Trinity General Hospital had the following number of patient admissions during the past 8 weeks Week Patient Admissions 1 120 2 145 3 95 4 112 5 130 6 110 7 100 8 140 a. Develop a 3-week weighted average forecast for Week 4 through 9 with weights W1 = 0.2 W2 = 0.3 W3 = 0.5 b. Forecast patient admissions for week 9 using simple exponential smoothing with α = 0.2. Assume that the forecast for Week 2 (F2) is the naïve forecast.

Trinity General Hospital had the following number of patient admissions during the past 8 weeks Week Patient Admissions 1 120 2 145 3 95 4 112 5 130 6 110 7 100 8 140 a. Develop a 3-week weighted average forecast for Week 4 through 9 with weights W1 = 0.2 W2 = 0.3 W3 = 0.5 b. Forecast patient admissions for week 9 using simple exponential smoothing with α = 0.2. Assume that the forecast for Week 2 (F2) is the naïve forecast.

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section: Chapter Questions

Problem 42P: The file P13_42.xlsx contains monthly data on consumer revolving credit (in millions of dollars)...

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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_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.
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?
4 Trinity General Hospital had the following number of patient admissions during the past 8 weeks Patient Week Admissions 1 120 145 3 95 4 112 5 130 6. 110 7 100 140 Develop a 3-weck weighted average forecast for Week 4 through 9 with weights a W1 = 0.2 W2 = 0.3 W3 = 0.5 Forecast patient admissions for week 9 using simple | exponential smoothing with a = 0.2. Assume that the forecast for Week 2 (F2) is the naïve forecast.
14 Community General Hospital finds itself treating many bicycle accident victims. Data from the last seven 24-hour periods is shown below: Day Bicycle Victims 1 8 2 14 3 8 4 14 5 18 6 15 With an alpha value of 0.31 and a starting forecast in day 3 equal to the 21 , what is the exponentially smoothed forecast for day 4? (Round to two decimal places) 15 What is the exponentially smoothed forecast for day 5? (Round to two decimal places) 16
- Sales of tablet computers at Ted Glickman's electronics store in Washington, D.C., over the past 10 weeks are shown in the table below: 6 10 Week 1 Demand 20 2 3 23 27 4 5 37 26 7 8 9 30 35 22 24 29 a) The forecast for weeks 2 through 10 using exponential smoothing with a = 0.55 and a week 1 initial forecast of 20.0 are (round your responses to two decimal places): Week 1 Demand 20 Forecast 20.0 2 3 23 27 4 37 5 26 6 30 7 35 8 22 9 24 10 29
Passenger miles flown on Northeast Airlines, a commuter firm serving the Boston hub, are shown for the past 12 weeks: 모 Week 1 2 3 4 5 6 7 8 10 11 12 Actual Passenger Miles (in thousands) 17 21 19 23 18 16 20 18 22 20 15 22 a) Assuming an initial forecast for week 1 of 17,000 miles, use exponential smoothing to compute miles for weeks 2 through 12. Use a = 0.2 (round your responses to two decimal places). Week 1 2 3 4. 6 7 8 9 10 11 12 Forecasted Passenger Miles (in thousands) 17.00 17.00 17.80 18.04 19.03 18.83 18.26 18.61 18.49 19.19 b) The MAD for this model = (round your response to two decimal places). c) Compute the cumulative forecast errors, cumulative MAD, and tracking signals (round your responses to two decimal places). Cumulative Cumulative Forecast Forecast Errors Tracking Signal Tracking Signal Week MAD Week Errors MAD 1 0.00 0.00 7 8.04 2.25 3.57 2 4.00 2.00 2.00 8 7.43 2.05 3.63 3 5.20 1.73 3.00 9 10.94 2.21 4.96 4 10.16 2.54 4.00 10 11.75 2.07 5.68 9.13 2.24 4.08 11 6…
The demand (in number of units) for Apple iPad over the past 6 months at BestBuy is summarized below. Month Nov 2019 Dec 2019 Demand 45 48 Jan 2020 50 Feb 2020 Mar 2020 Apr 2020 42 46 51 Consider the following three forecasting methods: • Two-month weighted moving average, with weights 6 and 2 (more weight assigned to more recent data) Exponential smoothing with a = 0.7. Let the initial forecast for Nov 2019 be 46. • A trend line projection in the form ŷ = a+bx . To simplify computations, transform the value of x (time) to simpler numbers – designate Nov 2019 as x=1, Dec 2019 as x= 2, etc. (a ) For each of the above methods, forecast the demand of Apple iPad for May 2020. (b) Consider only the two-month weighted moving average method, compute the MAD measure and the MSE measure using the data from Jan 2020. (c) Use the trend line to forecast the demand of Apple iPad for Dec 2020. Give your opinion regarding the reliability of the forecast.
Home-Style Cookies tracks monthly sales by type of cookie, see the following table. Month Chocolate Chip (000 units) Peanut Butter (000 units) Filled (000 units) Feb. 19 15 10 Mar. 18 14 9 Apr. 15 11 8 May 20 16 11 Jun. 18 14 10 Jul. 22 18 12 Aug. 20 16 10 Sep. Create a line chart in Excel showing monthly sales by type of cookies (each type will have its own line); then, forecast September sales volume for chocolate chip cookies using each of the following: the naïve approach; a 5-month moving average; a weighted average using .60 for August, .30 for July, and .10 for June; and a linear trend equation.
12-1. The Hartley-Davis motorcycle dealer in the Minneapolis- St. Paul area wants to be able to forecast accurately the de- mand for the Roadhog Super motorcycle during the next month. From sales records, the dealer has accumulated the data in the following table for the past year. Month January February March April Мay June Motorcycle Sales 7 10 8 7 12 July August September 10 11 12 October 10 November December 14 16 a Compute a three-month moving average forecast of demand for April through January (of the next year). b. Compore a five-month moving average forecast for June drough January. c. Compare the two forecasts computed in parts (a) and (b) using MAD. Which one should the dealer use for January of dhe next year?
Passenger miles flown on Northeast Airlines, a commuter firm serving the Boston hub, are shown for the past 12 weeks: Week 1 2 4 5 6. 7 8 9. 10 11 12 Actual Passenger Miles (in thousands) 17 21 19 23 18 16 20 18 22 20 15 22 a) Assuming an initial forecast for week 1 of 17,000 miles, use exponential smoothing to compute miles for weeks 2 through 12. Use a = 0.2 (round your responses to two decimal places). Week 1 2 3 4 6. 7 8. 10 11 12 Forecasted Passenger Miles (in thousands) 17.00 17.00 17.80 18.04 19.03 18.83 18.26 18.61 18.49 19.19