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
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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