How does this forecast differ from that using employment as the independent variable? What is the coefficient of determination for this scenario and what does it imply?
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Using data in the below in class example,
Questions:
- How does this forecast differ from that using employment as the independent variable?
- What is the coefficient of determination for this scenario and what does it imply?
|
|
|
Service Area |
|
|
Year |
GA Operations (Y) |
Employment (X1) |
Effective Buying Income (X2) |
|
2000 |
22,513 |
45,714 |
$8,433 |
|
2001 |
23,768 |
48,601 |
$9,207 |
|
2002 |
21,838 |
51,314 |
$10,106 |
|
2003 |
25,904 |
54,107 |
$10,457 |
|
2004 |
26,208 |
57,848 |
$11,055 |
|
2005 |
26,946 |
56,022 |
$11,016 |
|
2006 |
27,977 |
56,317 |
$11,399 |
|
2007 |
27,294 |
58,757 |
$11,883 |
|
2008 |
28,647 |
61,556 |
$12,119 |
|
2009 |
29,270 |
62,341 |
$12,213 |
|
Forecast |
|||
|
2010 |
66,533 |
$13,081 |
|
|
2015 |
73,491 |
$14,609 |
|
|
2020 |
82,189 |
$16,525 |
|
|
2030 |
99,584 |
$20,369 |
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- The file P13_42.xlsx contains monthly data on consumer revolving credit (in millions of dollars) through credit unions. a. Use these data to forecast consumer revolving credit through credit unions for the next 12 months. Do it in two ways. First, fit an exponential trend to the series. Second, use Holts method with optimized smoothing constants. b. Which of these two methods appears to provide the best forecasts? Answer by comparing their MAPE values.Under what conditions might a firm use multiple forecasting methods?The Baker Company wants to develop a budget to predict how overhead costs vary with activity levels. Management is trying to decide whether direct labor hours (DLH) or units produced is the better measure of activity for the firm. Monthly data for the preceding 24 months appear in the file P13_40.xlsx. Use regression analysis to determine which measure, DLH or Units (or both), should be used for the budget. How would the regression equation be used to obtain the budget for the firms overhead costs?
- The file P13_22.xlsx contains total monthly U.S. retail sales data. While holding out the final six months of observations for validation purposes, use the method of moving averages with a carefully chosen span to forecast U.S. retail sales in the next year. Comment on the performance of your model. What makes this time series more challenging to forecast?The file P13_29.xlsx contains monthly time series data for total U.S. retail sales of building materials (which includes retail sales of building materials, hardware and garden supply stores, and mobile home dealers). a. Is seasonality present in these data? If so, characterize the seasonality pattern. b. Use Winters method to forecast this series with smoothing constants = = 0.1 and = 0.3. Does the forecast series seem to track the seasonal pattern well? What are your forecasts for the next 12 months?The owner of a restaurant in Bloomington, Indiana, has recorded sales data for the past 19 years. He has also recorded data on potentially relevant variables. The data are listed in the file P13_17.xlsx. a. Estimate a simple regression equation involving annual sales (the dependent variable) and the size of the population residing within 10 miles of the restaurant (the explanatory variable). Interpret R-square for this regression. b. Add another explanatory variableannual advertising expendituresto the regression equation in part a. Estimate and interpret this expanded equation. How does the R-square value for this multiple regression equation compare to that of the simple regression equation estimated in part a? Explain any difference between the two R-square values. How can you use the adjusted R-squares for a comparison of the two equations? c. Add one more explanatory variable to the multiple regression equation estimated in part b. In particular, estimate and interpret the coefficients of a multiple regression equation that includes the previous years advertising expenditure. How does the inclusion of this third explanatory variable affect the R-square, compared to the corresponding values for the equation of part b? Explain any changes in this value. What does the adjusted R-square for the new equation tell you?
