a) Using a 3-year moving average, the forecasted registrations (in thousands) for years 4 through 12 are (round your responses to onedecimal place):YearForecast4YearForecast5465768b) Using a 3-year weighted moving average in which the registrations in the most recent year are given a weight of 2, and registrations in theother 2 years are each given a weight of 1, the forecasted registrations (in thousands) for years 4 through 12 are (round your responses toone decimal place):7981091110121112 Data collected on the yearly registrations for a Six Sigma seminar at the Quality College are shown in the following table:Year14.0YearForecast26.043 44.0 5.05Registrations (000)This exercise contains only parts a and b.a) Using a 3-year moving average, the forecasted registrations (in thousands) for years 4 through 12 are (round your responses to onedecimal place):6105 6 7 8 910.0 8.0 7.0 9.0 12.0 14.078910111115.012Db) Using a 3-year weighted moving average in which the registrations in the most recent year are given a weight of 2, and registrations in thenther 2 veare are each niven a weight of 1 the forecasted registratione (in thousande) for voare 4 through 12 are fround your roenoncos fo

a) Using a 3-year moving average, the forecasted registrations (in thousands) for years 4 through 12 are (round your responses to decimal place): Year Forecast 4 Year Forecast 5 4 6 5 7 6 8 b) Using a 3-year weighted moving average in which the registrations in the most recent year are given a weight of 2, and registratio other 2 years are each given a weight of 1, the forecasted registrations (in thousands) for years 4 through 12 are (round your respon one decimal place): 7 9 8 10 9 11 10 12 11 12

a) Using a 3-year moving average, the forecasted registrations (in thousands) for years 4 through 12 are (round your responses to decimal place): Year Forecast 4 Year Forecast 5 4 6 5 7 6 8 b) Using a 3-year weighted moving average in which the registrations in the most recent year are given a weight of 2, and registratio other 2 years are each given a weight of 1, the forecasted registrations (in thousands) for years 4 through 12 are (round your respon one decimal place): 7 9 8 10 9 11 10 12 11 12

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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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 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 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?
Suppose that a regional express delivery service company wants to estimate the cost of shipping a package (Y) as a function of cargo type, where cargo type includes the following possibilities: fragile, semifragile, and durable. Costs for 15 randomly chosen packages of approximately the same weight and same distance shipped, but of different cargo types, are provided in the file P13_16.xlsx. a. Estimate a regression equation using the given sample data, and interpret the estimated regression coefficients. b. According to the estimated regression equation, which cargo type is the most costly to ship? Which cargo type is the least costly to ship? c. How well does the estimated equation fit the given sample data? How might the fit be improved? d. Given the estimated regression equation, predict the cost of shipping a package with semifragile cargo.