Mathematics
QM662 University of Alabama Exponential Smoothing & Seasonal Indices HW

QM662

university of alabama

### Question Description

4. In #3, which method (if any) is most appropriate? (4)

a. Exponential smoothing.

b. Regression.

c. Regression with seasonal indices.

d. None of the above.

5. In #3, which of the following is most appropriate regarding sales? (4)

a. We should use all of the data in our model.

b. We should use only periods 5-16 in our model.

c. We should use only periods 9-16 in our model.

d. We should use only periods 13-16 in our model.

e. We should use only periods 1-12 in our model.

6. Refer to the Excel output on the final pages. Here, we are tracking the number of orders

placed by week for a 20-week period. The first set of output is for an exponential

smoothing model with

α

= 0.25. The second set of output is for a regression. Which of

the following is most appropriate? (3)

a. The exponential smoothing model is most appropriate.

b. The regression is most appropriate.

c. Another model would be more appropriate.

### Unformatted Attachment Preview

QM 662 Exam II 1. The table below features three forecasting models used on the same set of data. Model 1 Model 2 Model 3 Type Exponential Smoothing Regression Seasonal & Trend MSE 8755.3 4876.2 5945.8 Based solely on the information in this output, which of the following is the best answer? (5) a. b. c. d. e. The data set contains no trend or seasonality. The data set contains trend but no seasonality. The data set contains seasonality but no trend. The data set probably contains cyclicality. The data set contains both trend and seasonality. 2. In a forecasting application for 20 time periods, there are 10 negative errors and 10 positive errors. This indicates the model is performing well. (2) a. True b. False 3. Refer to the following graph: Quarterly Sales (in \$) 70000 60000 50000 40000 30000 20000 10000 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Which of the following apply? (8) a. b. c. d. The data contain a trend component. The data contain a seasonal component. The data ,contain a cyclical component. The data contain an irregular (random) component. 4. In #3, which method (if any) is most appropriate? (4) a. b. c. d. Exponential smoothing. Regression. Regression with seasonal indices. None of the above. 5. In #3, which of the following is most appropriate regarding sales? (4) a. b. c. d. e. We should use all of the data in our model. We should use only periods 5-16 in our model. We should use only periods 9-16 in our model. We should use only periods 13-16 in our model. We should use only periods 1-12 in our model. 6. Refer to the Excel output on the final pages. Here, we are tracking the number of orders placed by week for a 20-week period. The first set of output is for an exponential smoothing model with α = 0.25. The second set of output is for a regression. Which of the following is most appropriate? (3) a. The exponential smoothing model is most appropriate. b. The regression is most appropriate. c. Another model would be more appropriate. 7. The model with the lower MSE is always the most appropriate model. (2) a. True b. False 8. In a given application, we are using regression with seasonal indices. The regression model is y = 42 + 2.5t. The seasonal indices for quarters 1-4 are 0.85, 0.92, 0.98, and 1.25, respectively. The predicted value for period 20 is ___________. (5) 9. If our data contains seasonality but no trend, exponential smoothing is appropriate. (2) a. True b. False 10. Annual data can exhibit seasonality. (2) a. True b. False 11. We can assess quarterly seasonality with one year of data. (2) a. True b. False Week 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Orders 45 56 65 63 54 60 54 60 56 57 50 61 47 56 55 Forecast #N/A 45 47.75 52.0625 54.79688 54.59766 55.94824 55.46118 56.59589 56.44691 56.58519 54.93889 56.45417 54.09063 54.56797 Error #N/A 11 17.25 10.9375 -0.79688 5.402344 -1.94824 4.538818 -0.59589 0.553085 -6.58519 6.06111 -9.45417 1.909375 0.432031 Error^2 121 297.5625 119.6289 0.63501 29.18532 3.795648 20.60087 0.35508 0.305903 43.36467 36.73706 89.38128 3.645712 0.186651 16 17 18 19 20 52 57 58 61 47 54.67598 54.00698 54.75524 55.56643 56.92482 -2.67598 2.993017 3.244763 5.433572 -9.92482 7.160852 8.958153 10.52849 29.52371 98.50207 MSE = 48.47673 SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.139263 0.019394 -0.03508 5.524367 20 ANOVA df 1 18 19 SS 10.86466 549.3353 560.2 Coefficients 57.04211 -0.12782 Standard Error 2.566242 0.214226 Regression Residual Total Intercept Week MS 10.86466 30.51863 F 0.356001 t Stat 22.22787 -0.59666 P-value 1.54E-14 0.558166 Exponential Smoothing 70 60 Orders 50 40 30 Actual 20 Forecast 10 0 1 3 5 7 9 11 13 15 17 19 Week Significance F 0.558166112 ...
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Attached.

QM 662 Exam II

1. The table below features three forecasting models used on the same set of data.
Model 1

Model 2

Model 3

Type

Exponential
Smoothing

Regression

Seasonal & Trend

MSE

8755.3

4876.2

5945.8

Based solely on the information in this output, which of the following is the best answer? (5)
a.
b.
c.
d.
e.

The data set contains no trend or seasonality.
The data set contains trend but no seasonality.
The data set contains seasonality but no trend.
The data set probably contains cyclicality.
The data set contains both trend and seasonality.

2. In a forecasting application for 20 time periods, there are 10 negative errors and 10
positive errors. This indicates the model is performing well. (2)
a. True
b. False
3. Refer to the following graph:...

Carnegie Mellon University
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