Week Of Pints Used August 31 September 7 September 14 September 21 September 28 October 5 345 370 410 378 368 371 ) The forecasted demand for the week of October 12 using a 3-week moving average = 372.33 pints (round your response to two decimal places). ) Using a 3-week weighted moving average, with weights of 0.15, 0.30, and 0.55, using 0.55 for the most recent week, the forecasted demand for the week of October 12 = 371.15 pints (round your esponse to two decimal places and remember to use the weights in appropriate order- the largest weight applies to most recent period and smallest weight applies to oldest period.) If the forecasted demand for the week of August 31 is 345 and a = 0.25, using exponential smoothing, develop the forecast for each of the weeks with the known demand and the forecast for the week f October 12 (round your responses to two decimal places). Forecast for this Date Week Of Pints Used August 31 345 345 September 7 September 14 370 345.00 410 351.25 September 21 378

Week Of Pints Used August 31 September 7 September 14 September 21 September 28 October 5 345 370 410 378 368 371 ) The forecasted demand for the week of October 12 using a 3-week moving average = 372.33 pints (round your response to two decimal places). ) Using a 3-week weighted moving average, with weights of 0.15, 0.30, and 0.55, using 0.55 for the most recent week, the forecasted demand for the week of October 12 = 371.15 pints (round your esponse to two decimal places and remember to use the weights in appropriate order- the largest weight applies to most recent period and smallest weight applies to oldest period.) If the forecasted demand for the week of August 31 is 345 and a = 0.25, using exponential smoothing, develop the forecast for each of the weeks with the known demand and the forecast for the week f October 12 (round your responses to two decimal places). Forecast for this Date Week Of Pints Used August 31 345 345 September 7 September 14 370 345.00 410 351.25 September 21 378

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section13.7: Exponential Smoothing Models

Problem 29P: The file P13_29.xlsx contains monthly time series data for total U.S. retail sales of building...

See similar textbooks

Similar questions

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.
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_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 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_02.xlsx contains five years of monthly data on sales (number of units sold) for a particular company. The company suspects that except for random noise, its sales are growing by a constant percentage each month and will continue to do so for at least the near future. a. Explain briefly whether the plot of the series visually supports the companys suspicion. b. By what percentage are sales increasing each month? c. What is the MAPE for the forecast model in part b? In words, what does it measure? Considering its magnitude, does the model seem to be doing a good job? d. In words, how does the model make forecasts for future months? Specifically, given the forecast value for the last month in the data set, what simple arithmetic could you use to obtain forecasts for the next few months?
The file P13_28.xlsx contains monthly retail sales of U.S. liquor stores. 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 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?
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?
The file P13_25.xlsx contains the quarterly numbers of applications for home mortgage loans at a branch office of Northern Central Bank. 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? Is it guaranteed to produce better forecasts for the future?
Clinic administrator Marc Schniederjans wants you to forecastpatient demand at the clinic for week 7 by using this data. You decide to use a weighted moving average method to find this fore-cast. Your method uses four actual demand levels, with weights of 0.333 on the present period, 0.25 one period ago, 0.25 two peri-ods ago, and 0.167 three periods ago. a) What is the value of your forecast? PXb) If instead the weights were 20, 15, 15, and 10, respectively, howwould the forecast change? Explain why. c) What if the weights were 0.40, 0.30, 0.20, and 0.10, respec-tively? Now what is the forecast for week 7?