# Assignment

Assignment

This Assignment requires you to use Excel. Make sure to use the Unit 5 Assignment template located in Doc Sharing when you turn in your answers.

Question 1

Determine the error for each of the following forecasts. Then, calculate MAD and MSE.

Period Value Forecast Error

1 202 — —

2 191 202

3 173 192

4 169 181

5 171 174

6 175 172

7 182 174

8 196 179

9 204 189

10 219 198

11 227 211

Question 2

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

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

Then, use these data to develop forecasts for the 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:

a) What is the forecast for year 13 based on the 5-year moving average?

b) What is the forecast for year 13 based on the 5-year weighted moving average?

c) What is the MAD for the moving average forecast?

d) What is the MAD for the weighted moving average forecast?

e) Which forecasting model is better?

Year Factory

Orders

($ billion)

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 3,111.10

9 3,222.20

10 3,555.00

11 4,221.50

12 4,551.20

13 4,137.00

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 ﬁt the trend effects for these 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 ﬁt 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

Question 3

Used Excel Charting to fit a linear trendline, including the formula and r-squared.

Question 3

Used Excel Charting to fit a polynomial trendline, including the formula and r-squared.

Question 3

Recommended the better model with justification.

Question 1

Shown below are rental and leasing revenue ﬁgures for ofﬁce machinery and equipment in the United States over a 7-year period according to the U.S. Census Bureau. Use these data and the regression tool in the data analysis tool pack to run a linear regression.

Based on the formula you get from the regression output, answer the following questions:

a) What is the forecast for the rental and leasing revenue for the year 2011?

b) How confident are you in this forecast? Explain your answer by citing the relevant metrics.

Year Rental and Leasing ($ millions)

2004 5,860

2005 6,632

2006 7,125

2007 6,000

2008 4,380

2009 3,326

2010 2,642

Question 2

Suppose a researcher gathered survey data from 19 employees and asked the employees to rate their job satisfaction on a scale from 0 to 100 (with 100 being perfectly satisﬁed). Suppose the following data represent the results of this survey. Assume that relationship with supervisor is rated on a scale from 0 to 50 (0 represents a poor relationship and 50 represents an excellent relationship); overall quality of the work environment is rated on a scale from 0 to 100 (0 represents poor work environment and 100 rep resents an excellent work environment); and opportunities for advancement is rated on a scale from 0 to 50 (0 represents no opportunities and 50 represents excellent opportunities).

Answer the following questions:

A) What is the regression formula?

B) How reliable do you think the estimates will be based on this formula? Explain your answer by citing the relevant metrics.

C) Are there any variables that do not appear to be good predictors of job satisfaction? How can you tell?

D) If a new employee reports that her relationship with her supervisor is 40, rates her opportunities for advancement to be at 30, finds the quality of the work environment to be at 75, and works 60 hours per week, what would you expect her job satisfaction score to be?

55 27 65 50 42

20 12 13 60 28

85 40 79 45 7

65 35 53 65 48

45 29 43 40 32

70 42 62 50 41

35 22 18 75 18

60 34 75 40 32

95 50 84 45 48

65 33 68 60 11

85 40 72 55 33

10 5 10 50 21

75 37 64 45 42

80 42 82 40 46

Question 3

50 31 46 60 48

90 47 95 55 30

75 36 82 70 39

45 20 42 40 22

65 32 73 55 12

Investment analysts generally believe the interest rate on bonds is inversely related to the prime interest rate for loans; that is, bonds perform well when lending rates are down and perform poorly when interest rates are up.

Use the following data to construct a scatter graph and then fit a regression line to the data.

Report the regression formula and the r-squared values from the chart (right click on the data points, select Add Trend line, and select options to show these metrics).

Do you think the bond rate can be predicted by the prime interest rate? Justify your answer using the relevant metrics.

