Question: I need help with Question 2&3 Question 2 The U.S. Census Bureau publishes data on factory orders for all manufacturing, durable goods, and nondurable goods

I need help with Question 2&3

Question 2

The U.S. Census Bureau publishes data on factory orders for all manufacturing, durable goods, and nondurable goods industries. Shown below are factory orders in the United States over a 13-year period ($ billion).

First, use the data to develop forecasts for years 6 through 13 using a 5-year moving average.

Then, use the data to develop forecasts for years 6 through 13 using a 5-year weighted moving average. Weight the most recent year by 6, the previous year by 4, the year before that by 2, and the other years by 1.

Answer the following questions:

  1. What is the forecast for year 13 based on the 5-year moving average?
  2. What is the forecast for year 13 based on the 5-year weighted moving average?
  3. What is the MAD for the moving average forecast?
  4. What is the MAD for the weighted moving average forecast?
  5. Which forecasting model is better?

Year

Factory orders

1

2,512.70

2

2,739.20

3

2,874.90

4

2,934.10

5

2,865.70

6

2,978.50

7

3,092.40

8

3,052.60

9

3,145.20

10

3,114.10

11

3,257.40

12

3,654.00

13

Question 3

The "Economic Report to the President of the United States" included data on the amounts of manufacturers' new and unfilled orders in millions of dollars. Shown here are the figures for new orders over a 21-year period.

Use the charting tool in Excel to develop a regression model to t the trend effects for the data. Use a linear model and then try a polynomial (order 2) model. Make sure the charts show the line formula and the r-squared value. Include both charts in your report. Then, answer the following question:

  • How well does either model t the data? Which model should be used for forecasting? Explain using the relevant metrics.

Year

Total Number of New Orders

1

55,022

2

55,921

3

64,182

4

76,003

5

87,327

6

85,139

7

99,513

8

115,109

9

116,251

10

121,547

11

123,321

12

141,200

13

162,140

14

168,420

15

171,250

16

176,355

17

195,204

18

209,389

19

237,025

20

272,544

21

293,475

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