasts using only the regression line and using the formula  is (forecast-actual)/actual is  a 0.30 b 0.20 c 0.40 d 0.10

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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.

Why would it be a bad idea to use the linear regression line to make forecasts when looking at the data?

a

The linear trend line does not capture the fact that, on average, sales go up.

b

The linear trend line does not capture the seasonality of the data.

c

Both a. and b. are correct.

d

None of the above.

 

Decomposition forecasting decomposes data into which two factors?

a

Slope and intercept

b

Trend and seasonality

c

Past and future data

d

Decom and position

 

In decomposition forecasting, the calculated seasonal index for the first bi-annual period is

a

0.71

b

1.29

c

0.89

d

1.41

 

In decomposition forecasting, the calculated seasonal index for the second bi-annual period is

a

0.89

b

0.71

c

1.41

d

1.29

 

Using only the regression line, the predicted value for the first bi-annual term of forecast period year 4 (obs=7) is

a

15

b

16

c

17

d

14

 

Using decomposition forecasting, the predicted value for the first bi-annual term of forecast period year 4 (obs=7) is

a

13.49

b

21.93

c

10.65

d

27.09

 

Using only the regression line, the predicted value for the second bi-annual term of forecast period year 5 (obs=10) is

a

24

b

22

c

23

d

21

 

Using decomposition forecasting, the predicted value for the second bi-annual term of forecast period year 5 (obs=10) is

a

13.49

b

16.33

c

27.09

d

21.93

 

The Mean Absolute Percentage Error (MAPE) for all the forecasts using decomposition forecasting and using the formula  is (forecast- actual)/actual is 

a

0.40

b

0.10

c

0.30

d

0.20

 

The Mean Absolute Percentage Error (MAPE) for all the forecasts using only the regression line and using the formula  is (forecast-actual)/actual is 

a

0.30

b

0.20

c

0.40

d

0.10

 

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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