I need an explanation for this Economics question to help me study.
 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)
 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.
 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)
 True
 False
 Refer to the following graph:
Which of the following apply?(8)
 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.
 In #3, which method (if any) is most appropriate?(4)
 Exponential smoothing.
 Regression.
 Regression with seasonal indices.
 None of the above.
 In #3, which of the following is most appropriate regarding sales?(4)
 We should use all of the data in our model.
 We should use only periods 516 in our model.
 We should use only periods 916 in our model.
 We should use only periods 1316 in our model.
 We should use only periods 112 in our model.
 Refer to the Excel output on the final pages.Here, we are tracking the number of orders placed by week for a 20week 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)
 The exponential smoothing model is most appropriate.
 The regression is most appropriate.
 Another model would be more appropriate.
 The model with the lower MSE is always the most appropriate model.(2)
 True
 False
 In a given application, we are using regression with seasonal indices.The regression model is y = 42 + 2.5t.The seasonal indices for quarters 14 are 0.85, 0.92, 0.98, and 1.25, respectively.The predicted value for period 20 is ___________.(5)
 If our data contains seasonality but no trend, exponential smoothing is appropriate.(2)
 True
 False
 Annual data can exhibit seasonality.(2)
 True
 False
 We can assess quarterly seasonality with one year of data.(2)
 True
 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 
Pvalue 

Intercept 
57.04211 
2.566242 
22.22787 
1.54E14 

Week 
0.12782 
0.214226 
0.59666 
0.558166 
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