Based solely on the information in this output, which of the following is the best answer? (5) 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 pro

I need an explanation for this Economics question to help me study.

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)

1. The data set contains no trend or seasonality.
2. The data set contains trend but no seasonality.
3. The data set contains seasonality but no trend.
4. The data set probably contains cyclicality.
5. The data set contains both trend and seasonality.
1. 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)
1. True
2. False
1. Refer to the following graph:

Which of the following apply?(8)

1. The data contain a trend component.
2. The data contain a seasonal component.
3. The data ,contain a cyclical component.
4. The data contain an irregular (random) component.
1. In #3, which method (if any) is most appropriate?(4)
1. Exponential smoothing.
2. Regression.
3. Regression with seasonal indices.
4. None of the above.
1. In #3, which of the following is most appropriate regarding sales?(4)
1. We should use all of the data in our model.
2. We should use only periods 5-16 in our model.
3. We should use only periods 9-16 in our model.
4. We should use only periods 13-16 in our model.
5. We should use only periods 1-12 in our model.
1. 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)
1. The exponential smoothing model is most appropriate.
2. The regression is most appropriate.
3. Another model would be more appropriate.
1. The model with the lower MSE is always the most appropriate model.(2)
1. True
2. False
1. 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)
1. If our data contains seasonality but no trend, exponential smoothing is appropriate.(2)
1. True
2. False
1. Annual data can exhibit seasonality.(2)
1. True
2. False
1. We can assess quarterly seasonality with one year of data.(2)
1. True
2. False
 Week Orders Forecast Error Error^2 1 45 #N/A #N/A 2 56 45 11 121 3 65 47.75 17.25 297.5625 4 63 52.0625 10.9375 119.6289 5 54 54.79688 -0.79688 0.63501 6 60 54.59766 5.402344 29.18532 7 54 55.94824 -1.94824 3.795648 8 60 55.46118 4.538818 20.60087 9 56 56.59589 -0.59589 0.35508 10 57 56.44691 0.553085 0.305903 11 50 56.58519 -6.58519 43.36467 12 61 54.93889 6.06111 36.73706 13 47 56.45417 -9.45417 89.38128 14 56 54.09063 1.909375 3.645712 15 55 54.56797 0.432031 0.186651 16 52 54.67598 -2.67598 7.160852 17 57 54.00698 2.993017 8.958153 18 58 54.75524 3.244763 10.52849 19 61 55.56643 5.433572 29.52371 20 47 56.92482 -9.92482 98.50207 MSE = 48.47673
 SUMMARY OUTPUT Regression Statistics Multiple R 0.139263 R Square 0.019394 Adjusted R Square -0.03508 Standard Error 5.524367 Observations 20 ANOVA df SS MS F Significance F Regression 1 10.86466 10.86466 0.356001 0.558166112 Residual 18 549.3353 30.51863 Total 19 560.2 Coefficients Standard Error t Stat P-value Intercept 57.04211 2.566242 22.22787 1.54E-14 Week -0.12782 0.214226 -0.59666 0.558166

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