Professor Z needs to allocate time among several tasks next week to include time for students' appointments. Thus, he needs to forecast the number of students who will seek appointments. He has gathered the following data: Week Number of students 6 Weeks ago 51 5 Weeks ago 79 4 Weeks ago 83 3 Weeks ago 89 2 Weeks ago 71 Last Week 93 What is this week's forecast using exponential smoothing with alpha = 0.3 , if the forecast for two weeks ago was 77 ? a. 75.2 b. 78.8 c. 69.86 d. 80.54

Professor Z needs to allocate time among several tasks next week to include time for students' appointments. Thus, he needs to forecast the number of students who will seek appointments. He has gathered the following data: Week Number of students 6 Weeks ago 51 5 Weeks ago 79 4 Weeks ago 83 3 Weeks ago 89 2 Weeks ago 71 Last Week 93 What is this week's forecast using exponential smoothing with alpha = 0.3 , if the forecast for two weeks ago was 77 ? a. 75.2 b. 78.8 c. 69.86 d. 80.54

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section13.6: Moving Averages Models

Problem 22P: The file P13_22.xlsx contains total monthly U.S. retail sales data. While holding out the final six...

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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?
Under what conditions might a firm use multiple forecasting methods?
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.
At the beginning of each week, a machine is in one of four conditions: 1 = excellent; 2 = good; 3 = average; 4 = bad. The weekly revenue earned by a machine in state 1, 2, 3, or 4 is 100, 90, 50, or 10, respectively. After observing the condition of the machine at the beginning of the week, the company has the option, for a cost of 200, of instantaneously replacing the machine with an excellent machine. The quality of the machine deteriorates over time, as shown in the file P10 41.xlsx. Four maintenance policies are under consideration: Policy 1: Never replace a machine. Policy 2: Immediately replace a bad machine. Policy 3: Immediately replace a bad or average machine. Policy 4: Immediately replace a bad, average, or good machine. Simulate each of these policies for 50 weeks (using at least 250 iterations each) to determine the policy that maximizes expected weekly profit. Assume that the machine at the beginning of week 1 is excellent.
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 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?
A police station had to deploy a police officer for an emergency multiple times in the last four evenings. The table below shows the number of emergencies each evening. Weekday Number of calls each day Monday 5 Tuesday Wednesday Thursday 10 What would be their forecast for Friday using a naïve forecasting approach? Forecast for Friday calls
Professor Very Busy needs to allocate time next week to include time for office hours. He needs to forecast the number of students who will seek appointments. He has gathere the following data: Week 6 weeks ago 5 weeks ago 4 weeks ago 3 weeks ago 2 weeks ago # Students 83 110 95 80 65 50 Last week What is this week's forecast using trend adjusted (double) smoothing with alpha=0.5 and beta = 0.1, if the smoothed forecast for last week was 60, and the smoothed trend estimate for last week's forecast was -5? 49.3 O 50.6 47.3 65.4 42.7
A university lecturer needs to allocate time next week to include time for office hours. He needs to forecast the number of students who will seek appointments. He has gathered the following data: Week #students 6 weeks ago 83 5 weeks ago 100 4 weeks ago 95 3 weeks ago 80 2 weeks ago 65 Last week 50 What is this week's forecast using exponential smoothing with alpha = 2, if the forecast for two weeks ago was 90? O a. 49 O b. 77 O c. 52 O d. 50 O e. 65
The actual number of patients at Providence Emergency Medical Clinic for the first six weeks of this year follows: Week Actual No. of Patients 1 87 2 89 3 96 4 80 5 86 6 93 Clinic administrator Dana Schniederjans wants you to forecast patient numbers at the clinic for week 7 by using this data. You decide to use a weighted moving average method to find this forecast. Your method uses four actual demand levels, with weights of 0.333 on the present period, 0.250 one period ago, 0.250 two periods ago, and 0.167 three periods ago. Part 2 a) What is the value of your forecast? The value of the forecast is enter your response here patients (round your response to two decimal places). Part 3 b) If instead the weights were 20, 15, 15, and 10, respectively, how would the forecast change? A. The value of the forecast will increase. B. The…
Sales of Volkswagen's popular Beetle have grown steadily at auto dealerships in Nevada during the past 5 years (see table below). Using exponential smoothing with a smoothing constant (a) of 0.30 and a starting forecast of 415.00, the following sales forecast has been developed: Forecasted Year Sales Sales 2005 455 415.00 2006 510 427.00 2007 516 451.90 2008 570 471.13 2009 585 500.79 The MAD for a forecast developed using exponential smoothing with a = 0.30 is 74.04 sales (round your response to two decimal places). Forecasted sales for years 2006 through 2010 using exponential smoothing with a = 0.60 and a starting forecast of 415.00 are (round your responses to two decimal places): Year 2005 2006 2007 2008 2009 2010 Forecasted Sales 415.00 439 481.6 502.24 542.9 568.12 The MAD for a forecast developed using exponential smoothing with a = 0.60 is decimal places). sales (round your response to two
Sales of Volkswagen's popular Beetle have grown steadily at auto dealerships in Nevada during the past 5 years (see table below). Using exponential smoothing with a smoothing constant (a) of 0.30 and a starting forecast of 415.00, the following sales forecast has been developed: Forecasted Year Sales Sales 2005 455 415.00 2006 510 427.00 2007 516 451.90 2008 570 471.13 2009 585 500.79 The MAD for a forecast developed using exponential smoothing with a = 0.30 is sales (round your response to two decimal places).