1. Solve manually. The following gives the number of pints of type A blood used at Woodlawn Hospital in the past 6 weeks. Pints Used Week August 31 September 7 September 14 September 21 September 28 October 5 360 389 410 381 368 374 a. Forecast the demand for the week of October 12 using a 3-week moving average. b. Use a 3-week weighted moving average, with weights of 0.1, 0.3 and 0.6, using 0.6 for the most recent week. Forecast demand for the week of October 12. c. Compute the forecast for the week of October 12 using exponential smoothing with a forecast for August 31 of 360 and c=0.2. d. Compute the MAD for the 3-week moving average, 3-week weighted moving average and exponential smoothing models above.

1. Solve manually. The following gives the number of pints of type A blood used at Woodlawn Hospital in the past 6 weeks. Pints Used Week August 31 September 7 September 14 September 21 September 28 October 5 360 389 410 381 368 374 a. Forecast the demand for the week of October 12 using a 3-week moving average. b. Use a 3-week weighted moving average, with weights of 0.1, 0.3 and 0.6, using 0.6 for the most recent week. Forecast demand for the week of October 12. c. Compute the forecast for the week of October 12 using exponential smoothing with a forecast for August 31 of 360 and c=0.2. d. Compute the MAD for the 3-week moving average, 3-week weighted moving average and exponential smoothing models above.

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 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.
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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?
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_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?
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Under what conditions might a firm use multiple forecasting methods?
A small computer chip manufacturer wants to forecast monthly ozperating costs as a function of the number of units produced during a month. The company has collected the 16 months of data in the file P13_34.xlsx. a. Determine an equation that can be used to predict monthly production costs from units produced. Are there any outliers? b. How could the regression line obtained in part a be used to determine whether the company was efficient or inefficient during any particular month?
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