- Scenario 3 Ben Gibson, the purchasing manager at Coastal Products, was reviewing purchasing expenditures for packaging materials with Jeff Joyner. Ben was particularly disturbed about the amount spent on corrugated boxes purchased from Southeastern Corrugated. Ben said, I dont like the salesman from that company. He comes around here acting like he owns the place. He loves to tell us about his fancy car, house, and vacations. It seems to me he must be making too much money off of us! Jeff responded that he heard Southeastern Corrugated was going to ask for a price increase to cover the rising costs of raw material paper stock. Jeff further stated that Southeastern would probably ask for more than what was justified simply from rising paper stock costs. After the meeting, Ben decided he had heard enough. After all, he prided himself on being a results-oriented manager. There was no way he was going to allow that salesman to keep taking advantage of Coastal Products. Ben called Jeff and told him it was time to rebid the corrugated contract before Southeastern came in with a price increase request. Who did Jeff know that might be interested in the business? Jeff replied he had several companies in mind to include in the bidding process. These companies would surely come in at a lower price, partly because they used lower-grade boxes that would probably work well enough in Coastal Products process. Jeff also explained that these suppliers were not serious contenders for the business. Their purpose was to create competition with the bids. Ben told Jeff to make sure that Southeastern was well aware that these new suppliers were bidding on the contract. He also said to make sure the suppliers knew that price was going to be the determining factor in this quote, because he considered corrugated boxes to be a standard industry item. Is Ben Gibson acting legally? Is he acting ethically? Why or why not?Scenario 3 Ben Gibson, the purchasing manager at Coastal Products, was reviewing purchasing expenditures for packaging materials with Jeff Joyner. Ben was particularly disturbed about the amount spent on corrugated boxes purchased from Southeastern Corrugated. Ben said, I dont like the salesman from that company. He comes around here acting like he owns the place. He loves to tell us about his fancy car, house, and vacations. It seems to me he must be making too much money off of us! Jeff responded that he heard Southeastern Corrugated was going to ask for a price increase to cover the rising costs of raw material paper stock. Jeff further stated that Southeastern would probably ask for more than what was justified simply from rising paper stock costs. After the meeting, Ben decided he had heard enough. After all, he prided himself on being a results-oriented manager. There was no way he was going to allow that salesman to keep taking advantage of Coastal Products. Ben called Jeff and told him it was time to rebid the corrugated contract before Southeastern came in with a price increase request. Who did Jeff know that might be interested in the business? Jeff replied he had several companies in mind to include in the bidding process. These companies would surely come in at a lower price, partly because they used lower-grade boxes that would probably work well enough in Coastal Products process. Jeff also explained that these suppliers were not serious contenders for the business. Their purpose was to create competition with the bids. Ben told Jeff to make sure that Southeastern was well aware that these new suppliers were bidding on the contract. He also said to make sure the suppliers knew that price was going to be the determining factor in this quote, because he considered corrugated boxes to be a standard industry item. As the Marketing Manager for Southeastern Corrugated, what would you do upon receiving the request for quotation from Coastal Products?The file P13_26.xlsx contains the monthly number of airline tickets sold by the CareFree Travel Agency. a. Create a time series chart of the data. Based on what you see, which of the exponential smoothing models do you think will provide the best forecasting model? Why? b. Use simple exponential smoothing to forecast these data, using a smoothing constant of 0.1. c. Repeat part b, but search for the smoothing constant that makes RMSE as small as possible. Does it make much of an improvement over the model in part b?
- The following data show the number of liters of gasoline sold by Mackrin's in Colorado for the past 12 weeks. Which forecasting model would you recommend to Mackrin's manager? Why? What demand forecast would you recommend for week 13? Week Actual Demand (1000 liters) Forecasting (1000 liters) FSMA /A-F/ FWMA /A-F/ FES, α=0.3 /A-F/ 1 17 2 21 17 4 3 19 18.2 0.8 4 23 19 4 18.44 4.56 5 18 21 3 20.8 2.8 19.81 1.81 6 16 20 4 20 4 19.3 3.3 7 20 19 1 18.3 2.3 18.31 1.69 8 18 18 0 18.7 0.7 18.82 0.82 9 22 18 4 18.2 4.2 18.6 3.4 10 20 20 0 19.8 0.2 19.62 0.38 11 15 20 5 20.2 5.2 19.73 4.73 12 22 19 3 18.2 3.8 18.3 3.7 13 19 19.5 19.41 MAD 2.67 2.9 2.7 MSE 10.22…The table below shows the demand for a particular brand of microwave oven in a department store. Month 1 Demand27 31 29 36 35 42 a) Calculate a two-month weighted moving average for all possible months with weight assigned to oldest in order 04, 0.6. b) Apply exponential smoothing with a smoothing constant of 0.7 to derive a forecast for all possible months. c) Which of the two methods would you use to forecast and why?The following table contains the demand from the last 10 months. Calculate the exponential smoothing with trend forecast for these data, using an a of 0.30, a 8 of 0.30, an initial trend forecast (T1) of 1, and an initial exponentially smoothed forecast (F1) of 30. Round your intermediate calculations and answers to two decimal places. MONTH ACTUAL DEMAND Ft Tt 1 2 3 4 5 6 7 8 00 9 10 31 34 33 35 37 36 38 40 40 41 FITT