Bond Rate Prime Interest Rate

5% 16%

12 6

9 8

15 4

7 7

This Assignment requires you to use Excel. Make sure to use the Unit 5 Assignment template located in Doc Sharing when you turn in your answers.

Question 1

Determine the error for each of the following forecasts. Then, calculate MAD and MSE.

Period Value Forecast Error

1 202 — —

2 191 202

3 173 192

4 169 181

5 171 174

6 175 172

7 182 174

8 196 179

9 204 189

10 219 198

11 227 211

Question 2

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

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

Then, use these data to develop forecasts for the 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:

a) What is the forecast for year 13 based on the 5-year moving average?

b) What is the forecast for year 13 based on the 5-year weighted moving average?

c) What is the MAD for the moving average forecast?

d) What is the MAD for the weighted moving average forecast?

e) Which forecasting model is better?

Year Factory

Orders

($ billion)

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 3,111.10

9 3,222.20

10 3,555.00

11 4,221.50

12 4,551.20

13 4,137.00

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 ﬁt the trend effects for these 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 ﬁt 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

Question 3

Used Excel Charting to fit a linear trendline, including the formula and r-squared.

Question 3

Used Excel Charting to fit a polynomial trendline, including the formula and r-squared.

Question 3

Recommended the better model with justification.

Question 1

Shown below are rental and leasing revenue ﬁgures for ofﬁce machinery and equipment in the United States over a 7-year period according to the U.S. Census Bureau. Use these data and the regression tool in the data analysis tool pack to run a linear regression.

Based on the formula you get from the regression output, answer the following questions:

a) What is the forecast for the rental and leasing revenue for the year 2011?

b) How confident are you in this forecast? Explain your answer by citing the relevant metrics.

Year Rental and Leasing ($ millions)

2004 5,860

2005 6,632

2006 7,125

2007 6,000

2008 4,380

2009 3,326

2010 2,642

Question 2

Suppose a researcher gathered survey data from 19 employees and asked the employees to rate their job satisfaction on a scale from 0 to 100 (with 100 being perfectly satisﬁed). Suppose the following data represent the results of this survey. Assume that relationship with supervisor is rated on a scale from 0 to 50 (0 represents a poor relationship and 50 represents an excellent relationship); overall quality of the work environment is rated on a scale from 0 to 100 (0 represents poor work environment and 100 rep resents an excellent work environment); and opportunities for advancement is rated on a scale from 0 to 50 (0 represents no opportunities and 50 represents excellent opportunities).

Answer the following questions:

A) What is the regression formula?

B) How reliable do you think the estimates will be based on this formula? Explain your answer by citing the relevant metrics.

C) Are there any variables that do not appear to be good predictors of job satisfaction? How can you tell?

D) If a new employee reports that her relationship with her supervisor is 40, rates her opportunities for advancement to be at 30, finds the quality of the work environment to be at 75, and works 60 hours per week, what would you expect her job satisfaction score to be?

55 27 65 50 42

20 12 13 60 28

85 40 79 45 7

65 35 53 65 48

45 29 43 40 32

70 42 62 50 41

35 22 18 75 18

60 34 75 40 32

95 50 84 45 48

65 33 68 60 11

85 40 72 55 33

10 5 10 50 21

75 37 64 45 42

80 42 82 40 46

Question 3

50 31 46 60 48

90 47 95 55 30

75 36 82 70 39

45 20 42 40 22

65 32 73 55 12

Investment analysts generally believe the interest rate on bonds is inversely related to the prime interest rate for loans; that is, bonds perform well when lending rates are down and perform poorly when interest rates are up.

Use the following data to construct a scatter graph and then fit a regression line to the data.

Report the regression formula and the r-squared values from the chart (right click on the data points, select Add Trend line, and select options to show these metrics).

Do you think the bond rate can be predicted by the prime interest rate? Justify your answer using the relevant metrics.

Bond Rate Prime Interest Rate

5% 16%

12 6

9 8

15 4

7 7

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